Health outcomes of Experience Corps®: A high-commitment volunteer program

Social Science & Medicine 71 (2010) 414e420

Contents lists available at ScienceDirect

Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Health outcomes of Experience CorpsÒ: A high-commitment volunteer program S.I. Hong a, *, Nancy Morrow-Howell b a b

National University of Singapore, Social work, 3 Arts Link, BLK AS3, Level 4, Singapore George Warren Brown School of Social Work, Washington University, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 29 April 2010

Experience CorpsÒ (EC) is a high-commitment US volunteer program that brings older adults into public elementary schools to improve academic achievement of students. It is viewed as a health promotion program for the older volunteers. We evaluated the effects of the EC program on older adults’ health, using a quasi-experimental design. We included volunteers from 17 EC sites across the US. They were pre-tested before beginning their volunteer work and post-tested after two years of service. We compared changes over time between the EC participants (n ¼ 167) and a matched comparison group of people from the US Health and Retirement Study (2004, 2006). We developed the comparison group by using the nearest available Mahalanobis metric matching within calipers combined with the boosted propensity scores of those participating in the EC. We corrected for clustering effects via survey regression analyses with robust standard errors and calculated adjusted post-test means of health outcomes, controlling for all covariates and the boosted propensity score of EC participants. We found that compared to the comparison group, the EC group reported fewer depressive symptoms and functional limitations after two years of participation in the program, and there was a statistical trend toward the EC group reporting less decline in self-rated health. Results of this study add to the evidence supporting high-intensity volunteering as a social model of health promotion for older adults. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Older people Community-based health promotion Propensity score matching method Volunteer program effect USA Experience CorpsÒ

There is a growing body of research suggesting that volunteering improves the health of older adults (Corporation for National and Community Service [CNCS], 2007). Indeed, high-commitment volunteering has been proposed as a health promotion program for an aging society (Carlson et al., 2008; Fried et al., 2004). Experience CorpsÒ (EC), a high-commitment volunteer program, has been the focus of research on the health effects of volunteering since its original conceptualization as a social model of health promotion in 1995. The EC program is designed to bring older adult volunteers aged 55 and above into public elementary schools several times a week to provide tutoring for children at risk of reading failure. Currently, over 2000 EC tutors are serving approximately 20,000 students across 23 cities (see current description of the program at www.experiencecorps.org). In this study, we test the social model of health promotion by examining the effects of participating in the EC program on the selfrated health, functional limitations, and depressive symptoms of older adults. We included all program participants who joined the program for the first time in Fall, 2006, from 17 sites around the country. Specifically, we compared longitudinal changes in health outcomes over two years between EC volunteers who were engaged * Corresponding author. Tel.: þ65 9111 0925; fax: þ65 6778 1213. E-mail address: [email protected] (S.I. Hong). 0277-9536/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2010.04.009

in high-commitment volunteering and a matched sample of older adults from the Health and Retirement Study who were not engaged in high-commitment volunteering during the study period.

Background Health effects of volunteering The positive association of volunteering and well-being among older adults has been well-documented (CNCS, 2007). Volunteering has been associated with the following outcomes: lower mortality (Musick, Herzog, & House, 1999), higher physical function (Lum & Lightfoot, 2005; Moen, Dempster-McClain, & Williams, 1992), higher self-rated health (Morrow-Howell, Hinterlong, Rozario, & Tang, 2003), fewer depressive symptoms (Musick & Wilson, 2003), lower pain (Arnstein, Vidal, Wells-Federman, Morgan, & Caudill, 2002), and higher life satisfaction (Van Willigen, 2000). The argument that volunteering is positively associated with health status is bolstered by the fact that a growing number of studies, using various measures of health outcomes, support the same general conclusion. However, there are limits to the knowledge generated through this work. In general, the research cited above is based on longitudinal analyses of large nationally-

