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Psychology and Aging A Randomized Controlled Trial to Promote Volunteering in Older Adults Lisa M. Warner, Julia K. Wolff, Jochen P. Ziegelmann, and Susanne Wurm Online First Publication, August 18, 2014. http://dx.doi.org/10.1037/a0036486

CITATION Warner, L. M., Wolff, J. K., Ziegelmann, J. P., & Wurm, S. (2014, August 18). A Randomized Controlled Trial to Promote Volunteering in Older Adults. Psychology and Aging. Advance online publication. http://dx.doi.org/10.1037/a0036486

Psychology and Aging 2014, Vol. 29, No. 2, 000

© 2014 American Psychological Association 0882-7974/14/$12.00 DOI: 10.1037/a0036486

A Randomized Controlled Trial to Promote Volunteering in Older Adults Lisa M. Warner

Julia K. Wolff and Jochen P. Ziegelmann

German Centre of Gerontology, Berlin, Germany and Freie Universität Berlin

German Centre of Gerontology, Berlin, Germany

Susanne Wurm

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

German Centre of Gerontology, Berlin, Germany and Friedrich-Alexander University Erlangen-Nuremberg Volunteering is presumed to confer health benefits, but interventions to encourage older adults to volunteer are sparse. Therefore, a randomized controlled trial with 280 community-dwelling older German adults was conducted to test the effects of a theory-based social– cognitive intervention against a passive waiting-list control group and an active control intervention designed to motivate physical activity. Self-reports of weekly volunteering minutes were assessed at baseline (5 weeks before the intervention) as well as 2 and 6 weeks after the intervention. Participants in the treatment group increased their weekly volunteering minutes to a greater extent than participants in the control groups 6 weeks after the intervention. We conclude that a single, face-to-face group session can increase volunteering among older community-dwelling adults. However, the effects need some time to unfold because changes in volunteering were not apparent 2 weeks after the intervention. Keywords: volunteering, older adults, theory-based social– cognitive intervention, randomized controlled trial

Although almost half of the U.S. (Independent Sector, 2001) and almost one third of the German (Bundesministerium für Familie, Senioren, Frauen und Jugend, 2010) populations aged 65 and older engage in volunteer work, Gottlieb and Gillespie (2008) predict a potential shortage of volunteers in the future, with consequences for various areas of society. Given the salutary benefits of volunteering for society and for volunteers themselves (Okun, Yeung, & Brown, 2013), this predicted shortage of volunteers has led to calls for developing interventions to increase volunteering (e.g., Okun et al., 2013). Therefore, the aim of this study was to test the effects of a short, face-to-face group intervention to encourage community-dwelling older adults to volunteer. Because social– cognitive frameworks have been demonstrated to be valid in the domain of volunteering (Greenslade & White, 2005; Warburton, Terry, Rosenman, & Shapiro, 2001), the intervention was based on social– cognitive behavior change techniques.

Longitudinal and Experimental Evidence for the Benefits of Volunteering The benefits of volunteering for society are manifold and range from economic benefits for institutions that employ volunteers to lower depressive symptoms and isolation for recipients of volunteer services (Wheeler, Gorey, & Greenblatt, 1998). Moreover, volunteers profit themselves in terms of higher quality of life, social integration, self-rated health, ability to cope with stressful life events, and number of years lived without morbidity as well as lower levels of depressive symptoms and physical limitations (Onyx & Warburton, 2003), and this is perhaps especially true for older and retired adults (Sneed & Cohen, 2013; Van Willigen, 2000). A recent meta-analysis further confirms a reliable association between volunteering and mortality in older adults (Okun et al., 2013). Despite cross-sectional and prospective evidence, these findings are often confronted with the argument of reverse causality: Does volunteer work generate health benefits or are healthier individuals more likely to volunteer (Warburton & Peel, 2008)? Experimental studies suggest that volunteer work in fact contributes to better health and health behavior. A recent randomized controlled trial on healthy 10th-grade students showed that being assigned to weekly volunteering sessions with elementary schoolchildren improved cardiovascular reactivity after 2 months as compared with a waiting-list control group (Schreier, SchonertReichl, & Chen, 2013). For older adults, the Experience Corps Program found that participants who were randomly assigned to volunteer in elementary schools experienced greater gains than wait-list controls in terms of higher physical activity, muscle strength, cognitive performance, and social integration at 4- to 8-month follow-up assessments (Carlson et al., 2009; Fried et al., 2004; Tan, Xue, Li, Carlson, & Fried, 2006). Although both

