Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis

Xu, Selene; Nelson, Sandahl H; Kerr, Jacqueline; Godbole, Suneeta; Johnson, Eileen; Patterson, Ruth E.; Rock, Cheryl L.; Sears, Dorothy D.; Abramson, Ian

doi:10.1007/s12561-019-09237-3

Cited by 18 publications

(19 citation statements)

References 25 publications

(39 reference statements)

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“…A few studies investigated diurnal patterns of PA (data collected over 5-7 days) and health [64][65][66][67]. Distinct temporal PA patterns observed in this study have been detected by two other studies that used kmeans and x-means clustering approaches to derive clusters using overall activity measured by metabolic equivalent of tasks (METs) and timing of PA [64,65].…”

Section: Discussionsupporting

confidence: 51%

Temporal physical activity patterns are associated with obesity in U.S. adults

Aqeel

Guo

Lin

et al. 2021

Preventive Medicine

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Background: Few attempts have been made to incorporate multiple aspects of physical activity (PA), including timing and volume, to classify patterns that link to health. Temporal PA patterns integrating time and activity counts were created to determine their association with health. Methods: PA accelerometry data obtained from the cross-sectional National Health and Nutrition Examination Survey 2003-2006 was used to pattern PA counts and time of activity from 1,999 nonpregnant adults with one random valid weekday of activity. Constrained dynamic time warping with Sakoe-Chiba band and kernel k-means clustering grouped participants to 4 clusters representing temporal PA patterns. Multivariate regression models controlling for potential confounders and adjusting for multiple comparisons (p<0.05/6) determined associations between clusters and health status indicators and conditions obesity, type 2 diabetes, and metabolic syndrome.Results: Participants in Cluster 2, represented by a temporal PA pattern with activity counts reaching >1.2e 5 counts/ h (cph) and tapering off through the day, had lower mean body mass index (BMI) (p<0.001), waist circumference (WC) (p<0.01), and 65% lower odds of obesity relative to normal weight status compared with participants in Cluster 1 with the lowest PA counts reaching 4.8e 4 cph from 6:00 to 23:00 (OR: 0.3; 95% CI: 0.2, 0.8). Cluster 3, characterized by a temporal PA pattern with activity counts reaching 9.6e 4 -1.2e 5 cph between 16:00 to 21:00, was associated with lower mean BMI (p<0.001) and WC (p<0.01), and 60% lower odds of obesity relative to normal weight status compared to Cluster 1 (OR: 0.4; 95% CI: 0.2, 0.8). Cluster 4 characterized by activity counts reaching 9.6e 4 cph between 8:00 to 11:00 was associated with lower BMI and WC compared to Cluster 1 (both p<0.05).Conclusions: U.S. adults with temporal PA patterns of higher activity counts ranging between 9.6e 4 ->1.2e 5 cph performed early (8:00 to 11:00), late (16:00 to 21:00), or throughout the day had signi cantly lower mean BMI and WC compared with adults with a temporal PA pattern of the lowest PA counts reaching 4.8e 4 cph from 6:00 to 23:00. Temporal PA patterns created by integrating time with PA counts throughout a day meaningfully link to health status.

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Section: Discussionsupporting

confidence: 51%

Temporal physical activity patterns are associated with obesity in U.S. adults

Aqeel

Guo

Lin

et al. 2021

Preventive Medicine

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“…We identified a few studies that examined diurnal patterns of PA/ SB, (i.e., patterns based on the time of day that active/ sedentary time was accumulated) and health. Using functional principal components analysis, Xu et al reported that higher evening (vs mid-day) activity was associated with worse mental quality-of-life [26]. Zeitzer et al found principal components-derived patterns that were predictive of changes in sleep and cognition, as well as cardiovascular-related mortality and all-cause mortality [63].…”

Section: Discussionmentioning

confidence: 99%

“…At study entry, mean (SD) daily sedentary time and MVPA were respectively 597 (103), and 50 (34) minutes. Mean (SD) physical functioning scores at baseline were 69 (26).…”

Section: Study Sample and Baseline Descriptive Characteristicsmentioning

confidence: 99%

“…While there is a paucity of evidence relating diurnal patterns of SB with health, emerging evidence suggests that diurnal patterns of physical activity (PA) are associated with fatigability [24]; early-stage Alzheimer's disease [25]; insulin and C-reactive protein levels, and quality of life [26]; sleep efficiency, cognition, and all-cause mortality [27]. There have also been reports that diurnal patterns of SB vary by sociodemographic variables; for example, older adults accumulate more SB during the day than younger adults, with smaller age group differences in evening SB accumulation [28].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diurnal patterns of sedentary behavior and changes in physical function over time among older women: a prospective cohort study