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

representative data sets, where information on volunteering is limited to a few basic descriptors of the volunteer experience such as hours in the last year and type of organization. A wide range of volunteering is thus included, and it is not clear what constitutes the health-promoting intervention. Although the health outcomes are generally captured through standardized measures of health, mental health, and life satisfaction, the outcomes are assessed in time frames consistent with the observation periods of the studies as opposed to time frames directly corresponding to the volunteer experience. Thus, specific outcomes associated with the volunteer experience per se are not known. Further, there are very few experimental designs reported in the literature on the effects of volunteering, given the difficulty of creating two comparable groups of volunteers and non-volunteers. In sum, the current literature presents a convincing argument that volunteering produces health, but we will be hampered in developing programs and policies without more specific information about what types of volunteer programs produce what kinds of effects, under what circumstances. Previous work on health effects of volunteering in Experience CorpsÒ Baltimore The limitations cited above are addressed in current work on the effects of the EC program in Baltimore, Maryland. The Baltimore program is one of the oldest EC programs, and it is described in detail in various publications (Carlson, Seeman, & Fried, 2000; Freedman & Fried, 1999; Frick et al., 2004; Fried et al., 2004; Glass et al., 2004; Martinez et al., 2006; Rebok et al., 2004; Tan, Xue, Li, Carlson, & Fried, 2006). In general, it is similar to other EC programs (described below) in that older adults are recruited, trained, and monitored in their work with young students in public elementary schools. All participants commit 15 h a week throughout the academic year and receive a small stipend to offset costs of travel and meals. In a pilot randomized trial, 149 new recruits to the program were assigned to EC or to a waitlist. One-year outcomes showed that EC volunteers reported increased physical strength, an increase in the number of people they could turn to for help, and less TV watching. They also showed less decline in walking speed than the control group (Fried et al., 2004); and a trend toward improved cognitive function (Carlson et al., 2008). Participants reported being more physically active (Tan et al., 2006); and after three years, EC members sustained increased physical activity compared to a match group of comparisons from another study of older adults (Tan et al., 2009). Rationale for the current study The available evidence from the evaluation of the Baltimore Experience CorpsÒ suggests that the program promotes the health of its older volunteers. We build on these findings in several ways. Most importantly, we include participants from all EC programs across the United States (at the time of the study). Programs across the country share certain essential elements, but there are variations in terms of roles in the schools, time requirements, stipend status, and administrative structures. It is important to establish the extent to which outcomes can be achieved across multiple programs sites, as differences will continue to exist as local communities and school districts implement the program. We also increase sample size to improve statistical power, utilize different measures of health outcomes, and employ a two year observation period. Our work draws on the social model of health promotion (Fried et al., 2004), proposing that volunteering produces positive outcomes through activities associated with the volunteer role d that is, through physical, cognitive, and social pathways. EC participation involves cognitive activity (interaction with students during tutoring sessions; preparation for sessions; ongoing training events), social

415

activity (connections with EC school personnel and other volunteers, including group meetings and events); and physical activity (travelling to school two to three times a week; walking around the school building with students). Based on this conceptualization, we hypothesized that older adults who participate in the EC program for two years will experience more positive health outcomes than comparable older adults who do not participate in the program. Overview of the Experience CorpsÒ program The Experience CorpsÒ program is a national program, with a Chief Executive Officer and program staff in Washington, DC. Local communities implement EC programs, which are usually hosted at the city level by nonprofit or public agencies. For example, a nonprofit organization, Generations Incorporated, Inc. operates a program in Boston; the South East Texas Regional Planning Commission, a voluntary association of local governments, operates a program in Port Arthur, Texas; and the City Community Services Department operates a program in Tempe, Arizona. In all cases, school districts and elementary schools choose to participate in the program. To be a part of the Experience CorpsÒ national program, local sites adhere to program missions and standards, participate in national training and support activities, and receive assistance with fund-raising and expansion efforts. The local programs share common elements, but there are also variations that reflect the nature and preferences of the local communities. At each site, paid EC staff members coordinate the program. They identify elementary schools that want to participate; recruit, train, and monitor the volunteers; and assign low-reading students referred by the teachers to EC volunteers. The volunteer tutors are recruited from the local neighborhoods through word-of-mouth and advertising. In order to participate, potential volunteers must submit an application and references. They must pass a background check and be interviewed by EC staff members. Volunteers receive training on literacy as well as on relationship-building and behavior. Volunteers work with the children individually and in small groups, and assist the teacher in the classroom as needed. Between the cities, the size and administration of the programs differ as do the range of services and levels of engagement of the EC volunteers. In the sample of EC volunteers included in this survey, the large majority of EC members (85%) provided one-on-one tutoring. Almost 38% provided small group academic help, and 11% reported serving as an assistant to the teacher. Volunteers averaged about 12 h per week working for the EC program (ranging from 5 to more than 16 h a week). The modal time commitment per week was 15 h, with 30% of volunteers providing this level of service. Some, but not all, programs provide stipends to the EC participants; and monies for stipends are obtained from the federal AmeriCorps program, private foundations, and school districts. In this sample, 69% of EC participants received a stipend. There are no explicit income eligibility requirements to receive a stipend, and the programs award stipends in exchange for a commitment of a certain amount of hours per week for the entire academic year. For example, to receive the AmeriCorps stipends, members must sign up for 10- or 12-month terms for 15 h per week. They receive a monthly taxable stipend of about $290.Members who receive non-AmeriCorps stipends make similar time commitments; stipend rates vary from city to city, but average about $2.77 an hour. Methods Research design We used a quasi-experimental two-group pre-post test design to assess outcomes associated with participation in the EC program.

