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Positive Interventions 1 Running head: WHAT ARE POSITIVE INTERVENTIONS What are Positive Interventions and How Effective Are They? A Conceptual Discussion of the “Positive” and a Meta-Analysis of the Effectiveness of Positive Interventions Stephen M. Schueller Major Area Qualifying Exam Paper 1 Committee: Martin E. P. Seligman, Ph.D. (Advisor) Michael J. Kahana, Ph.D. (Chair) Robert J. DeRubeis, Ph.D.

Transcript of Positive Interventions 1 Running head: WHAT ARE POSITIVE ...positive interventions increase...

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Positive Interventions 1

Running head: WHAT ARE POSITIVE INTERVENTIONS

What are Positive Interventions and How Effective Are They? A Conceptual Discussion

of the “Positive” and a Meta-Analysis of the Effectiveness of Positive Interventions

Stephen M. Schueller

Major Area Qualifying Exam

Paper 1

Committee:

Martin E. P. Seligman, Ph.D. (Advisor)

Michael J. Kahana, Ph.D. (Chair)

Robert J. DeRubeis, Ph.D.

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Abstract

One focus of positive psychology is to promote the good life. Several researchers have

developed interventions aimed at promoting well-being. This paper offers a definition of

a positive intervention consistent with subjective well-being (SWB) approaches to

defining happiness. From this perspective, a positive intervention is a cognitive or

behavioral strategy that promotes happiness, positive affect, or satisfaction with life, all

key components to subjective well-being. A meta-analysis computed the average effect

size of studies that met this definition. 58 research studies with a total of 4,502

participants were included in this analysis. This study found that positive interventions

lead to small-to-moderate boosts in subjective well-being with an average effect size of

.44 and moderate effects for reduction of depressive symptoms.

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What are Positive Interventions and How Effective Are They? A Conceptual Discussion

of the “Positive” and a Meta-Analysis of the Effectiveness of Positive Interventions

Ten years ago, Martin Seligman established the field of positive psychology to

foster research on “positive” aspects of living and to create a psychology of human

strengths and flourishing. Seligman noted that since World War II, psychological

research has focused on pathology, learning much about the predisposing factors for

pathology, yet neglecting the study of factors associated with well-being. However, with

the advent of the positive psychology enterprise, research on these topics has flourished.

The measurement of once fuzzy constructs, such as happiness, has advanced to the point

that we now have well-validated and widely used measures (see Diener & Suh, 1997).

This advancement in measurement has promoted research and expanded knowledge on

well-being and happiness. A search of PsycINFO finds 1233 articles with “happiness” as

a descriptor published since Seligman’s 1998 American Psychological Association

Presidential Address, compared to only 921 articles published prior to it. This increase in

research has prompted for the creation of new journals, such as the Journal of Positive

Psychology and the Journal of Happiness Studies, to serve as an outlet for these studies.

Research has advanced our understanding of happiness and the factors that

promote it (see Diener, Suh, Lucas, & Smith, 1999; Lyubomirsky, Schkade, & Sheldon,

2005). Researchers have used this knowledge to develop interventions based on positive

psychology principles. This trend in empirical research has been mirrored in public

interest. Several self-help books dot the shelves of bookstores, only a few of which have

any empirical grounding (see Lyubomirsky, 2007; Seligman, 1991, 2002). Others offer

advice with little scientific backing yet still draw immense popular attention, such as the

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New York Times bestseller, The Secret. In order to separate the science of happiness

from self-proclaimed self-help gurus whose advice is not empirically grounded, positive

psychologists must continue to tighten definitions of, and better operationalize key

constructs, which would aid theoretical and empirical progress.

As positive psychology advances into its second decade, an important indicator of

its usefulness to psychology will be its staying power. Is positive psychology merely a

fad or does it still have something to offer? In order to better promote research,

investigators interested in positive psychology must better operationalize the terms used

by the field.

Despite many attempts, a satisfactory definition of what exactly is “positive”

about positive psychology is still lacking. The dominant paradigm in the treatment of

mental disorders follows a medical model that focuses on correcting illness, but not on

improving individuals once they become illness-free. Positive psychology offers an

alternative to the medical model by adopting a strengths-perspective that builds on what

individuals do well. Furthermore, positive psychology emphasizes building positive

emotions and positive character traits that promote flourishing and optimal human

functioning. In the terminology of positive psychology, interventions using this

promotion-focused view are called “positive interventions.” A wide variety of

interventions could be “positive” depending on the definition adopted. This term faces

the same hurdle facing positive psychology. What exactly is “positive” about a positive

intervention?

The aim of this paper is two-fold. First, I will attempt to reach a theoretical

definition of what is meant by “positive,” especially with regards to positive

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interventions. I will focus mostly on how “positive” applies to intervention studies - that

is, studies that impart some change in the individual to increase well-being. I will

consider 6 different definitions of what constitutes a positive intervention. Second, I will

analyze the effectiveness of these interventions through meta-analytic techniques.

Defining a “Positive” Intervention

My definition of a positive intervention is as follows: a positive intervention is a

cognitive or behavioral strategy, or a collection of cognitive and behavioral strategies that

attempts to promote well-being – building happiness, satisfaction with life, or positive

affect through processes we have learned that lead to well-being such as engagement,

meaning, and pleasure. This definition is a better descriptor of the science of positive

psychology than 5 alternative definitions that will be considered. These alternative

definitions fall into different categories. The first three are definitions that agree that

positive interventions increase well-being, but that differ on the definition of well-being.

Several definitions for well-being exist, including what Dolan and colleagues (2006,

2007) characterize as wanting, needing, and liking theories. Therefore, any definition of a

positive intervention has to address which of these perspectives best captures the meaning

of well-being. I adopt a “liking” theory of well-being, an approach built on a subjective

well-being perspective. Alternative definitions of positive interventions can be built on

each of these different perspectives and a brief review of each perspective and the

implications for definitions of positive interventions will be considered.

Besides disagreeing about the nature of well-being, the fourth definition of a

positive intervention, offered by Pawelski (2007), states that positive interventions either

focus on fostering what is good or advances someone who is already well further along

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the continuum. For example, many interventions attempt to address a deficit and move a

person back to a zero point of a scale. Positive psychology, following from this second

aspect of the definition, attempts to move individuals to higher positive values on the

continuum.

The term positive generally refers to adding something as opposed to subtracting

something, or fixing something that is broken. The fifth definition of a positive

intervention is an enhancement view of positive psychology. Following from this view,

positive psychology is a commission approach, an active approach to creating something

new as opposed to an omission approach, which addresses something that is not working.

The definition helps clarify the difference between a “negative” intervention and a

“positive” intervention.

One last definition of a positive intervention is that a positive intervention is one

that has a good effect. It improves a person’s life in some way. As this definition is the

most encompassing of all the proposed definitions, it will be considered first.

The broadest definition of a positive intervention is that it leads to improvement

in people’s lives. Although this is the purpose of positive interventions, this is the general

aim of interventions as a whole, namely to improve functioning and decrease distress. As

psychologists, our efforts are motivated by the same mindset of the physicians who

follow the Hippocratic Oath, primum non nocere, “first, do no harm.” Indeed, the Ethics

Code of the American Psychological Association instructs psychologists to “take

reasonable steps to avoid harming their clients/patients.” (APA, 2002, p. 1065).

Following from this, all of the interventions proposed by psychologists are intended to

benefit those who receive the intervention. The general goal of therapy is not to remove

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disorder, but to increase the well-being of the patients involved (Kazdin, 1992; Strupp,

1996). Therefore, this definition is too broad and fails to exclude any interventions.

Positive psychology cannot lay claim to all interventions that aim to improve individuals.

Instead, there must be something distinctive about positive interventions.

What is “Positive” About Positive Interventions?

Positive psychology emphasizes building strengths and positive emotions as

opposed to relieving negative states. This simple statement, however, is both theoretically

rich and empirically complicated. What is the difference between building the positive

and alleviating the negative and does this difference matter? In order to examine this

difference, I will first offer different definitions of what is “positive” and consider the

implications on defining a “positive intervention.” I will then conceptually discuss the

benefits of building the positive as opposed to addressing the negative.

What is positive?

The word “positive” has several definitions. The definition most commonly

associated with positive psychology is that “positive” refers to something that has a good

effect. Following from this definition, positive psychology is the scientific study of

processes that have a good effect in our lives. This is consistent with Gable and Haidt’s

(2005) definition that positive psychology “is the study of the conditions and processes

that contribute to the flourishing and optimal functioning of people” (pg. 104). This

definition, however, leads to another conceptual problem: If “positive” means that an

intervention will have a good effect and a good effect is defined as flourishing and

optimal functioning of people, then what exactly does it mean to flourish or function

optimally? As mentioned previously, merely stating that positive interventions do “good”

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fails to exclude any definitions. Furthermore, defining positive psychology using terms

such as flourishing and well-being without offering a clear definition of these concepts is

hardly a definition either. The concept of well-being is a difficult one to unpack and a

complete analysis is beyond the scope of this paper; however, given that positive

psychology is interested in studying the processes that contribute to flourishing and

optimal functioning, and positive interventions are interventions that augment well-being

through promoting these processes, some discussion of theories of well-being is relevant.

As mentioned previously, definitions of well-being and by extension, positive

interventions, can be divided into categories: liking, wanting, and needing theories.

Liking (or Subjective Well-Being Theories)

Liking theories of well-being are those most often adopted by psychology because

it focuses on the mental-state of the individual. These accounts view well-being as

characterized by the presence of pleasure and the absence of pain. Pleasure, however,

does not merely include the experience of pleasant affect or the fulfillment of drives.

Instead, pleasure encompasses subjective evaluations about one’s life and one’s

conditions as well. This approach has the considerable advantage of being face valid

(Dolan & White, 2007). An individual is considered happy if he reports himself as such

and each individual is considered the expert on his or her own happiness (Kesebir &

Diener, 2008; Myers & Diener, 1995).

Well-being, however, is comprised of more than just happiness1. Well-being includes

two components – a subjective cognitive evaluation (such as global happiness or life

1 Some authors use the terms happiness and well-being interchangeably (Lyubomirsky,

2001; Seligman, 2003). In my usage, however, happiness refers to subjective happiness,

an individual’s evaluation of how happy one is (Lyubomirsky & Lepper, 1999). Well-

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satisfaction) along with an affective component, namely the presence of positive emotions in

the absence of negative emotions (Diener, 1984; 1994). Therefore a person high in subjective

well-being both evaluates her life to be good as well as experiences more positive emotions

compared to negative emotions.

Although some would argue that subjective well-being is not sufficient for defining a

good life, adopting the SWB approach confers several advantages (Jayawickreme, 2008). First,

as mentioned previously, it has face validity and makes the measurement of subjective well-

being straight forward. Researchers use several measures of subjective well-being depending

on the context and specific component that interests researchers. Measures can assess

differences in the presence of positive and negative moods using measures such as the Positive

and Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1998) or the Profile of Mood

States (POMS; McNair, Lorr, & Droppleman, 1971, 1992). These measures assess the affective

component of subjective well-being. Other measures tap the cognitive or evaluative component

of well-being. These measures use a variety of approaches to assess how people feel and make

judgments about their satisfaction including generalized feelings (e.g., Satisfaction with Life;

Diener, Emmons, Larsen, & Griffin, 1985) versus domain satisfaction (Quality of Life

Inventory; Frisch, 1992), social comparisons ( e.g. “Compared to most my peers, I consider

myself: less happy to more happy;” Subjective Happiness Scale, Lyubomirsky & Lepper,

1999) versus temporal evaluations, or even single item measures such as Bradburn’s (1969)

global happiness item “Taking all things together, how would you say things are these days?”

or Andrews & Withey’s (1976) Delighted-Terrible Scale (e.g., “How do you feel about your

being is broader and akin to Diener’s (1984) definition of subjective well-being that

includes both cognitive and affective components – high life satisfaction, high positive

affect, and low negative affect.

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life right now?”). Despite focusing on the individual as the expert of his happiness, self-ratings

of happiness converge with ratings made by significant others (Lepper, 1998; Sandvik, Diener,

& Seidlitz, 1993) and even observers with minimal contact (Redelmeier & Kahneman, 1996).

Therefore, although self-evaluations are thought of as the most important indicator of well-

being, these ratings conform to what others observe. This supports the validity of the self-

report, suggesting that those individuals whom we see as happy tend to in fact be happy.

Liking accounts have often been accused of reducing well-being to hedonism - that

well-being is achieved when pleasure is maximized. Researchers, however, use subjective

well-being as a proxy for pleasure. This avoids the criticism that happiness from a subjective

well-being account is hedonism because subjective well-being involves a cognitive evaluation

of how one’s life is living up to one’s expectations in addition to just maximizing pleasure and

minimizing pain. Inclusion of a reflective and evaluative component is important because well-

being is achieved and maintained through a variety of cognitive processes. These cognitive

processes help individuals maintain levels of well-being in the face of a variety of threats to

well-being. This is a “top-down” approach that emphasizes that events and experiences

influence our well-being only after they pass through our cognitive evaluations (Lyubomirsky,

2001). Therefore, the intensity and valence of emotional experience is important and correlated

with our evaluations in that it is more likely we will have a positive evaluation of a positive

experience, and individuals who are happy will tend to see things in a positive light (Taylor &

Brown, 1988).

These approaches differ from “bottom-up” approaches that posit that well-being is the

sum of moment-to-moment positive emotions. From this perspective, well-being is calculated

by integrating the area under the curve of a plot of well-being measures assessed frequently

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over a given time period. One method of obtaining this data is the day reconstruction method

(DRM) that requires participants to systematically reconstruct a day to reduce biases inherent

in recall (Kahneman, Krueger, Schkade, Schwarz, & Stone, 2004). Kahneman’s (1999) view of

well-being focuses on adding up these momentary experiences to achieve a measure of the

“experiencing self” as an indicator of well-being. One problem with this approach is that

examining momentary positive and negative affect can produce different conclusions than

evaluate approaches on how much well-being an individual derives from certain experiences

and may produce prescriptions that could ultimately decrease well-being when using amore

evaluative measure. For example, using the DRM, Kahneman and colleagues (2004) found that

individuals reported more negative affect caring for children than commuting or doing

housework, with caring for children producing more momentary negative affect than any other

activity assessed besides working. Furthermore, interactions with children produced less

positive affect than any other close relationships (spouses, friends, and family) and were only

rated slightly higher than clients or customers. Following from Kahneman’s approach to taking

momentary ratings of affect to form the assessment of well-being, children would pose a

serious detriment to well-being, although, most people consider their children as an important

source of well-being. Subjective well-being approaches that include an evaluative component

of life satisfaction avoids reducing the construct to mere hedonism, focusing on increasing

pleasure in the moment, and instead allows a chance to evaluate how much we enjoy the

circumstances we experience. Therefore, even if children do correspond to a high degree of

negative affect and low positive affect in the moment, our evaluations of how much we like

having children allow our children to contribute to our well-being. Evaluative approaches to

well-being allow for the individual’s preferences, goals, desires, expectations, and mental

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states to drive the final assessment of well-being. These approaches, therefore, allow the

individual the final say in whether or not her life is “happy”.

Despite being subjective, these measures of well-being are correlated with a host of

other benefits. Recent evidence suggests that subjective well-being may cause benefits in work,

relationships, and health (see Lyubomirsky, Diener, & King, 2005; Pressman & Cohen, 2005

for a review). Subjective well-being has been linked to better health, longer life spans (Danner,

Snowden, & Friesen, 2001); and better work performance, better social relationships, and more

ethical behavior (Diener, 2007). Furthermore, individuals rate happier lives as more desirable

and believe that happier individuals are more likely to go to Heaven (King & Napa, 1998).

Individuals throughout the world rate happiness and well-being as more valuable than income

(Diener, 2000). Therefore, individuals put great value on subjective well-being and seek out

experiences that maximize their satisfaction.

Needing (or Objective List Theories) and “Hybrid” Theories

Needing accounts of well-being theories focus on the content of one’s life as an

important determinant of well-being. These theories list objective circumstances required for a

good life. Needing approaches have been referred to elsewhere as objective list theories of

well-being (Nussbaum, 1992; Sen, 1985, 1999). In these approaches, well-being is achieved

through satisfaction of these needs which can be listed a priori. Different theorists propose

different elements that should be included on these objective lists. For example, Maslow’s

(1954/1970) hierarchy of needs is an objective list that argues for the fulfillment of basic needs

before people can meet their full potential. The capabilities approach argues similarly for

essential needs including food, shelter, health, security, and freedom as a condition for

individuals to have the ability to create their own well-being. Objective lists highlight the

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importance of certain things that are truly valuable to well-being, irrespective our value

assigned to it. A list could include several elements, such as education, relationships, career

success, democracy, beauty, and material comforts (Seligman & Royzman, 2003).

