Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women

Full, Kelsie M; Moran, Kevin; Carlson, Jordan; Godbole, Suneeta; Hipp, J. Aaron; Glanz, Karen; Mitchell, Jonathan A.; Laden, Francine; James, Peter; Kerr, Jacqueline

doi:10.1371/journal.pone.0218595

Cited by 13 publications

(12 citation statements)

References 37 publications

(37 reference statements)

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“…Future longitudinal studies could identify trajectories/profiles of 24-h time-use behaviors (see e.g. [ 92 ]) and study their associations with other lifestyle choices.…”

Section: Discussionmentioning

confidence: 99%

Life-course leisure-time physical activity trajectories in relation to health-related behaviors in adulthood: the Cardiovascular Risk in Young Finns study

et al. 2021

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Background Evidence on whether leisure-time physical activity (LTPA) facilitates individuals’ adoption of multiple healthy behaviors remains scarce. This study investigated the associations of diverse longitudinal LTPA trajectories from childhood to adulthood with diet, screen time, smoking, binge drinking, sleep difficulties, and sleep duration in adulthood. Methods Data were drawn from the Cardiovascular Risk in Young Finns Study. Participants were aged 9–18 years (N = 3553; 51% females) in 1980 and 33–49 years at the latest follow-up in 2011. The LTPA trajectories were identified using a latent profile analysis. Differences in self-reported health-related behaviors across the LTPA trajectories were studied separately for women and men by using the Bolck-Croon-Hagenaars approach. Models were adjusted for age, body mass index, education level, marital status, total energy intake and previous corresponding behaviors. Results Persistently active, persistently low-active, decreasingly and increasingly active trajectories were identified in both genders and an additional inactive trajectory for women. After adjusting the models with the above-mentioned covariates, the inactive women had an unhealthier diet than the women in the other trajectories (p < 0.01; effect size (ES) > 0.50). The low-active men followed an unhealthier diet than the persistently and increasingly active men (p < 0.01; ES > 0.50). Compared to their inactive and low-active peers, smoking frequency was lower in the increasingly active women and men (p < 0.01; ES > 0.20) and persistently active men (p < 0.05; ES > 0.20). The increasingly active men reported lower screen time than the low-active (p < 0.001; ES > 0.50) and persistently active (p < 0.05; ES > 0.20) men. The increasingly and persistently active women reported fewer sleep difficulties than the inactive (p < 0.001; ES > 0.80) and low-active (p < 0.05; ES > 0.50 and > 0.80, respectively) women. Sleep duration and binge drinking were not associated with the LTPA trajectories in either gender, nor were sleep difficulties in men and screen time in women. Conclusions Not only persistently higher LTPA but also an increasing tendency to engage in LTPA after childhood/adolescence were associated with healthier diet and lower smoking frequency in both genders, having less sleep difficulties in women and lower screen time in increasingly active men. Inactivity and low activity were associated with the accumulation of several unhealthy behaviors in adulthood. Associations were stronger in women.

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“…Future longitudinal studies could identify trajectories/profiles of 24-h time-use behaviors (see e.g. [ 92 ]) and study their associations with other lifestyle choices.…”

Section: Discussionmentioning

confidence: 99%

Life-course leisure-time physical activity trajectories in relation to health-related behaviors in adulthood: the Cardiovascular Risk in Young Finns study

et al. 2021

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“…There are a limited number of previous studies examining profiles or phenotypes of activity in adult populations using LCAs of objectively measured activity data. Prior studies have examined variations in activity during waking hours (29)(30)(31), or activity-sleep patterns during a 24-hour period (32). All of these studies identified distinct activity profiles (3 to 6 profiles) for time spent in different types or intensity when awake, with 1 identifying a distinct profile of poor sleepers.…”

Section: Discussionmentioning

confidence: 99%

“…To date, there are a limited number of studies outlining variations in patterns of 24-hour activity and sleep within defined adult health populations using objective measures (29)(30)(31)(32). To the best of our knowledge, no previous study has examined how objectively measured 24-hour activity and sleep profiles may vary in adults with arthritis.…”

mentioning

confidence: 99%

Twenty‐Four Hour Activity and Sleep Profiles for Adults Living with Arthritis: Habits Matter