Lisa M. Warner, German Centre of Gerontology, Berlin, Germany and Department of Health Psychology, Freie Universität Berlin, Berlin, Germany; Julia K. Wolff and Jochen P. Ziegelmann, German Centre of Gerontology; Susanne Wurm, German Centre of Gerontology and Institute of Psychogerontology, Friedrich-Alexander University Erlangen-Nuremberg, Berlin, Germany. This work was supported by the German Federal Ministry of Education and Research. The authors thank the German Centre of Gerontology for logistic support and a team of highly motivated student assistants for helping to conduct the study. Correspondence concerning this article should be addressed to Lisa M. Warner, Health Psychology, Freie Universität Berlin, Habelschwerdter Allee 45, 14195 Berlin, Germany. E-mail: [email protected] 1


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WARNER, WOLFF, ZIEGELMANN, AND WURM

studies were conducted in the field, they were highly structured, and participants were randomized to specific volunteer tasks (e.g., support in schools) to ensure comparable doses of volunteering (e.g., at least 15 hr/week). However, in everyday life, most older adults organize their volunteer work themselves. This provides them with the chance to select volunteer tasks that are meaningful to them and to self-determine the type and amount of work, but it requires high motivation as well as self-regulatory and social skills to find a suitable volunteer position and to maintain it. Therefore, interventions that aim to promote older adults’ motivation for volunteering should be more sustainable if they prompt selfregulatory strategies to help them find and maintain volunteer work by themselves rather than enrolling them in specific volunteer work for a defined study period.

Motivation to Volunteer Among Older Adults Earlier studies on people’s motivation to volunteer mainly focused on various functions of volunteering. They found that older adults are motivated to volunteer out of altruistic reasons (e.g., helping people in need or contributing to society), but that they also pursue self-oriented motives such as structuring and filling leisure time, being socially integrated, gaining social approval, and boosting their self-esteem (Okun, 1994; Warburton et al., 2001). However, simply asking current volunteers for their motives to volunteer neglects other cognitions, such as the costs of volunteering, whether people feel capable to volunteer, or whether or not they are supported by their environment (Greenslade & White, 2005). Therefore, more recent studies investigated the explanatory value of social– cognitive theories for volunteering. Their main findings are that the motivation to volunteer is based on positive attitudes toward volunteering, self-efficacy beliefs, and social support (Warburton et al., 2001). Hence, people who expect benefits of volunteering, feel capable of volunteering, and perceive that their significant others approve of and support volunteering are more likely to be motivated to volunteer than others (Grano, Lucidi, Zelli, & Violani, 2008; Greenslade & White, 2005; Morrow-Howell, Hong, & Tang, 2009). Although there is comprehensive research on the factors that predict volunteering, these studies typically use correlative designs and consider only people who volunteer (Fisher & Ackerman, 1998). Therefore, they give little insight into how organizations or interventionists could encourage older adults to initiate or maintain volunteering. One of the few randomized controlled trials to promote volunteering without assigning participants to specific volunteer work prompted volunteering among adolescents (Wilson, Allen, Strahan, & Ethier, 2008). In two 80-min interactive group sessions delivered over a 2-week period, interventionists provided information about the benefits of volunteering and encouraged debates about strategies on how to overcome barriers to volunteer. Compared with an active control group that debated body image in an identical setting, the intervention group reported higher intentions for various kinds of volunteer work 1 week later. Two further experimental studies in the laboratory (in samples of undergraduate students and parents of 4- to 17-year-old children) varied the amount of expected social recognition and perceived needs for volunteer services in specific organizations on flyers and posters (Fisher & Ackerman, 1998). The intention to

volunteer was higher among participants in the condition with high expected social approval and high organizational need, confirming the authors’ hypothesis of social influences on volunteering motivation. Considering the gap between intentions and behavior (Sheeran, 2002), the evaluation of intervention effects in terms of short-term changes in intentions to volunteer rather than actual involvement in volunteer work provides little guidance on how to prompt volunteering behavior. Therefore, on the basis of broad correlational evidence (but sparse experimental evidence), social– cognitive interventions to increase volunteering should contain behavior change techniques that focus on positive outcome expectancies of volunteering, self-efficacy, and social support. A wellestablished social– cognitive framework that incorporates these constructs is the Health Action Process Approach (HAPA), which was found to be valid in various health behaviors (Schwarzer et al., 2007). Research on the effects of interventions to increase volunteering should further evaluate their success in terms of the amount of volunteer work rather than intentions and have a longitudinal design.

