Who Volunteers? Results From an Internet-Based Cognitive Monitoring Study of Community-Based Older Adults

Abstract

Objectives. Volunteering promotes well-being and may provide added purpose to life after retirement. Limited evidence exists regarding the characteristics and study adherence among those who participate in longitudinal studies while also volunteering outside the study. We assessed characteristics and adherence of older adults who volunteered outside of participation in a regular monthly cognitive monitoring study. Method. All 124 participants with complete data were included. Participants were from a regular cognitive monitoring study that required completion of a 15-min monthly online cognitive assessment. Analysis of covariance and logistic regression analysis were performed to examine differences between volunteers and nonvolunteers. Results. Those who volunteered outside the study were significantly less likely to be college-educated (although all participants were highly educated) but the two groups were cognitively similar. Volunteers had significantly lower scores for neuroticism. Those who volunteered also were significantly less likely to drop out but had poorer study adherence. The most frequent type of volunteering was religious activities. Volunteers were motivated mainly by altruism, although most reported multiple reasons. Conclusion. Older adults who enroll in a longitudinal research study and volunteer outside the study have similar personal characteristics as those who opt out of additional volunteering, with somewhat less education and more favorable personality traits. However, they may be more likely to drop out and need more reminders. Therefore, those who volunteer outside a study may need more attention from study administrators and potentially a more individualized schedule that works around their volunteer obligations.

Keywords

older adults volunteering characteristics personality health adherence

Retirement represents a major transition that alters everyday life and personal identity (N. D. Anderson et al., 2014). Working adults often identify themselves through their occupation and retirement constitutes a role loss that is sometimes difficult to replace. This transition may have negative health consequences, including increased difficulties with mobility and daily activities, more illnesses, declines in mental health (Dave et al., 2007), and potentially worse cognition (Rohwedder & Willis, 2010).

Volunteering may soften the transition from work to retirement, possibly offsetting some of the presumed negative effects of retirement by providing a new postretirement life role, new routine, and offering opportunities to stay active to promote healthy aging (Fried et al., 2004; Guiney & Machado, 2018). The role theory posits that active engagement can substitute for the potential loss of activity during retirement (N. D. Anderson et al., 2014). Through social mechanisms, behavioral expectations are set by individuals and become roles (Sieber, 1974). Continuous engagement in roles helps form a role identity that then becomes a key part of a person’s identity (Burke & Tully, 1977). Volunteering may alleviate loss of roles experienced during retirement by promoting civic engagement (L. A. Anderson & Prohaska, 2014). Volunteering is defined as, “Time individuals give without pay to activities performed either through an organization or directly for others outside their own household” (International Labour Organization, 2011, p. 13) and supports societal economic and health benefits, including increases in longevity and standard of living (Shmotkin et al., 2003).

Past evidence suggests that volunteering may play an important role in reducing age-related cognitive decline, especially after retirement (Rohwedder & Willis, 2010). A longitudinal study concluded that retired older adults who continuously volunteered were less likely to be prescribed anti-dementia treatments or self-report cognitive complaints (Griep et al., 2017). Shmotkin et al. (2003) and Infurna et al. (2016) found that volunteering was associated with better cognition and lower risk of cognitive impairment. Both subjective and objective cognitive outcomes have been used with a general preference for objective measures (N. D. Anderson et al., 2014; Guiney & Macahdo, 2018). The effects of volunteering on cognitive performance for processing speed, visual attention/learning, and working memory have not been addressed, although processing speed is associated with cognitive aging (Finkel et al., 2007; Salthouse, 1996).

Sociodemographic characteristics influence the propensity to volunteer. Older adult volunteers tend to be younger (Shmotkin et al., 2003) and women (Einolf, 2011; Parkinson et al., 2010), although some suggest that women and men simply pursue different types of volunteer activities (Einolf, 2011; Parkinson et al., 2010). For example, women volunteer in religious activities, human services, and education, whereas men volunteer in sports and recreation. Marriage increases engagement in volunteering activities, possibly due to greater social networks and financial resources (Choi, 2003; Dury et al., 2015). Finally, higher education may facilitate volunteering through more available resources, opportunities, and better physical and cognitive health (Choi, 2003; Curvers et al., 2018; Dury et al., 2015; Shmotkin et al., 2003).