416

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

We could not establish a randomly assigned control group or a waitlist control group because there are more students referred to the program than there are EC volunteers. The program recruits, trains, and places volunteers well into the school year, and still there are students referred by teachers who cannot participate due to lack of available tutors (Morrow-Howell, Jonson-Reid, McCrary, Lee, & Spitznagel, 2009). Under these circumstances, we sought an alternative counterfactual: we established a comparison group through matching individuals participating in the Health and Retirement Study (Fonda & Herzog, 2004). EC sample recruitment Inclusion criteria for the EC sample included adults over the age of 50 years who began serving in the EC program in the 2006e2007 academic year and were first-time participants in the EC program. We included only those who were new to the program instead of returning volunteers to capture the outcomes from a pre-program baseline health status. We excluded volunteers less than 50 years old and anyone who initially expressed interest in volunteering but was not trained and placed in a school to begin service in fall 2006. Program staff from 17 sites sent us names and contact information of new volunteers. In the parent study, 348 individuals qualified for and began the EC program in fall, 2006. Most of these individuals finished the school year (n ¼ 285; an 82% completion rate). For this analysis, we focus on EC participants who served in the program for two years. There is only a one-year commitment requested at the time of enrollment into the program. Thus, this group of returning volunteers valued participation and found it feasible to continue to serve beyond the expected commitment of one term. In this study, we followed 185 volunteers who completed the first year and returned for a second year of service (two program sites closed after the 2006e2007 academic year, so 31 volunteers did not have an opportunity to serve another year; thus 185 is approximately a 73% return rate from one year to the next). In the second year, 167 (90%) of these people completed the program and participated in the follow-up interview. Of 18 dropouts, 11 cited family and logistical challenges while 7 mentioned health concerns as the reason for dropping out. We compared the study sample of 167 with the 18 EC participants who began a second term of service but did not complete it. The groups were not different on demographics (age, ethnicity, gender, marital status, education, income, and employment status), volunteer history, and health outcomes at baseline (self-rated health, functional limitations, and depressive symptoms). EC data collection Trained interviewers followed a structured protocol to conduct baseline assessments via 45-min telephone interviews. The pretest assessment included demographics, volunteer history, and measures of health status, including self-rated health, functional limitations, and depressive symptoms. At the end of the 2007e2008 academic year, we re-interviewed those volunteers who continued for a second year of service. The post-test assessment included the same health assessments as well as information about the level of volunteering. All procedures were approved by the Institutional Review Board at Washington University (E05-133). Measurement Three standardized measures of health were employed, and the questions were identical between the EC and Health and Retirement Study (HRS) participants. Functional limitations were assessed with

questions originally derived from the research of Nagi (1969, 1976), Rosow and Breslau (1966), Katz et al. (1963), and Lawton and Brody (1969). Eleven items assessed functional difficulties caused by physical health conditions in the following areas: walking, running, climbing stairs, stooping, sitting for long periods, lifting arms, getting up, lifting weights, and picking up a dime. Each item is assessed using a four-point scale ranging from no difficulties to extreme difficulty. Items were summed to create a measure of functional limitations (Cronbach’s alpha ¼ 0.76 for the EC sample and 0.81 for the HRS sample), with higher scores representing more disability. Self-rated health was measured with a scale from excellent (5) to poor (1). This measure is commonly used and valid for studies of older adults (Eriksson, Unden, & Elofsson, 2001). Depression was measured with 9 items from the Center for Epidemiologic Studies Depression Scale (CES-D) (Radloff, 1977). Cronbach’s alpha was 0.82 for the EC sample and 0.81 for the HRS sample. Older adults’ age, gender, race, marital status, education, employment status, and annual family income were measured as covariates, comparably across both data sets. In the HRS, survey participants indicated if they had ever volunteered as well as their current volunteer status and level of volunteering in the past 12 months (less than 50 h; more than 50 and less than 100 h; more than 100 and less than 200 h; and more than 200 h). We created the variable of volunteer history to indicate if HRS participants had volunteered before the 2004e2006 observation period and if EC participants had volunteered before joining the EC program. Identification of an HRS comparison group We identified a matched sample from the Health and Retirement Survey (HRS), a biennial survey of a large nationally-representative sample of adults over the age of 50 conducted by the Survey Research Center since 1992 in cooperation with the National Institute on Aging and the University of Michigan (National Institute on Aging & U.S. Department of Health and Human Services, 2007). In addition to demographic and health measures, the HRS also has measures of volunteering, allowing this important variable to be used in matching as described below. We first selected the pool of HRS participants to consider for matching one-to-one with the EC participants. We started with observations that had complete data in two consecutive waves, 2004 and 2006 (N ¼ 17,560). We excluded HRS observations who died or were lost to follow-up or who were hospitalized at the time of the interview. We excluded HRS individuals who were outside of lower boundaries of the EC sample in terms of age, income and education. That is, we excluded HRS individuals below the age of 50 and with less than the minimum EC income (less than $5000 a year) or education levels (less than 6 years of schooling). To allow for consideration of unmeasured characteristics associated with volunteering, we did not exclude from the matching pool HRS participants with a history of volunteering or those currently volunteering. However, we excluded from the matching pool HRS participants who were high-commitment volunteers, meaning that they volunteered more than 200 h in the last 12 months (the highest category of volunteer hours in the HRS data set). Thus, this strategy allowed for the matching procedure to consider HRS participants who may or may not be current or past volunteers; and it enabled us to compare high-commitment volunteering to comparable older adults who were not currently in a high-commitment volunteer situation. These inclusion and exclusion criteria resulted in an HRS subsample of 7312 available for matching. We next employed a stringent matching procedure, the nearest available Mahalanobis metric matching within calipers defined by the propensity score (see D’Agostino, 1998) to identify 167 HRS participants comparable to the EC participants. The propensity

