Self-Efficacy for Physical Activity Self-Efficacy for Physical Activity in Special Populationsin Special Populations

Meta Analysis and Implications for Physical Education

Alicia Dixon & Miyoung Lee, PhD

Department of Nutrition and Exercise SciencesMovement Studies in Disability

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Presentation Outline

Introduction– Background: Self-efficacy as a determinant of physical activity

Meta-Analysis Review– Purpose– Methods – Analysis– Results

Discussion– Sources of self-efficacy specific to special populations choosen

Implications– Physical education strategies for increasing efficacy

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– Confidence a person has in his or her ability to perform a behavior and overcome barriers

Self-Efficacy

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– One of the strongest correlates to physical activity

– Stems from personal factors (age, gender, health) & environmental factors (safe facilities, transportation, social support)

(Anderson, Wojcik, Winett, & Williams, 2006; Sallis & Owen, 1999)

Self-Efficacy for Physical Activity

Special populations have more challenges with these factors

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Purpose of Review

To investigate the relationship of self-efficacy on physical activity behavior in special populations by employing a meta analysis

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MethodsMethods

– Articles reviewed through Pubmed, Medline, and Ebscohost databases

• Keywords: physical activity, self-efficacy, older adult, intellectual disabilities, physical disabilities, and single names of disabilities (e.g., multiple sclerosis, spina bifida, spinal cord injuries, etc.)

– Special populations chosen for review:• Intellectual disabilities• Physical disabilities• Neurological disabilities• Older adults (65+ years)

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Criteria for inclusion1.Self-efficacy as a mediator to PA behavior2.Inclusion of correlation coefficients (self-

efficacy and PA )3.Sample size larger than 150 4.Participants over the age of 18 years5.Recently published (2000 to current)

Methods – cont.Methods – cont.

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Data AnalysisData Analysis

– Fisher’s (Zr) transformed correlations

– Calculated Zr transformed to average correlation coefficient (r)

– Example of Data Analysis

rrESZr 1

1ln5.

11

2

2

Zr

Zr

ES

ES

eer

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ResultsResults

– Out of 610 articles searched, the following studies met the criteria and were used for meta analysis

Fisher’s (Zr) transformed correlation

Zr = 0.49 Back transformed average correlation

r = 0.45

Lead Author Population Sample Age Correlation Coefficient

Morris, K.S.(2008) Multiple Sclerosis, 173(F) 46.1yrs r = 0.34

Nosek, M.A. (2006) Physical Disabilities 386(F) 18-65yrs r = 0.50

Peterson, J.J. (2008) Intellectual Disabilities 73(F) 79(M) 37.2yrs r = 0.33

Resnick, B. (2000) Older Adults 32(M) 155(F) 85yrs r = 0.56

Table 1. Summary of Research Findings met the Inclusion Criteria

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DiscussionDiscussion

Sources of Self-Efficacy 1.Performance attainments2.Vicarious experiences3.Verbal persuasion and social influence4.Psychological states

Bandura (1987)

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Self-efficacy for Physical Activity in Special Populations:Self-efficacy for Physical Activity in Special Populations:

Intellectual disabilities

1) Performance attainments– Positive experiences in early environments

2) Vicarious experiences– Role modeling through peers and supports

3) Verbal persuasion and social influence– Need supports to “show them how” to be active, provide encouragement, and provide tangible support

4) Psychological states– Lack of perceived personal skills

(J. Peterson et al., 2008; Temple, 2008)

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Self-efficacy for Physical Activity in Special Populations:Self-efficacy for Physical Activity in Special Populations:

Physical Disabilities

1) Performance attainments– Gain knowledge and recall positive PA experiences

2) Vicarious experiences– Role modeling

• Observing others succeeding in PA• Becoming a role model

3) Verbal persuasion and social influence– Insufficient supports– Important to be apart of a social network

4) Psychological states– Pain, fatigue, anxiety, and level of neurologic impairment or limitations– Negative beliefs about the benefits

(Kersten et al., 2006; Kroll et al., 2007; Middleton et al., 2007; Nosek,et al., 2006)

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Self-efficacy for Physical Activity in Special Populations:Self-efficacy for Physical Activity in Special Populations:

Multiple Sclerosis

1) Performance attainments– Previous PA attainments and knowledge prior to onset of disease – Acknowledging accomplishments w/ MS

2) Vicarious experiences– Role models

• seeing others with MS succeed • providing confidence for others

3) Verbal persuasion and social influence– Support from others participating in a PA program– Feeling comfortable and accepted– Social supports are critical for women with MS

4) Psychological states– Symptoms (fatigue, spasticity, mobility, depression, and pain)

(Kasser, 2009; Motl et al., 2006; Snook, et al., 2007 )

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Self-efficacy for Physical Activity in Special Populations:Self-efficacy for Physical Activity in Special Populations:

Older Adults (65+ years)

1) Performance attainments– Previous participation in PA means continued confidence

later in life• Older women (today) don’t recall these experiences

2) Vicarious experiences– Role models- variability in this influence

• Seeing others “worse off” doing better may think negatively about their progressOR

• Positive influence- believe “If they can do it, I can do it!”

3) Verbal persuasion and social influence– Women tend to be more active with supports

4) Psychological states– Outcome expectations– “too old” to exercise, no benefits, may cause

injury– Self-evaluation and perceptions of self – Mental and physical health

(Lee et al., 2008; Resnick, et al., 2000; Resnick, 2002)

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ImplicationsPhysical education strategies

for increasing self-efficacy

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Implications: Strategies for the Classroom Strategies for the Classroom

1. Be aware of previous history or experiences in PE setting

2. Reduce competition

3. Provide positive specificreinforcements and encouragement

4. Choose activities that are appropriate

5. Provide a welcoming and “safe” environment

6. Gradually decrease the use of assistance (Block, 2007)

(Block, 2007)

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Alicia Dixon Oregon State UniversityEmail: dixona@onid.orst.edu

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Example: Transforming r and calculating the average