A definition of a positive intervention from an objective list theory would include

interventions that increase or provide elements included in these objective lists. For example, if

an objective list included education, sending one’s child to college would be a positive

intervention regardless if the child enjoyed attending college or not. Objective lists approaches

have two advantages. First, like Maslow’s hierarchy of needs, it highlights the need for

addressing primary needs before indulging in higher pursuits. This emphasizes the importance

of providing basic resources for individuals - food, shelter, safety - to promote well-being as

opposed to focusing on higher level processes such as promoting emotions such as elevation

and awe. In certain circumstances, addressing these basic needs may be the most beneficial

approach as opposed to focusing on higher psychological processes. In the aftermath of

Hurricane Katrina, Edna Foa, an expert on the treatment of post-traumatic stress disorder,

emphasized this point, suggesting initial efforts should address basic needs as opposed to

providing psychological services to the survivors (Medscape, 2005). The second advantage is

that these lists do provide objective indicators of well-being. That is, one cannot be considered

“happy” unless he or she is objectively well-off in some sense. Seligman and Royzman (2003)

provide an example of orphan children living on the streets who might be subjectively “happy”

by engaging in activities that provide momentary pleasure with little concern for the future.

The appeal of an objective list approach is that even if these individuals report being happy

they would fall short of being considered as such based on a lack of objective criteria.

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Despite this appeal of objective lists, there are limitations as well. First, there is no

general consensus on which exact items should be included on objective lists. Although some

needs may be universal, other needs are likely to differ across cultures or across individuals

who have different values. Second, objective lists accounts of well-being are often linked to

objective outcomes to determine well-being. Therefore measuring well-being requires the

assessment of outcomes such as literacy rates or crime rates. Again, the relative importance of

these outcomes may differ by individual values, which introduces a subjective element into this

assessment. Lastly, objective lists ignore any subjective evaluation of well-being, which in the

end is a too strict criteria for developing a theory of well-being.

This final critique, that objective lists are too strict, is addressed by theories that

combine subjective appraisals with need approaches. These theories expand upon the

constructs of subjective well-being and happiness that leave out several important components

of the good life and consider the content of one’s life as well. These theories can be thought of

as “hybrid” theories because they combine liking and needing approaches. I propose that

combining an objective list approach with a liking approach encompasses more aspects than

what are included in subjective well-being accounts and makes up a construct that I call

“wellness.”

Wellness incorporates additional hallmarks of the good life including positive mental

health, flourishing, positive physical health, adaptive functioning, quality of life, and

psychological well-being as well as happiness and subjective well-being. Wellness, therefore,

is a multifaceted concept that integrates signs of well-being from several objective conditions

of one’s life in addition to subjective evaluations. A complete discussion of wellness is beyond

the scope of this paper; however, because my definition of a positive intervention is an

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intervention that builds well-being through promoting and enhancing the factors that lead to it,

it is important to differentiate well-being from wellness. Interventions could promote an aspect

of wellness without enhancing well-being and would not be considered a positive intervention.

Therefore, a brief discussion of the concept of wellness will follow.

In her seminal work on positive mental health, Jahoda (1958) identified 6 factors that

contribute to the mental health of an individual. These factors include attitudes towards the

self, self-actualization, integration of self, autonomy, perception of reality, and environmental

mastery. Ryff (1989) found that many of these concepts are absent in definitions of happiness

or well-being. Drawing on past theoretical concepts and her own empirical investigations, Ryff

concluded that there should be at least six dimensions of well-being: self-acceptance, positive

relations, environmental mastery, purpose in life, and personal growth. Furthermore, research

supports that models that only address satisfaction with life and affect may be insufficient at

capturing the multifaceted nature of positive functioning (Ryff & Keyes, 1995).

This conception of mental health is similar to the term “competency” used within

community psychology. Competency refers to individuals or communities that have a

repertoire of resources and the knowledge and desire to utilize these resources effectively

(Iscoe, 1974). Being a competent individual and having a range of available resources is an

important contributor to the functioning and wellness of an individual.

Furthermore, the ability to adapt and respond to the challenges of life is one important

component of wellness. Often individuals are thought to be well if they are able to withstand

the several stressors of life and demonstrate resilience. Lorion (2000) describes wellness using

of a river. Those who are “well” are able to respond the ebbs and flow of the river. According

to this conceptualization of wellness, preferred psychological functioning is showing resilience

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in the face of life stresses. This is an important component as life stresses may be a crucible

that promotes further positive developments.

Another perspective on wellness is that wellness is a state characterized by strengths

and virtues. Seligman and Peterson (2004) identified and classified strengths that are pervasive

across cultures. A taxonomy of character strengths was developed as a parallel to the

Diagnostic and Statistical Manual of Mental Disorders (DSM), which focuses on

psychopathology. These strengths are characteristics definitive of high functioning and

flourishing individuals. Seligman and Peterson identified 24 different virtues or strengths of

character that are thought to transcend cultures. In addition to identifying the virtues, they

developed a questionnaire that assesses these strengths of character. Exhibiting a large number

of these character strengths is another way to conceptualize wellness.

Cowen (1994, 1999, 2000) has differentiated wellness enhancement from the efforts of

primary prevention of psychopathology and maladaptation. In order to better understand what a

positive intervention is, it is useful to distinguish it from wellness enhancement. Wellness

enhancement assumes that by increasing competencies and functioning, psychological well-

being will increase, which in turn will buffer the individual against pathology. This buffering is

accomplished by helping the individual withstand or better deal with life stressors or other

aspects that might contribute to pathology. Cowen (1991) defines wellness based on two

clusters of indicators. The first cluster include an assessment of how well the individual is

functioning – being able to satisfy basic needs such as eating and sleeping as well as

performing life’s tasks well, a notion Cowen ties to Freud’s conception of “Leben und

Arbeiten” or “to love and to work.” The second set of indicators includes evaluative measures,

such as life satisfaction. Life satisfaction is often included as an important factor in definitions

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of subjective well-being (Diener, 1984). Cowen’s definition incorporates the cognitive

component of subjective well-being, but leaves out the affective component.

Positive emotions, one of the three pillars of study in positive psychology, are a crucial

component to think of when considering wellness. Research supports the notion that those who

frequently experience positive emotions consider themselves happy (Diener et al., 1999;

Diener, Larsen, Levine, & Emmons, 1985; Larsen & Ketelaar, 1991). Indeed, positive affect

may actually be one of the most important contributors to a person’s overall wellness.

Increasing positive affect may not only be beneficial for increasing emotions that people desire

on an abstract, common-sense level (i.e., individuals report liking to experience positive

emotions such emotions as joy, awe, love) but also act as markers of flourishing and success

(Cantor et al., 1991; Carver & Scheier, 1998; Clore, Wyer, Dienes, Gasper, & Isbell, 2001).

Positive emotions may undo the effects of negative emotions and may broaden-and-build

resources (Fredrickson, 1998, 2001). Inducing positive emotions in the short-term leads

increased to creativity and cognitive flexibility (see Fredrickson, 1998, for a review). Recent

evidence, however, suggests that positive emotions may not only be markers of success but

that positive emotions may actually lead to success, better job performance, social relationships

(Lyubomirsky, King, & Diener, 2005) and better health (Pressman & Cohen, 2005).

Therefore, positive emotions are important to optimal psychological functioning and well-

being and increasing positive emotions is an important goal of positive interventions.

Consistent with the notion that mental health is not merely the opposite of mental

illness, Keyes’ (2005, 2006a, 2006b, 2007) proposes a complete state model that includes

a conception of flourishing in addition to a continuum of pathology. These dimensions

are correlated, yet are separate dimensions. Keyes modeled his definition of flourishing

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on the DSM definition of depression: To be diagnosed with flourishing, a person must

have at least one symptom of hedonia as defined by high positive affect and happiness or

life satisfaction and six or more symptoms of positive functioning that include self-

acceptance, social acceptance, personal growth, social actualization, purpose in life,

social contribution, environmental mastery, social coherence, autonomy, positive

relations with others, and social integration (Keyes, 2005). This concept emphasizes

positive emotions as one of the hallmark signs of flourishing, much as sadness is a

hallmark symptom of depression. This collection of flourishing “symptoms” represents a

synthesis of different conceptions of mental health and encompasses all the aspects of

wellness discussed in this paper. A person is considered to be languishing if he or she

lacks the criteria for flourishing. Therefore, flourishing and languishing are a bipolar

continuum that— although related to presence and absence of mental health— is a

separate identifiable factor. A confirmatory factor analysis of different theories testing

the relationship between mental health (as defined by flourishing) and DSM disorders

found that a two axis, oblique solution was the only model with good fit compared to

models of independence, single axis and orthogonal, two axes solutions (Keyes, 2005).

Flourishing has also been shown to be related to positive outcomes such as fewer days off

work, increasing intimacy, and better goal pursuit; whereas languishing and mental

illness combined are a much better predictor of days of work missed and helplessness

than mental illness alone. Furthermore, Keyes (2007) found that in the absence of mental

illness, those who were identified as languishing functioned worse than those with mental

illness who demonstrated a moderate level of mental health. Keyes’ research emphasizes

the importance of distinguishing the positive from the negative and also highlights the

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importance of subjective well-being – which includes an affective component - as a core

feature of definitions of wellness.

This distinction between subjective well-being and wellness is similar to that in the

literature between hedonic and eudamonic conceptions of well-being (see Ryan & Deci, 2001).

Hedonic views follow liking theories, whereas eudamonic conceptions involve an objective

component and are either needing or hybrid theories. Hedonic well-being is often reduced to

equating well-being with pleasure; however, most research within hedonic conceptions of well-

being use subjective well-being as a proxy for pleasure. Therefore, in a hedonic view, well-

being is a summation of positive cognitive evaluations of one’s life (in the form of subjective

happiness or life satisfaction), and the presence of positive affect and the absence of negative

affect. Eudamonic views of well-being define well-being as living a good and virtuous life.

Eudamonic well-being is often paralleled with Ryff’s conception of psychological well-being.

Although, eudamonic conceptions of well-being are important to consider and are important

components of positive mental health, eudamonic well-being is a pathway to subjective well-

being. That is to say, aspects of eudamonic well-being foster subjective well-being. Indeed, one

analysis found that subjective well-being fully mediated the relationship between

psychological well-being and measures of quality of life (Ring, Höfer, McGee, Hickey, &

O’Boyle, 2007). Therefore, eudemonic well-being contributes to quality of life only insofar as

these activities contribute to subjective well-being, such as using one’s signature strengths and

pursuing meaning. Thus, paths to eudamonic well-being are important to consider, but the

ultimate goal of interventions is to increase subjective well-being.

Positive interventions can increase several aspects of wellness. However, positive

interventions are ultimately defined by the fact that they promote this core component of

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flourishing, namely subjective well-being. This definition allows us to distinguish positive

interventions from interventions based on objective list accounts. I see subjective well-being as

the central feature of building the positive as presented by positive psychology. Because

subjective well-being is the building block of the good life, it is the most important feature to

increase. Increases in eudamonic conceptions of well-being may even lead to moment-to-

moment and long-term increases in hedonic well-being. This is similar to Kahneman’s (1999)

notion of well-being as the sum of moment-to-moment positive experiences. Therefore, a

positive intervention must build well-being through the promotion of happiness and positive

emotions, which in turn involves increasing factors that support them.

Wanting (or Desire-Fulfillment Theories)

Wanting or desire-fulfillment accounts define well-being as the ability to fulfill

one’s desires. These approaches dominate economics, where the perspective on well-

being is that it comes from satisfying one’s preferences. Economists use money as a

proxy for well-being because money helps individuals satisfy their preferences. Desire

theories overlap with subjective well-being theories when we desire happiness or

subjective well-being. Desire-fulfillment accounts, however, depart from subjective well-

being accounts in many instances. Liking accounts contend that maximizing subjective

well-being is beneficial even if this is not what we desire, whereas desire-fulfillment

accounts contend that getting what we want contributes to well-being even if it does not

increase pleasure. Desire-fulfillment accounts explain how one can consider his life

happy, even if it is filled with displeasure. If one is reaching one’s goals and obtaining

one’s preferences, then her life will be considered a happy one. The desire-fulfillment

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account is appealing because assessment involves examining a person’s choices directly

(Dolan & White, 2007).

A positive intervention defined according to a desire-fulfillment account would be

an intervention that fulfills people’s desires. This could be thought of as an intervention

that increases utility for a person. One potential problem with this definition is that we

believe certain things will increase our well-being, but in actuality these things do not. In

this case, our desires and expectations do not match with the eventual experience. This is

due to several biases that distort our expectations about what emotions will be triggered

by various outcomes and how long these emotions will last (Wilson & Gilbert, 2003).

Two experimental examples help illustrate that when individuals are given

freedom to make choices, they act in ways that do fail to promote well-being (as defined

by other theories). In one study, participants planned a menu of snacks they would

receive when they returned to the laboratory over the following three weeks (Read &

Loewenstein, 1995). Participants tended to pick a variety of snacks as opposed to picking

their favorite snack for all three weeks. If participants were picking snacks to eat all at

once, this would be a good strategy as snacks have diminishing marginal returns, since

the third Snickers bar hardly satisfies as much as the first. Over a longer time period,

however, snacks do not have this same effect and participants are disappointed when they

return to the laboratory and receive something other than their favorite snack. This study

demonstrates that our long-term choices are often overly influenced by our evaluations of

what we need in the moment as opposed to being able to properly predict what our

experience will be like when it occurs. In this example, participants made an error in

predicting what they would want and make a choice that in the end left them

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disappointed. This illustrates what Gilbert and Wilson (2007) term miswanting or a

disparity between what we say want (by the choices we make) and what will make us

happy.

Another problem in determining well-being using desire-fulfillment accounts is

that individuals focus on means as opposed to ends, despite the fact that, in most cases,

ends ultimately contribute to our well-being. To illustrate this, participants with a

preference for vanilla over pistachio ice cream were randomly assigned to two conditions

(Hsee, Yu, Zhang, & Zhang, 2003). In both conditions participants had to choose

between a low-effort and a high-effort task. Participants were rewarded for completing

these tasks in different ways. In the no-medium conditions, participants received

pistachio ice cream for completing the low-effort task and vanilla ice cream for the high-

effort task. In the medium condition, participants received points for completing these

tasks: 60 points for completing the low-effort task and 100 points for completing the high

effort task. These points could be used to buy ice cream. The favored vanilla ice cream

cost 60 points whereas the pistachio ice cream cost 100 points. In the no-medium

condition, most participants chose the low-effort task in order to receive their preferred

reward, vanilla ice cream. In the medium condition, however, participants were more

likely to complete the high-effort task, even though the extra points would not help

receive their stated preference. In this example, even though participants know what they

want, they focus on the means rather than the ends of utility, a phenomenon Hsee and

colleagues call “medium maximization.” This experiment offers an interesting

comparison to how money might effect decision making. Although money does allow for

fulfillment of desires, the focus on money as the means as opposed to our desires as the

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ends might lead individuals to allocate their time and resources in ways that do not

ultimately maximize well-being.

One approach to define a positive intervention based on desire-fulfillment

accounts would be to say that a positive intervention increases the number of available

choices available to individuals. Therefore, it is not increasing desires per se, but giving

the individual the opportunity to have the most available options to choose from. Desire-

fulfillment accounts believe that increasing the number of available choices should

increase well-being. Schwartz (2004) argues that increasing the number of options,

however, actually leads to decreases in well-being because it is harder to choose from

multiple options. This paradox of choice leads to procrastination and post-choice regret

which results in less satisfaction with the eventual choice and lower levels of well-being

(Iyengar & Lepper, 2000; Schwartz, Ward, Lyubomirsky, Monterosso, White, &

Lehman, 2002).

These studies highlight the drawbacks of desire-fulfillment accounts. When given

the opportunity to make choices and fulfill desires, individuals do not act in ways that

increase well-being. Furthermore, increasing the number of available choices, an

intervention that would be beneficial according to desire-fulfillment accounts leads to

depressed levels of well-being.

The specific benefits of adopting a subjective well-being approach will be

discussed at more length later as this forms the core of my definition of a positive

intervention. I will consider two similar definitions. These definitions do not attempt to

define well-being.

Pawelski’s Definition

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Pawelski’s (2007) is the only theorist to directly tackle the question of what is

positive psychology and to offer a definition of a positive intervention. This approach is

similar to mine in that it focuses on interventions that increase well-being (although does

not specify which type of well-being) but uses two distinct characteristics of the

intervention to determine whether or not an intervention is “positive.” Pawelski states

that positive psychologists use the term “positive” in reference to two properties. The first

is the point of application. Positive psychology focuses on people who are well but want

to become better. Interventions can be aimed towards individuals with identifiable

deficits or individuals who merely want to better themselves. Pawelski describes the

latter as “normal weather” interventions because they are used when things are going

well. The second is what Pawelski refers to as “green-cape” approaches, or approaches

that make things better by focusing on what’s there and helping developing it – building

strengths as opposed to alleviating pathology. These two definitions highlight different

aspects of an intervention. Therefore an intervention could either meet the requirements

of one of these definitions, both, or neither. Positive interventions are those interventions

that are either “green-cape” approaches or “normal weather” interventions or they are

both. I disagree with this definition as interventions that are “positive” in application but

not “positive” in focus are nothing more than standard interventions applied to non-

clinical populations. Therefore, Learned Optimism based programs, which teach

cognitive behavioral therapy (CBT) skills to individuals who are not depressed should not

be considered a positive intervention. I will consider each of the two dimensions included

in this definition in turn. “Positive” in application refers to interventions that help people

who are already doing well do better by further reducing the negative in their lives

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Positive Interventions 25

(Pawelski, 2007). Interventions that are “positive” in methods refers to the content of the

intervention.