Feehan

Xie

et al. 2020

Arthritis Care & Research

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Objective. To identify 24-hour activity-sleep profiles in adults with arthritis and explore factors associated with profile membership. Methods. Our study comprised a cross-sectional cohort and used baseline data from 2 randomized trials studying activity counseling for people with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), or knee osteoarthritis (OA). Participants wore activity monitors for 1 week and completed surveys for demographic information, mood (Patient Health Questionnaire 9), and sitting and walking habits (Self-Reported Habit Index). A total of 1,440 minutes/day were stratified into minutes off body (activity unknown), sleeping, resting, nonambulatory, and intermittent or purposeful ambulation. Latent class analysis determined cluster numbers; baseline-category multinomial logit regression identified factors associated with cluster membership. Results. Our cohort included 172 individuals, including 51% with RA, 30% with OA, and 19% with SLE. We identified 4 activity-sleep profiles (clusters) that were characterized primarily by differences in time in nonambulatory activity: high sitters (6.9 hours sleep, 1.6 hours rest, 13.2 hours nonambulatory activity, and 1.6 hours intermittent and 0.3 hours purposeful walking), low sleepers (6.5 hours sleep, 1.2 hours rest, 12.2 hours nonambulatory activity, and 3.3 hours intermittent and 0.6 hours purposeful walking), high sleepers (8.4 hours sleep, 1.9 hours rest, 10.4 hours nonambulatory activity, and 2.5 hours intermittent and 0.3 hours purposeful walking), and balanced activity (7.4 hours sleep, 1.5 hours sleep, 9.4 hours nonambulatory activity, and 4.4 hours intermittent and 0.8 hours purposeful walking). Younger age (odds ratio [OR] 0.95 [95% confidence interval (95% CI) 0.91-0.99]), weaker occupational sitting habit (OR 0.55 [95% CI 0.41-0.76]), and stronger walking outside habit (OR 1.43 [95% CI 1.06-1.91]) were each associated with balanced activity relative to high sitters. Conclusion. Meaningful subgroups were identified based on 24-hour activity-sleep patterns. Tailoring interventions based on 24-hour activity-sleep profiles may be indicated, particularly in adults with stronger habitual sitting or weaker walking behaviors. INTRODUCTION Adults with arthritis may not be able to spend much time in higher intensity physical activities due to activity limitations (1). Consequently, they may not be able to meet the aerobic activity guideline of getting at least 2.5 hours of moderate-to-vigorous physical activity (MVPA) every week (2-5). Arthritis-related pain, fatigue, limited joint and muscle function, or mood disruption may contribute to increased sedentary activity and less walking or impact sleep quality and quantity (1,6-8). Such pain may, in turn, have implications for a person's overall health, as being physically inactive and having insufficient (<6 hours) or excessive (>9 hours) sleep are independently associated with higher risk of all-cause mortality (8-14), including in people with arthritis (15). Supporting someone to be ph...

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“…Model-based approaches that could segment physicians into different phenotype groups may allow for tailoring of behavioral interventions to improve patient care. For example, latent class analysis has been used to classify phenotypes using clinical [5,6], behavioral [7,8], and activity data [9,10].…”

Section: Introductionmentioning

confidence: 99%

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study

et al. 2020

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Latent profile analysis of accelerometer-measured sleep, physical activity, and sedentary time and differences in health characteristics in adult women

Cited by 13 publications

References 37 publications

Life-course leisure-time physical activity trajectories in relation to health-related behaviors in adulthood: the Cardiovascular Risk in Young Finns study

Life-course leisure-time physical activity trajectories in relation to health-related behaviors in adulthood: the Cardiovascular Risk in Young Finns study

Twenty‐Four Hour Activity and Sleep Profiles for Adults Living with Arthritis: Habits Matter

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study

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