Research suggests that those with higher levels of extraversion, agreeableness, openness to new experiences, and conscientiousness volunteer more frequently (Carlo et al., 2005; King et al., 2015). Individuals with high levels of agreeableness are typically motivated by compassion (Moore et al., 2014) possibly increasing their desire to volunteer. Extroverted individuals may be more inclined to participate in social activities provided by volunteering (Bekkers, 2010). Individuals who are conscientious generally seek to stay busy, making them more likely to volunteer (King et al., 2015). Being open to new experiences has been linked to engagement in new activities such as volunteering (King et al., 2015). Neuroticism is associated with negative moods and social anxiety, negatively impacting volunteering (McCann, 2017).

Finally, volunteering may influence adherence for participation in other activities. Studies testing adherence to medication (Aronson et al., 2020), exercise (Orzech et al., 2013), and cognitive monitoring regimens (Sadeq et al., 2018; Valdes et al., 2016) found that depressive symptoms, social motivation, conscientiousness, and limited time reduced study adherence. Perhaps other responsibilities, like those presented by additional volunteering beyond the research study, may further reduce adherence. Conversely, as the role identity theory posits, the identity of a volunteer contributes to other prosocial behaviors (Piliavin et al., 2002), which may improve research adherence, although such evidence is missing.

Current Study

Building on previous research, we examined personal characteristics, cognition, and study adherence in relation to volunteer status among community-dwelling participants from a regular cognitive monitoring study (Valdes et al., 2016). Using role theory (N. D. Anderson et al., 2014), we hypothesized that those volunteering outside the cognitive monitoring study would be younger, women, have higher education and better cognitive function, married, and exhibit more favorable personality traits than nonvolunteers. We also investigated study adherence, expecting that volunteers would be more likely to adhere to the study protocol. Finally, we explored the most common types of volunteer activities and reasons for volunteering.

Method

Participants

Included participants had to be 55 years of age or older and without cognitive impairment or diagnosis of Alzheimer’s disease or Parkinson’s disease. Cognitive impairment was determined by scoring at least 24 on the Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005). Participants were not excluded based on a traumatic brain injury, education, or occupational history. Of 218 participants screened, 51 were ineligible due to low MoCA scores, leaving 167 eligible participants. A total of 16 participants dropped out before the study and seven spouses of enrolled participants were added, resulting in 158 participants. Participants were excluded due to an incomplete annual survey (n = 15), three missed consecutive Cogstate sessions (n = 7), time conflict (n = 4), computer issues (n = 3), health (n = 2), deceased (n = 1), and other (n = 2), resulting in a final sample of 124 participants. Excluded participants did not significantly differ on sociodemographic variables (ps > .05). However, the seven who were excluded based on missing three consecutive Cogstate sessions were less likely to be married, χ²(1, N = 131) = 12.58, p < .001.

Procedures

The study was conducted over 6 years, 2013–2019. The program included an in-person training session for the participants (Valdes et al., 2016) with a member of the research team, followed by monthly sessions self-administered at home. Each month, participants were emailed a user-specific link to the CogState Brief Battery 1 week prior to each monthly session. If participants failed to complete a session within a week of the session date, they were provided with phone/email reminders 5 days, 7 days, and 12 days after receiving the user-specific link. If the session was not completed within 5 days of receiving the third reminder, the session was coded as skipped. Participants who skipped three consecutive monthly sessions were dropped from the study. Annual interviews were conducted at the end of each year. The Year 1 annual interview was enriched with additional items for Cogstate Brief Battery feasibility. Years 2 to 4 annual interview collected health and volunteering data that may have changed annually. The study is compliant with the ethical standards of the Committee on Human Experimentation of a large public university in the United States and was approved by the Institutional Review Board (USF Pro00012918). All eligible participants provided written informed consent.

Measures

Demographics

Information about age (years), gender (men/women), race/ethnicity (White vs. non-White), marital status (married/not married), depressive symptoms, and education (college educated vs. not) was collected during baseline.