Aims of the Current Study The current randomized controlled trial was conducted to test a short, face-to-face group intervention to increase volunteering among community-dwelling older adults by means of prompting social cognitions based on the HAPA (Schwarzer, 2008). The intervention targeted participants’ self-regulatory skills to choose and organize volunteer work on their own initiative without imposing any specific volunteer services on them. It was hypothesized that participants in the intervention group would show higher levels of volunteering at the 2- and 6-week follow-up than participants who attended the active or passive control group.

Method Participants and Procedure A sample of community-dwelling adults aged 64 and older was recruited via newspaper articles and advertisements in a large German city. The study was named “Active Retirement,” and the communicated study purpose was that volunteering and physical activity in retirement and their relation to health and well-being would be discussed in randomized groups. Interested retirees called the research institute and left their contact details to be called back. In this telephone interview, potential participants were selected as eligible for participation if they were 64 years or older, not acutely physically impaired or disabled, not exercising on a regular basis, and not seriously cognitively impaired. In total, 647 older adults were assessed for eligibility. The CONSORT flow diagram in Figure 1 displays exclusion and dropout rates. Within the first telephone interview, participants were randomized into three groups using the software R (http:// cran.r-project.org/) via the function “sample” of the R package “base” with predefined group sizes for the volunteering intervention group (VIG), the active control group (ACG), and the passive control group with no intervention (PCG). Three hundred and ten participants provided informed consent and completed the baseline paper-and-pencil questionnaire (T1) at the research institute in


VOLUNTEERING INTERVENTION IN OLDER ADULTS

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Figure 1. CONSORT flow diagram.

Spring 2012. All participants of T1 were remunerated with €25. The intervention session and the ACG session took place 5 weeks after T1. Sessions for the ACG were randomized into sessions that had parallel content to the VIG and sessions that were nonparallel with the VIG. Participants in the nonparallel sessions (n ⫽ 30) were not considered in the analyses for this study. Therefore, the final sample for this study comprises 280 individuals. All three groups received follow-up questionnaires via mail with prepaid return envelopes 2 (T2) and 6 weeks (T3) after the intervention (the PCG at respective time intervals). The first follow-up (T2) was completed by 253 participants, and 244 participants completed T3. Participants were on average 70.29 years of age (SD ⫽ 4.95, range ⫽ 64 –92 years) and reported 4.65 (SD ⫽ 2.78) illnesses at T1. Most were women (76%) and high-school graduates (59%); 44% lived with a partner. Ethical consent was granted from the Ethics Commission of the German Psychological Society (DGPs-SW 02_2012).

Experimental Conditions The intervention development was based on previous correlational and experimental evidence and the HAPA model (Schwarzer, 2008). This model postulates that behavioral intentions are built on risk perception (not assumed to be relevant for volunteering), outcome expectancies, and self-efficacy in the motivational phase, whereas intentions are translated into behavior via selfregulatory strategies such as planning and self-monitoring in the volitional phase. Because volunteers and nonvolunteers should be addressed likewise, motivational and volitional strategies were chosen. The following behavior change techniques were used in interactive group intervention sessions to prompt volunteering (Michie et al., 2011): information about the benefits of volunteering in old age (prompt for outcome expectancies), focus on past success (prompt for self-efficacy in biography worksheet), goalsetting behavior and outcome (prompt for intention formation in worksheet), action planning and use of cues (prompt for if-then


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implementation intentions in worksheet), and modeling behavior (prompt for self-efficacy in a 5-min video clip with older person as role model). Furthermore, informational material on volunteering opportunities for older adults in their residential area was available for free, and self-monitoring of behavior was prompted between T2 and T3 (10-day volunteering diary). Because volunteering can have detrimental effects on health if performed excessively, participants were also informed about keeping weekly volunteer minutes to an amount that would not burden them (Musick, Herzog, & House, 1999). The ACG received the same behavior change techniques (including the diary) as the VIG in interactive group sessions with similar length, but all material was adapted to physical activity. The PCG received neither intervention session nor informational material nor a diary during that time. Two female psychologists with doctoral degrees developed the intervention content and were the main interventionists for all group sessions. Both learned the session protocols by heart and read the manual again before every session. Further, sessions were structured by standardized PowerPoint presentations to ensure comparability of session content. Most sessions were conjointly led by those two women (n ⫽ 25 sessions), but some were conducted with only one of them leading the session (e.g., because of illness) while a trained psychology student assisted with content-unrelated tasks such as writing benefits on the whiteboard or starting the video (n ⫽ 9 sessions).