“Positive” in Application

“Positive” in application refers to the context in which interventions are applied.

Positive psychology is an alternative to clinical psychology that focuses on

psychopathology and mental illness. Instead, positive psychology focuses on learning

more about what normal people do well and how normal people flourish (Seligman &

Csikszentmihalyi, 2000). Following from this, one aspect of the Pawelski definition is

that positive interventions are aimed at people without any deficits or psychopathology,

that is, normal people who want to better themselves. For example, people who are not

depressed, but could be happier or individuals who are good at math, but wish they could

be better. I argue that although these motivations to build an already existing strength are

admirable, it is an inaccurate definition of positive interventions. This definition is

inaccurate as it focuses on characteristics of the population as opposed to an aspect of the

intervention. Using only this definition, an intervention that aims to increase well-being

in depressed populations would not be considered a positive intervention.. Furthermore,

according to such a definition, whether or not an exercise is considered a positive

intervention (e.g., increasing gratitude) would fully depend on the population in which it

is practiced. One reason to include these two dimensions in the definition of what

constitutes a positive intervention is to be able to include standard “negative”

interventions used in non-pathological or well-functioning populations. I believe positive

psychology cannot lay claim to these practices.

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Positive Interventions 26

To further illustrate the weaknesses of this definition, consider again CBT for

depression. As mentioned previously, CBT is a “negative” intervention because it

attempts to correct maladaptive cognitions in order to alleviate depression. If we consider

the characteristics of the population receiving the intervention as defining if the

intervention is positive or not, then once an individual is no longer depressed, it would

become a positive intervention. There is overwhelming support that using techniques

from CBT prior to a depressive episode can prevent subsequent depressive episodes in

both children and adults (see the Penn Resiliency Program, Jaycox, Reivich, Gillham, &

Seligman, 1994; APEX program, Seligman, Schulman, DeRubeis, & Hollon, 1999;

Seligman, Schulman, & Tyron, 2007). Therefore, it is not to say that applying cognitive

therapy to individuals who are not depressed will have no beneficial effect. The

classification of the intervention as positive or not, however, should not change based on

who is receiving it.

Therefore, applying CBT techniques to individuals before they are depressed

should not be considered a positive intervention, since such efforts simply apply the same

“negative” intervention of learning skills to dispute thoughts and fix maladaptive

cognitions before the occurrence of a full-blown depressive disorder. In order to be

considered a positive intervention, the intervention must be positive in more than just the

point of application, it must be positive in its methods and mechanism of action.

“Positive” in Methods

What does it mean for an intervention to be “positive” in methods? Interventions

that are positive in methods are interventions that attempt to promote happiness and well-

being through increasing the factors that contribute to them such as positive emotions and

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Positive Interventions 27

character strengths. In his definition, Pawelski posits that these are interventions that

“help good things to grow” (pg. 7). Positive psychology from this perspective looks for

what an individual does well and strengthens those existing resources.

One particularly important and oft overlooked approach to promote happiness is

changing cognitive and motivational processes. As previously discussed, happiness is a

subjective judgment; each individual is considered an expert of his own happiness (Myers

& Diener, 1995). Several cognitive processes influence happiness such as social

comparisons, postdecisional rationalization, event construal, and self-reflection (see

Lyubomirsky, 2001, for a review). In much the same way that the cognitive theory of

depression conceptualizes depression as a result of negative views about the self, world,

and future (Beck, 1974), a construal approach to happiness posits that happiness is the

result of self-enhancing cognitions that moderate the objective impact of events on our

subjective judgments. Cognitive therapy attempts to correct cognitive distortions that lead

to depression. Positive interventions, instead, consider the causes of well-being and

attempt to increase these.

Fordyce (1977, 1983) took this approach in constructing one of the first

empirically tested positive interventions. Fordyce created a program deemed the 14

Fundamentals by observing the behaviors of happy people and assigning individuals to an

intervention where they engaged in these behaviors. These recommendations included:

socialize, strengthen close relationships, be outgoing, be a better friend, develop a healthy

personality, lower expectations, be optimistic, make happiness a goal, be active, create

meaning, get organized and make plans, develop a present orientation, reduce negative

feelings, and stop worrying. Included in these recommendations are both positive and

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negative intervention suggestions. Some suggestions, such as create meaning and develop

a present orientation, involve creating something new and developing new approaches

that promote happiness and well-being; whereas others, such as stop worrying and reduce

negative feelings, are clearly measures aimed at reducing the negative and addressing

deficits. In Fordyce’s study, although there was a great variety in which strategies each

person preferred and received the most benefit from, positive strategies such as being

optimistic, trying new activities and being more active, being more social, and

developing a more outgoing and extraverted personality were the most often reported

beneficial approaches. Fordyce’s program is a positive intervention because the majority

of recommendations conform to the definition of a positive intervention. Just as

“negative” interventions at times may engage in an exercise that is positive in focus, there

is a place in positive psychology for reducing the negative as well.

Consider Seligman and colleagues (2006) recent attempt to create a positive

psychotherapy. In positive psychotherapy, individuals discuss troubles and therapist and

client engage in problem-solving to help address these concerns; however, the major

focus of therapy was on promoting the positive. Therefore, although some of the

individual therapeutic interventions applied can be considered either positive or negative,

the overall focus of the therapy is on the positive. These approaches are all considered

positive interventions.

Enhancement Definitions

A second definition of “positive” however should also be considered. According

to Merriam-Webster, positive also means “indicating, relating to, or characterized by

affirmation, addition, inclusion, or presence rather than negation, withholding, or

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absence.” In this way, “positive” means building something new as opposed to

correcting something that is existing but maladaptive. This definition is an enhancement

perspective of positive psychology and positive interventions. Following from this

definition, a positive intervention builds something new or enhances something good as

opposed to fixing a deficit.

Incorporating this definition can help distinguish the difference between a

“negative” and “positive” intervention. Consider a sports analogy to the game of baseball.

If a pitcher wants to improve his game, he could accomplish this by focusing on different

aspects of his pitching repertoire. This pitcher could attempt to fix the mechanics of his

curveball, learn how to throw a proper knuckleball, or develop his already effective

change up to increase the speed between his slowball and his fastball. A “negative”

intervention would be noticing the mechanics on his curveball are poor and working to

use the proper release to apply the proper spin to the ball. A “positive” intervention could

either develop something new, adding a new pitching to the repertoire such as learning

how to throw a knuckleball or developing something that is already good, such as

increasing the speed between one’s slowball and fastball to develop a more effective

change up. All of these interventions would have the net effect of improving one’s

overall pitching; however, the focus of the interventions are different. This mirrors the

focus of therapy in having a net effect of improving one’s life (Kazdin, 1992; Strupp,

1996).

Traditional therapy techniques focus on correcting maladaptive patterns of

thoughts and behaviors. CBT, for example, is based on the notion that maladaptive

thinking patters are the root of depressive symptoms and that fixing those patterns will

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lead to relief (Beck, 1967). Positive psychology, however, believes that psychology

should go beyond the relief of a negative state and instead focus on promoting positive

states. Part of promoting positive states involves the creation of something new.

DeRubeis (2000) echoes this distinction proposing that “a focus on the positive very

often involves a focus on learning to do things, or learning to do things well” (pg. 266).

This aspect of the definition is important, yet in my view incomplete. A proper definition

of what is “positive” about positive psychology needs to combine both meanings of the

word “positive.” That is to say, simply focusing on learning to do new things or

becoming better at what one already does well is not sufficient to be considered a positive

intervention. One could become better at doing things which do not contribute to one’s

sense of happiness or wellness. Instead, a positive intervention must teach an individual a

new skill that contributes to their happiness through increasing pleasure, engagement, or

meaning.

What is a Positive Intervention?

A positive intervention is a cognitive or behavioral strategy, or a collection of

cognitive and behavioral strategies that attempts to promote well-being – building

happiness, satisfaction with life, or positive affect through processes we have learned that

lead to well-being such as engagement, meaning, and pleasure. Furthermore, a positive

intervention attempts to build these factors for the long-term and looks to increase tonic

as opposed to phasic levels of happiness and well-being. Mood induction procedures are

widely used in psychological research; however, they provide only short-term boosts in

positive emotions. Watching a funny movie clip is not a positive intervention because this

boost in mood is short lived. However, a positive intervention could be designed out of a

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short-term intervention. For example, a person could find a show that consistently makes

him or her laugh and make sure to schedule watching this show each day. Over time, this

extra few minutes of positive emotions could help promote happiness in the long-term

and could then be considered a positive intervention.

This definition expands upon the definition Duckworth and colleagues (2005) presented

when coining the term “positive interventions. They specified that a “positive intervention” is a

technique that helps build the pleasant life, engaged life, and meaningful life. A positive

intervention is an intervention that aims to build well-being through the promotion of pleasure,

engagement, and meaning.

Furthermore, positive interventions should add something new, either a new skill

or a new way to foster these aspects of flourishing or increase expertise or strengths in an

area to better promote well-being. The core of this definition, however, is in increasing

subjective well-being.

Although many existing therapies may indeed increase positive emotions, these

therapies are focused on combating disorder and are not included as positive

interventions. Therapies that focus on building well-being and positive emotions such as

well-being therapy (Fava, 1999) and positive psychotherapy (Seligman, Rashid, & Parks,

2006) are included under the definition of positive interventions. This is a similar

distinction to that made by Durlak and Wells (1997) in distinguishing between preventive

programs that prevent behavioral and social problems by either reducing deficits or by

promoting positive behaviors that confer a protective benefit to the individual. Therefore,

the overall end state of an individual after undergoing an intervention is not enough to

distinguish it into either a positive intervention or otherwise. Psychotherapy can promote

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well-being and positive interventions can prevent disorder. But the focus of the

intervention on promoting the building blocks of positive mental health, subjective well-

being, is the critical factor for defining an intervention as “positive.”

What is Included in this Definition?

Now I will turn to a clarification of what is included in this definition of a positive

intervention. As mentioned previously, the building block of positive psychology is

subjective well-being with increasing happiness, life satisfaction, and perhaps most

importantly, positive emotions as the thrust of positive interventions. This is consistent

with Keyes’ conception of flourishing, which highlights hedonia as the hallmark feature

of flourishing. An individual must possess either high positive affect or high happiness or

life satisfaction to be considered flourishing. Building this hedonic conception of well-

being is the most important aspect of a positive intervention.

In order to discuss what is included in this definition and why, it is important to

consider what the field knows about increasing well-being. One finding from the research

is that affect is increased through “doing, not thinking” (Watson, 2002). This corresponds

to Seligman’s (2002) pleasure route to happiness, which has the goal of introducing

something new to the client that will get them activated (e.g. behavioral activation).

Theoretically, this arousal is related to increases in positive affect. The second principle

is that mood is a result of the complex interactions based on goal pursuits. We strive to

achieve things we desire (a goal) or to avoid things that are undesirable (an anti-goal).

Working towards our goals and thinking we are doing well will lead to elation and joy

whereas failing to progress towards our goals will lead to depression. Furthermore,

avoiding our anti-goals and preventing what we fear will lead to relief, whereas believing

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we are approaching these undesired events will cause anxiety. Setting goals can help

provide meaning in our lives and pursuing these goals can help produce engagement.

From this view, setting goals and engaging in activity to pursue these goals are important

aspects of positive interventions.

Positive Interventions as Throughputs

I argue that subjective well-being is the core component of the good life. As

mentioned previously, positive emotions do not only correlate with several indicators of

life success but experimental and longitudinal research suggests that positive emotions

cause success. Subjective well-being, therefore, is the ultimate outcome of positive

interventions but what role do interventions play in theories of well-being?

In an attempt to integrate several theories of well-being, Jayawickreme (2008)

proposes a model that divides well-being into the different processes that contribute to

achieving well-being. These processes include inputs, throughputs, and outputs.

Throughputs are reactions to and choices an individual makes in response to his or her

environment. Therefore, throughputs represent the processes that help foster subjective

well-being.

Why Don’t We Only Focus on Studies that Increase Happiness?

Focusing only on studies that increase happiness would confound the definition

of positive intervention with which variables the researchers choose to measure. In the

case of prevention programs based on CBT, the main outcome variables include

optimism as well as depression. In research studies on cognitive therapy, researchers

measure changes in depressive symptoms. A research team could just as easily measure

changes in optimism while conducting an outcome study of CBT. Indeed, research

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supports that patients experience changes in measures of optimism over the course of

cognitive therapy that correspond to improvements in depressive symptoms (Seligman et

al., 1988).

It is important, however, to define a positive intervention on more than just the

outcome measured in a study. A positive intervention cannot be only thought of as an

intervention that increases well-being – positive emotions, life satisfaction, happiness, or

other components. Many interventions may increase many aspects of well-being yet the

studies may not assess these as outcomes. Continuing CBT even after the depression

subsides could continue to benefit life satisfaction. When studies are conducted,

researchers decide on which outcomes to measure. In any instance, researchers could just

as easily measure a different outcome, but this does not change the nature of the

intervention. Therefore, just as defining a positive intervention by point of application

could lead an intervention to be considered positive in some cases and negative in others,

this same problem could occur by defining a positive intervention by outcome.

Is Increasing the Positive Different (and Better) than Decreasing the Negative?

Positive psychology, which focuses on strengths and what people do well, is often

presented as an alternative to the medical model that focuses on deficits and where people

need improvement. Both of these approaches have merit and the goal of positive

psychology is not to replace the medical model but to offer a complimentary approach.

Approaching deficits and focusing on reducing the negative has helped create

interventions to treat a host of psychological disorders. Existing interventions can remedy

at least 14 psychiatric disorders (Seligman, 1994) and guidelines for developing

empirically-supported treatments will likely lead to additional treatments for disorders

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that still are in need of long-lasting treatments (Chambless et al., 1998). Positive

psychology and positive interventions, however, offer another approach to combat the

burden of psychological distress. Increasing positive emotions complements decreasing

negative emotions as an important strategy for preventing and treating problems

(Fredrickson, 2000). Therefore, positive interventions represent another tool in the

toolbox of psychologists to both promote well-being in addition to combating mental

disorders.

It should be noted that the difference between positive and negative may be

purely semantic. From this view the main benefit of positive psychology would be to

provide a different language for health care professionals to present interventions to a

client. For example, an intervention might “promote optimistic thinking” as opposed to

“reducing negative cognitions” or working towards “happiness” and “meaning” rather

than attempting to address a “depression.” Even if the only difference in a positive

psychology approach is the language presented to clients, this does not necessarily

undermine the possible importance of this shift in terminology. It is consistent with

cognitive models that the language we use makes a significant impact on our

interpretations of events and subsequent emotional reactions (Beck, 1995). Socializing

clients with positive terms rather than deficits language may make clients more amenable

to the process. Further research is needed on the use the importance of framing

interventions differently and assessing impact of treatment outcome.

Another important consideration is whether there is any inherent benefit to

building the positive rather than addressing the negative. In some circumstances, negative

and positive thoughts and emotions may be a zero-sum game and increasing the positive

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may be the most efficient way to increase overall well-being (Harris & Thoresen, 2006).

Positive states undo negative states in addition to the benefits that come from the positive

states themselves. This is consistent with the undoing aspect of positive emotions

proposed by the broaden-and-build theory of positive emotions (Fredrickson, 2001,

2002). Positive emotions serve to undo the deleterious effects of negative emotions.

Negative emotions tend to lead to specific responses whereas positive emotions broaden

the available mental options in a situation. Furthermore, positive emotions reduce

accelerated heart rate that accompanies negative emotions. Evidence suggests that

although positive affect and negative affect are orthogonal in the long-term, momentary

experience of positive affect is negatively correlated with negative affect (Larsen, Diener,

Emmons, 1986; Watson, Clark, & Tellegen, 1984; Watson & Tellegen, 1985).

Furthermore, knowledge of increasing positive emotions may be important for

disorders in which deficits in positive emotions provide specificity from other disorders.

For example, low positive affect, rather than high negative affect, is a specific feature of

some disorders, such as depression. Low positive affectivity separates individuals with

depression from individuals with other disorders (Clark, Watson, & Mineka, 1994;

Watson, Clark, & Carey, 1988). Individuals with anxiety disorders, for example, have

high levels of negative affect but do not experience reduced positive affect. Increasing the

positive, therefore, might be an important tool that is not currently employed or studied

often enough in the context of depression.

