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Resilience in Rural Community-DwellingOlder AdultsMargaret Wells, PhD, RN, NP1

ABSTRACT: Context: Identifying ways to meet thehealth care needs of older adults is important because theirnumbers are increasing and they often have more healthcare issues. High resilience level may be one factor thathelps older adults adjust to the hardships associated withaging. Rural community-dwelling older adults often faceunique challenges such as limited access to health careresources. Purpose: To determine the resilience level ofrural community-dwelling older adults and to determineif socio-demographic factors, social networks, and healthstatus are associated with resilience. Methods: Data werecollected from 106 registered voters, aged 65 years or overfrom a rural area in New York State using a cross-sectional design. The instruments used in the studyinclude the Resiliency Scale, the SF-12v2, and the LubbenSocial Network Scale-Revised. Findings: The meanresilience level of the sample was high. Resilience was notcorrelated with any of the socio-demographic factors whichincluded gender, age, income, education, marital, andemployment status. There was a weak positive correlationbetween social networks and resilience levels of rural olderadults. Both physical and mental health status werepositively correlated with resilience. In a regression model,mental health status was the strongest predictor ofresilience levels. Conclusion: If low resilience levels areidentified in rural community-dwelling older adults,interventions to build resilience may be helpful inpromoting independence; however, further research isneeded to determine this.

Americans, in both rural and urban areas,are living longer. Finding cost-effectiveways to meet the health care needs of thisgroup is of great importance. As peopleage, they often experience hardships due

to decline in physical health. In addition, losing aspouse or close friend can cause significant distress andimpairment of mental health. Some people, when facedwith stressors and life changes, are able to adjust andbecome even more capable, while others, when facedwith similar situations, are not able to adapt to theseoccurrences. Those who are able to adapt with little

disruption to their lives often possess high levels ofresilience. Resilience is viewed as “a personalitycharacteristic that moderates the negative effects ofstress and promotes adaptation.”1

According to the Resiliency model, if individualsexperience disruption to their lives when a stressor isencountered, they rely on internal protective factors,such as self-reliance, as well as external protectivefactors such as social networks to restore balance intheir lives; this process is referred to as resilientreintegration.2 Based on the process described in theResiliency model, interventions to promote protectivefactors can be initiated to help individuals adjust tohardships in a resilient manner. Screening older adultsfor their level of resilience may identify those whocould have difficulty adjusting when faced with lifestressors.

Social networks have been found to be associatedwith resilience in multiple studies; 3-10 however, somestudies found no relationship.11,12 It appears that socialnetworks may serve as a protective factor forindividuals when faced with adversity. Several studiesfound a relationship between physical health status andresilience.3,5,6,8,13-17 In general, better self-reportedphysical health status was associated with higher levelsof resilience. As one ages, functional ability and healthstatus may decline; thus, studying the relationshipbetween health status and resilience in the older adultpopulation is relevant. Mental health status andresilience were found to be related in numerous studies.Many studies found a relationship between positiveemotions and resilience.9,12,17-21 An inverse relationship

1College of Nursing, SUNY Upstate Medical University, Syracuse,N.Y.

The author acknowledges Paula F. Rosenbaum, PhD, for assistancewith statistical analyses and editing of the manuscript. Thanks toPam Stewart Fahs, PhD, RN, for guidance in development of thisresearch project. For further information, contact: Margaret Wells,PhD, RN, NP, 750 East Adams St, Syracuse, NY 13210; e-mailwellsm@upstate.edu.

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between mental health disorders, such as depression,and resilience was found in many studies.9,13-15,22,23

The level of resilience and associated factors inrural older adults has not yet been determined. Thedearth of literature on resilience in ruralcommunity-dwelling older adults supports the need forresearch. The purpose of this research study was 2-fold:(1) to determine the level of resilience of ruralcommunity-dwelling older adults, and (2) to identify ifsocio-demographic factors, social networks, and healthstatus are associated with resilience in ruralcommunity-dwelling older adults.

MethodsDesign and Recruitment. This cross-sectional

study was initiated after receiving approval from theUniversity Human Subjects Review Committee.Systematic sampling was used to randomly selectadults aged 65 years and over from voter registrationlists of rural residents in New York State. The criterionused to determine the degree of rurality of a countywas the rural-urban continuum codes developed by theEconomic Research Service, US Department ofAgriculture (ERS, USDA).24 The central and southerntier area of New York State, in which the data werecollected, is coded as 6, indicating that the area isnonmetropolitan. A survey containing items ondemographics, resilience, social networks, and healthstatus was mailed to 300 registered voters in thedesignated area. Reminder postcards were sent, andthen 60 additional surveys were mailed to obtain anadequate sample size.