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

score, defined as the conditional probability of being in the highvolunteer group (in our case, a volunteer in the EC program) was used to select comparisons most similar to the program participants. With the same propensity score, EC and HRS participants are assumed to be randomly assigned to each group in the sense of being equally likely to be in the high-volunteer or comparison group. The propensity score was first calculated using sociodemographics (age, gender, marital status, race, employment status, income, and education), health status at baseline (depression, functional limitations, and self-rated health), and previous volunteering experience. Because the accurate estimation of propensity scores is obstructed by a large numbers of covariates and tentative functional forms for their associations with program selection, we supplemented the estimation with a boosting technique. Boosting estimates propensity score weights to eliminate group differences at pre-test, which enhances the accuracy of estimated propensity scores (McCaffrey, Ridgeway, & Morral, 2004). The boosted propensity scores were used as a covariate to calculate the Mahalanobis metric distances. All comparison subjects within a caliper of the EC participant’s estimated propensity scores were selected. Without replacement, this sampling process was repeated (D’Agostino, 1998). After completing the matching method, we had 167 pairs of EC and HRS participants. All matching sampling processes incorporated by the boosted propensity scores were performed using the psmatch2 process in Stata. 10.0. To assess comparability of the matched sample, we used independent t-tests and chi-squares tests. The final data set contained 334 observations, with equal numbers of EC and HRS participants. This sample allowed us to proceed with a quasi-experimental design to compare change over a two-year period between the two groups on health outcomes. Method of analysis Missing data Missing data were imputed separately for high-volunteer and comparison groups using Markov Chain Monte Carlo Multiple Imputation to complete all missing values of the study variables (Allison, 2002). The EC data set had missing values less than 5% of the time across all the study variables. In the HRS, some variables (education, income, and depression) had missing values 20%e28% of the time. Using multiple covariates related to these missing values, the imputation method created five different datasets respectively for the EC and HRS data sets. The unconditional ANOVA tests indicated no significant difference in outcome measures among these multiple data sets; thus, we selected only the first implicate dataset to use in the matching procedures. Testing effects of the EC participation To assess change over two years on the outcome measures, we used unadjusted paired t-tests. To assess the effects of participation in the EC program, multiple regressions were used to compare post-test scores for the EC and the comparison group, adjusting for pre-test health status, socio-demographics, and previous volunteering experience. To further control for any difference between these two groups, the boosted propensity score was also included in the analytic model. We examined the adjusted post-test scores for statistical significance and calculated effect sizes using Hedge’s G (Rosenthal & Rosnow, 1991). We assessed the covariates for multicollinearity; and all correlations were lower than 0.40, with the exception of the correlation between self-rated health and functional limitations at pre-test (r ¼ 0.57).

417

Correcting for clustering The data in the EC sample were clustered by EC programs. Outcomes of individual members within a same cluster are likely to be correlated, and a failure to incorporate within-cluster correlations into the analytic model leads to incorrect standard errors and p-values (Peters, Richards, Bankhead, Ades, & Sterne, 2003). Based on this notion, estimates and corresponding p-values were adjusted by survey regression analysis with robust standard errors using the PROC SURVEY procedure in the SAS 9.1 (SAS Institute Inc., 2009). Results Characteristics of EC and HRS comparison sample As shown in Table 1, no statistical differences in pre-test variables were found between the EC group and the HRS comparison group, with the exception of education. For both groups, average age of the sample was 65 years. Females comprised over 80% of the sample, and most were not working. About 60% were non-white. About half of each group had volunteered before the observation period of this study (50% of EC volunteers were volunteers before joining EC and 46% of comparisons had volunteered before the 2004 HRS data collection). During the two-year observation period, 65% of HRS comparisons were not current volunteers, 14% volunteered less than 50 h in the last year, 13% volunteered between 50 and 100 h, and 8% volunteered between 100 and 200 h. EC volunteers averaged about 12 h a week over two academic years during this period. It was not possible to identify an HRS group that matched on education as well as other socioeconomic indicators. The EC group is more educated than the general population, while still being economically and ethnically diverse. Forty-five percent of EC have more than 16 years of schooling, while 44% of HRS controls have less than 12 years. It is important to note that there are no differences between the two groups on the health outcomes of interest. Program effects on health outcomes We found that both groups made statistically significant changes in health indicators over the two-year observation period (Table 2). For the EC members, both post-test means of depression and functional limitations significantly decreased from pre-test. In contrast, the HRS comparison reported worse health status on these two measures over two years. The two-year change in selfrated health was not statistically significant for either group. Table 3 indicates that, when adjusting for pre-test health status, socioeconomic status, volunteer history, and boosted propensity scores, EC participants experienced more positive health outcomes than the comparison group. Specifically, EC members experienced more positive changes in functional limitations and depressive symptoms at post-test. Differences in self-rated health were marginally significant (p ¼ .09), but the trend is toward EC members registering fewer declines than the comparison group. The relationships of the covariates to health outcome are expected. Older study participants experienced a decrease in selfrated health and an increase in functional limitations. Females experienced an increase in functional limitations compared to males. Lower education was related to an increase in depressive symptoms and a decrease in self-rated health. In addition, higher levels of family income were associated with a decrease in depression and functional limitations. The propensity score was not significant in any model, indicating that the matching procedure and statistical control offered by the covariates eliminated observable differences in the groups.