Another important contribution of positive psychology is that building and

working with an individual’s strengths may be more beneficial than working on

weaknesses. A commonly used positive intervention is using a signature strength (a

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Positive Interventions 37

signature strength is a strength in the individual’s top 5 strengths as assessed by the

Values in Action (VIA) Strengths Questionnaire; Peterson & Seligman, 2004) in a new

way. Research suggests that this intervention leads to increases in happiness and

decreases in depressive symptoms (Seligman et al., 2005; 2006). One study compared the

benefits of building on strengths versus working on a weakness (Haidt, 2002). Students

were randomly assigned to either use a signature strength or work on a weakness (a

strength in the bottom 5 on the VIA). Although there were no significant differences in

changes in well-being between individuals who worked on a strength or weakness, those

who worked on a strength reported significantly greater enjoyment than those who

worked on a weakness. Enjoyment may be an important long-term predictor of benefit

from interventions as it leads participants to be more motivated, which other studies have

found is an important mediator of intervention effectiveness (Dickerhoof, Lyubomirsky,

Sheldon, 2007). In a follow-up study, individuals who were assigned to engage in an

activity that matched to a signature strength reported more intense and longer lasting

pleasure from the activity (Haidt, 2004). Matching to strengths, therefore, contributes to

positive affect and engagement while doing an activity and can form the basis of a

positive interventions.

Results from the National Institute of Mental Health Treatment of Depression

Collaborative Research Program suggest that matching to strengths may be more

effective than addressing a deficit. Analysis from this study found that individuals with

the lowest levels of cognitive dysfunction responded better to CBT whereas interpersonal

therapy (IPT) was a better treatment modality for individuals with high social skills. In

both of these instances, these baseline predictors are the actual skills that CBT and IPT

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Positive Interventions 38

aim to increase. This supports that capitalizing on strengths and doing what one does well

is an important basis and tool for interventions.

Should Positive Interventions Work?

In addition to debating the definition of well-being, researchers disagree over

whether well-being is stable over time or amenable to change. This debate is important to

consider before evaluating the effectiveness of positive interventions because if well-

being is resistant to long-term changes then it would be futile to develop interventions to

enhance well-being. Researchers who conclude that well-being is immutable use three

main arguments: (1) well-being is due largely to genetic factors, (2) well-being is

strongly related to personality, (3) life circumstances may have immediate impacts, but

individuals adapt to any changes and return to previous levels of well-being.

The first argument is that an individual’s level of subjective well-being is due to a

genetically-determined set point (or set range). Events push individuals higher or lower

within their set ranges but ultimately individuals return to their predetermined level of

well-being. Lykken and Tellegen (1996) present evidence from twin and adoption studies

that suggests the genetic contributions to well-being corresponds to a heritability estimate

as high as 80% (although several other studies find the estimate is more likely to be 50%;

see Braungart, Plomin, DeFries, & Fulker, 1992; Tellegen et al., 1988). Both of these

estimates, however, suggest that a large portion of the variance in individual levels of

well-being is due to genetic factors. This suggests that levels of well-being are likely to

be stable across time. Indeed, data from a four-wave panel study found that individuals

tend to return to their baseline level of well-being after the initial psychological impact of

the events dissipates (Headey & Wearing, 1989). Lykken (2000) compares this process to

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Positive Interventions 39

waves passing through water, events may cause the water to swell but eventually the

water will settle to its initial levels.

The stability of well-being is also due to the strong relationship between well-

being and personality traits. Traits are stable patterns of thinking, feeling, and behaving.

Recent research suggests that as much as two-thirds of the variance in subjective well-

being is accounted for by personality (Steel, Schmidt, Shultz, 2008; cf. DeNeve &

Cooper, 1998; Ozer & Benet-Martínez, 2006). Costa and McCrae (1990) have presented

evidence that supports the long-term stability of personality traits, especially for

neuroticism and extraversion. These two traits have the most overlap with subjective

well-being as they are strongly related to levels of positive and negative affect (Lucas &

Fujita, 2000; Watson & Clark, 1992; Tellegen & Waller, 1992; Yik & Russell, 2001).

Furthermore, personality tends to shape the type of events people experience. Headey and

Wearing (1989) found that the same life events tend to happen repeatedly to the same

people. This commonality of circumstances helps contribute to the stability of well-being.

Barring the occurrence of any unusual events, situations repeat themselves and well-

being remains constant.

The last evidence supporting long-term stability of well-being is the notion of the

hedonic treadmill (Brickman & Campbell, 1971). The hedonic treadmill refers to the

tendency for individuals to adapt to hedonically relevant stimuli over time, returning to

initial levels of well-being. One of the most famous studies supporting this conclusion

involved lottery winners and accident victims (Brickman, Coates, & Janoff-Bulman,

1978). Results of this study are often cited to show that after the initial psychological

impact of the event wore off, the lottery winners and the accident victims who became

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Positive Interventions 40

quadriplegic were no different in terms of happiness. In fact, the accident victims were

significantly lower on well-being than the lottery winners. The researchers concluded,

however, that the accident victims were not as unhappy as one would expect. The group

mean of the accident victims was still above the midpoint of the scale. This finding,

however, is not surprising given that most individuals consider themselves happy and rate

themselves well above the midpoint of any happiness scale (Diener & Diener, 1996).

Therefore, this study is often miscited and used as strong support for the hedonic

treadmill when in actuality this study supports that individuals do not adapt completely to

becoming a quadriplegic.

Despite the fact that the results of this study are often misreported, considerable

evidence supports the notion of the hedonic treadmill. As mentioned previously, life

events have little long-term impact on well-being (Headey & Wearing, 1989; Suh,

Diener, & Fujita, 1996). A number of studies examining reactions to the death of a

spouse show that most individuals demonstrate considerable psychological resilience and

that their initial emotional reactions fade over time (Bonanno et al., 2002; Bonanno,

Wortman, & Nesse, 2004; Lucas, Clark, Georgellis, & Diener, 2003). Thus, despite some

evidence to the contrary, the hedonic treadmill has received considerable support (see

Fredrick & Loewenstein, 1999, for a review).

Support in Opposition of Set-Point Theory

Recently, the notion that well-being is unalterable has been challenged. Evidence

suggests that well-being increases across the lifespan (Fujita & Diener, 2005). Several

life events do lead to lasting changes in life satisfaction including marriage (Lucas et al.,

2003), divorce (Lucas, 2005), and unemployment (Lucas, Clark, Georgellis, & Diener,

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Positive Interventions 41

2004). Furthermore, some countries show considerable variability in nation-wide levels

of well-being across time. Objective measures of the success of nations predict national

levels of well-being, including wealth, democracy, and productivity (Diener, Diener, &

Diener, 1995). Nations even show considerable changes in well-being that correspond to

historical events that improve or decrease the standard of living in those countries, such

as the fall of communism in Russia and Eastern Europe.

Lyubomirsky and colleagues (2005) present a framework of well-being that

suggests that although 50% of the variance in subjective well-being is due to genetic

factors, a further 10% of the variance is due to life circumstances, and 40% of the

variance is due to intentional activity. One important implication of this theory is that

individuals can perform cognitive and behavioral strategies to boost their levels of well-

being above their set points. Research can help identify which strategies contribute to

well-being and help design interventions to lead to long-term changes in well-being.

How Effective Are Positive Interventions?

Thus far, the aim of this paper has been to describe what a positive intervention is

and what it is not. In summary my view of a positive intervention is as follows:

(1) A positive intervention aims to increase well-being

(2) Well-being is defined based on a liking approach that emphasizes subjective

well-being as the core feature of well-being

(3) A positive intervention is a cognitive or behavioral strategy that looks to

increase subjective well-being

(4) Positive interventions add something new or build on a strength through

increasing and promoting these factors that lead to subjective well-being

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Positive Interventions 42

This definition expands upon previous definitions of positive interventions because it

gives a clear objective for what positive interventions aim to increase, namely subjective

well-being. Prior definitions have often either been too broad, such as the improvement

definition that states that positive interventions aim to improve an individual’s life and

therefore fail to exclude anything, or are not operationally defined, such as Duckworth

and colleagues (2005) definition that positive interventions are those that lead to pleasure,

meaning, or engagement. The definition presented in this paper clearly outlines the focus

of positive interventions as subjective well-being. Furthermore, this definition leaves out

a group of interventions that many consider positive interventions or instances of positive

psychology that I believe are misclassified. These are interventions that adopt the same

strategies and exercises as typically used in clinical populations but apply these

techniques to a novel, non-suffering population. Examples of these interventions include

the Penn Resiliency Program and the APEX program that teach cognitive-behavioral

skills to individuals before a depressive episode. As mentioned previously, these

approaches are not positive interventions because defining them as such involves a

classification of the context of the intervention as opposed to the content. A definition of

positive interventions should revolve around the content of the intervention. Lastly,

although my definition does adopt an enhancement approach, it restricts enhancement

definitions to only those that target subjective well-being. Table 1 presents a summary of

all of the definitions of positive interventions considered thus far in this paper.

One distinction between my definitions and other possible definitions of positive

interventions is that I believe positive interventions refer to interventions that aim to

increase well-being. The Pawelski approach and Enhancement definitions both describe a

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Positive Interventions 43

feature that some theorists posit is the real distinction of positive interventions - that it is

an intervention that aims to increase or better something. The differences between these

definitions and the definition adopted by this paper are based on a theoretical rationale as

to what is the goal of positive psychology. I see positive psychology as focusing on

finding ways to enhance and promote well-being, others view positive psychology as a

framework for improving things in general. Once adopting an approach that focuses on

building well-being then empirical research can bear on understanding what the best

definition of well-being is. Nevertheless, I can find no empirical way to ground the notion

that positive psychology should build well-being or should find how to better things in

general. This is a theoretical question and open for further debate. However, the evidence

presented in this paper suggests that well-being is something that individuals care very

deeply about and that research in the domain of positive psychology supports an be

increased. Furthermore, many researchers within the field of positive psychology have

developed interventions that focus on this very component, as opposed to the

enhancement view; therefore if the types of studies researchers conduct can be used as

evidence of their views, then the subjective well-being approach appears to be well

supported. This definition is adopted for the rest of the paper that will focus on

summarizing and describing the existing literature through a meta-analysis of the

effectiveness of these interventions.

Meta-Analysis of Positive Interventions

Selection of Studies

The primary purpose of a meta-analysis is to summarize the average relationship

in a given research domain. In order to accurately estimate the average effect, researchers

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Positive Interventions 44

must make an attempt to compile the existing studies that bear on this relationship. In

order to do so I employed several different search techniques. First, I conducted a

PsycINFO search using the following search terms: happiness, well-being, subjective

well-being, quality of life, positive intervention, positive emotions, life satisfaction,

intervention, and positive psychology. The PsycINFO search was conducted using various

search features until the search produced 300 or fewer hits. Each term was entered into

PsycINFO first searching for the term anywhere. If this search produced more than 300

hits I used advanced features to limit the search to first only empirical articles, then using

the term as a descriptor, and lastly using the term as a descriptor selecting only empirical

articles. Once a search produced 300 or fewer hits I scanned the titles and abstracts for

acceptability. In cases where the next level search was not completely nested in the

previous search (for example if the first search that produced 300 hits was all empirical

articles using that term) I then preceded to the next level to search for additional articles.

After each using each term individually, I created 45 two-term pairs by producing all

combinations of the 9 search terms. These pair of terms were searched using a similar

procedure in which each pair was examined first in all instances, then all empirical

instances, then using the first term as a descriptor, then all empirical instances of the first

term as a descriptor, then using the second term as a descriptor, then all empirical

instances of the second term as a descriptor, then both terms as a descriptor, and lastly all

empirical instances using both terms as a descriptor. Again, searches were reduced until it

produced 300 hits or fewer at which point the titles and abstracts of all studies retrieved

were scanned for acceptability. Table 2 presents the number of articles produced for each

aspect of this search. Summing across the highest level of all searches produced 471,436

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Positive Interventions 45

hits. Counting only the searches that produced 300 or fewer hits, 9,404 titles and abstracts

were scanned for acceptability. Of these studies, I deemed 140 studies acceptable for

further analysis and obtained the full article to determine if it met the inclusion/exclusion

criteria.

In addition to a PsycINFO search I obtained articles from additional sources. I

conducted a hand search of all issues of two prominent journals in positive psychology

(The Journal of Positive Psychology and The Journal of Happiness Studies). I also

reviewed all articles from a database that a student created of positive psychology articles

(positivepsycharticles.com) for fulfillment of the program requirements for the Masters

of Applied Positive Psychology Program at the University of Pennsylvania. Furthermore,

I searched all of the Masters projects that had been completed in this program’s two-year

history. I conducted a search of titles (and abstracts when available) of three conferences:

The Positive Psychology Summit (past 9 years), and the past five years for the American

Psychological Association and the Association for Psychological Science. Additionally, I

searched the references from a recently published book on positive psychology

interventions titled The How of Happiness: A Scientific Approach to Getting the Life you

Want (Lyubomirsky, 2007). I also completed a reference search of all articles that were

found in order to find additional articles. I also requested unpublished data from

prominent positive psychology laboratories and contacted experts for other suggestions

for sources of positive interventions.

All of these search procedures together produced 236 studies that I examined to

determine if they were acceptable to enter the meta-analyses based on the inclusion and

exclusion criteria.

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Positive Interventions 46

Inclusion/Exclusion Criteria

From this pool of 236 studies I examined each study to determine if it met

eligibility for inclusion in the meta-analysis. For inclusion in the study each study had to

meet the following criteria:

1. The study must have included an intervention that is either a cognitive or

behavioral strategy that attempts to promote well-being – building happiness, satisfaction

with life, or positive affect through processes we have learned that lead to well being

such as engagement, meaning, and pleasure as well as cognitive and behavioral strategies

we have linked to increased well-being.

2. The study must have been an experiment and included a suitable comparison

control group, such as a neutral control activity or a no-treatment control group.2

3. The study must have a measurement of subjective well-being, i.e., happiness,

positive affect, or life satisfaction OR depression. Many of these studies contained

measures of depression. Very few studies included other measures of psychopathology.

One study reported results on changes in anxiety symptoms and 1 study reported changes

in alcohol consumption. Because depression was by far the most widely assessed

measure of psychopathology it was included in the analysis.

4. The intervention must attempt to build these factors in the long-term; that is,

increase tonic as opposed to phasic levels of happiness and well-being. Mood induction

procedures are widely used in psychological research; however, such inductions provide

only short-term boosts in positive emotions.

2 This exclusion criterion was not applied in the search phase of the meta-analysis.

Therefore, the initial results include studies with active control groups (For example,

CBT and treatment as usual). This will be discussed further in the results section.

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Positive Interventions 47

5. The study must contain statistical information sufficient to compute an effect

size.

6. Although studies on exercise and behavioral activation fit the definition

proposed in the introduction of a positive intervention, these studies were excluded from

the meta-analysis for the following reasons.

(1) Many of these studies have only measures of depression and not of well-being.

(2) The link between positive emotions and activity is best understood at a

biological level as opposed to a psychological level.

(3) Given the large amount of studies of this nature, the average effect size would

be strongly weighted by these studies.

(4) Several recent meta-analyses of the effectiveness of exercise and behavioral

activation have already been conducted (see Cuijpers, van Straten, &

Warmerdam, 2007; Puetz, O’Connor, & Dishman, 2006; Stathopoulou, Powers,

Berry, Smits, & Otto, 2006), therefore the average effect size for positive

interventions can be compared to the average effect size of exercise without

overly weighting the effect size computed from this study to reflect the effects of

exercise and behavioral activation.

After applying these criteria to each study 58 research studies were selected for

data extraction.

Data Extracted

I extracted several aspects of each study that met the inclusion criteria. Each study

received a rating of study quality based on a 7-point scale. Aspects of the study

considered for study quality included: whether or not the study used random assignment

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Positive Interventions 48

to condition, the quality of the statistical analysis, internal validity, external validity,

strength of the comparison condition, adequacy of measures used, and the mode of

administration of the intervention. One point was awarded for each aspect of the study

allowing the scores to range from 0 to 7. The quality scores for the studies included in

this analysis ranged from 3 to 7. The intervention of each study was classified into one of

14 different groups based on the construct that the intervention targeted. Furthermore,

each intervention was classified was either a behavioral, cognitive, or cognitive-

behavioral intervention. Other variables included length of the intervention and the type

of sample used (either patient sample versus non-patient sample). Lastly for each study a

measure of effect size was extracted. The calculation of effect sizes followed principles

set forth by Hedges and Olkin (1985) and Rosenthal (1991).