Measures Weekly volunteering minutes were assessed with two items adapted from Ayalon (2008): (a) “During the past 4 weeks, on how many days per week did you do volunteer work?” and (b) “If you did volunteer work, how many minutes did one session last on average?” They were multiplied and divided by 4 (weeks) to create a score for the average weekly volunteering minutes. Outliers were truncated to 2 SD above the respective M within each measurement point in time (results do not change with raw data). Covariates were participants’ age, gender, education, partner status (1 ⫽ partner; 0 ⫽ without partner), and number of illnesses at T1 because these variables were shown to be associated with volunteering in previous studies (Thoits & Hewitt, 2001). Education was assessed and classified according to the International Standard Classification of Education (ISCED; Unesco, 1997), with 1 indicating low education (ⱕ9 years school education), 2 indicating medium education (secondary school), and 3 indicating high education (qualifying for university admission). The number of illnesses was assessed with a list of 25 illnesses mentioned either in the Charlson Comorbidity Index (Charlson, Szatrowski, Peterson, & Gold, 1994) or the Functional Comorbidity Index (Groll, To, Bombardier, & Wright, 2005) and summed up.

Data Analyses The intervention effect was tested with latent change scores, modeling change in volunteering minutes from T1 to T2 and from T2 to T3, and level of volunteering at T2 in Mplus (Mun, von Eye, & White, 2009). Missing values on volunteering (T1 ⫽ 8.1%, T2 ⫽ 17.1%, T3 ⫽ 25.2%) were imputed via full information maximum likelihood estimation (FIML) in Mplus because FIML

makes use of all available data in model estimation (Arbuckle, 1996). Two dummy variables (ACG and PCG) using the VIG as the reference group were entered as predictors of the latent change scores. Significant associations of ACG versus VIG or PCG versus VIG with changes in volunteering can be interpreted as treatment effects. The model was statistically controlled for participants’ age, gender, education, partner status, and number of illnesses at T1.

Attrition Analysis Those 36 participants who dropped out were examined for significant differences compared with participants with complete data at T1. There were no statistically significant differences between participants who dropped out and those who stayed in the study in terms of weekly volunteering minutes t(255) ⫽ 0.49, p ⫽ .26 (Mdropouts ⫽ 120.30 min, SDdropouts ⫽ 164.27 min; Mcompleters ⫽ 141.85 min, SDcompleters ⫽ 228.49 min), age t(275) ⫽ 0.26, p ⫽ .64 (Mdropouts ⫽ 70.09 years, SDdropouts ⫽ 5.11 years; Mcompleters ⫽ 73.32 years, SDcompleters ⫽ 4.93 years), gender ␹2(1) ⫽ 0.22, p ⫽ .64 (dropouts 75.8% women, completers 77.8% women), education t(276) ⫽ ⫺0.44, p ⫽ .57 (Mdropouts ⫽ 2.51, SDdropouts ⫽ 0.70; Mcompleters ⫽ 2.46, SDcompleters ⫽ 0.72), partner status ␹2(1) ⫽ 0.25, p ⫽ .62 (dropouts 44.3% with partner, completers 38.9% with partner), or number of illnesses t(273) ⫽ ⫺1.41, p ⫽ .19 (Mdropouts ⫽ 5.31, SDdropouts ⫽ 3.13; Mcompleters ⫽ 4.57, SDcompleters ⫽ 2.75). The main self-reported reasons for dropout were hospital admissions, serious health problems, and time constraints.