Instruments. The Resilience Scale (RS) was used tomeasure the resilience level of the participants.1 It wasdeveloped from the findings of a qualitative study ofolder women who had successfully adapted to a majorloss.25 These women were found to have 5characteristics, which included equanimity,self-reliance, perseverance, meaningfulness of life, andexistential aloneness. The RS has 25 items, which arescored on a 7-point scale from 1, strongly disagree, to 7,strongly agree. Scores on the RS range from 25 to 175.1

The Cronbach’s alpha of the RS, for this study, was 0.94,which indicates strong internal consistency of the itemsin the scale.

The Short-Form revised (SF-12v2) Health Surveywas used to measure health status.26 The SF-12v2 has 2health summary components, which include thePhysical Component Summary (PCS) and the MentalComponent Summary (MCS). In this study, theCronbach’s alpha coefficients for each summarycomponent of the SF-12v2 were as follows: 0.87 for the

MCS and 0.89 for the PCS, which indicate stronginternal consistency.

The 12 item Lubben Social Network Scale-Revised(LSNS-R) was the instrument used to measure socialnetworks.27 Items on the LSNS-R are rated on a scale of0 to 5, with 0 indicating “never” or “none” and 5indicating “always” or “9 or more.” The total scoreranges from 0 to 60, and the subscales of friends andfamily networks, each range in scores from 0 to 30. Inthis study, the Cronbach’s alpha, for the total LSNS-R,was 0.90. For the family and friend subscales, theCronbach’s alpha was calculated to be 0.89 and 0.88respectively.

Analysis. Statistical analyses were carried out usingSPSS R© 14.0 (SPSS Inc., Chicago, Ill). The level ofsignificance was set at 0.05. Initial descriptive analysesincluded frequencies, means, and standard deviations.Continuous data were analyzed using t-tests andone-way analysis of variance (ANOVA) to assessdifferences in resilience across demographic categoriesas well as in the SF-12v2 subscales, and the LSNS-Rtotal score and subscales. Pearson product-momentcorrelation coefficients were calculated to determine ifthere was an association between resilience and theother continuous variables. Multiple linear regressionmodels were built to evaluate independent predictorsof resilience with control for demographic factors.Initially, all the predictor variables were entered intothe model and a backward elimination approach wasused, removing any variable with α > 0.15. The finalmodel included the predictors remaining in the firstmodel as well as age and gender. Because participantswere randomly selected from voter registration listsand no identifying information was used, it was notpossible to compare responders with non-responders;however, select participant data were compared tocensus data.

ResultsThe sample size was 106, and the overall return rate

was 30%. The majority of participants were female(54%), married (63%), and not employed (80%). Themean age of the participants was 75 years.Demographic characteristics of participants are shownin Table 1. Seven participants omitted their incomelevel, and 3 omitted their age.

Participants were similar to the populationaccording to the 2000 US Census data; however, incomeand education levels varied somewhat. Only 6% of theparticipants had incomes below $10,000, while 11% ofthe population comprised this category. The educationvariation, most worth noting, involved those with

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Table 1. Demographic Characteristics ofParticipants (N = 106)

Percentage

SexFemale 54Male 46

Income rangeLess than $10,000 6$10,000-$24,999 31$25,000-$34,999 23Greater than $35,000 39

Years of educationLess than high school degree 6High school degree 33Some college 27Associate or bachelor’s degree 13Graduate or professional 21

Marital statusMarried 63Not married 37

EmployedNo 81Yes 19

Age ( x̄ 75, SD 6.4)65-74 5675-84 3185-94 13

graduate degrees: 21% of the participants had graduatedegrees while only 6% of those in the population hadgraduate degrees.

Table 2 displays the mean scores, standarddeviations, and range of scores for the instruments usedin the study. The mean score of the RS indicated highlevels of resilience, while total and subscale scores onthe LSNS-R indicated moderate numbers of socialnetworks. Participants had higher MCS scores andslightly lower PCS scores than norm-based data ofolder adults in the general population.26

None of the socio-demographic factors (age,gender, income, education, marital status, andemployment status) were found to be significantly

Table 2. Instrument Scores

Instruments Means SD Range

RS (resilience) 149.4 18.2 88-175SF-12v2: PCS (physical health) 41.5 11.1 10-61SF-12v2: MCS (mental health) 53.9 9.4 26-70LSNS-R: Total score (social networks) 37.2 10.3 0-60LSNS-R: Family subscale 19.8 5.9 0-30LSNS-R: Friend subscale 17.4 6.1 1-30