Effect Size Calculation

For each study an effect size was calculated based on the post-intervention

difference between the treatment and control group using the following methods. For

studies that reported the post-treatment means and standard deviations, effect sizes were

calculated by subtracting the mean of the control group ( CX ) from mean of the treatment

group ( TX ) and dividing by the pooled standard deviation ( pooledσ ). This produces a

Cohen’s d estimation of effect size for each study.

pooled

CT XXd

σ

−=

For some studies, this information was not provided and the effect sizes were estimated

using the following equations that allow conversion from standard statistical tests

(Rosenthal, 1991):

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Positive Interventions 49

df

td

2=

df

Fd

x),1(2 ⋅=

If the authors provided neither the means and standard deviations nor a test statistic and

corresponding degrees of freedom, I estimated the effect size by converting the p-value to

a relevant effect size using the following equations (Rosenthal, 1991):

N

Zr =

21

2

r

rd

=

If the results were reported as significant the p-value was considered to be equal to the

alpha value reported. In studies that reported the results as non-significant then the effect

size for the study was assumed to be zero. This is a rather conservative estimate for the

effect sizes therefore reducing the value of the overall effect size estimated as opposed to

biasing the estimation in favoring of finding an effect.

After calculating the Cohen’s d for each outcome the effect size was adjusted for

small sample sizes using a correction suggested by Hedges and Olin (1985). This

correction produces a Hedge’s g from a Cohen’s d with the following equation:

−+−=

9)(4

31

21 nndg

In addition to applying this correction, each effect size requires an estimation of the

variance component corresponding to that effect size. These variance components were

estimated using the following equations:

)1()1( 221112

21

ppnppn

nnw

−+−=

2'1 d

Nw

−=

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Positive Interventions 50

Lastly the overall average effect size was calculated by weighting each individual effect

size by its variance.

j

jj

w

gwg

Σ

Σ=

This overall effect size corrects for differences in sample sizes and variance, giving more

weight to effect sizes from large studies that are estimated more accurately. Table 3

displays the information from each study extracted from the original article including the

values of the effect size computed and any moderator variables used in subsequent

analyses. I will now discuss the results of computing these average effect sizes.

Results

There were 58 research studies combined in this meta-analysis that included a

total of 4,502 participants. All studies met my criteria for a “positive intervention”;

however, the type of intervention varied greatly from study to study. This feature of the

data suggests the analyses should use random effects as opposed to fixed effects. Fixed

effects models assume that there is one true effect size for all studies included in the

analysis. This might be the case for several replications of the same intervention,

however, with several distinct interventions it is unlikely that one true effect size exists

for all the studies. It has been suggested that if there is likely to be variability amongst the

interventions, a random effects model is more appropriate (Hedges & Olkin, 1985). A

random effects model produces a summary of the average effect sizes of the studies as

opposed to estimating the true effect size. One way to support this empirically is to

examine the amount of dispersion in effect sizes that is between studies. This is

accomplished by analyzing for significant homogeneity between the studies using

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Positive Interventions 51

Cochran’s Q statistic. The Q statistic is a measure of the total amount of variance

amongst the effect sizes. A non-significant Q statistics suggests that the effect sizes are

similar enough to compute a true population effect sizes (Cochran, 1954). In the case

where a Q statistic is non-significant and there is theoretical justification to do so an

estimate of the true effect size in the population is computed using a fixed effects model.

If the Q statistic is significant, a random effects model that calculates the average effect

size of a distribution of effect sizes is more appropriate.

Calculating the overall effect size of positive interventions on well-being using a

random effects model produced an effect size of .54 (95% confidence interval .40, .68),

which is a moderate sized effect (Cohen, 1977). This effect size was based on 51 studies

that reported measures of well-being. In addition to determining the average effect size

the distribution of effect sizes between studies is another consideration. Figure 1 displays

a Forest plot of the effect sizes for the individual studies. There was significant

heterogeneity in this distribution (Q(50) = 201.53, p < .001). In order to quantify the

amount of heterogeneity the I2 value was computed. The I

2 is a ratio of the amount of

variance that is between effect sizes compared to the total variance (Higgins, Thompson,

Deeks, & Altman, 2003). Cut-offs proposed for evaluating the magnitude of an I2 are I

2 =

25 is low, I2 = 50 is moderate, and I

2 = 75 is high. For the overall estimation of the well-

being effect size, I2 = 75.19 suggesting that a large amount of the total variation in the

effect sizes is between-studies. Given that over three-fourths of the total variance is

represented by between-study variation, moderation analysis should consider aspects of

the studies that can explain this between-study difference. Moderator analyses will be

examined in turn.

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Positive Interventions 52

One criticism often raised against meta-analyses is that many studies on the topic

are conducted yet only those that produce statistically significant findings are published.

This is referred to as the file-drawer problem and is an issue because this would bias the

effect sizes because often only the published (and significant) findings are used to form

the overall effect size (Rosenthal, 1991). The most extreme form of this criticism has

claimed that for every published study, an unpublished study exists with an effect size of

the same magnitude in favor of control (Rosenthal & Rubin, 1978).

This study attempted to address this criticism in several ways. One is through

extensive literature searching and inclusion of unpublished data. This includes poster

presentations, doctoral dissertations, and master’s theses. Of the 58 studies included, 4

were unpublished doctoral dissertations, 1 was a poster presentation, 1 was an

unpublished master’s thesis, and 2 were manuscripts in preparation based on unpublished

data. Although these studies have not undergone the peer review process, their inclusion

helps address the concern that the effect size obtained is based on the few significant

findings that were published.

Furthermore, fail-safe values were computed to describe the number of studies

that would have to exist (yet not included) with null findings to reduce the given effect

size to zero (Rosenthal, 1991). The classic fail-safe N for the effect size on well-being

was 2617, meaning there would have to be 2617 studies not included with null findings to

reduce the effect size to zero. Given the effort to include unpublished data and the

number of published studies found, this number of unpublished studies is unlikely. The

classic fail-safe N, however, assumes that the unpublished studies have a d = .00 and

examines the number needed to reduce to effect size to zero. This may not be of interest

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Positive Interventions 53

to researchers because effect sizes larger than zero may still be considered uninteresting

and unpublished studies may have non-zero effect sizes that would still reduce the value

of the overall effect size considerable. To address these concerns, another approach,

Orwin’s (1983) fail-safe N, determines the number of studies with a specified effect size

that would be needed to reduce the effect size to a specified value that is considered to be

uninteresting to the researcher. I calculated two values of this fail-safe N, one examined

the number of studies with a d = .00 that would have to exist to reduce the average effect

size to .199 and the other the number of studies with a d = .10 that would have to exist to

reduce the average effect size to .199. These fail-safe N’s produced values of 49 studies

and 99 studies respectively. Again, given the number of studies found and the number of

unpublished studies included it is unlikely that this number of unpublished studies with

such small effect sizes exist yet were not obtained for this analysis.

Other than computing fail-safe values, additional methods of assessing

publication bias compare the obtained effect size to the variance of each study. These

methods assume that if the obtained effect sizes were from the sample of studies that

happened to be published only because they obtained significant results due to chance,

then studies with high standard errors (or small studies) would be associated with the

largest effect sizes. The first method is to assess this graphical by plotting the obtained

effect sizes versus the standard errors. This is referred to as a funnel plot due to the shape

it produces (Light, Singer, & Willett, 1994). Figure 2 displays the funnel plot of the effect

sizes for measures of well-being. If this sample included studies from the complete

population of studies then the effect sizes should cluster symmetrically around the line

indicating the combined effect size. If this sample was biased by only including published

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Positive Interventions 54

and significant studies then there would be asymmetry in the plot. In this case the effect

sizes appear to be symmetrically distributed around the line, which supports the notion

that this analysis is not subject to publication bias. Although funnel plots provide a

graphical display of the data it is left to the researcher to interpret whether or not the

funnel plot indicates a significant bias or not. Quantitative assessments of the relationship

between the effect sizes and the standard errors also aid in the interpretation. Begg and

Mazumdar (1994) suggest calculating a correlation between the standardized effect size

and the variances of these effects. The value of Begg and Mazumdar’s rank correlation

test is positive if large effect sizes correspond to studies with small variances and

negative if large effect sizes come from studies with large variances. For the effect sizes

of well-being, τ = .30, p = .002, suggesting that the largest effect sizes come from the

studies with smaller variances. The results of these statistics support that the overall

effect size was unlikely to be biased by only sampling the significant and published

studies.

This conclusion is not surprising because this analysis included 8 unpublished

studies (or roughly 16% of the included studies). In order to further address whether that

average effect size could be biased due to the existence of unpublished studies

moderation analysis compared the effect sizes for published studies to unpublished

studies for this sample. The average effect size for the 8 included unpublished studies

was .26 (95% confidence interval .06, .46), whereas the average effect size for published

studies was .49 (95% confidence interval .37, .62). The average effect sizes for published

versus unpublished were not statistically different at the standard α = .05 level, Q(1) =

3.71, p = .054. However, there was a trend suggesting that unpublished studies have

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lower effect sizes than published studies. This finding suggests that if anything the

average effect size computed for this meta-analysis is a conservative estimate given that a

large effort was made to include unpublished studies. Furthermore, the average effect size

for unpublished studies included in this study was still in the small-to-moderate range and

was significantly different from zero.

In addition to analyzing the effects of positive interventions on well-being, I

calculated an average effect size for differences in depressive symptoms between

treatment and control post-intervention. Twenty-five studies reported measures of

depressive symptoms. Again, a random effects model estimated the overall average effect

size for depressive symptoms, which was equal to .64 (95% confidence interval .40, .88)

this is a moderate sized effect. There was significant heterogeneity in the distribution of

effect sizes between studies as well, Q(24) = 77.70, p < .001, I2 = 69.11.

An analysis of the indicators of publication bias raised greater concern, however,

for measures of depressive symptoms than for the well-being effect. The classic fail-safe

N had a value of 435 studies. Orwin’s fail-safe N was 20 studies to reduce the average

effect size to .199 if the unretrieved studies had an effect size of .00 and 39 studies if the

unretrieved studies had an effect size of .10. Given the number of studies that measured

depressive symptoms in the sample it is unlikely that this number of unretrieved studies

exist, however, these values are much lower than those found for the measures of well-

being.

Figure 4 displays the funnel plot for effect sizes of measures of depressive

symptoms. This funnel plot raises the concern that there could be additional studies not

included in the analysis that would lead to a reduction in the overall effect size. A

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majority of the effect sizes are clustered around the right side of the line with only three

effect sizes below the average effect size. Begg and Mazumdar’s rank correlation test is τ

= .40, p = .005 which suggests that the studies with the largest effect sizes had the smaller

variances. Therefore, with regards to depressive symptoms, there is some concern that

these studies are biased. One possibility is that because most of these studies use well-

being as the primary outcome measure the measures of depressive symptoms are reported

only when the effects are significant. Again, while the funnel plot and Begg and

Mazumdar’s rank correlation reach different conclusions, the fact that several sources of

data suggests some bias merits concern.

Again, I compared the average effect sizes on depressive symptoms for published

versus unpublished studies. Only 2 of the 8 unpublished studies reported measures of

depressive symptoms for the sample. The average effect size for changes in depressive

symptoms from published studies was .79 (95% confidence interval .52, 1.07). For

unpublished studies the average effect size was .12 (95% confidence interval -.52, .76).

The average effect size for unpublished studies was not statistically significantly different

from zero. Although, the average effect sizes were not significantly different from each

other, there was a trend suggesting that unpublished studies have smaller effect sizes on

depressive symptoms, Q(1) = 3.53, p = .06. Given concerns raised from other measures

of publication bias, this suggests the average effect size for depressive symptoms should

be interpreted cautiously; however, the inclusion of unpublished studies in this analyses

supports the view that the estimate presented is conservative.

Given that there was significant heterogeneity in effect sizes for both measures of

well-being and depressive symptoms and that a large proportion of the variance was

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between effect sizes, the next set of analyses analyzed moderator variables. Before I

conducted this moderation analysis, some studies included in the initial analyses were

excluded. First, 6 studies were excluded from this analyses because the control

comparisons used were active comparison conditions (either CBT, treatment as usual, or

a partial intervention control group). This violates one of the exclusion criteria however I

decided to run an initial run of data analyses including these studies. Furthermore,

extreme values of effect sizes were assessed using the sample-adjusted meta-analytic

deviancy (SAMD) statistic (Huffcutt & Arthur, 1995). The SAMD statistic is a ratio of

the raw deviancy of a given study divided by the sampling error of that difference. It is

based on a similar logic to the difference-in-fit standardized (DFFITS) statistic used to

assess outliers in regression analysis. The value of the SAMD statistic fits a t distribution

and therefore values of greater than 2 should be considered as outliers. For meta-

analysis, however, there is a trade-off in excluding outliers because one goal of meta-

analysis is to search for meaningful moderator variables to characterize the variability of

effect sizes. Excluding too many studies can eliminate possibly interesting variation in

effect sizes, however, very extreme values could be due to a variety of errors (such as a

value being entered incorrectly) that could bias the overall effect size. An analysis of a

scree plot is required in order to determine if a value is too extreme. Figure 5 displays the

screen plot of SAMD statistics based on well-being effect sizes. Figure 6 displays the

same plot for effect sizes of depressive symptoms. Two studies with well-being measures

and 1 study with depression measures were excluded on the basis of being extreme

outliers. The studies for well-being had effect sizes of d = 5.00 and d = 2.98. The study

with a measure of depressive symptoms had an effect size of d = -.12. This reduces the

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number of studies in the moderation analysis to 49. Table 4 displays the overall

estimations of average effect sizes after excluding these studies.

Five variables were coded from the studies to be considered as moderators of the

effectiveness of positive interventions – construct measured, study population, target of

the intervention, type of intervention, length of the intervention and quality of the study.

Given that subjective well-being is a multi-faceted construct I also computed

individual effect sizes for different components of subjective well-being. Table 5 displays

the results for average effect size on each component that contributes to subjective well-

being, happiness, life satisfaction, positive affect, well-being, and composites of the

previous measures. Well-being measures are those measures that assessed a construct that

represents some combination of both affective and cognitive measures. Therefore, these

effect sizes are based on a theoretical a priori combination of these components in a

single measure and cannot be parsed out. Composite measures come from studies that

combined different measures of well-being into a single value to calculate an effect size.

Construct was a significant moderator of the average effect sizes, Q(4) = 15.62, p = .004.

By far the largest average effect size was on measures of happiness (.71). Life

satisfaction had the lowest overall effect size (.44). One noteworthy finding was that

studies that combined measures into composite values (.21) had lower effect sizes than

any of the individual measures of subjective well-being.

Another potential moderator was the study population. Studies were divided into

those studies that use a patient population versus a normal sample. Table 6 displays the

data relevant to the average effect size of well-being measures in both patient populations

and normal populations. Type of sample was not a significant moderator of differences in

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effect size for well-being, Q(1) = 1.82, p = .18. The estimated average effect size in the

normal populations was .46; whereas the estimated average effect size in patient

populations was .29. The difference in effect sizes for depressive symptoms was quite

similar, although both effect sizes had greater magnitudes. Table 7 displays the values for

measures of depressive symptoms. The patient population had an average effect size of

.89, whereas the non-patient sample had an effect size of .71. These effect sizes were not

significantly different, Q(1) = .41, p = .52. Although these effects did not reach

significance dividing the sample into patient versus non-patient populations did decrease

the I2 values in each of the subgroups. For well-being measures, studies using patient

samples had a homogenous distribution of effect sizes, Q(6) = 3.74, p = .71.

The interventions were classified into 14 different categories based on the target

of the intervention. Table 8 displays the results for average effect sizes for interventions

aimed at targeting different constructs. Reminiscence interventions comprised the largest

group of studied interventions. For well-being measures, target of intervention was not a

significant moderator, Q(13) = 13.40, p = .42. Table 9 displays the results of the

moderation analysis for effect sizes of depressive symptoms. Studies that assessed

depressive symptoms used only 9 different types of positive interventions. Again, target

of intervention was not a significant moderator of the effectiveness of positive

interventions with regards to depressive symptoms, Q(8) = 13.92, p = .08.

Each intervention also was classified into either a behavioral, cognitive, or

cognitive-behavioral intervention. Table 10 displays the average effect size of well-being

measures broken up by type of intervention. Type of intervention was a significant

moderator of the effectiveness of positive interventions using well-being measures as an

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outcome, Q(2) = 15.1, p = .001. Behavioral interventions had the lowest average effect

size of .15 whereas cognitive interventions had an average effect size of .47 and

cognitive-behavioral interventions had an average effect size of .56. This same pattern

did not hold for measures of depressive symptoms. Table 11 displays the results for the

effect sizes of depressive symptoms. Type of intervention was not a significant

moderator, Q(2) = .77, p = .68. Although behavioral interventions still had the smallest

average effect size, all effect sizes for depressive symptoms were in the moderate to large

range.

Length of study was coded in terms of number of days the intervention lasted.

Given that length of study is a continuous variable, meta-regression was used to

determine if there was a relationship between effect size and length of study. Figure 7

displays the values of effect size for well-being plotted against the length of the study.