418

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

Table 1 Comparison of volunteer program and comparison groups at pre-test (N ¼ 334). EC volunteer group (n ¼ 167)

Variable

Mean (SD) Range Age

HRS comparison group (n ¼ 167) N (%)

64.78 (7.44) 51e83

Gender Male Female

Mean (SD) Range

Test statistic N (%) t ¼ 0.50

65.14 (7.17) 51e84 23 (13.77) 144 (86.23)

27 (16.17) 140 (83.83)

c2 ¼ 0.27

65 (38.92) 42 (25.15) 60 (35.93)

81 (48.50) 25 (14.97) 61 (36.53)

c2 ¼ 4.80

Employment Yes No

22 (13.17) 145 (86.83)

32 (19.16) 135 (80.84)

c2 ¼ 1.93

Race White Non-whitea

63 (37.72) 104 (62.28)

65 (38.92) 102 (61.08)

c2 ¼ 0.003

43 (25.75) 49 (29.34) 75 (44.91)

73 (43.71) 35 (20.96) 59 (35.33)

c2 ¼ 13.62*

Marital Status Married Widowed Never married

Education 0e12 years 13e15 years 16 years or more Annual family income Less than $5000 $5000e$10,000 $10,000e$15,000 $15,000e$20,000 $20,000e$25,000 $25,000e$30,000 $30,000e$35,000 $35,000e$40,000 $40,000e$50,000 $50,000e$75,000 $75,000 more

7.04 (3.05) 1e11

Volunteer history No Yes Self-rated health Excellent Very good Good Fair Poor

t ¼ 0.55

7.26 (3.14) 2e11 2 12 16 14 15 12 14 13 18 26 25

(1.20) (7.19) (9.58) (8.38) (8.98) (7.19) (8.38) (7.78) (10.78) (15.57) (14.97)

0 (0.00) 18 (10.78) 12 (7.19) 17 (10.18) 11 6.59) 6 (3.59) 6 (3.59) 11 (6.59) 41 (24.55) 13 (7.78) 32 (19.16)

82 (49.10) 85 (50.90)

90 (53.89) 77 (46.11)

3.64 (0.96) 1e5

t ¼ 1.39

3.44 (0.85) 1e5 33 64 51 16 3

(19.76) (38.32) (30.54) (9.58) (1.80)

c2 ¼ 0.97

23 44 85 14 1

(13.77) (26.35) (50.90) (8.38) (0.60)

Depression

5.08 (2.28) 0e9

5.88 (2.48) 0e9

t ¼ 1.17

Functional limitations

2.06 (2.16) 0e10

2.48 (2.25) 0e10

t ¼ 1.76

Note. *p < .001. a Non-white group includes Black/African-American, Asian, & multiracial.

Table 2 Mean (SD) and t-value concerning pre-post changes for volunteer and comparison groups. Outcome Variable

Group

Pre-test

Posttest

t-statistic

Depression

Volunteer (N ¼ 167) Comparison (N ¼ 167)

5.08 (2.28)

4.14 (3.15)

4.36**

5.88 (2.49)

6.60 (2.60)

4.68**

Volunteer (N ¼ 67) Comparison (N ¼ 167)

2.06 (2.16)

1.71 (1.61)

2.02*

2.48 (2.26)

2.80 (2.45)

2.32*

Volunteer (N ¼ 167) Comparison (N ¼ 167)

3.64 (0.96)

3.61 (0.94)

0.69

3.44 (0.85)

3.38 (0.98)

0.97

Functional limitations

Self-rated health

Note. *p < .05, **p < .001.