Depressive Symptoms

Depressive symptoms indicate psychological well-being, which may be affected by volunteering. The 15-item Geriatric Depression Scale (GDS; Sheikh & Yesavage, 1986), which has been validated for middle-aged and older adults (Rule et al., 1989), was administered.

CogState Brief Battery

The CogState Brief Battery is a computer-based cognitive test that has been used in a clinician-/researcher-supervised manner with cognitively normal older adults and individuals with mild cognitive impairment (Lim et al., 2013; Maruff et al., 2013). The 15-min battery consists of four tasks designed to assess psychomotor function, attention, working memory, and visual learning. Each task utilizes stimuli in the form of playing cards. Stimuli characteristics (e.g., color and suit) are manipulated based on the requirements of each task. The Detection Task is a simple reaction time task that measures psychomotor function and speed of processing. The Identification Task is a choice reaction time task that measures visual attention and vigilance. The One Card Learning Task is a measure of visual learning and memory. The One Back Task is a measure of attention and working memory (Valdes et al., 2016). Cogstate performance was measured using the score at or closest to Month 12, which coincided with the separate completion of the volunteer questionnaire also given at Month 12.

MoCA

The MoCA (Nasreddine et al., 2005) is used as a screening tool for detecting mild cognitive impairment and mild dementia. This tool measures eight domains of cognition, with a total possible score of 30; scores 26 and above are considered normal (Nasreddine et al., 2005). This brief assessment (~10 min) has been validated and used extensively in clinical practice and research (Hoops et al., 2009). Initially, those who scored lower than 26 were excluded. This was lowered to 24 for a more cognitively diverse sample.

Big Five Inventory (BFI)

The BFI is a 44-item questionnaire used to assess several personality dimensions: openness to new experiences, conscientiousness, extraversion, agreeableness, and neuroticism (John & Srivastava, 1999). Example includes, “I see myself as someone who. . . generates a lot of enthusiasm, is reserved, perseveres until the task is finished.” Responses are measured on a 5-point Likert-type scale ranging from agree strongly to disagree strongly. A composite score was used (Warr et al., 2005), with higher scores indicating a stronger association with the personality domain being measured. The BFI has strong reliability and validity (John & Srivastava, 1999).

Study Adherence

Information about study adherence to the protocol requiring participants to complete the Cogstate Brief Battery monthly included number of days needed to complete monthly assessments, assessments completed late, skipped assessments, and reminder phone calls. For these four measures, the number of instances per year (i.e., number of days needed to complete assessment) were totaled for each year of the study and then averaged across years to reflect participants’ study adherence across the entire study period. Based on previous cognitive monitoring (Sadeq et al., 2018; Valdes et al., 2016) and a telephone-based monitoring study (Mundt et al., 2007), successful adherence was operationalized as skipping no more than two monthly assessments annually. Dropout rate was calculated using a binary variable (drop out = 1; no drop out = 0; Matas et al., 2015).

Volunteering

Information about volunteering was collected during the annual interview in only Years 2 to 4 of the study because Year 1 prioritized feasibility. Participants were asked, “Aside from our study, do you participate in any other volunteering?” Those who reported yes were asked, “Please describe your volunteer activities in a few words.” Using content analysis (Hsieh & Shannon, 2005), responses were coded separately by two research members and then compared to resolve discrepancies through consensus. Volunteering was described as “any educational, religious, or other group organization activities or independent activities such as caregiving.” Participants who stated “yes” (n = 56) in Year 2 were also asked about their reasons for volunteering. Responses included giving back to the community, building social relationships, feeling important, and helping others. Additional questions added were the following: “Did you retire in the past year?” and “Are you still doing any kind of work, including volunteering?” See Supplemental Appendix for both versions.