The value of the slope for the regression was .00043, and was not significant, p = .65.

This suggests that a linear trend between effect size and length of intervention was not

present for well-being measures. Figure 8 displays the effect sizes of depressive

symptoms plotted against length of the study. The estimate for the slope of the line of

best fit was .00038, and this value was not significant, p = .33.

Lastly, the average effect sizes for both well-being and depression overall were

recalculated using the quality ratings of the study as weights. For the well-being effect

sizes, the correlation between effect size and quality ratings was not significant, r (43) = -

.07, p = .66. Adjusting for quality, however, increased the overall effect size for well-

being = .51, and there was no longer significant heterogeneity between the effect sizes,

Q(42) = 38.49, p = .67, I2 = 0.00. This suggests that some of the heterogeneity in effect

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Positive Interventions 61

sizes for well-being measures was the varying quality of the studies combined to produce

an average effect size. For depressive symptoms, the correlation between quality ratings

and effect sizes was r(17) = .22, p = .39. The average effect size was .93 although there

was still considerable heterogeneity between these effect sizes even after accounting for

quality of the studies, Q(16) = 35.71, p < .001, I2 = 55.19.

Discussion

The results of this meta-analysis support the view that positive interventions lead

to significant increases in well-being and decreases in depressive symptoms. This

synthesis of the existing literature on positive interventions highlights the variety of

exercises used to increase subjective well-being. It is not surprising, therefore, that there

was considerable heterogeneity amongst study effect sizes. Furthermore, analysis of

moderators showed that differences in aspects of the interventions could explain some of

the differences of the effect sizes. Specifically, this study found that exercises that

included a cognitive component (either by itself or in combination with a behavioral

component) corresponded to larger effect sizes than behavioral interventions.

After accounting for significant outliers the overall average effect size on

measures of well-being was .44 (.51 correcting for quality of study). This suggests that on

average, positive interventions lead to small-to-moderate boosts in subjective well-being

compared to inactive or no-treatment control conditions. These effect sizes are smaller

than those reported by Okun and colleagues (1990) who meta-analyzed interventions

designed to increase subjective well-being among the elderly and found an average effect

size of .67. These effect sizes, however, compare favorably to prevention programs

including prevention programs focused on wellness (average effect size of .41; MacLeod

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& Nelson, 2000), programs for children (average effect size of .34; Durlak & Wells,

1997), and depression prevention programs (average effect size of .16; Horowitz &

Garber, 2006). Furthermore, moderate effect sizes, such as those reported for measures of

depressive symptoms, are often found in meta-analyses of psychotherapy (average effect

size of .68; Smith & Glass, 1977). The results of this meta-analysis support that positive

interventions are as effective as many other forms of interventions and the size of the

effects are comparable to many prevention programs.

It is not surprising that we did not observe large effects of positive interventions

given the populations used most often in these studies. A majority of studies investigate

interventions applied to samples of college students. College students on average are

neither depressed, nor particularly unhappy, and although many Americans do report the

desire to be happier (see Lyubomirsky et al., 2005), a vast majority of these participants

did not seek out these interventions. Therefore, the effectiveness of these interventions

may be smaller than what would be expected in an applied setting where individuals are

more devoted to the goal of becoming happier and motivated to complete the

intervention.

Furthermore, these interventions lead to a moderate decrease in depressive

symptoms that corresponds to an average effect size of .77 (.93 correcting for quality of

study). This is larger than the effect size found for measures of well-being. There is some

concern, however, that this effect size may be inflated due to publication bias based on

the results of the fail-safe calculations as well as the funnel plot. One possibility is that

researchers only report the results for depression if they are significant.

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Another important comparison are those interventions which fit the definition

proposed for a “positive” intervention but were excluded due to the fact that recent meta-

analyses were already available on these specific interventions. This includes exercise

and behavioral activation. A recent meta-analysis of behavioral activation found that

activity scheduling compared to control conditions corresponded to an effect size of .87

(Cuijpers et al., 2007). This effect size is comparable to the effect size found for measures

of depressive symptoms in this study and is almost identical to the effect size of .89

reported for reduction of depressive symptoms in patient populations. Therefore, even

though behavioral activation studies were excluded from the analysis, their effectiveness

was on par with the positive interventions synthesized in this study. Meta-analyses of

exercise have found similar ranges of effect with small to moderate effects of increases in

affect and decreases in depressive symptoms in non-depressed populations (North,

McCullagh, & Tran, 1990; Puetz et al., 2006) and large combined effects for reduction of

depressive symptoms in clinical samples (Stathopoulou et al., 2006). The results of this

study found a large effect size corresponding to reduction of depressive symptoms in

patient populations for positive interventions. Note, however, that this effect size was still

smaller than the effects of exercise on depression.

The moderation analyses provided some insight into which aspects of the

interventions affect how efficacious the interventions are and which measures of well-

being positive psychology exercises have the largest effects on. The length of the

intervention was not related to the size of the effect. This is consistent with the notion

that positive psychology exercises are brief interventions that can lead to significant

boosts in well-being and decreases in depressive symptoms (Schueller & Seligman, in

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press; Seligman et al., 2005, 2006). Given the low cost of applying these interventions,

which are easily disseminated through modalities with no therapist or human contact,

small-to-moderate effects are impressive. Prentice and Miller (1992) suggest that even

small effects are quite important when they involve any small manipulation of the

independent variable leading to any explainable variance. Although, the average length of

time for interventions included in this analysis was one month, the duration of over half

of the interventions was shorter than 3 weeks. This supports the notion that the

interventions included in this analyses were brief and involved little manipulation of the

independent variable yet still produced significant effects in the outcomes of interest.

Although it failed to reach significance, the effect sizes for measures of well-

being was lower for those interventions applied to patient populations compared to non-

patient populations. This could be because the most effective intervention for patient

populations is to focus on their given disorder first. That is, for individuals with

depression it may be more effective to first target the depression and then apply a positive

intervention to help prevent relapse and promote recovery. Recovery from a disorder

includes more than just a reduction of symptoms (Fava, Ruini, & Belaise; 2007; Fava,

Tomba, & Grandi, 2007). “Recovery” often refers to when an individual no longer meets

full criteria for a disorder, but this concept has come to include definitions of

psychological well-being as well (Fava, 1996). Therefore, to truly achieve a state of

recovery, existing interventions may be enhanced by combining positive interventions to

both decrease disorder and increase well-being. Well-being therapy, an intervention

based on Ryff’s model of psychological well-being, espouses this very philosophy, as it

designed to be used in the treatment of affective disorders during the residual phase of

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therapy (Fava, 1999; Fava & Ruini, 2003). The results of this meta-analysis support that

positive interventions might be best applied during this period because they have larger

effect sizes in non-patient populations. Further investigation of the use of positive

interventions in patient populations following treatment offers rich questions for future

research. For example, are positive interventions an effective form of prevention of new

episodes following a proper trial of cognitive-behavioral therapy? Would positive

interventions lead to increases in well-being to supplement the reduction of depressive

symptoms? These are exciting, yet untested, empirical questions.

Exercises that included a cognitive component had significantly larger effect sizes

for well-being than behavioral only interventions. These findings are inconsistent with

the argument that well-being may be increased more by doing than thinking (Watson,

2002). For increasing happiness, changing an individual’s pattern of thinking may be

more effective at leading to short term boosts in well-being than a behavioral

intervention. This is consistent with cognitive theories of happiness that implicate

cognitive processing patterns as an important determinant of an individual’s level of

happiness (see Lyubomirsky, 2001; Veenhoven, 2006). An important caveat to this

conclusion is that all measures of well-being for this meta-analysis compared treatment to

control post-intervention. Behavioral interventions may have a stronger influence on

affective components of well-being that may be slower acting, but ultimately more

important to overall well-being. Veenhoven (2006) argues that affective theories of

happiness not only account for a larger portion of the variance in measures of life

satisfaction, but are a more important foundation for intervention; if one could change

happiness by changing thinking than happiness is divorced from the actual conditions of

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one’s life. This leads to the rather dangerous philosophical position that one could be

happy in Hell if one just changed their thoughts about living in Hell. Although, this

argument is an extreme one, it is important to remember that interventions should not just

be increasing subjective judgments in isolation of concern for other aspects of an

individual’s life.

Of the studies examined the only measure assessed that could address the other

theories of well-being discussed was health and only 4 studies reported these outcomes.

Therefore, it is difficult to directly compare theories of well-being empirically to

determine the effectiveness of interventions that met the definition of positive

interventions according to other definitions proposed for well-being. This speaks to the

the types of researchers typically conducting studies on interventions. The subjective

well-being approach is heavily favored in the psychological literature because it focuses

on subjective mental states assessed with self-report measures. Other conceptions of well-

being would include changes in health, literacy, education, beauty, etc. as a result of

positive interventions. Furthermore, desire-fulfillment accounts would require some

assessment of what an individual wants and if the intervention is helping achieve those

goals. In order to determine the effects of positive interventions on other conceptions of

well-being researchers need to include more diverse measures in studies. A more

complete conception of well-being including objective measures could provide support

for the important of objective lists or underscore the value of adopting a subjective well-

being perspective. Future research should include more objective measures in order to

answer this question empirically.

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By contrast, there were no significant differences between behavioral, cognitive,

and cognitive behavioral approaches for measures of depression symptoms, which

suggests that behavioral approaches are appropriate for decreasing depression. In this

way, positive psychology interventions may be acting through a similar mechanism as

behavioral activation. These findings support the view that all of these approaches are

reasonable to combat depression and positive interventions may be yet another approach.

Positive interventions contribute most strongly to measures of happiness and least

strongly to measures of life satisfaction. This smaller effect on life satisfaction is not

surprising given that life satisfaction is a global and stable cognitive appraisal of how

good one’s life is (Diener et al., 1985). Furthermore, the fact that life satisfaction

measures were the least malleable component of subjective well-being provides support

that these interventions do not simply make an individual feel better about his or her life

conditions, but do lead to increases in positive mood and happiness. Such an intervention

effect is preferable to finding increases in life satisfaction with no changes in mood

because such gains may be more likely to fade over time (R. Veenhoven, personal

communication, March 29, 2008). Additionally, the fact that interventions have larger

effects on happiness and mood compared to life satisfaction contradicts the criticism that

positive interventions are simply making individuals feel better about living in Hell.

The fact that composite measures of happiness corresponded to the smallest effect

sizes is difficult to interpret. Multiple indicators of subjective well-being should lead to a

more accurate estimation of the true well-being construct. One problem with this

approach as it is often implemented in the literature is that researchers typically use an

equal weighting of the different components to compute a well-being composite. The use

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of a well-being composite should be constructed through either a theoretical or empirical

weighting of the different aspects of the constructs. Most often, when multiple indicators

are combined to form a latent construct, a measurement model helps guide the researcher

to the nature of the relationship between the variables and suggests values for weighting.

Thus the equal weighted composite used in studies included in this analysis is not

necessarily more reliable than the individual measures. More caution should be used to

have a clear theoretical or empirical rational before constructing a composite variable and

the effects on different aspects of well-being should be reported. In this meta-analysis, if

the studies reported the individual components of well-being, those values were entered

into the analyses instead of the composite.

Directions for Future Research

This meta-analysis provides support for the effectiveness of positive psychology

interventions. Furthermore, it suggests that cognitive (and cognitive-behavioral)

interventions are more effective than behavioral interventions. Future research should

attempt to understand why these interventions are effective. What are the processes of

change involved in positive psychology interventions and how are these changes leading

to improvements in well-being? Furthermore, although relatively long interventions were

included (length of intervention ranged from 1 day to 180 days with an average length of

approximately 1 month), there was no moderating effect of length of intervention,

suggesting that positive interventions may be a cost- and time-effective way of increasing

well-being.

As mentioned previously, to directly compare conceptions of well-being, more

studies need to include outcomes that address both needing and wanting theories of well-

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being. Psychological accounts of well-being focus almost exclusively on well-being and

thus researchers are biased in not considering other accounts. Although I do agree that a

subjective well-being approach is the best conception of well-being and most relevant to

positive interventions, including variables that tap other accounts of well-being could aid

empirical support for this position. Furthermore, even if positive interventions are defined

using a subjective well-being approach, understanding the connections between positive

interventions and objective criteria for well-being may help positive interventions shape

public policy.

Limitations

One limitation of this study is that it included only post-intervention outcomes.

Whether or not changes in happiness can be maintained is an important research

questions. Very few studies assess follow-up boosts on well-being, although some more

recent studies are beginning to follow participants for longer periods after the

intervention. Although several researchers have reached the pessimistic conclusion that

happiness is the product of genes and personality and that individuals quickly adapt to

any boosts more recent evidence supports that long-term changes in happiness are

achievable and that adaptation is not as ubiquitous or inevitable as once thought (see

Diener, Lucas, & Scollon, 2006; Lyubomirsky et al., 2005). Once more positive

interventions have assessed long-term changes, however, this can be further assessed

with future meta-analyses.

Another limitation of this study is that the findings regarding publication bias on

measures of depressive symptoms were inconclusive. The possibility remains that

researchers do not report measures of depressive symptoms if the findings are not

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significant. Although this cannot be determined based on this data, the effect size of

measures of depressive symptoms should be interpreted with caution. Inclusion of

unpublished studies in this analysis partially accounts for addressing this concern.

Overall, approximately 1 out of 8 studies included in this analysis were unpublished

studies. Furthermore, although there was a trend of lower effect sizes in unpublished

studies as compared to published studies, the effect sizes obtained from both sources

were not significantly different. Lastly, inclusion of unpublished studies supports that the

effect sizes obtained are conservative in regards to addressing publication bias.

Lastly, several studies were excluded from this analysis because they did not have

adequate assessment of outcomes. This is because positive psychology is a relatively

young field and many of the efforts in intervention research are still pilot studies. Future

intervention studies should include proper measures to complete a quantitative analysis of

the effectiveness of the intervention. This will help positive psychology grow through

empirical investigation of its tenets and practices. Some of the omissions are studies that

positive psychologists often have an interest in determining if data can support the

effectiveness of these interventions. These include self-help groups such as large group

awareness training (such as Erhard Seminars Training and Forum). In cases where there

were empirical studies on these interventions they lacked the outcome measures to be

included in this analyses. In most instances, these interventions are investigating through

testimonials or case studies rather than rigorous empirical testing (see Finkelstein,

Wenegrat, & Yalom, 1982; Fisher et al., 1989). In order to compare these interventions to

positive psychology interventions research studies must include adequate assessments of

outcome.

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Conclusions

Overall, this study found support for the effectiveness of positive interventions

with effect sizes on par to average effects found in other forms of prevention and

treatment. Furthermore, cognitive and cognitive-behavioral forms of positive

interventions were significantly more effective at increasing measures of subjective well-

being than behavioral interventions. The accumulation of research on positive

intervention supports the view that fostering the positive aspects can lead to increases in

subjective well-being and decreases in depressive symptoms. Although more studies can

help provide support for the usefulness of these interventions in addition to other forms of

treatment, the existing evidence points to these forms of intervention as a useful tool for

psychologists in attempting to promote well-being.

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Figure 1. Standardized effect sizes of positive interventions on well-being compared to control conditions at

post-test.

