Journal of Aging and Physical Activity, 2014, 22, 364-371 http://dx.doi.org/10.1123/JAPA.2012-0302 © 2014 Human Kinetics, Inc.
Official Journal of ICAPA www.JAPA-Journal.com ORIGINAL RESEARCH
Community-Based Exergaming Program Increases Physical Activity and Perceived Wellness in Older Adults Kara A. Strand, Sarah L. Francis, Jennifer A. Margrett, Warren D. Franke, and Marc J. Peterson Exergaming may be an effective strategy to increase physical activity participation among rural older adults. This pilot project examined the effects of a 24-wk exergaming and wellness program (8 wk onsite exergaming, 16-wk wellness newsletter intervention) on physical activity participation and subjective health in 46 rural older adults. Sociodemographic data and self-reported physical activity were analyzed using descriptive statistics and Cochran’s Q, respectively. Qualitative data were reviewed, categorized on the basis of theme, and tabulated for frequency. Increased physical activity and perceived health were the most reported perceived positive changes. Significant increases in physical activity participation were maintained among participants who were physically inactive at baseline. Best-liked features were physical activity and socialization. Findings suggest that this pilot exergaming and wellness program is effective in increasing physical activity in sedentary rural older adults, increasing socialization, and increasing subjective physical health among rural older adults. Keywords: intergenerational, exercise, rural-residing seniors Technological advancements in the medical field and aging baby boomers are resulting in a rapidly growing older adult population. Older adults (age 65 and older) are expected to make up 20% of the U.S. population by 2030 (U.S. Census Bureau, 2011a). In addition, an estimated 14% of this population resides in rural communities (U.S. Census Bureau, 2011b), which may pose additional challenges to physical activity and resulting health. In the United States, more than 87% of adults age 65 and older and 94% of adults age 75 and older are physically inactive, and 35% are classified as overweight or obese (Fakhouri, Ogden, Carroll, Kit, & Flegal, 2012). Estimates in Iowa are similar; the percentage of older Iowans ages 65–74 classified as not meeting the recommended physical activity levels is 87.1%, and the percentage of Iowans age 75 and older is slightly higher at 88.1% (Shepherd, 2011). The percentage of older Iowans classified as overweight or obese is much higher than the national average, at 72.4% of adults ages 65–74 and 62.9% of adults age 75 and older, respectively (Shepherd, 2011). Moreover, in Iowa, one third (33.1%) of adults age 65 and older had heart disease, 14.6% reported having had a stroke, and 42.3% were diagnosed with some type of cardiovascular disease (Shepherd, 2011). Sedentary lifestyle behaviors increase the risk of chronic disease, which can result in about $25,000 annual health care costs for those with five or more chronic conditions (Federal Interagency Forum on Aging-Related Statistics, 2010). Physical activity is a key modifiable behavior for lowering these health care expenses; it can decrease chronic disease risk, improve physical functioning, and reduce depressive symptoms in older adults (Rosenberg et al., 2010). Participating in regular physical activity can attenuate numerous chronic diseases and improves physical fitness (e.g., strength, flexibility, and balance; Physical Activity Guidelines Advisory Committee, 2008). All these abilities
Strand, Francis, and Peterson are with Dept. of Food Science and Human Nutrition; Margrett, the Dept. of Human Development and Family Studies; and Franke, the Dept. of Kinesiology, Iowa State University, Ames, IA. Address author correspondence to Sarah Francis at [email protected]
are crucial for maintaining independence, improving health-related quality of life, and increasing performance in activities of daily living (Dionigi, 2007). Moreover, it is cost effective in that inactive adults who engage in 90 min of weekly activity could save an estimated $2,200 on annual health care costs (Centers for Disease Control and Prevention, 2003). According to the Centers for Disease Control and Prevention (2011), an inactive individual is defined as one not engaging in either 150 min of moderate-intensity physical activity or 75 min of vigorous-intensity physical activity every week. The need for physical activity programs is high, especially in rural areas, because rural-residing older adults are half as likely to be physically active as their urban-residing counterparts (Shores, West, Theriault, & Davison, 2009). In Iowa, adults age 75 years and older had the lowest percentage of moderate physical activity participation (37.1%) of all age groups (Shepherd, 2011). The limited availability of physical activity programs for older adults in rural areas is likely associated with the higher incidence of chronic disease and disability, which leads to lower quality of life and increased health care expenditures. Older adults often refrain from participating in regular physical activity because of perceived barriers to physical activity and exercise programs that prevent them from realizing the benefits associated with being