Results Preliminary Analyses A univariate analysis of variance on weekly volunteering minutes at T1 revealed that randomization was not completely effective because those assigned to PCG (M ⫽ 149.80 min, SD ⫽ 192.15 min) reported higher weekly volunteering minutes than the ACG (M ⫽ 73.30 min, SD ⫽ 135.36 min; F(2, 252) ⫽ 4.84, p ⬍ .05). However, neither control group differed significantly from the treatment group at baseline (M ⫽ 106.16 min, SD ⫽ 153.44 min). The intervention sessions for the treatment group and ACG were comparable in length with M ⫽ 167.45 (SD ⫽ 17.63) min in the treatment group sessions and M ⫽ 165.73 (SD ⫽ 15.32) min in the ACG sessions, t(186) ⫽ ⫺0.71, p ⫽ .48. Sessions had an equal number of participants with M ⫽ 5.12 (SD ⫽ 1.32) participants in the treatment groups and M ⫽ 5.74 (SD ⫽ 1.18) participants in the ACG, t(186) ⫽ 0.98, p ⫽ .33. The perceived usefulness of the sessions was also comparable with M ⫽ 2.97 (SD ⫽ 0.51) on a rating scale from 1 to 4 in the treatment group and M ⫽ 3.07 (SD ⫽ 0.59) in the ACG, t(126) ⫽ 1.01, p ⫽ .31. Participants in the VIG filled in the 10-day diary on similar days as the ACG, with the VIG starting M ⫽ 37.43 (SD ⫽ 10.89) days and the ACG starting M ⫽ 37.34 (SD ⫽ 13.06) days after the group session; t(149) ⫽ ⫺0.5, p ⫽ .96.

Treatment Effects The unconditional latent change model (without introducing the treatment groups as predictors) fitted the data well ␹2(2) ⫽ 3.61,


VOLUNTEERING INTERVENTION IN OLDER ADULTS

p ⫽ .16, root mean square error of approximation (RMSEA) ⫽ 0.05, comparative fit index (CFI) ⫽ 0.99, standardized root mean square residual (SRMR) ⫽ 0.02. The latent mean for volunteering at T2 as a reference point was 120.83 min, p ⬍ .001. The latent change from T1 to T2 was 14.93 min, p ⫽ .17, and the latent change from T2 to T3 was 31.85 min, p ⫽ .02, meaning that the amount of volunteering did not change significantly from T1 to T2 but increased reliably from T2 to T3 when aggregated across the whole sample. The conditional model also fit the data, ␹2(2) ⫽ 4.41, p ⫽ .11, RMSEA ⫽ 0.07, CFI ⫽ 0.99, SRMR ⫽ 0.02. The group variables did not significantly predict the intercept at T2 and change from T1 to T2, indicating that all three groups developed similarly from T1 to T2 and had a similar level of volunteering at T2. The latent change variable from T2 to T3 was significantly associated with the group variable ACG versus VIG (␤ ⫽ ⫺.23, SE ⫽ .10, p ⫽ .02) and with the group variable PCG versus VIG (␤ ⫽ ⫺.26, SE ⫽ .10, p ⫽ .01), indicating a steeper increase of weekly volunteering minutes from T2 to T3 in the VIG as compared with both other groups (see Figure 2). The explained variance in volunteering change from T1 to T2 was R2 ⫽ .003 and from T2 to T3 it was R2 ⫽ .06. The VIG changed by 84, the ACG by 2, and the PCG by 13 weekly volunteering minutes from T2 to T3, as can be seen in Table 1. The percentage of nonvolunteers (reporting 0 min of volunteer work per week) in the VIG stayed the same from T1 to T2 with 44.6% and 44.7%, respectively, but it reduced considerably from T2 to T3 because only 26.2% reported to be nonvolunteers at T3. The percentage of nonvolunteers stayed similar in the ACG and PCG from T1 to T3 (ACG: T1 ⫽ 58.5%, T2 ⫽ 53.9%, T3 ⫽ 54.2%; PCG: T1 ⫽ 38.0%, T2 ⫽ 44.8%, T3 ⫽ 34.5%).

Discussion Volunteering contributes to society by supporting social services and charitable organizations, and it could confer mental and physical health benefits for the volunteer— especially in old age (Okun et al., 2013; Onyx & Warburton, 2003; Wheeler et al., 1998). Therefore, the study presented here aimed to increase volunteering among community-dwelling older adults by a short, social– cognitive, face-to-face group intervention in a randomized controlled trial that was based on a health behavior change theory and compared this intervention to an ACG and a PCG.

Figure 2. Manifest means of weekly volunteering minutes by time and group. Analyses were controlled for age, gender, education, health status, and baseline weekly volunteering minutes.