Study name Outcome Statistics for each study Std diff in means and 95% CI

Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value

Algoe, 2005 Happiness 0.051 0.152 0.023 -0.246 0.348 0.336 0.737

Aylett, 2004 Life Satisfaction 0.344 0.217 0.047 -0.082 0.770 1.583 0.113

Bedard et al. 2003 Well-Being 0.341 0.662 0.438 -0.956 1.638 0.515 0.606

Bennett & Maas, 1988 Life Satisfaction 2.979 0.570 0.325 1.862 4.096 5.229 0.000

Bryant et al., 2005 Happiness 0.650 0.282 0.080 0.097 1.203 2.304 0.021

Burton & King, 2004 Well-Being 1.300 0.232 0.054 0.844 1.756 5.593 0.000

Cook, 1998 Life Satisfaction 0.830 0.367 0.135 0.111 1.549 2.263 0.024

Davis, 2004 Life Satisfaction 0.870 0.559 0.313 -0.226 1.966 1.556 0.120

Dickerhoof et al., 2007 Well-Being 0.140 0.117 0.014 -0.089 0.369 1.199 0.230

Eells, 2006 Life Satisfaction 0.793 0.186 0.034 0.429 1.157 4.271 0.000

Elizabeth, 2006 Well-Being 5.000 0.524 0.275 3.972 6.028 9.535 0.000

Emmons & McCullough, 2003 Study 2 Positive Affect 0.588 0.200 0.040 0.195 0.981 2.935 0.003

Emmons & McCullough, Study 3 Combined 0.632 0.255 0.065 0.133 1.131 2.483 0.013

Fallot, 1979-1980 Happiness 0.484 0.370 0.137 -0.242 1.210 1.306 0.191

Fava et al., 1998 Well-Being 0.000 0.447 0.200 -0.877 0.877 0.000 1.000

Fava et al., 2007 Combined 1.000 0.587 0.345 -0.151 2.151 1.702 0.089

Fordyce, 1977 Study 1 Happiness 0.572 0.168 0.028 0.242 0.902 3.396 0.001

Fordyce, 1977 Study 2 Happiness 0.741 0.253 0.064 0.245 1.237 2.925 0.003

Fordyce, 1983 Study 4 Combined 0.424 0.214 0.046 0.004 0.844 1.978 0.048

Fordyce, 1983 Study 5 Combined 0.389 0.262 0.069 -0.125 0.903 1.484 0.138

Frieswijk et al., 2006 Well-Being 0.265 0.145 0.021 -0.018 0.548 1.833 0.067

Goldwurm et al., 2003 Combined 0.450 0.211 0.045 0.036 0.864 2.128 0.033

Green et al., 2006 Combined 0.808 0.295 0.087 0.230 1.385 2.741 0.006

Grossman et al., 2007 Positive Affect 0.680 0.327 0.107 0.039 1.321 2.079 0.038

Guse et al., 2006 Combined 0.495 0.299 0.090 -0.092 1.082 1.653 0.098

Haight, 1984 Life Satisfaction 2.051 0.713 0.509 0.653 3.449 2.876 0.004

Harris et al., 2006 Positive Affect 0.299 0.125 0.016 0.054 0.544 2.391 0.017

King & Miner, 2000 Positive Affect 0.470 0.187 0.035 0.104 0.836 2.517 0.012

King, 2001 Combined 0.780 0.235 0.055 0.319 1.241 3.319 0.001

Kremers et al., 2006 Well-Being 0.262 0.170 0.029 -0.070 0.594 1.545 0.122

Lichter et al., 1980 Study 1 Combined 0.684 0.434 0.188 -0.166 1.534 1.577 0.115

Lichter et al., 1980 Study 2 Combined 0.496 0.294 0.086 -0.079 1.071 1.690 0.091

Low et al., 2006 Positive Affect 0.185 0.313 0.098 -0.429 0.799 0.591 0.555

Lyubomirsky et al., 2004 Well-Being 0.360 0.209 0.044 -0.050 0.770 1.722 0.085

Lyubomirsky et al., 2006 Combined 0.016 0.265 0.070 -0.503 0.535 0.060 0.952

MacDonald & Settin, 1978 Life Satisfaction 0.660 0.459 0.211 -0.240 1.560 1.437 0.151

Mitchell et al., 2007 Well-Being 0.450 0.396 0.157 -0.327 1.227 1.135 0.256

Otake et al., 2006 Happiness 0.407 0.189 0.036 0.037 0.777 2.157 0.031

Ruini et al., 2006 Positive Affect 0.180 0.190 0.036 -0.193 0.553 0.946 0.344

Savelkoul et al., 2001 Life Satisfaction 0.130 0.164 0.027 -0.191 0.451 0.794 0.427

Scates et al., 1986 Life Satisfaction 0.000 0.343 0.118 -0.672 0.672 0.000 1.000

Schwartz & Sendor, 1999 Life Satisfaction 0.250 0.464 0.215 -0.660 1.160 0.539 0.590

Seligman et al., 2006 Study 1 Life Satisfaction 0.420 0.320 0.102 -0.207 1.047 1.312 0.189

Seligman et al., 2006 Study 2 Combined 0.855 0.399 0.160 0.072 1.638 2.140 0.032

Sheldon & Lyubomirsky, 2006 Positive Affect 0.340 0.259 0.067 -0.168 0.848 1.313 0.189

Smith et al., 1995 Combined 2.092 0.402 0.161 1.305 2.878 5.209 0.000

Spence & Grant, 2007 Combined 0.610 0.277 0.077 0.068 1.152 2.205 0.027

Tkach, 2005 Well-Being 0.004 0.126 0.016 -0.243 0.251 0.032 0.975

Updegraff & Suh, 2007 Life Satisfaction 0.290 0.205 0.042 -0.112 0.692 1.413 0.158

Wing et al., 2006 Life Satisfaction -0.190 0.163 0.027 -0.510 0.130 -1.165 0.244

Campbell & Donovan, 2007 Combined 0.310 0.712 0.507 -1.086 1.706 0.435 0.663

0.540 0.070 0.005 0.403 0.677 7.729 0.000

-1.00 -0.50 0.00 0.50 1.00

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Figure 2. Funnel plot of well-being effect sizes.

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6

0.0

0.2

0.4

0.6

0.8

Sta

nd

ard

Err

or

Std diff in means

Funnel Plot of Standard Error by Std diff in means

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Figure 3. Standardized effect sizes of positive interventions on depressive symptoms compared to control

conditions at post-test.

Study name Outcome Statistics for each study Std diff in means and 95% CI

Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value

Bedard et al. 2003 Depression 0.312 0.661 0.437 -0.984 1.608 0.472 0.637

Cheavens et al., 2006 Depression 0.730 0.365 0.133 0.014 1.446 1.999 0.046

Davis, 2004 Depression 2.750 0.746 0.556 1.289 4.211 3.689 0.000

Dickerhoof et al., 2007 Depression -0.120 0.117 0.014 -0.349 0.109 -1.028 0.304

Fallot, 1979-1980 Depression 0.918 0.384 0.147 0.166 1.670 2.391 0.017

Fava et al., 1998 Combined 0.668 0.469 0.220 -0.252 1.587 1.424 0.155

Fava et al., 2007 Combined 0.820 0.521 0.271 -0.200 1.840 1.575 0.115

Fordyce, 1983 Study 4 Depression 0.309 0.213 0.046 -0.109 0.727 1.448 0.148

Fordyce, 1983 Study 5 Depression 0.558 0.264 0.070 0.040 1.076 2.112 0.035

Freedman & Enright, 1996 Depression 1.204 0.627 0.394 -0.026 2.434 1.919 0.055

Grossman et al., 2007 Depression 0.560 0.325 0.106 -0.077 1.197 1.723 0.085

Grosssman et al., 2007 Depression2 0.630 0.326 0.106 -0.009 1.269 1.932 0.053

Guse et al., 2006 Depression 0.670 0.303 0.092 0.076 1.264 2.211 0.027

Hebl & Enright, 1993 Depression 0.704 0.422 0.178 -0.123 1.531 1.668 0.095

Hedgepeth & Hale, 1983 Depression 0.128 0.258 0.067 -0.379 0.635 0.495 0.620

Lichter et al., 1980 Study 2Depression 0.407 0.292 0.085 -0.165 0.979 1.394 0.163

Lin et al., 2004 Depression 1.751 0.629 0.395 0.519 2.983 2.785 0.005

Mitchell et al., 2007 Depression 0.570 0.399 0.159 -0.213 1.353 1.428 0.153

Ruini et al., 2006 Depression -0.290 0.191 0.036 -0.664 0.084 -1.519 0.129

Schwartz & Sendor, 1999 Depression 0.730 0.468 0.219 -0.186 1.646 1.561 0.118

Seligman et al., 2006 Study 1Depression 0.530 0.322 0.104 -0.101 1.161 1.645 0.100

Seligman et al., 2006 Study 2Combined 1.265 0.415 0.173 0.451 2.079 3.045 0.002

Smith et al., 1995 Depression 1.998 0.395 0.156 1.224 2.772 5.056 0.000

Stevens-Ratchford, 1993 Depression 0.605 0.417 0.174 -0.213 1.423 1.449 0.147

Surway et al., 2005 Depression 0.575 0.496 0.246 -0.397 1.547 1.160 0.246

0.641 0.121 0.015 0.403 0.879 5.280 0.000

-1.00 -0.50 0.00 0.50 1.00

Fav ours A Fav ours B

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Figure 4. Funnel plot of depression effect sizes.

-3 -2 -1 0 1 2 3

0.0

0.2

0.4

0.6

0.8

Sta

nd

ard

Err

or

Std diff in means

Funnel Plot of Standard Error by Std diff in means

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SA

MD

Figure 5. Scree plot analysis for sample-adjusted meta-analytic deviancy (SAMD) statistics for

well-being measures.

0

2

4

6

8

10

12

14

16

18

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45

Rank-order Position

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SA

MD

Figure 6. Scree plot analysis for sample-adjusted meta-analytic deviancy (SAMD) statistics for

depression measures.

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Rank-order Position

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Figure 7. Meta-regression Assessing Length of Intervention as a Moderator, Measures of Well-Being

Regression of Length - Days on Std diff in means

Length - Days

Std

dif

f in

me

an

s

-16.90 4.58 26.06 47.54 69.02 90.50 111.98 133.46 154.94 176.42 197.90

3.00

2.68

2.36

2.04

1.72

1.40

1.08

0.76

0.44

0.12

-0.20

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Figure 8. Meta-regression Assessing Length of Intervention as a Moderator, Measures of Depression

Regression of Length - Days on Std diff in means

Length - Days

Std

dif

f in

me

an

s

-6.70 4.94 16.58 28.22 39.86 51.50 63.14 74.78 86.42 98.06 109.70

3.00

2.70

2.40

2.10

1.80

1.50

1.20

0.90

0.60

0.30

0.00

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Table 1. A summary of the definitions of positive interventions considered

Definition Description Strengths Weaknesses

Improvement A positive intervention leads

to improvement in an

individual’s life

Describes everything that

individuals would intuitively

want positive psychology to

include

Does not leave out any

interventions. All

interventions presumably

look to improve an

individual’s life

Well-Being:

Liking

A positive intervention

improves well-being which

is defined as subjective well-

being

Gives a clear definition of

what the goal of a positive

intervention is as well as a

way to measure well-being

Relies on self-report, which

is subjective. One is

considered happy if he or she

reports being happy

Well-Being:

Needing

A positive intervention

improves well-being which

is defined using an objective

list

Provides objective criteria to

base judgments of well-being

on

Still have to decide which

objective list to use and

which to measure to

determine effectiveness of

intervention

Well-Being:

Wanting

A positive intervention

improves well-being which

is defined as fulfilling one’s

desires

Allows well-being to be

based on criteria besides

subjective well-being that

individuals desire. Can

account for a “happy” yet

miserable life if individual

accomplishes one’s goals

and meets one’s desires

People are bad at knowing

what consequences events

will have on their well-being.

People often make decisions

that do not promote well-

being

Pawelski’s A positive intervention is an

intervention that either (1)

build or improves upon what

is good (2) is applied to an

individual that lacks deficits

in that area

Accounts for both positive

psychology definitions build

on well-being approaches as

well as those that believe

positive interventions build

or promote something good

(besides well-being)

No definition of what the

good is. Allows for

interventions to be positive

in some instances but not

others depending on the

context in which it is applied

Enhancement A positive intervention

builds something new or

enhances something that is

present as opposed to fixing

a deficit

Captures definitions of

positive interventions that do

not include well-being as a

component.

No definition of what the

good is. Hard to determine

what the difference is

between something that is

broken and something that is

not present in many

situations

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Table 2. Search terms and results from PsycINFO search

Term 1 Term 2 All Empirical Descriptor 1 Empirical Descriptor 2 Empirical Descriptor - Both Empirical Articles Found

happiness 13604 7514 2162 1186 -

well-being 64443 41941 11050 7615 -

subjective well-being 7551 5200 0 0 -

quality of life 43143 28203 12896 8948 -

positive intervention 59 - - - 5

positive emotions 1023 700 0 0 -

emotions 58661 26972 17585 6692 -

life satisfaction 12184 9014 4007 3411 -

intervention 152153 79377 20779 8790 -

positive psychology 3802 1475 669 104 9

happiness well-being 5551 3320 786 418 1695 1084 365 157 5

happiness subjective well-being 2630 1641 438 252 0 0 0 0 10

happiness quality of life 1966 1143 307 129 593 341 133 - 6

happiness positive intervention 8 - - - - - - - 0

happiness positive emotions 648 321 115 - 0 0 0 0 2

happiness emotions 3855 2181 530 327 909 522 134 - 1

happiness life satisfaction 2168 1457 431 231 795 613 247 - 6

happiness intervention 1791 807 114 - 141 - - - 22

happiness positive psychology 1287 491 217 - 277 - - - 8

well-being subjective well-being 7551 5200 2120 1587 0 0 0 0 -

well-being quality of life 11891 8138 2383 1662 3897 2837 947 581 -

well-being positive intervention 29 - - - - - - - 1

well-being positive emotions 1035 537 246 - 0 0 - - 3

well-being emotions 7145 3840 1035 612 1167 621 198 - 2

well-being life satisfaction 6230 4573 1811 1377 1769 1481 683 522 -

well-being intervention 14756 8223 1895 1181 1573 688 227 - 23

well-being positive psychology 2137 911 683 280 373 59 203 - 12

subjective well-beingquality of life 2301 1560 0 0 - - - - -

subjective well-beingpositive intervention 6 - 0 0 - - - - 1

subjective well-beingpositive emotions 419 210 0 0 0 0 - 6

subjective well-beingemotions 1704 943 0 0 238 - - - 1

subjective well-beinglife satisfaction 2544 1843 0 0 882 748 - - -

subjective well-beingintervention 1411 777 0 0 94 - - - 7

subjective well-beingpositive psychology 896 434 0 0 138 - - - 2

quality of life positive intervention 8 - - - - - - - 1

quality of life positive emotions 261 - - - - - - - 6

quality of life emotions 2093 1129 341 224 265 - 53 - 9

quality of life life satisfaction 3791 2796 1594 1204 1117 871 611 432 -

quality of life intervention 11002 6413 2187 1476 1241 653 225 - 14

quality of life positive psychology 707 292 182 - 139 - - - 2

positive interventionpositive emotions 7 - - - - - - - 0

positive interventionemotions 18 - - - - - - - 0

positive interventionlife satisfaction 4 - - - - - - - 0

positive interventionintervention 103 - - - - - - - 5

positive interventionpositive psychology 6 - - - - - - - 0

positive emotionsemotions 2405 1407 0 0 499 265 0 0 3

positive emotionslife satisfaction 251 - - - - - - - 6

positive emotionsintervention 457 169 0 0 43 - 0 0 12

positive emotionspositive psychology 721 296 0 0 117 - 0 0 10

emotions life satisfaction 1224 698 149 - 248 - - - 4

emotions intervention 6895 3031 957 478 598 249 84 - 14

emotions positive psychology 1318 493 211 - 184 - - - 5

life satisfactionintervention 1869 1160 298 - 130 - - - 34

life satisfactionpositive psychology 610 297 224 - 122 - - - 5

intervention positive psychology 1104 350 161 - 170 - - - 16

Total 471436 278

Excluding Repeats 140

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Table 3. Summary of studies included in the meta-analysis

Study N N Well-being Depression Quality Participants Length Description Target Type

Treatment Control d p d p (Patient / Non-patient) (Days)

Algoe, 2005 174 87 87 .051 .737 - - 5 Undergraduates

(Non-patient)

1 Roommates recruited and

randomly assigned to write

gratitude letter to roommate

or emotionally neutral topic

Gratitude B

Aylett, 2004 86 43 43 .344 .113 - - 3 Undergraduates

(Non-patient)

1 Undergraduates given

intervention focused on

identifying strengths and

goals and examining effects

on well-being

Hope and

Goals

C

Bédard et al.,

2003

13 10 3 .341 .606 .312 .637 4 Community sample -

individuals with mild

to moderate brain

injuries (Patient)

84 Individuals who

experienced traumatic brain

injuries were exposed to

meditation

Mindfulness C-B

Bennett &

Maas, 1988

26 13 13 2.979 .000 - - 4 Elderly Women,

Nursing Home

Residents

(Non-patient)

42 Elderly women residing in

nursing homes or hostels

randomly assigned to verbal

review or music life review

Reminiscence C

Bryant et al.,

2005

55 33 22 .650 .021 - - 6 Undergraduates

(Non-patient)

7 Students randomly assigned

to reminiscence sessions

twice a week over 1 week

period

Reminiscence C

Burton &

King, 2004

90 48 42 1.300 .000 - - 7 Undergraduates

(Non-patient)

3 Students wrote about

intensely positive

experiences for 3 days

Reminiscence C

Campbell &

Donovan,

2007

9 6 3 .310 .663 - - 3 Couples

(Non-patient)

14 Randomly assigned to

blessings exercise or waitlist

control. Participants in

intervention group shared

three good things with their

partner at end of each day

Gratitude C

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Cheavens et

al., 2006

32 16 16 - - .730 .046 6 Community Sample

(Non-patient)

8 Individuals randomly

assigned to receive hope-

based, group therapy

protocol (eight-sessions

emphasizing building goal

pursuit skills)

Hope and

Goals

C-B

Cook, 1998 36 12 24 .830 .024 - - 4 Elderly Women,

Nursing Home

Residents over Age of

65 (Non-patient)

112 Elderly woman assigned to

a reminiscence condition of

positive and pleasant

experiences

Reminiscence C

Davis, 2004 14 7 7 .870 .120 2.750 .000 5 Individuals with

cerebral vascular

accidents

(Patient)