physically active. Lack of adherence, environment, low self-efficacy, lack of knowledge to feel confident in exercising alone, and financial constraints are all well-documented barriers to participation in physical activity perceived by older adults (Dionigi, 2007; Dorgo, Robinson, & Bader, 2009; Hildebrand & Neufeld, 2009; Hughes et al., 2005; Rosenberg et al., 2010; Shores et al., 2009). Having readily available community-based physical activity programs specifically tailored to older adults can help reduce the aforementioned barriers and thereby increase the opportunities this age group has for being active (Layne et al., 2008). Older adult–specific physical activity programs provide the guidance and supervision needed to help older adults properly exercise and gain knowledge in a nonintimidating and friendly environment (Dorgo et al., 2009). One means of promoting physical activity and active aging is through the incorporation of intergenerational
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activity (Butts & Chana, 2007). Intergenerational programs create an opportunity to integrate generations and promote valuable and meaningful relationships that are often lacking (Kaplan, Liu, & Radhakrishna, 2003). In addition, exergaming, or video gaming with exercise incorporated into game play, is a relatively new way to increase physical activity time. However, most research examining the effects of exergaming on energy expenditure has primarily been conducted with youths. A meta-analysis by Barnett, Cerin and Baranowski (2011) reported that exergaming elicited energy expenditures of 3 metabolic equivalents (METs) or higher, thereby supporting the assertion that exergaming activities are moderate-intensity activities. Similarly, Miyachi et al. (2010) suggested that about one third of exergames (including Wii Fit Plus and Wii Sports) could contribute toward the goal of 30 min of moderate-intensity daily physical activity. Exergaming can lead to improved health outcomes and higher physical activity frequency and intensity (Anderson-Hanley, Arciero, Brickman, Nimon, et al., 2012). Rather than focusing on the exertion and work being done, exergaming brings the focus on the fun of video gaming while still providing the health benefits of physical activity (Graves et al., 2010). Anderson-Hanley et al. (2012) found that older adult participants enjoyed the challenge of competing with the virtual trainer and liked the visual stimulation offered by the game. Group-based exergaming has been found in some studies to result in increased effort levels in youths when compared with individual-based exergames (Mandryk, Inkpen, & Calvert, 2006). In younger adults, exergaming has been classified as low- to medium-intensity exercise, but in deconditioned populations, such as physically inactive older adults, the relative intensity of activity provided through exergaming may be higher (O’Donovan et al., 2012). Graves et al. (2010) reported that exer-
gaming with the Wii can help older adults reach the goal of at least 30 min daily of light-intensity activity. Several health benefits for older adults, including a reduction in the risk for cardiovascular disease and Type 2 diabetes, can be provided through exergaming (Graves et al., 2010). Little research has been conducted on the effectiveness of exergaming programs on physical activity participation and subjective health for older adults; however, they are becoming increasingly popular in senior centers and retirement communities. Given the barriers to physical activity in rural older adults, this pilot study tested a community-based, intergenerational exergaming program for older adults (the Living well through Intergenerational Fitness and Exercise [LIFE] Program) at seven rural community-based locations. More specifically, this pilot study examined the effects of the LIFE program on older adult self-reported physical activity participation and subjective health. The hypothesis for this research objective was that in combining the three health promotion approaches of intergenerational group design, exergaming (Wii Active), and theory-based wellness newsletters, we would evoke changes in older adults’ physical activity participation and enhance subjective health.
Method Program Design The LIFE Program was a 25-week (including data collection) pilot study with a crossover design of a theory- and community-based intergenerational exergaming program for older adults age 60 and older at seven rural locations (five congregate meal sites and two senior apartment complexes; Table 1). The basis for the LIFE
Table 1 LIFE Program Description Program component
Onsite exergaming (8 weeks)
Used Wii Active. Included age-appropriate aerobic, strength training (using resistance bands), and fundamental of sports (e.g., tennis) activities. The exercises could be performed sitting in a chair if needed. Activities gradually increased in duration weekly on the basis of the Wii EA Active program timings ranging from 13 min at Session 1 to 23 min at Session 16 (this does not include instructional demonstration and technical challenges that increased the time). Participants performed the designated number of repetitions stated by Wii on their own in the event that the remote would lock.