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Table 1 M, SD, and n of Weekly Volunteering Minutes Per Group and Point of Measurement T1 Group VIG ACG PCG

M

SD

T2 n

M

SD

T3 n

M

SD

n

106.16 153.44 90 124.34 174.74 74 207.81 220.33 64 73.30 135.36 93 96.92 144.29 88 109.77 170.32 82 149.80 192.15 69 161.48 221.03 65 163.78 190.12 56

The results show that the weekly amount of time devoted to volunteering developed comparably across the three groups within the first 2 weeks after the intervention. However, the treatment group significantly increased its weekly volunteering minutes in the subsequent 4 weeks as compared with the ACG and PCG. These results indicate that a group-based face-to-face intervention containing behavior change techniques that were based on the HAPA (Schwarzer, 2008) can be effective in increasing the volunteering levels of community-dwelling older adults. Furthermore, evaluating the effects of the treatment group not only against a PCG but (also) against an ACG that attended an intervention session with comparable length and behavior change techniques rules out the alternative explanation—that nonspecific factors such as the inclusion into a scientific study, mere contact with interventionists, or discussing active aging in a social atmosphere caused the effects. The time-lagged effect of the intervention must be interpreted in light of the social– cognitive intervention: Participants were not assigned to concrete volunteering organizations or services. The self-regulation strategies taught in the intervention, such as making plans to search for volunteer services that match one’s interests and to self-monitor progress, need time to unfold. Therefore, the first postintervention measurement point 2 weeks after the group session might have been too early to detect changes in behavior. Further, the intervention was not only effective in increasing the amount of volunteering in previous volunteers, but it also motivated nonvolunteers to take up volunteer activities. Because all three groups contained at least one third nonvolunteers at T1 and because not only the overall minutes per week but (also) the number of volunteers increased in the intervention group as compared with both other groups, the intervention can be considered effective for maintenance and initiation of volunteering. Therefore, future intervention studies that aim at targeting nonvolunteers might consider using motivational prompts (e.g., information about the benefits of volunteering, focus on past success, modeling, and goal-setting) and volitional techniques (e.g., if-then implementation intentions and self-monitoring) whereas interventions that aim to increase volunteering frequency or duration among older volunteers might focus only on volitional techniques. To further investigate these processes, preparatory behaviors (e.g., informing oneself about volunteering opportunities) and social cognitions (e.g., self-efficacy or outcome expectancies) might be assessed in future studies (Warburton et al., 2001). This would allow unraveling the mechanisms that lead to the initiation and maintenance of volunteering via mediation analyses. To investigate whether volunteering and physical activities impede each other because they are both time- and energy-consuming, or

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whether they synergize and allow for a good balance between mental and physical challenges in retirement, future studies might want to include an intervention group that receives social– cognitive prompts for both behaviors.


Limitations and Strengths Although this study aimed at producing empirical evidence for the feasibility to increase volunteering in a randomized controlled trial among older adults under naturalistic conditions (hence without assigning them to volunteering services), some limitations must be kept in mind when the results are interpreted. First of all, the results of this study are based on self-report data. Because the purpose of the intervention was obvious for all participants as it included information about the benefits of volunteering and various prompts to increase its likelihood, self-reports of volunteering may be considerably biased by social desirability. However, it is unlikely that our findings can be ascribed to social desirability because the increase in volunteering was observed only at T3 and not at T2. Nevertheless, future studies should gather data provided by volunteering organizations in addition to selfreports. With only 6 weeks of follow-up, the time frame of the current study was relatively short; therefore, only short-term intervention effects could be observed. To evaluate whether the intervention effects can be maintained for longer time periods, future intervention studies should consider having more follow-up measurement points at later points in time. Furthermore, the effect size of the intervention was small. Given the relatively low dosage of the intervention, with only one session of on average less than 170 min, the results are encouraging. The explained amount of variance in change over time was very small, and it indicates that volunteering is subject to factors not controlled for in this study, such as seasonal influences (e.g., weather conditions, summer holidays). The randomization at baseline did not work out completely because the PCG started out with higher volunteering levels than the ACG. However, the treatment group did not differ in terms of weekly volunteering minutes from the other groups at baseline, indicating that it was unlikely that this randomization drawback affected the results. Because this study included self-selected community-dwelling older adults, the results cannot be generalized to less healthy and less educated individuals from different age groups. Future studies that include less healthy and functionally impaired older adults should keep possible negative effects of very high volunteering levels in mind and communicate these to participants to avoid unnecessary pressure or other negative outcomes (Musick et al., 1999; Warburton & Peel, 2008). However, the fact that this short, 2-hr, face-to-face group intervention session produced a significant increase in weekly volunteering minutes 6 weeks after the intervention is promising. Studies with more than one intervention session might be able to produce longer lasting effects, especially if they are conducted in groups, because the group setting might encourage participants to exchange volunteering plans and experiences, and they might support each other in the process of finding a suitable volunteer position (Haski-Leventhal & Cnaan, 2009). Within this study, very few participants met again after the group session (one participant

