Chronotype refers to the time of day that people prefer to be active or to sleep and varies predictably across the lifespan. In younger samples, the morning-chronotype is related to greater levels of physical activity (PA) and improved health outcomes. It is unclear whether this pattern holds in older adults, a group that commonly exhibits an “early bird” preference. We investigated differences in PA patterns between chronotypes in 109 older adults (Mage = 70.45 years) using wrist-worn ActiGraphs in a free-living environment. ActiGraphs captured data about PA and sleep using a novel approach to measuring chronotype with the mid-point of the sleep interval. We categorized participants as morning-, intermediate-, or evening-chronotypes. We used ANCOVA to predict total and average peak PA from chronotype, adjusting for age, sex, education, and BMI. Total PA significantly differed between chronotypes such that evening-types engaged in less PA than both morning- and intermediate-types, F (2,102) = 4.377, p =.015. Average peak activity did not differ between chronotypes, p =.112. Consistent with findings in younger samples, our evening type participants engaged in less overall activity. A unique finding was that evening-types did not differ from their morning- and intermediate-chronotype peers in peak activity levels. This implies a key distinction between total activity and peak activity levels consistent with recent trends in PA research using a 24-hour-a-day framework instead of average or total activity levels. Future research should consider whether these differences in activity patterns translate into meaningful differences in health benefits in this age group.
Advancements in body-worn activity devices make them valuable for objective physical activity measurement. Research-grade monitors utilize software algorithms developed with younger populations using waist-worn devices. ActiGraph offers the low frequency extension (LFE) filter which reduces the movement threshold to capture low acceleration activity that is more common in older adults. It is unclear how this filter changes activity variable calculations in older adults. We investigated the effects of the LFE filter on wrist-worn activity estimates in this population. Participants were 21 older adults who wore the GT9X on their non-dominant wrist for 7 days in a free-living environment. Activity counts were estimated both with and without the LFE filter. Paired samples t-tests revealed that the LFE estimated significantly higher number of counts than non-LFE calculated counts per minute on all three axes (p < .001). Step count estimates were higher with (M = 20,780.09, SD = 5300.85) vs. without (M = 10,896.54, SD = 3489.45) the LFE filter, (t (20) = -22.21, p < .001). These differences have implications for calculations based on axis counts (e.g., Axis-1 calculated steps, intensity level classifications) that rely on waist-worn standards. For example, even without the filter, the GT9X calculated an average of 10,897 steps, which is likely an overestimate in this population. This suggests that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should aim to develop separate wrist and waist-worn standard estimates of these variables in older adult populations.
As a default setting, many body-worn research-grade activity monitors rely on software algorithms developed for young adults using waist-worn devices. ActiGraph offers the low-frequency extension (LFE) filter, which reduces the movement threshold to capture low acceleration activity, which is more common in older adults. It is unclear how this filter changes activity estimates and whether it is appropriate for all older adults. The authors compared activity estimates with and without the LFE filter on wrist-worn devices in a sample of 34 older adults who wore the ActiGraph GT9X on their nondominant wrist for 7 days in a free-living environment. The authors used participant characteristics to predict discrepancy in step count estimates generated with and without the LFE filter to determine which individuals are most accurately characterized. Estimates of steps per minute were higher (M = 21, SD = 1), and more activity was classified as moderate to vigorous intensity (M = 5.03%, SD = 3.92%) with the LFE filter (M = 11, SD = 1; M = 4.27%, SD = 3.52%) versus without the LFE filter (all ps < .001). The findings suggest that axes-based variables should be interpreted with caution when generated with wrist-worn data, and future studies should develop separate wrist and waist-worn standard estimates in older adults. Participation in a greater amount of moderate to vigorous intensity physical activity predicted a larger discrepancy in step counts generated with and without the filter (p < .009), suggesting that the LFE filter becomes increasingly inappropriate for use in highly active older individuals.
Actigraphy has become a popular, non-invasive means of continuously monitoring physical activity and sleep. One optional setting, the low frequency extension (LFE) filter, reduces the movement threshold to capture low acceleration activity that is common in older adults. This filter significantly alters physical activity outcomes (e.g., step counts), but it is unclear if this has implications for sleep interval calculations that rely upon accurate differentiation between physical activity and sleep. We investigated the effects of the LFE filter on wrist-worn sleep estimates in older adults. Participants were 9 older adults who wore the ActiGraph GT9X on their non-dominant wrist for 7 days in a free-living environment. Raw data was processed with and without the LFE filter enabled, and sleep intervals were calculated by a proprietary ActiGraph algorithm. Paired samples t-tests demonstrated that the LFE filter generated significantly later bedtimes, fewer minutes spent in bed, shorter sleep duration, and fewer awakenings during the night compared to when the filter was disabled (all p < .043). Use of the LFE filter did not lead to differences in arise time, sleep latency, efficiency, or wake after sleep onset (all p > .052). While the LFE filter was designed to improve accuracy of physical activity estimates in more sedentary populations, these findings suggest that the LFE filter also has the potential to impact sleep estimates of older adults. Researchers using ActiGraph-calculated sleep would benefit from careful consideration of this software-dependent impact.
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