“Go4Life” exercise counseling, accelerometer feedback, and activity levels in older people☆
Introduction
It is generally recommended that older adults be moderately or vigorously active for 150 min each week (Sun, Norman, & White, 2013). A systematic review demonstrated that only 20–60% of older people are achieving this goal (Sun et al., 2013). These studies determined adherence to physical activity recommendations by questionnaire. Using NHANES data, it has been demonstrated that older people meet activity recommendations 62% of the time using a self-report questionnaire compared to 9.6% of the time when measured by accelerometry (Tucker, Welk, & Beyler, 2011). Thus, objective measures suggest that older people are falling even more short of the goal than previously thought.
Most studies have measured moderate and vigorous activity. However, light activity or NEAT (non-exercise activity thermogenesis) also has an important effect on health (Levine, Eberhardt, & Jensen, 1999). For example, increased energy expenditure was associated with lower mortality in community-dwelling older adults (Manini et al., 2006). More than half of the extra energy expenditure in the high energy expenditure group came from non-exercise (light) activity. In addition to reduced total mortality, increased light and moderate activity has been associated with better cognitive function (Weuve et al., 2004), reduced fracture rate (Gregg et al., 1998), less cardiovascular disease (Fossum et al., 2007), and weight loss (Morey et al., 2009) in older people. A meta-analysis of middle-aged and older adults has demonstrated greater all-cause mortality with increased sitting time (Chau et al., 2013). Thus, any strategy which can increase activity (whether light or more vigorous) has the potential to save lives and improve quality of life for older adults.
A variety of devices have been used to measure physical activity. A tri-axial accelerometer measures movement in three dimensions. Studies comparing tri-axial accelerometers with uni-axial accelerometers and pedometers demonstrate that only certain tri-axial accelerometers provide a reliable assessment of energy expenditure (Plasqui, Bonomi, & Westerterp, 2013). This is usually due to failure to detect light activity. Since light activity accounts for a substantial portion of older people's energy expenditure (Colbert et al., 2013), measuring activity with a questionnaire or measuring steps with a pedometer do not provide an accurate reflection of activity in older people.
A recent review concluded that there is only weak evidence that physical activity can be improved (Moyer and US Preventive Services Task Force, 2012). Since increasing both light and moderate activity benefit older people, studies demonstrating that physical activity can be improved are urgently needed. Since accelerometry is the best way to accurately assess light activity, we performed a study to determine if an activity counseling program and using an accelerometer which gives feedback on physical activity, can result in an increase in light and moderate activity in older people. We also sought to determine whether counseling and accelerometer feedback would result in weight loss, change in % body fat, glucose, hemoglobin A1c, insulin, and fasting lipid profile.
Section snippets
Methods
Sedentary subjects age 65 and older were entered into a randomized cross-over trial (see Fig. 1). Randomization was done using a table of random numbers and sealed opaque envelopes. Subjects were recruited from independent living sections of assisted living facilities (n = 35) and the general public (n = 14) in the Rochester, MN area. Subjects volunteered after hearing presentations from the authors at assisted living facilities or seeing posters placed in the community.
A research grade tri-axial
Results
49 participants were randomized and 48 participants completed the study (Fig. 1). There were no significant differences between the two groups on any baseline variable (Table 1). Fig. 2 demonstrates a significant correlation between age and activity at baseline among all 48 participants (r = 0.42, p = 0.003). The older participants were significantly less active than the younger participants expending 109 fewer activity units per year (p = 0.003). This represents a 2.4% drop in activity each year.
The
Discussion
Older adults are the fastest growing segment of the population in the US, but few meet the minimum recommended 30 min of moderate activity on 5 days or more per week (Centers for Disease Control and Prevention, 2002). Our study found that within the geriatric population, activity declines as people age. We saw a 2.4% decline per year cross-sectionally. This finding agrees with a recent cohort study (Bachman et al., 2014). In that study, the annual decline accelerated with increasing age. Thus,
Conclusions
Governments are interested in developing programs for older adults to forestall disability, reduce healthcare utilization and improve quality of life in the aging population (Goetzel et al., 2007). The present study confirms what has been shown previously. Increasing physical activity in older adults results in demonstrable benefit; however, effective programs for doing so remain elusive.
Conflict of interest statement
The authors have no conflicts of interest to declare.
Acknowledgments
This work was supported by a grant from the JR Albert Foundation and by the Division of Preventive, Occupational, and Aerospace Medicine at Mayo Clinic, Rochester. The financial sponsors played no role in the design, collection, analysis and interpretation of data, in the writing of the manuscript, or in the decision to submit the manuscript for publication. This project was supported by Grant Number UL1 TR000135 from the National Center for Advancing Translational Sciences (NCATS). Its
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2020, Archives of Gerontology and GeriatricsCitation Excerpt :One study identified no statistically significant difference between the groups in the time spent on MVPA (Ashe et al., 2015), although a significant difference was identified in daily step count in the same study. One study reported no significant within-group or between-group differences in any of the PA-related variables (Thompson et al., 2014). When compared with an active control group (i.e., giving the participants a simple pedometer with no connection to online interactive platforms), statistically significant between-group differences were identified in daily step count in three studies (Bickmore et al., 2013; Martin et al., 2015; Rowley et al., 2017) and in the time spent on MVPA in one study (Martin et al., 2015).
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Clinical Trials.gov ID # NCT01502007.