Onsite trainer-led program (8 weeks)
Led by younger adult trainers (ages 19–26 years) who were trained before program start (2–3 trainers/location). Met twice weekly for 8 weeks (60 min total weekly activity using Wii EA Active). Included additional 30 min of interactive games (e.g., icebreakers, storytelling activities, strategy and mind games) weekly to promote camaraderie, problem solving, and communication skills in an intergenerational setting. Attendance recorded daily. Research team member supervised first 2–3 weeks to ensure program was implemented as designed.
Volunteer program for participants to continue the LIFE program after the onsite program ended. Six onsite leaders were trained through integrated training sessions during the youth trainer-led program.
Newsletter intervention (16 weeks)
Participants received twice monthly wellness newsletters. Four fitness/nutrition newsletters offered ways to create a healthy plate and at-home exercising and stretching suggestions. Four cognitive/social newsletters included cognitive exercises and tips on how to keep mentally and socially active. Participants were encouraged to continue using the Wii onsite under the guidance of the onsite leaders. The use of the Wii at the sites was recorded and stored using Scan Disk storage cards (data are not reported here).
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366 Strand et al.
Program intervention included two theoretical behavior change models: the whole person wellness model (WPWM) and the transtheoretical model. The WPWM is a comprehensive approach to healthy aging that is being used with senior living communitybased wellness programming (Montague, Piazza, Peters, Eippert, & Poggiali, 2002). The WPWM has the potential to serve as a basis for transdisciplinary wellness programs such as the LIFE Program. It incorporates personal wellness concepts including personal choice, self-efficacy, and self-responsibility and multiple dimensions reflecting holistic health (e.g., physical, emotional, spiritual, intellectual, occupational, and social; Montague et al., 2002). Basing programs on the WPWM is proposed to be a means to reach a broader audience because the model offers multidimensional programs (Montague et al., 2002). Research has suggested that participation in whole-person wellness programs by older adults may promote independence (Edelman & Montague, 2006). The LIFE Program focused on reaching four of the six wellness dimensions: physical, emotional, intellectual, and social. When using the WPWM, incorporating other health behavior change models and techniques, such as the transtheoretical model of behavior change (Edelman & Montague, 2006), is encouraged. The transtheoretical model acknowledges that behavior change occurs over time rather than at one moment (Prochaska, DiClemente, & Norcross, 1992). Change occurs in a series of five stages (precontemplation, contemplation, preparation, action, and maintenance; Prochaska et al., 1992). The model uses individual decision-making processes as a basis to explain intentional behavior change. The LIFE Program targeted these processes in contemplation through preparation and sought to move the participants into action or maintenance; however, participants were not excluded from participating on the basis of their initial stage classification. The onsite program was primarily targeted to the preparation and action stages, and the newsletter intervention was geared toward maintenance. For participating in the study, participants received a variety of items to promote physical wellness including a healthy eating cookbook in Week 2 (intended to encourage healthful cooking), Exercise and Physical Activity: Your Everyday Guide From the National Institute on Aging in Week 3 (National Institute on Aging, 2009; intended to provide more instruction on how to do activities at home), and the corresponding exercise DVD in Week 8 (intended to encourage continued physical activity at home in addition to the LIFE Program). Participants and trainers provided consent before starting the program. All study protocols were approved by the institutional review board of Iowa State University.
Recruitment Older Adults. We used both direct (i.e., in-person presentations)
and indirect (i.e., flyers, posters, press releases, word of mouth from family and friends) recruitment strategies and recruited 56 and 44 prospective participants, respectively, from local congregate meal site programs and among those who lived in U.S. Department of Housing and Urban Development–qualified senior apartments. For the 32 prospective participants who did not join, no data were collected as to why they elected not to start the program. Although cognitive status was not measured, these individuals met the locations’ requirements for participation, which included being able to function independently and take care of personal needs as well as being able to communicate needs and comprehend information and directions for an activity. Prospective participants included
those who met the following criteria: (a) age 60 and older and eligible to participate in congregate meal sites; (b) literate; (c) able to participate as determined by the Physical Activity Readiness Questionnaire (American College of Sports Medicine, 2007), received physician permission, or both; and (d) able to complete questionnaires at multiple time points (Weeks 1, 8, and 25). Trainers. Trainers were university students ages 19–26 recruited about 4 months before the program started through indirect (i.e., flyers, university newspaper article, and e-mails sent to student-centered e-mail distribution lists) and direct (i.e., in-class presentations) recruitment means. A total of 48 students expressed interest, and 18 provided informed consent to participate. No data were collected from the 30 who initially expressed interest but did not participate. Trainers had to (a) complete a 1-day training workshop, (b) be available 8 consecutive weeks during the onsite program, (c) be able to lead an onsite physical activity program twice weekly for 2 hours each visit, (d) have reliable transportation, (e) participate in a follow-up focus group about their experience, and (f) complete questionnaires at three time points (Weeks 1, 8, and 25).