The post-test means adjusted for all covariates were used to calculate effect sizes. The effect sizes associated with program participation were 0.42 for functional limitations, and 0.73 for depressive symptoms. Discussion The study findings demonstrate that high-commitment volunteering produces enhanced health outcomes for older adults. This work is consistent with previous research on the positive effects of volunteering. Yet this work adds some insight regarding the healthpromoting aspects of volunteering. First of all, it suggests that the intensity of volunteering may be important, given that the EC group committed 12 h per week on average to volunteering over the two year observation period while the comparison group volunteered quite a bit less, if at all, in that same period. Further, we know that the EC volunteers in the study committed this time to a range of physical, cognitive and social activities associated with working

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

419

Table 3 Unstandardized beta estimates (SError) and their significance levels showing the contribution of EC volunteering on depression, self-rated health and functional limitations. Variable

Depression

Self-rated Health

Beta (SE)

Beta (SE)

EC Participation

2.109 (0.257)***

0.118* (0.70)y

Previous volunteer history

0.070 (0.242)

0.014 (0.072)

Socio-demographics Age Gender (Female)

0.021 (0.020) 0.017 (0.306)

0.005 (0.006)y 0.179 (0.118)

Marital Status (Married) Widowed Never married Employment (Non-employed) Race (Non-White)

0.177 0.422 0.447 0.263

0.168 0.047 0.055 0.063

Education (16 years or above) 0e12 years 13e15 years

(0.350) (0.379) (0.499) (0.282)

0.530 (0.282)y 0.783 (0.293)**

Annual family income

0.143 (0.052)**

Health outcomes at pre-test Self-rated health Depression Functional limitations Propensity score R-square

0.063 0.634 0.181 0.081 52.42

(0.138) (0.089)*** (0.081)* (0.093)

(0.107) (0.088) (0.124) (0.089)

Functional Limitations Beta (SE) 0.880 (0.195)*** 0.302 (0.207) 0.029 (0.014)* 0.569 (0.229)* 0.049 0.256 0.068 0.282

(0.288) (0.228) (0.324) (0.266)

0.345 (0.101)*** 0.160 (0.098)

0.457 (0.288) 0.129 (0.252)

0.003 (0.161)

0.075 (0.045)*

0.592 0.053 0.055 0.008 56.51

(0.052)*** (0.022)* (0.019)** (0.022)

0.054 0.080 0.476 0.009 40.95

(0.138) (0.055) (0.069)*** (0.065)

Note: Reference group is indicated in parentheses. yp < .10; *p < .05; **p < .01; ***p < .001.

with children and teachers and with the training and support activities provided by the program. Previous work on the EC program demonstrated that the volunteers became more physically active (Tan et al., 2006). In terms of social pathways, 34% of EC volunteers reported that involvement increased their circle of friends and 47% felt more meaningful engaged in their communities (Morrow-Howell et al., 2008). The findings support the social model of health promotion which suggests that volunteering produces positive outcomes through activities associated with the volunteer role d that is, through physical, cognitive, and social pathways. In this study, high-commitment volunteering occurred through one program, the Experience Corps program. Accordingly, we cannot draw conclusions about the health-promoting effects of the amount of time commitment independent of other aspects of the EC volunteer experience. Further study is needed to add specificity to knowledge about the health-promoting features of the volunteer experience. A very attractive feature of a social model of health promotion, exemplified by the EC program, is that older adults are motivated to participate in the program to be “generative and make valued contributions to society” (Carlson et al., 2008, p. 799), not to participate in a health promotion program per se. The literature on health promotion programs, like those focused on physical exercise, suggests that although health outcomes can be attained, participation and retention rates are low (Conn, Minor, Burks, Rantz, & Pomeroy, 2003; Van der Bij, Laurant, & Wensing, 2002). These obstacles to health promotion programs can be minimized in a high-intensity volunteering program (Fried et al., 2004; Tan et al., 2009, 2006). This study lends evidence to the argument that attracting people to health-promoting activity in the form of meaningful volunteer work may indeed overcome problems of participation associated with more traditional health promotion. The EC sample used here were diverse in terms of income and ethnicity. Further, the retention rate was high, in that over 80% finished one year, and of those volunteers, over 70% decided to sign up for another year. This contrasts with estimates that 22%e76% of participants in

traditional exercise programs drop out within six months (Center for the Advancement of Health, 2006). In sum, high-commitment volunteering may increase the diversity of participants and increase the length of participation, mainly because participants get and stay involved, first and foremost, to help the children rather than to improve their health. Effect sizes associated with statistically significant program effects on depression and functional limitations are meaningful (0.73 and 0.42, respectively). It is suggested that an effect size of 0.50 on health-related quality of life measures translates to clinically important differences (Norman, Sloan, & Wyrwich, 2003). More specifically, the impact of the program on physical functioning is comparable to effects suggested by a recent meta-analysis of intentional exercise trials (average effect size ¼ 0.41) (Kelley, Kelley, Hootman, & Jones, 2009). In regards to depression, metaanalyses of psychological treatments and behavior activation (Cuijpers, van Straten, & Smit, 2006, Cuijpers, van Straten, & Warmerdam, 2007) yield effect sizes between 0.72 and 0.87. It is noteworthy that after two years of participation in the EC program, participants experienced a reduction in depression symptoms and functional limitations, while the comparison group experienced an increase on these two measures. It was expected that a pattern of maintenance of health versus decline in health would emerge, as EC participation may not reverse but rather postpone loss associated with age-related conditions. The expected pattern of maintenance (or less decline) was observed on the selfrated health variable (although only as a trend, p ¼ .09), where EC members reported less decline than the comparison group. These findings need to be considered in light of several study limitations. First of all, we did randomly assign people to the two groups. Although the matched comparison group was largely equivalent on all measured characteristics and our statistical methods corrected for covariates, the EC participants might be different from their matched HRS comparisons on unobserved characteristics. Also, because of our study strategy to obtain matched observations for the HRS, we needed a two-year observation on the EC sample. The EC program requests one year of