in the VIG and three in the ACG), which is probably related to the fact that the intervention only comprised one group session. Future studies with several group sessions should assess participants’ contact rates outside of the intervention group setting to investigate the possible active ingredients of social support and companionship in addition to the core intervention content.

Conclusion This study demonstrates that volunteering can be increased through one short, face-to-face group intervention among older community-dwelling adults by using behavior change techniques from health behavior change theory. Because volunteering can be considered a strategy for successful aging because of its associations with health, well-being, and longevity, future studies should follow up on this line of research (Okun et al., 2013; Warburton & Peel, 2008).

References Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides and R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243–277). Mahwah, NJ: Erlbaum. Ayalon, L. (2008). Volunteering as a predictor of all-cause mortality: What aspects of volunteering really matter? International Psychogeriatrics, 20, 1000 –1013. doi:10.1017/S1041610208007096 Bundesministerium für Familie, Senioren, Frauen und Jugend. (2010). Report German Survey on Volunteering 2009: Civil Society, Social Capital and Volunteering in Germany [in German]. Berlin, Germany: Author. Carlson, M. C., Erickson, K. I., Kramer, A. F., Voss, M. W., Bolea, N., & Mielke, M. (2009). Evidence for neurocognitive plasticity in at-risk older adults: The Experience Corps program. The Journals of Gerontology: Series A: Biological Sciences and Medical Sciences, 64A, 1275– 1282. doi:10.1093/gerona/glp117 Charlson, M., Szatrowski, T. P., Peterson, J., & Gold, J. (1994). Validation of a combined comorbidity index. Journal of Clinical Epidemiology, 47, 1245–1251. doi:10.1016/0895-4356(94)90129-5 Fisher, R. J., & Ackerman, D. (1998). The effects of recognition and group need on volunteerism: A social norm perspective. Journal of Consumer Research, 25, 262–275. doi:10.1086/209538 Fried, L. P., Carlson, M., Freedman, M., Frick, K., Glass, T., & Hill, J. (2004). A social model for health promotion for an aging population: Initial evidence on the experience corps model. Journal of Urban Health, 81, 64 –78. doi:10.1093/jurban/jth094 Gottlieb, B. H., & Gillespie, A. A. (2008). Volunteerism, health, and civic engagement among older adults. Canadian Journal on Aging, 27, 399 – 406. doi:10.3138/cja.27.4.399 Grano, C., Lucidi, F., Zelli, A., & Violani, C. (2008). Motives and determinants of volunteering in older adults: An integrated model. The International Journal of Aging & Human Development, 67, 305–326. doi:10.2190/AG.67.4.b Greenslade, J. H., & White, K. M. (2005). The prediction of above-average participation in volunteerism: A test of the theory of planned behavior and the volunteers functions inventory in older Australian adults. The Journal of Social Psychology, 145, 155–172. doi:10.3200/SOCP.145.2 .155-172 Groll, D. L., To, T., Bombardier, C., & Wright, J. G. (2005). The development of a comorbidity index with physical function as the outcome. Journal of Clinical Epidemiology, 58, 595– 602. doi:10.1016/j.jclinepi .2004.10.018