Measures Data collection was performed at three different time points throughout the program, including Week 1 (before the program began), Week 8 (completion of the onsite program), and Week 25 (completion of the newsletter intervention) by research team members and trainers who received training on how to collect data during the initial workshop. Data included sociodemographic background questions and self-reported physical activity participation. Sociodemographic background questions pertained to age, sex, ethnicity, marital status, general perceived health, living arrangements, and frequency of interaction with younger adults. Self-reported physical activity participation was assessed using the question “Do you currently engage in regular physical activity?” from the Cancer Prevention Research Center’s (2010) Stages of Change for Physical Activity Questionnaire. For this questionnaire, regular physical activity was defined for the participants as physical activity done for 30 min at a time (or more) per day and done at least 4 days per week. The intensity did not have to be vigorous, but it needed to increase the participant’s heart rate, breathing level, or both. Stage of change at each time point was measured but is not reported here. Written evaluations, given at Weeks 8 and 25, contained four open-ended questions, two of which asked participants what lifestyle changes (positive or negative) they made as a result of the LIFE Program and what they liked best and least about the LIFE Program. Participants were able to provide more than one response for the questions.
Data Analysis We analyzed data using SPSS for Windows, Version 17.0 (SPSS, Inc., Chicago, IL). Demographic information was analyzed with descriptive statistics (n = 46 participants). Responses to the openended evaluation questions were reviewed, categorized on the basis of theme, and tabulated for frequency (n = 46 participants). We used a one-way analysis of variance test to determine differences among age, self-reported health status, trainer influences, seasonality, and attendance on physical activity participation at Week 1 (n = 46 participants). Cochran’s Q was used to assess changes in self-reported physical activity over the three time points for
Exergaming, Physical Activity, and Perceived Wellness 367
those who self-identified as physically inactive at baseline (n = 21 participants). Significance was determined at p ≤ .05 for one-way analysis of variance. A Bonferroni p value of p ≤ .0167 was used for Cochran’s Q. We included only participants who completed a questionnaire at each time point.
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Participants Sixty-eight participants started the program, and 46 completed it (67.6% completion rate). Reasons for attrition included (a) health (n = 9; 40.9%), (b) limited time (n = 3; 13.6%), (c) participation in other exercise classes (n = 2; 9.1%), (d) moved away (n = 1; 4.5%), or (e) unknown (n = 7; 31.8%). The majority of participants were female (n = 40; 87%), White (n = 46; 100%), and not currently married (n = 30; 65.2%) and self-identified as physically active at baseline (n = 25; 54.3%; Table 2).
Physical Activity Self-reported physical activity participation increase significantly among participants who reported being inactive at the start of the program (Weeks 1–25, Bonferroni p = .001; Weeks 8–25, Bonferroni p = .014; Figure 1). Twenty-one participants (45.7% of all participants) self-identified as being physically inactive at Week 1. Of these, five (23.8%) were active by Week 8, and an additional six, for a total of 11 participants (52.4%), were active by Week 25. None of these participants regressed to being inactive after Week 8. By Week 25, there was an overall 22% increase in self-reported physical activity participation by all participants; however, this overall increase was not statistically significant. An analysis of variance test indicated that self-reported health status, age, and attendance did not influence the changes noted. In addition, we identified no significant differences among site characteristics, seasonality, and trainer influence.
Perceived Physical Wellness Table 2 Participant Characteristics Characteristic
apartment or home
other community-based living arrangement
several times a day
active (≥30 min/day × 4 days)