Table 3. Final Regression Model

Beta 95% CI PPredictor Coefficient of Beta Value

Age −0.21 (−0.68, 0.26) .37Gender∗ 0.14 (−5.90, 6.18) .96Friend networks 0.33 (−0.17, 0.83) .19(PCS) Physical health 0.24 (−0.04, 0.51) .09(MCS) Mental health 1.01 (0.70, 1.32) .00

∗male = 0 and female = 1.

correlated with resilience. Total LSNS-R scores wereweakly correlated with resilience levels (r = 0.20),P =.04. The family subscale of the LSNS-R andresilience levels were not significantly correlated, whilethe relationship between the friend subscale of theLSNS-R and resilience level did demonstrate astatistically significant weak correlation (r = 0.20), P =.04. The correlation between physical health andresilience was (r = 0.24), P = .02, while mental healthand resilience had a correlation coefficient of (r = 0.58),P = .00.

In the initial multiple regression model, 3 variablesremained and included LSNS-R friend subscale, MCS,and PCS. In the final model, the linear combination ofage, gender, LSNS-R friend subscale, PCS, and MCSwas significantly related to resilience, F(3.92) = 17.3, P= .00. The R2 was 0.38 indicating that 38% of thevariance of resilience levels can be accounted for by thelinear combination of predictors. Higher perceivedmental health status was the strongest predictor ofresilience. The results of the final regression model areincluded in Table 3.

DiscussionThe mean resilience level was 149, indicating this

sample of rural community-dwelling older adults hadhigh levels of resilience. It does not appear thatresilience levels decrease as one ages. In fact, resiliencelevels may actually increase, but further research isneeded to support this.

Similar to findings in the literature, this studyfound a relationship between resilience and socialnetworks; however, it was a weak relationship. Thismay be due in part to the belief that rural dwellers tendto be self-reliant.28 Within the Resilience Scale,self-reliance is a measure of resilience. Thus, those withhigh resilience levels tend to have high levels ofself-reliance. Social networks consisting of friends werefound to be correlated with resilience in rural older

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adults, but not social networks consisting of family.Many young adults move from rural to metropolitanareas. Because rural older adults are often separatedfrom their adult children, they may tend to rely onneighbors and friends when help is needed.

This study found that perceived physical andmental health status were correlated with resilience,and this is well supported in the literature. Thecorrelation between resilience and physical health wasweak, indicating that declining health status may notreduce resilience levels. Mental health status had thestrongest association with resilience, and multiplestudies support this relationship. Future research onresilience should include identification of recent lossesor stressors experienced by the participants becausethis may influence their resilience levels. In addition,ascertaining whether a recent hardship wasexperienced may help in understanding the process ofresilience.

While this study could not identify causalrelationships, associations among the protective factorsand resilience were determined. Post-hoc poweranalysis was performed using SamplePower v2(software). For a regression model with 2 covariates(age and gender) and 3 main variables (PCS, MCS, andLSNS-R subscale friend), an alpha of 0.05 and an effectsize of R2 = 0.38, only 30 people were needed for apower of 80%.

Limitations. Although this study has adequatepower, there are some limitations. Mailed surveys maynot have captured the true resilience levels of ruralcommunity-dwelling older adults. Because the samplewas selected from a rural area in New York State,results may not be generalizable to other ruralpopulations. To complete the surveys, subjects had tobe able to read and have adequate visual acuity tocomplete the surveys. Furthermore, the education levelof the participants is much higher than those in thepopulation. Finally, directions on the second page of theResilience Scale were incorrect and this is a threat tointernal validity.

Implications for Practice. Primary care providers(PCP) in rural areas are in ideal positions to screen theirolder adult patients for resilience levels. Patients withlow levels of resilience should have individualizedtreatment plans. When appropriate, patients should bereferred to mental health providers who can offerstrategies to help build resilience and to providetreatment for mental health disorders such asdepression. Those with untreated depression may havelower resilience levels, which could lead to moredifficulty adjusting when faced with adversity.

Furthermore, when appropriate, providers mayconsider encouraging their rural older adult patientswith low resilience levels to build social networks bybecoming involved with social groups through seniorcommunity centers or churches.

In conclusion, this study found correlates ofresilience in rural elders. The next step should includedeveloping and testing interventions to increaseresilience in rural older adults with low levels. Becauseperceived mental health status was the factor moststrongly associated with resilience, it seems logical tofocus on mental health when developing interventionsto help increase resilience levels. If these interventionsare effective in increasing resilience levels, more ruralcommunity-dwelling older adults may be able tomaintain independence in their communities.

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