Statistical Analysis

Analyses of covariance (ANCOVAs) were performed to assess differences between volunteers and nonvolunteers across continuous demographic variables, personality traits, and cognitive performance measures. Age, sex, education level, marital status, GDS score, and volunteer status were covariates in all analyses. Logistic regressions were conducted to assess differences between volunteers and nonvolunteers for categorical demographic variables as well as dropout rates, with binary logistic regression used for two-level variables (sex and marital status), multinomial logistic regression is calculated for variables measured with nominal values (race), and ordinal logistic regression is used for variables that have an ordered sequence (marital status). Analyses were conducted using SAS Version 9.4 (SAS Institute, Cary, NC, USA).

Results

Participant Characteristics

On average, participants were 76.87 years of age (SD = 7.47), mostly women (66.94%), married (76.61%), and college-educated (76.61%). Table 1 displays descriptive statistics for the analytic sample of volunteers and nonvolunteers.

Table 1.

Sample Description.

Variable	Volunteers(n = 80)	Nonvolunteers(n = 44)	p value
Variable	M (SD) or n (%)	M (SD) or n (%)	p value
Demographic characteristics
Age	76.41 (7.61)	77.70 (7.24)	.754
Women	56 (70.00%)	27 (61.36%)	.340
Caucasian	79 (98.75%)	43 (97.73%)	.943
Married	62 (77.50%)	33 (75.00%)	.416
College-educated	58 (72.50%)	37 (84.09%)	.043*
GDS total score	0.73 (1.79)	0.70 (1.21)	.917
Cognitive measures
Identification speed	2.75 (0.09)	2.75 (0.12)	.811
Identification accuracy	1.45 (0.18)	1.42 (0.18)	.644
Detection speed	2.60 (0.10)	2.59 (0.10)	.804
Detection accuracy	1.53 (0.08)	1.53 (0.08)	.921
One Card learning speed	3.05 (0.10)	3.06 (0.09)	.738
One Card learning accuracy	0.95 (0.11)	0.95 (0.10)	.976
One Back speed	2.90 (0.09)	2.89 (0.08)	.326
One Back accuracy	1.38 (0.14)	1.43 (0.15)	.091
MoCA total score	26.66 (1.66)	27.07 (1.69)	.198
Personality traits
Neuroticism	2.23 (0.66)	2.41 (0.74)	.023*
Conscientiousness	4.15 (0.64)	4.22 (0.58)	.554
Openness to new experiences	3.68 (0.55)	3.90 (0.50)	.113
Agreeableness	4.31 (0.49)	4.15 (0.58)	.055
Extraversion	3.63 (0.71)	3.37 (0.90)	.096
Study adherence
Total days to complete	3.71 (2.36)	3.62 (2.52)	.801
Total late assessments	9.48 (8.26)	4.30 (4.96)	.000***
Total skipped assessments	2.76 (3.01)	1.27 (2.35)	.007**
Total reminder calls	10.01 (10.25)	5.29 (6.42)	.013**
Dropout rate	17 (22.08%)	24 (55.81%)	.001**

Note. The p values for ANCOVA and logistic regression analysis adjusted for age, sex, education level, marital status, GDS composite score, and volunteer status. ANCOVA = analysis of covariance; GDS = Geriatric Depression Scale; MoCA = Montreal Cognitive Assessment.

p < .05. **p < .01. ***p < .001.

Of the 124 participants, 80 (64.52%) reported volunteering at least once over the span of 4 years outside of the research study. Volunteers and nonvolunteers did not significantly differ on age, F(1, 122) = 0.10, p = .754; gender (OR = 1.52, 95% CI = [0.64, 3.60], p = .340); marital status (OR = 0.68, 95% CI = [0.27, 1.73], p = .416); or depressive symptoms F(1, 122) = 0.03, p = .917. Participants who volunteered were significantly less likely to be college-educated (OR = 2.09, 95% CI = [1.02, 4.26], p = .043).

Cognitive Measures

Volunteers and nonvolunteers scored similarly across all Cogstate tasks, including identification speed, F(1, 122) = 0.77, p = .382; identification accuracy, F(1, 122) = 2.20, p = .141; detection speed, F(1, 122) = 0.19, p = .662; detection accuracy, F(1, 122) = 1.62, p = .206; One Card learning speed, F(1, 122) = 1.97, p = .163; One Card learning accuracy, F(1, 122) = 1.64, p = .203; One Back speed, F(1, 122) = 3.19, p = .077; and One Back accuracy, F(1, 122) = 0.79, p = .377. In addition, volunteers and nonvolunteers performed about equally on the MoCA, F(1, 122) = 1.67, p = .198, which also did not yield significance.