3 Individuals with right

hemisphere cerebral

vascular accidents were

administered life review

therapy

Reminiscence C

Dickerhoof et

al., 2007

332 222 110 .140 .230 -.120 .304 5 Undergraduates

(Non-patient)

56 Undergraduates allowed to

either sign up for a

happiness increasing study

or recruited from the subject

pool and randomly assigned

to either control or a

gratitude or optimism

intervention

Gratitude and

Optimism

B

Eells, 2006 130 52 78 .793 .000 - - 5 Undergraduates

(Non-patient)

1 Undergraduate couples

recruited and randomly

assigned to 1 of 3

expressive writing

conditions: trauma, falling

in love, or control

Reminiscence C

Elizabeth,

2006

60 30 30 5.000 .000 - - 4 Undergraduate

females

(Non-patient)

60 Individuals randomly

assigned to spiritual well-

being intervention based on

loving-kindness meditation

or control program

Mindfulness C-B

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Positive Interventions 106

Emmons &

McCullough,

2003

Study 2

104 52 52 .588 .003 - - 6 Undergraduates

(Non-patient)

70 Undergraduates randomly

assigned to gratitude,

hassles, or social

comparison condition

Gratitude C

Emmons &

McCullough,

2003

Study 3

65 33 32 .632 .013 - - 4 Community sample

(Patient)

21 Participants from the UC

Davis Medical Center

mailing list mailed

questionnaires containing

either gratitude intervention

or control condition

Gratitude C

Fallot, 1979-

1980

30 15 15 .484 .191 .918 .017 4 Community

participants

(Non-patient)

3 Verbal reminiscing

compared with talking about

the present or future in

female participants

Reminiscence C

Fava et al.,

1998

20 10 10 .000 1.000 .668 .155 6 Remitted patients with

affective disorders

(Patient)

112 Effects of well-being

therapy

Therapy C-B

Fava et al.,

2005

16 8 8 1.000 .089 .820 .115 7 Outpatients with GAD

(Patient)

112 Randomly assigned to either

a protocol of CBT or CBT +

WBT

Therapy C-B

Fordyce,

1977

Study 1

154 94 60 .572 .001 - - 5 Undergraduates

(Non-patient)

14 Randomly assigned to 1 of 4

groups: education about

happiness, fundamentals

program, activities program

(engage in self-selected

happiness activities), or

control

14

Fundamentals

C-B

Fordyce,

1977

Study 2

68 39 29 .741 .003 - - 5 Undergraduates

(Non-patient)

42 Participants were assigned

by class to the 14

fundamentals program,

expanded form the "nifty

nine" of study 1, or

expectancy control

14

Fundamentals

C-B

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Positive Interventions 107

Fordyce,

1983

Study 4

98 64 34 .424 .048 .309 .148 4 Undergraduates

(Non-patient)

77 Participants randomly

assigned by class to either

the 14 fundamentals

(consists of introductory

training and detailed

elaboration 14

fundamentals) or

information only control

14

Fundamentals

C-B

Fordyce,

1983

Study 5

71 50 21 .389 .138 .558 .035 4 Undergraduates

(Non-patient)

17 Randomly assigned to full

14 fundamentals program or

a control with instruction in

some fundamentals but not

the full program

14

Fundamentals

C-B

Freedman &

Enright, 1996

12 6 6 - - 1.204 .055 7 Community sample -

survivors of incest

(Patient)

100 Survivors of incest

randomly assigned to

forgiveness intervention

Forgiveness C-B

Frieswijk et

al., 2006

193 97 96 .265 .067 - - 5 Community-dwelling

elderly population

(Non-patient)

70 The effects of bibliotherapy

intervention designed

around self-management

ability of well-being

Cognitive-

Intervention

C

Goldwurm et

al., 2003

92 45 47 .450 .033 - - 3 Students of

psychotherapy -

psychologists and

medical doctors

(Non-patient)

180 Randomly assigned to either

Fordyce style subjective

well-being program or

general information control

14

Fundamentals

C-B

Green et al.,

2006

50 25 25 .808 .006 - - 6 Community sample

volunteers responding

to an ad for the "Coach

Yourself" program

(Non-patient)

70 Volunteers randomly

assigned to life coaching or

waitlist control

14

Fundamentals

C-B

Grossman et

al., 2007

52 39 13 .680 .038 .595 .068 5 Patients with

fibromyalgia

(Patient)

56 Randomly assigned to either

mindfulness-based stress

reduction or social support

Mindfulness C-B

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Positive Interventions 108

control

Guse et al.,

2006

46 23 23 .495 .098 .670 .027 4 Expectant mothers

(Non-patient)

6 Pregnant women selected

into semi-randomized test of

a hypnotherapeutic program

aimed to increase strengths

Strengths B

Haight &

Bahr, 1984

12 6 6 2.051 .004 - - 4 Elderly Community

Members

(Non-patient)

30 Elderly participants

randomly assigned to either

life review therapy or

visitation and test on

measures of life satisfaction

Reminiscence C

Harp Scates

et al., 1985-

1986

34 17 17 .000 1.000 - - 5 Elderly Community

Members

(Non-patient)

21 Senior citizens randomly

assigned to cognitive-

behavioral group,

reminiscence treatment

group, or activity control

group

Reminiscence C

Harris et al.,

2006

259 134 125 .299 .017 - - 5 Community Sample

(Non-patient)

42 Adults who experienced a

hurtful interpersonal

transgression from were

randomized to forgiveness-

training program or no-

treatment control group

Forgiveness C-B

Hebl &

Enright, 2003

24 13 11 - - .704 .095 6 Community sample -

church members

(Non-patient)

56 Randomly assigned to group

forgiveness intervention or

control group

Forgiveness C-B

Hedgepeth &

Hale, 1983

60 30 30 - - .128 .620 3 Elderly Women from

community settings

(Non-patient)

3 Elderly females randomly

assigned to 1 of 3 groups:

positive reminiscing,

present experiences, or

control group.

Reminiscence C

King, 2001 79 41 38 .780 .001 - - 7 Undergraduates

(Non-patient)

4 Students randomly assigned

to write about their best

possible self or non-

Optimism C

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Positive Interventions 109

emotional control topic

King &

Miner, 2000

118 57 61 .470 .012 - - 7 Undergraduates

(Non-patient)

3 Students randomly assigned

to write about perceived

benefits of events

Optimism C

Kremers et

al., 2006

142 63 79 .262 .122 - - 4 Single community-

dwelling women, age

55 or older

(Non-patient)

6 Single women randomly

assigned to either self-

management of well-being

intervention or control

group

Cognitive-

Intervention

C

Lichter et al.,

1980

Study 1

23 10 13 .684 .115 - - 5 Community

participants

(Non-patient)

28 Examining and

implementing pro-happy

beliefs

Cognitive-

Intervention

C

Lichter et al.,

1980

Study 2

48 25 23 .496 .091 .407 .163 5 Community

participants

(Non-patient)

14 Rehearsal of Positive

Feeling Statements

Cognitive-

Intervention

C

Lin et al.,

2004

14 7 7 - - 1.751 .005 7 Residential treatment

facility individuals

with substance abuse

(Patient)

42 Randomly assigned to either

forgiveness therapy or

alternative treatment

Forgiveness C-B

Low et al.,

2006

41 21 20 .185 .555 - - 7 Breast cancer patients

(Patient)

21 Effects of writing about

benefit finding in cancer

Optimism C

Lyubomirsky

et al., 2004

104 35 69 .360 .085 - - 5 Undergraduates

(Non-patient)

42 Effects of thinking, writing,

or talking about a positive

experience compared to

neutral condition

Kindness B

Lyubomirsky

et al., 2006

58 26 32 .016 .952 - - 5 Undergraduates

(Non-patient)

3 Randomly assigned to 6-

week acts of kindness

intervention

Reminiscence C

MacDonald

& Settin,

1978

20 10 10 .660 .151 - - 4 Community sample,

nursing home

residents

(Non-patient)

35 Effects of two psychosocial

treatments compared to a

control. One was a sheltered

workshop where individuals

Kindness B

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Positive Interventions 110

created goods for others.

Mitchell et

al., 2007

27 11 16 .450 .256 .570 .153 4 Visitors to online

website

(Non-patient)

35 A website was designed to

deliver positive psychology

exercises online for

participants

Strengths B

Otake et al.,

2006

119 71 48 .407 .031 - - 4 Japanese community

sample, all female

(Non-patient)

7 Japanese women assigned to

either count acts of kindness

or control group; effects on

well-being were examined

Kindness C

Ruini et al.,

2006

111 57 54 .180 .344 -.290 .129 6 Middle school

students

(Non-patient)

4 One middle-school had 6

classes volunteer to

participate in a RCT of

well-being therapy versus

CBT principles in the

classroom

Therapy C-B

Savelkoul et

al., 2001

168 56 112 .130 .427 - - 5 Patients with chronic

rheumatic diseases

(Patient)

10 Randomly assigned to cope

actively with their problems

Coping C-B

Schwartz &

Sendor, 1999

72 5 67 .250 .590 .730 .118 3 Individuals with

disability

(Patient)

4 Participants recruited to

help individuals with the

same chronic disease they

have

Kindness B

Seligman et

al., 2006

Study 1

40 19 21 .420 .189 .530 .100 5 Undergraduates with

mild-to-moderate

depressive symptoms

(Patient)

42 Individuals with mild-to-

moderate depressive

symptoms randomly

assigned to a group PPT

versus no-treatment control

Therapy C-B

Seligman et

al., 2006

Study 2

28 13 15 .855 .032 1.265 .002 6 Clients from

University Counseling

Center (Patient)

84 Randomly assigned to

individual PPT, treatment as

usual, or TAU + medication

Therapy C-B

Sheldon &

Lyubomirsky,

2006

67 44 23 .340 .189 - - 7 Undergraduates

(Non-patient)

4 Students randomly assigned

to express gratitude or

visualize their best possible

Optimism C-B

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Positive Interventions 111

self

Smith et al.,

1995

46 34 12 2.092 .000 1.998 .000 3 Undergraduates

(Non-patient)

42 Individuals assigned to

control group, personal

happiness enhancement

program, or PHEP plus

meditation

14

Fundamentals

C-B

Spence &

Grant, 2007

63 43 20 .610 .027 - - 6 Community

participants

(Non-patient)

70 Randomly assigned to

undergo either professional

coaching or peer coaching

based on the GROW Model

Coaching C-B

Stevens-

Ratchford,

1987

24 12 12 - - .605 .147 5 Elderly Adults from a

Retirement

Community

(Non-patient)

21 Older adults randomly

assigned to a reminiscence

intervention and control

Reminiscence C

Surway et al.,

2005

17 9 8 - - .575 .246 4 Patients with chronic

fatigue syndrome

(Patient)

56 Patients with Chronic

Fatigue Syndrome on a

waiting list for CBT were

randomly assigned into a

mindfulness based stress

reduction intervention

Reminiscence C

Tkach, 2006 285 191 94 .004 .975 - - 4 Undergraduates

(Non-patient)

63 Randomly assigned to

perform acts of kindness at

different variety and

frequency

Kindness B

Updegraff &

Suh, 2007

96 48 48 .290 .158 - - 6 Undergraduates

(Non-patient)

1 Instructed to take a concrete

or an abstract self-focused

mindset

Cognitive-

Intervention

C

Wing et al.,

2006

175 120 55 -.190 .244 - - 5 Undergraduates and

community members

(Non-patient)

3 Randomly assigned to write

about positive emotional

experiences or control

groups

Reminiscence C

Note: B = Behavioral, C = Cognitive, C-B = Cognitive Behavioral

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Positive Interventions 112

Table 4. Average effect sizes with excluded Studies

Analyses

Random Effects

Variance Standard

Error

95% Confidence

Interval

Q-

value

df p I2

Combined

Effect

Size Lower Upper

Well-Being (43) .44 .001 .06 .33 .55 98.59 42 .000 57.40

Depression (17) .77 .02 .13 .51 1.03 28.99 16 .024 44.81

Note: The number of studies included for each variable is included in parentheses.

Table 5. Average effect sizes of positive interventions – by components of well-being

95% Confidence

Interval

Analyses

Random Effects

Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-

value

df p I2

Happiness (11) .71 .025 .158 .40 1.02 42.83 10 .000 76.65

Life Satisfaction (20) .44 .008 .089 .27 .62 35.42 19 .012 46.36

Positive Affect (16) .56 .009 .097 .37 .75 29.70 15 .013 49.49

Well-Being (7) .48 .012 .110 .26 .69 8.23 6 .222 27.12

Composite (6) .21 .004 .066 .08 .34 5.53 5 .354 9.65

Note: The number of studies included for each variable is included in parentheses.

Table 6. Moderation Analysis of Type of Sample, Well-Being Measures, N = 43

95% Confidence

Interval

Analyses

Random Effects

Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-

value

df p I2

Non-Patient Sample (36) .46 .004 .062 .34 .58 94.33 35 .000 62.90

Patient Sample (7) .29 .013 .114 .06 .51 3.74 6 .712 0.00

Note: The number of studies included for each variable is included in parentheses.

Table 7. Moderation Analysis of Type of Sample, Depression Measures, N = 17

95% Confidence

Interval

Analyses

Random Effects

Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-

value

df p I2

Non-Patient Sample (9) .71 .029 .171 .37 1.04 17.02 8 .030 52.99

Patient Sample (8) .89 .050 .223 .45 1.32 11.42 7 .121 38.69

Note: The number of studies included for each variable is included in parentheses.

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Positive Interventions 113

Table 8. Moderation Analysis of Target of Intervention, Well-Being Measures, N = 43

95% Confidence

Interval

Analyses

Random Effects

Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-value df p I2

14 Fundamentals (5) .84 .044 .210 .43 1.25 14.14 4 .007 71.72

Coaching (1) .61 .077 .277 .07 1.15

Cognitive

Intervention (5)

.31 .008 .090 .13 .48 1.33 4 .856 0.00

Coping (1) .13 .027 .164 -.19 .45 - - - -

Forgiveness (1) .30 .016 .125 .05 .54 - - - -

Gratitude (5) .29 .016 .125 .05 .54 7.68 4 .104 47.95

Hope and Goals (1) .34 .047 .217 -.08 .77 - - - -

Kindness (5) .24 .013 .114 .01 .46 5.22 4 .26 23.41

Mindfulness (2) .61 .086 .293 .04 1.19 .21 1 .646 0.00

Optimism (3) .51 .022 .149 .22 .81 2.44 2 .295 18.02

Optimism and

Gratitude (1)

.34 .067 .259 -.17 .85 - - - -

Reminiscence (10) .59 .041 .203 .19 .99 43.75 9 .000 79.43

Strengths (2) .48 .057 .239 .01 .95 .01 1 .93 0.00

Positive Therapy (1) .42 .102 .320 -.21 1.05 - - - -

Note: The number of studies included for each variable is included in parentheses.

Table 9. Moderation Analysis of Target of Intervention, Depression Measures, N = 17

95% Confidence

Interval

Analyses

Random Effects

Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-

value

df p I2

14 Fundamentals (1) 2.00 .156 .395 1.22 2.77 - - - -

Cognitive

Intervention (1)

.41 .085 .292 -.16 .98 - - - -

Forgiveness (3) 1.07 .094 .306 .47 1.67 1.97 2 .373 0.00

Hope and Goals (1) .73 .133 .365 .01 1.45 - - - -

Kindness (1) .73 .219 .468 -.19 1.65 - - - -

Mindfulness (3) .55 .063 .252 .06 1.04 .15 2 .927 0.00

Reminiscence (4) .90 .173 .416 .08 1.71 12.30 3 .006 75.62

Strengths (2) .63 .058 .241 .16 1.11 .040 1 .842 0.00

Positive Therapy (1) .53 .104 .322 -.10 1.16 - - - -

Note: The number of studies included for each variable is included in parentheses.

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Positive Interventions 114

Table 10. Moderation Analysis of Type of Intervention, Well-Being Measures, N = 43

95% Confidence

Interval

Analyses Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-value df p I2

Behavioral (8) .15 .004 .066 .02 .28 5.96 7 .544 0.000

Cognitive (23) .47 .007 .081 .31 .63 51.36 22 .000 57.16

Cognitive-

Behavioral (12)

.56 .012 .109 .34 .77 26.30 11 .006 58.17

Note: The number of studies included for each variable is included in parentheses.

Table 11. Moderation Analysis of Type of Intervention, Depression Measures, N = 17

95% Confidence

Interval

Analyses Combined

Effect

Size

Variance Standard

Error

Lower Upper

Q-value df p I2

Behavioral (3) .65 .046 .214 .23 1.07 .073 2 .964 0.00

Cognitive (5) .73 .088 .297 .14 1.31 12.51 4 .014 68.04

Cognitive-

Behavioral (9)

.90 .037 .191 .52 1.27 13.59 8 .093 41.14

Note: The number of studies included for each variable is included in parentheses.