420

S.I. Hong, N. Morrow-Howell / Social Science & Medicine 71 (2010) 414e420

service; thus the sample in the study are those volunteers who selfselected to serve another year. A one-year observation period would yield more generalizable findings to all older adults who come forward to serve in the EC program. We were restricted in the analyses to variables available in both the EC and HRS data sets. Thus, we excluded variables that may be important in matching and model specification, like health behaviors such as smoking and diet. Further, the health outcomes were all self-report, and self-rated health was a single item. More objective measures of health (i.e., physiologic measures, functional strength, and balance) may yield different findings. Finally, we suspect that outcomes associated with the EC program vary by site, given local differences in leadership, school district support, etc. This analysis did not address differences in outcomes associated with within-site variations (see Hong, Morrow-Howell, Tang, & Hinterlong, 2009). By including multiple EC programs from around the country and using a stringent analytic approach, this study adds to growing evidence that supports the health-promoting effects of volunteering. Acknowledgements This project was funded by The Atlantic Philanthropies. The authors would like to acknowledge the staff members at Mathematica Policy Research, Inc. for data collection services. References Allison, P. D. (2002). Missing data (Sage University Papers Series on Quantitative Applications in the Social Sciences, Series no.07-136). Thousand Oaks, CA: Sage. Arnstein, P., Vidal, M., Wells-Federman, C., Morgan, B., & Caudill, M. (2002). From chronic pain patient to peer: benefits and risks of volunteering. Pain Management Nursing, 3(3), 94e103. Carlson, M. C., Saczynski, J. S., Rebok, G. W., Seeman, T., Glass, T. A., McGill, S., et al. (2008). Exploring the effects of an “everyday” activity program on executive function and memory in older adults: Experience CorpsÒ. The Gerontologist, 48, 793e801. Carlson, M., Seeman, T., & Fried, L. P. (2000). Importance of generativity for healthy aging in older women. Aging Clinical and Experimental Research, 12, 132e140. Center for the Advancement of Health. (2006). A new vision of aging (Issue Brief #2). Washington, DC: Center for the Advancement of Health. Conn, V., Minor, M., Burks, K., Rantz, M., & Pomeroy, S. (2003). Integrative review of physical activity intervention research with aging adults. Journal of the American Geriatrics Society, 51, 1159e1168. Corporation for National and Community Service [CNCS], Office of Research and Policy Development. (2007). The health benefits of volunteering: A review of recent research. Washington, DC. Cuijpers, P., van Straten, A., & Smit, F. (2006). Psychological treatment of late-life depression: a meta-analysis of randomized controlled trials. International Journal of Geriatric Psychiatry, 21, 1139e1149. Cuijpers, P., van Straten, A., & Warmerdam, L. (2007). Behavioral activation treatments of depression: a meta-analysis. Clinical Psychology Review, 27, 318e326. D’Agostino, R. B. (1998). Tutorial in biostatistics propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17, 2265e2281. Eriksson, I., Unden, A., & Elofsson, S. (2001). Self-rated health: comparisons between three different measures. Results from a population study. International Journal of Epidemiology, 30, 326e333. Fonda, S., & Herzog, A. R. (2004). HRS/AHEAD documentation report. Ann Arbor, MI: Survey Research Center, University of Michigan. Freedman, M., & Fried, L. P. (1999). Launching Experience Corps: Findings from a twoyear pilot project mobilizing older Americans to help inner-city elementary schools. Oakland, California: Civic Ventures. Frick, K. D., Carlson, M. C., Glass, T. A., McGill, S., Rebok, G. W., Simpson, C., et al. (2004). Modeled cost-effectiveness of the Experience Corps Baltimore based on a pilot randomized trial. Journal of Urban Health, 8(1), 106e117. Fried, L., Carlson, M., Freedman, M., Frick, K., Glass, T., Hill, J., et al. (2004). A social model for health promotion for an aging population: initial evidence on the Experience Corps model. Journal of Urban Health, 81, 64e78. Glass, T. A., Freedman, M., Carlson, M., Hill, J., Frick, K. D., Ialongo, N., et al. (2004). Experience Corps: design of an intergenerational program to boost social capital and promote the health of an aging society. Journal of Urban Health, 81, 94e105.