Personality

Results revealed volunteers reported significantly lower scores than nonvolunteers for neuroticism, F(1, 122) = 2.17, p = .023. Volunteers had marginally significant higher scores for agreeableness, F(1, 122) = 3.75, p = .055. Volunteers and nonvolunteers scored similar and did not significantly differ on measures of extraversion, F(1, 122) = 2.81, p = .096; openness to new experiences, F(1, 122) = 2.56, p = .113; and conscientiousness, F(1, 122) = 0.35, p = .554. See Figure 1.

Figure 1.

Personality traits.

Study Adherence

Study adherence data comprise the entire length of the study. Results indicated that volunteers had significantly lower dropout rates over the course of the study compared with nonvolunteers (OR = 0.22, 95% CI = [0.09, 0.53], p = .001). Interestingly, nonvolunteers adhered to the study protocol more closely, skipping slightly more than one assessment compared with volunteers skipping almost three, F(1, 122) = 7.50, p = .007. On average, volunteers also had significantly more late monthly assessments, F(1, 122) = 14.59, p < .001, and volunteers needed twice as many reminder phone calls than nonvolunteers, F(1, 122) = 6.45, p = .013.

Types of Volunteering

Table 2 portrays the type of volunteer activities participants reported. The most common volunteer activities belonged to religious (n = 24; 29.27%), health and medical (n = 19; 23.17%), and senior citizen/related (n = 15; 18.29%) organizations. A total of 23 (40.24%) participants reported multiple volunteer activities.

Table 2.

Volunteer Characteristics.

Volunteer variables	n (%)
Types of volunteering^a
For-profit organizations
Business	9 (10.98)
Health and medical	19 (23.17)
Legal	5 (6.10)
Technical	5 (6.10)
Total	38 (46.34)
Nonprofit organizations
Public charities	12 (14.63)
Caregiver	10 (12.20)
Religious	24 (29.27)
Education	10 (12.20)
Military	2 (2.44)
Senior citizen/related organization	15 (18.29)
Total	73 (89.00)
Other/not specified
Other	7 (8.54)
Not specified	10 (12.20)
Total	17 (20.73)
Reasons for volunteering^b
Giving back to the community	27 (71.05)
Feeling important	2 (5.26)
Important to help others	25 (65.79)
Building social relationships	12 (31.58)
Other	14 (36.84)

Participating in multiple volunteering activities explains overlapping percentages.^bReporting multiple reasons for volunteering explains overlapping percentages and reflects a different sample size of n = 38.

Reasons for Volunteering

Table 2 displays the results from our post hoc analyses with 56 volunteers who responded to a detailed set of questions regarding reasons for volunteering. The most common reason participants reported they volunteered was to give back to the community (n = 27; 71.05%), followed by the importance to help others (n = 25; 65.79%), other reasons (n = 14; 36.84%), building social relationships (n = 12; 31.58%), and feeling important (n = 2; 5.26%). If participants chose “other,” they were further asked to specify their reasons for volunteering. Examples of responses when selecting “other” included personal enjoyment, having a sense of purpose, and feeling needed.

Discussion

The goal of this study was to explore characteristics of older adults who engaged in volunteering outside of participation in a monthly cognitive monitoring study. We also explored the association between volunteering and study adherence. Overall, those who volunteered outside our study and those who did not were quite similar in main personal characteristics, but those who volunteered outside the research study were less likely to be college-educated but also less neurotic and slightly more agreeable compared with those who did not. No substantial differences in cognition were observed, although this may be a function of the fact that all participants were high cognitively functioning.

Notably, volunteers were less likely to drop out from the study than nonvolunteers, suggesting that the extra volunteering activity outside a research study does not carry extra burden that could facilitate attrition. Rather, it may be that the individuals who take on these multiple volunteer roles are inherently equipped to handle these activities, potentially by more favorable personality traits (Ozer & Benet-Martínez, 2006). However, those who volunteered outside of our study were also more likely to need reminders to complete sessions on time.