Reuter

Bellettiere

Liles

et al. 2020

Int J Behav Nutr Phys Act

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Background: Sedentary behavior (SB) is linked to negative health outcomes in older adults. Most studies use summary values, e.g., total sedentary minutes/day. Diurnal timing of SB accumulation may further elucidate SB-health associations. Methods: Six thousand two hundred four US women (mean age = 79 ± 7; 50% White, 34% African-American) wore accelerometers for 7-days at baseline, yielding 41,356 person-days with > 600 min/day of data. Annual follow-up assessments of health, including physical functioning, were collected from participants for 6 years. A novel two-phase clustering procedure discriminated participants' diurnal SB patterns: phase I grouped day-level SB trajectories using longitudinal k-means; phase II determined diurnal SB patterns based on proportion of phase I trajectories using hierarchical clustering. Mixed models tested associations between SB patterns and longitudinal physical functioning, adjusted for covariates including total sedentary time. Effect modification by moderate-vigorous-physical activity (MVPA) was tested. Results: Four diurnal SB patterns were identified: p1 = high-SB-throughout-the-day; p2 = moderate-SB-with-lower-morning-SB; p3 = moderate-SB-with-higher-morning-SB; p4 = low-SB-throughout-the-day. High MVPA mitigated physical functioning decline and correlated with better baseline and 6-year trajectory of physical functioning across patterns. In low MVPA, p2 had worse 6-year physical functioning decline compared to p1 and p4. In high MVPA, p2 had similar 6-year physical functioning decline compared to p1, p3, and p4. Conclusions: In a large cohort of older women, diurnal SB patterns were associated with rates of physical functioning decline, independent of total sedentary time. In particular, we identified a specific diurnal SB subtype defined by less SB earlier and more SB later in the day, which had the steepest decline in physical functioning among participants with low baseline MVPA. Thus, diurnal timing of SB, complementary to total sedentary time and MVPA, may offer additional insights into associations between SB and physical health, and provide physicians with early warning of patients at high-risk of physical function decline.

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“…As physical activity varies substantially over the course of a week [37,38], an observational study design was considered appropriate to investigate the daily relationship of physical activity and sleep quality in individuals suffering from RLS. Both physical activity levels and sleep quality of a given individual vary over the course of a week [39]. Thus, the co-variation between physical activity during the day and quality of sleep during the subsequent night should be taken into account.…”

Section: Study Design and Recruitmentmentioning

confidence: 99%

Accelerometer-measured physical activity and its impact on sleep quality in patients suffering from restless legs syndrome

Reimers

Heidenreich²,

Bittermann³

et al. 2021

BMC Neurol

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Background The primary symptoms of restless legs syndrome (RLS) are sleep onset insomnia and difficulty to maintain sleep. Previous studies have shown that regular physical activity can reduce the risk of developing RLS. However, the relationship between physical activity and sleep quality parameters in individuals suffering from RLS has not yet been investigated by applying accelerometry. Thus, the present study investigates the impact of physical activity (measuring both intensity levels and duration of physical activity) during the day (7–12 h, 12–18 h, 18–23 h) on sleep quality in patients suffering from idiopathic RLS by applying a real-time approach. Methods In a sample of 47 participants suffering from idiopathic RLS, physical activity and sleep quality were measured over one week using accelerometers. For data analysis, physical activity levels and step counts during three periods of the day (morning, afternoon, evening) were correlated with sleep quality parameters of the subsequent night. Results This observational study revealed that in most instances physical activity was not correlated with sleep parameters (two exceptions exist: steps taken in the morning were negatively correlated with periodic leg movements during sleep, and physical activity in the evening was negatively correlated with total sleep time). The physical activity levels of the participants in this study, however, were unexpectedly high compared to population-level data and variance in physical activity was low. The average activity was 13,817 (SD = 4086) steps and 347 (SD = 117) minutes of moderate physical activity per day in females, and 10,636 (SD = 3748) steps and 269 (SD = 69) minutes of moderate physical activity in males, respectively. Participants did not engage in any vigorous physical activity. Conclusions Further interventional studies are needed to investigate the daily effects of different intensities of physical activity on RLS symptoms.

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Modeling Temporal Variation in Physical Activity Using Functional Principal Components Analysis

Cited by 18 publications

References 25 publications

Temporal physical activity patterns are associated with obesity in U.S. adults

Temporal physical activity patterns are associated with obesity in U.S. adults

Diurnal patterns of sedentary behavior and changes in physical function over time among older women: a prospective cohort study

Accelerometer-measured physical activity and its impact on sleep quality in patients suffering from restless legs syndrome

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