VOLUNTEERING INTERVENTION IN OLDER ADULTS Haski-Leventhal, D., & Cnaan, R. A. (2009). Group processes and volunteering: Using groups to enhance volunteerism. Administration in Social Work, 33, 61– 80. doi:10.1080/03643100802508635 Independent Sector. (2001). Giving and volunteering in the United States. Washington, DC: Author. Michie, S., Ashford, S., Sniehotta, F. F., Dombrowski, S. U., Bishop, A., & French, D. P. (2011). A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: The CALO-re taxonomy. Psychology & Health, 26, 1479 –1498. doi:10.1080/08870446.2010.540664 Morrow-Howell, N., Hong, S.-I., & Tang, F. (2009). Who benefits from volunteering? Variations in perceived benefits. The Gerontologist, 49, 91–102. doi:10.1093/geront/gnp007 Mun, E. Y., von Eye, A., & White, H. R. (2009). An SEM approach for the evaluation of intervention effects using pre-post-post designs. Structural Equation Modeling, 16, 315–337. doi:10.1080/10705510902751358 Musick, M. A., Herzog, A. R., & House, J. S. (1999). Volunteering and mortality among older adults: Findings from a national sample. Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 54B, S173–S180. doi:10.1093/geronb/54B.3.S173 Okun, M. A. (1994). The relation between motives for organizational volunteering and frequency of volunteering by elders. Journal of Applied Gerontology, 13, 115–126. doi:10.1177/073346489401300201 Okun, M. A., Yeung, E. W., & Brown, S. (2013). Volunteering by older adults and risk of mortality: A meta-analysis. Psychology and Aging, 28, 564 –577. doi:10.1037/a0031519 Onyx, J., & Warburton, J. (2003). Volunteering and health among older people: A review. Australasian Journal on Ageing, 22, 65– 69. doi: 10.1111/j.1741-6612.2003.tb00468.x Schreier, H. M. C., Schonert-Reichl, K. A., & Chen, E. (2013). Effect of volunteering on risk factors for cardiovascular disease in adolescents: A randomized controlled trial. Journal of the American Medical Association Pediatrics, 167, 327–332. doi:10.1001/jamapediatrics.2013.1100 Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology: An International Review, 57, 1–29. doi:10.1111/j.14640597.2007.00325.x Schwarzer, R., Schüz, B., Ziegelmann, J. P., Lippke, S., Luszczynska, A., & Scholz, U. (2007). Adoption and maintenance of four health behaviors: Theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Annals of Behavioral Medicine, 33, 156 –166. doi:10.1007/BF02879897

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Sheeran, P. (2002). Intention-behavior relations: A conceptual and empirical review. In W. Stroebe & M. Hewstone (Eds.), European review of social psychology (Vol. 12, pp. 1–36). Hove, United Kingdom: Psychology Press/Taylor & Francis. doi:10.1080/14792772143000003 Sneed, R. S., & Cohen, S. (2013). A prospective study of volunteerism and hypertension risk in older adults. Psychology and Aging, 28, 578 –586. doi:10.1037/a0032718 Tan, E. J., Xue, Q.-L., Li, T., Carlson, M., & Fried, L. (2006). Volunteering: A physical activity intervention for older adults—the Experience Corps Program in Baltimore. Journal of Urban Health, 83, 954 –969. doi:10.1007/s11524-006-9060-7 Thoits, P. A., & Hewitt, L. N. (2001). Volunteer work and well-being. Journal of Health and Social Behavior, 42, 115–131. doi:10.2307/ 3090173 Unesco. (1997). International Standard Classification of Education. Retrieved February 7, 2011, from http://www.uis.unesco.org/publications/ ISCED97 Van Willigen, M. (2000). Differential benefits of volunteering across the life course. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55, S308 –S318. doi:10.1093/geronb/55.5 .S308 Warburton, J., & Peel, N. M. (2008). Volunteering as a productive ageing activity: The association with fall-related hip fracture in later life. European Journal of Ageing, 5, 129 –136. doi:10.1007/s10433-0080081-9 Warburton, J., Terry, D. J., Rosenman, L. S., & Shapiro, M. (2001). Differences between older volunteers and nonvolunteers: Attitudinal, normative, and control beliefs. Research on Aging, 23, 586 – 605. doi: 10.1177/0164027501235004 Wheeler, J. A., Gorey, K. M., & Greenblatt, B. (1998). The beneficial effects of volunteering for older volunteers and the people they serve: A meta-analysis. The International Journal of Aging & Human Development, 47, 69 –79. doi:10.2190/VUMP-XCMF-FQYU-V0JH Wilson, A. E., Allen, J. W., Strahan, E. J., & Ethier, N. (2008). Getting involved: Testing the effectiveness of a volunteering intervention on young adolescents’ future intentions. Journal of Community & Applied Social Psychology, 18, 630 – 637. doi:10.1002/casp.970

Received August 9, 2013 Revision received January 22, 2014 Accepted January 24, 2014 䡲