Volunteers and nonvolunteers did not vary on age, gender, or marital status. This is not consistent with previous research that has found that volunteers tend to be younger, women, and married (Parkinson et al., 2010; Shmotkin et al., 2003). This divergence in results could be explained by the fact that our sample was highly homogeneous across these characteristics. Participants were also high functioning and had higher education, which is typical for this type of longitudinal study.

With respect to the finding that volunteers were somewhat less likely to have college education, this is contrary to previous research, which suggests volunteers tend to be more educated (Choi, 2003; Curvers et al., 2018; Dury et al., 2015; Shmotkin et al., 2003). However, it is of note that the sample used here was overall highly educated and that the overrepresentation of volunteers among women compared with men, rather than lower education itself, may explain this finding.

Volunteering and Cognition

Cognition was assessed in two ways—with Cogstate and MoCA, objectively. With respect to Cogstate, volunteerism did not provide a cognitive benefit cross-sectionally or longitudinally. These findings do not support the role theory (N. D. Anderson et al., 2014) or previous research that has suggested volunteering is associated with cognitive benefits (Griep et al., 2017; Guiney & Machado, 2018; Infurna et al., 2016; Shmotkin et al., 2003). There was no significant association between volunteering and cognition measured by the MoCA, although this may be because an MoCA cutoff was used as inclusion criteria.

Previous research has shown a positive association between volunteering and global measures of cognition (Lee et al., 2016). One possible explanation for the discrepancy between ours and previous findings could be that volunteers were less educated than nonvolunteers. Given that more education relates to better cognition, it may be that volunteering helped eliminate the gap in objective cognition between those with varying levels of education (Huang & Zhou, 2013; Lee et al., 2016). Another possibility is that the fact that all our participants were relatively high functioning cognitively masked any benefits of extra volunteering.

The Role of Personality on Volunteering

Volunteers scored significantly lower on measures of neuroticism, which is in line with previous research (Carlo et al., 2005; King et al., 2015; Moore et al., 2014). Given that neuroticism is typically accompanied by worrying, getting easily upset, and social anxiety, these characteristics may discourage engagement in volunteering activities (Carlo et al., 2005; McCann, 2017). There was a trend suggesting that volunteers may also be more agreeable and extroverted, although these results were not significant (agreeableness: p = .055) and extroversion: p = .096), possibly due to our small sample. Agreeable individuals have been conceptually linked to having higher levels of altruism, compliance with requests, and being trusting (Carlo et al., 2005), which may lead to more prosocial behaviors. Levels of extraversion did not differ based on volunteer status that seems questionable considering extraversion is highly correlated with increased levels of sociability, positivity, and activity, is a predictor for volunteering (Bekkers, 2010; Carlo et al., 2005). The discrepancy between previous findings regarding openness to new experiences and conscientiousness may be due to these traits being less conceptually related to volunteering (Carlo et al., 2005).

Volunteering and Adherence

Although volunteers were less likely to dropout, they had lower study adherence compared with nonvolunteers. Overall, these findings partially support the role identity theory and previous research where volunteers are more committed to participating in studies long-term (Vecina & Chacón, 2017), but their lower study adherence warrants further investigation (Piliavin et al., 2002). Several plausible explanations address this finding. Given that volunteers were more likely to stay in the study, they may have felt compelled to continue despite events that would have made a nonvolunteer quit. However, this may have also made them more prone to needing reminders to complete sessions. Moreover, volunteers may have more commitments than nonvolunteers, making them more likely to complete sessions late. Volunteers typically have more social obligations such as multiple volunteering organizations, caregiving, education/training programs, or bridge jobs, which may have interfered with monthly study adherence (Morrow-Howell, 2010; Morrow-Howell et al., 2014).