Hong, S., Morrow-Howell, N., Tang, F., & Hinterlong, J. (2009). Engaging older adults in volunteering: conceptualizing and measuring institutional capacity. Nonprofit and Voluntary Sector Quarterly, 38(2), 200e219. Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A., Jaffe, M. W., & Cleveland, M. A. (1963). Studies of illness in the aged. The index of ADL: a standardized measure of biological and psychosocial function. Journal of the American Medical Association, 185, 914e919. Kelley, G. A., Kelley, K. S., Hootman, J. M., & Jones, D. L. (2009). Exercise and healthrelated quality of life in older community-dwelling adults: a meta-analysis of randomized controlled trials. Journal of Applied Gerontology, 28(3), 369e394. Lawton, M. P., & Brody, E. M. (1969). Assessment of older people: self-maintaining and instrumental activities of dialing living. The Gerontologist, 9, 179e186. Lum, T. Y., & Lightfoot, E. (2005). The effects of volunteering on the physical and mental health of older people. Research on Aging, 27(1), 31e55. Martinez, I. L., Frick, K., Glass, T. A., Carlson, M., Tanner, E., Ricks, M., et al. (2006). Engaging older adults in high impact volunteering that enhances health: recruitment and retention in the Experience CorpsÒ Baltimore. Journal of Urban Health, 83(5), 941e953. McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403e425. Moen, P., Dempster-McClain, D., & Williams, R. M. (1992). Successful aging: a lifecourse perspective on women’s multiple roles and health. The American Journal of Sociology, 97(6), 1612e1638. Morrow-Howell, N., Hinterlong, J., Rozario, P. A., & Tang, F. (2003). Effects of volunteering on the well-being of older adults. Journals of Gerontology, 58B(3), A137eS145. Morrow-Howell, N., Jonson-Reid, M., McCrary, S., Lee, Y., & Spitznagel, E. (2009). Evaluation of Experience Corps: Student reading outcomes (CSD Research Report 09-01). St. Louis, MO: WashingtonUniversity, Center for Social Development. Morrow-Howell, N., McCrary, S., Gonzales, E., McBride, A., Hong, S., & Blinne, W. (2008). Experience Corps: Benefits of volunteering. St. Louis, MO: WashingtonUniversity, Center for Social Development. Musick, M. A., Herzog, A. R., & House, J. S. (1999). Volunteering and mortality among older adults: findings from a national sample. Journal of Gerontology: Social Science, 54B(3), S173eS180. Musick, M. A., & Wilson, J. (2003). Volunteering and depression: the role of psychological and social resources in different age groups. Social Science & Medicine, 56(2), 259e269. Nagi, S. Z. (1969). Disability and rehabilitation: Legal, clinical and self concepts and measurement. Columbus, OH: Ohio State University Press. Nagi, S. Z. (1976). An epidemiology of disability among adults in the United States. Milbank Memorial Fund Quarterly/Health and Society, 54, 439e467. National Institute on Aging & U.S. Department of Health and Human Services. (2007, March). The Health & retirement study: Growing older in America. NIH Publication No. 07e5757. Norman, G., Sloan, J., & Wyrwich, K. (2003). Interpretation of changes in healthrelated quality of life. Medical Care, 41, 582e592. Peters, T. J., Richards, S. H., Bankhead, C. R., Ades, A. E., & Sterne, J. A. (2003). Comparison of methods for analysing cluster randomized trials: an example involving a factorial design. International Journal of Epidemiology, 32(5), 840e846. Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385e401. Rebok, G. W., Carlson, M. C., Glass, T. A., McGill, S., Hill, J., Wasik, B. A., et al. (2004). Short-term impact of Experience Corps participation on children and schools. Journal of Urban Health, 81(1), 79e93. Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis (2nd ed.). New York: McGraw Hill. Rosow, I., & Breslau, N. (1966). A Guttman health scale for the aged. Journal of Gerontology, 21, 556e559. SAS Institute Inc.. (2009). The SURVEYREG procedure in the SAS/STAT user’s guide, Version 9.1.3. Retrieved March 03, 2009 from. http://support.sas.com/rnd/app/ da/new/dasurvey.html. Tan, E. J., Rebok, G. W., Yu, Q., Frangakis, C. E., Carlson, M. C., Wang, T., et al. (2009). The long-term relationship between high-intensity volunteering and physical activity in older African American women. Journals of Gerontology: Social Sciences, 64(2), B304eB311. Tan, E. J., Xue, Q.-L., Li, T., Carlson, M. C., & Fried, L. P. (2006). Volunteering: a physical activity intervention for older adults e the Experience CorpsÒ program in Baltimore. Journal of Urban Health, 83(5), 954e969. US Health and Retirement Study. (2004, 2006). Health and Retirement Study: A longitudinal study of health, retirement, and aging sponsored by the National Institute on Aging. Retrieved March 03, 2008 from. http://hrsonline.isr.umich. edu/index.php. Van der Bij, A., Laurant, M., & Wensing, M. (2002). Effectiveness of physical activity interventions for older adults. American Journal of Preventive Medicine, 22, 120e133. Van Willigen, M. (2000). Differential benefits of volunteering across the life course. Journals of Gerontology, 55B, 308e318.