Types of Volunteering

Religious activities were most frequently reported. Consistent with previous research, one of the most commonly reported volunteering settings is religious (Yeung, 2018). However, 40% of participants in our study indicated multiple forms of volunteer settings and activities. These findings suggest that older adult volunteers seem to prefer participating in different types of volunteering (Hinterlong, 2008), which may be due to personal factors such as commitment to a specific organization that contributes to the likelihood of volunteering in various types of settings.

Reasons for Volunteering

Although participants reported a multitude of reasons for volunteering, the most common reason was to give back to the community, followed by the importance of helping others, other reasonings, to build social relationships, and to feel important. Similarly, prior research has found some common reasons for volunteering are the importance to help others (Nichols and King, 1999) and to feel important (Okun, 1994). These findings coincide with Clary et al.’s (1992) multidimensional theory of motivation to volunteer. Perhaps the complexity of a wide variety of reasons (Celdrán & Villar, 2007) could be better analyzed as two main dimensions to further understand the mechanisms involved.

Strengths and Limitations

This study has several strengths, including objective measurement of several cognitive domains and longitudinal measures of study adherence. However, the results should be interpreted with caution. First, the study lacks a diverse demographic sample of participants. A total of 96% of the sample were Caucasian with the majority in their mid-70s. Future studies should recruit a more diverse sample to generalize findings to other racial backgrounds and age ranges. Second, these data were collected in Years 1 to 5 of a longitudinal study. Thus, individuals partaking in this study have an overall better health status compared with participants who have withdrawn in previous years. Previous research suggests that participants who continuously participate in a study have less health and mental health problems (Parkinson et al., 2010; Radler & Ryff, 2010). Third, the sample of volunteers and nonvolunteers were relatively similar given that they were already volunteers of a research study. Finally, we had only yes/no information about volunteering. Having more details regarding the mean number, frequency, and duration of volunteering would have been preferable. This would allow comparison between those who engage in volunteering frequently and those who volunteer a limited amount. Future research should address this by examining differences between older adults who volunteer frequently compared with those who do not.

Implications for Practice

The findings offer several implications for practice. Research participants who also volunteer in other activities do not drop out more frequently but do exhibit difficulty with task completion. This can be offset by focus on convenience scheduling (Shen et al., 2020) such as offering more time to complete research tasks or using an online format to provide more flexibility. As altruism primarily drives volunteering in older adults, researchers/organizations may increase participation by promoting that their involvement will benefit others (Paylor, 2011). Educating older adults about the health benefits of volunteering may also facilitate participation in both research and volunteering. Religious organizations, which seem to be a popular venue for volunteering, may play an important role in encouraging volunteering across the broad range of options.

Conclusion

This study explored characteristics and study adherence among retired older adults who volunteered outside of a cognitive research study. Older adult volunteers were less educated, had lower levels of neuroticism, and were less likely to drop out, but also had lower study adherence. The volunteers and nonvolunteers did not differ in cognitive performance. Formal activity in religious settings was most frequently reported and giving back to the community was the most common reason for volunteering. Future research may expand on study adherence and retention to promote volunteering postretirement.

Supplemental Material

sj-docx-1-heb-10.1177_10901981221101355 – Supplemental material for Who Volunteers? Results From an Internet-Based Cognitive Monitoring Study of Community-Based Older Adults

Supplemental material, sj-docx-1-heb-10.1177_10901981221101355 for Who Volunteers? Results From an Internet-Based Cognitive Monitoring Study of Community-Based Older Adults by Britney Veal, Nasreen A. Sadeq, Taylor J. Atkinson and Ross Andel in Health Education & Behavior

Footnotes

Acknowledgements

The authors would like to express their gratitude to Devina Basdeo, Sanjay Chakkoli, Jerry Ray Logemann, Brian Musch, Sean Briceno, Nourleen Goubrial, and Cortny Marchant for their help with data collection and other aspects of this project.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the USF Health Byrd Alzheimer’s Institute at the University of South Florida.

Ethical Statement

This work was approved by the Committee on Human Experimentation of the University of South Florida.

IRB Protocol/Human Subjects Approval Number

USF Pro00012918.

ORCID iD

Britney Veal

Supplemental Material

Supplemental material for this article is available online at .

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