Dataset Information

Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices.

ABSTRACT: Getting enough sleep, exercising, and limiting sedentary activities can greatly contribute to disease prevention and overall health and longevity. Measuring the full 24-h activity cycle-sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA)-may now be feasible using small wearable devices.This study compared nine devices for accuracy in a 24-h activity measurement.Adults (n = 40, 47% male) wore nine devices for 24 h: ActiGraph GT3X+, activPAL, Fitbit One, GENEactiv, Jawbone Up, LUMOback, Nike Fuelband, Omron pedometer, and Z-Machine. Comparisons (with standards) were made for total sleep time (Z-machine), time spent in SED (activPAL), LPA (GT3X+), MVPA (GT3X+), and steps (Omron). Analysis included mean absolute percent error, equivalence testing, and Bland-Altman plots.Error rates ranged from 8.1% to 16.9% for sleep, 9.5% to 65.8% for SED, 19.7% to 28.0% for LPA, 51.8% to 92% for MVPA, and 14.1% to 29.9% for steps. Equivalence testing indicated that only two comparisons were significantly equivalent to standards: the LUMOback for SED and the GT3X+ for sleep. Bland-Altman plots indicated GT3X+ had the closest measurement for sleep, LUMOback for SED, GENEactiv for LPA, Fitbit for MVPA, and GT3X+ for steps.Currently, no device accurately captures activity data across the entire 24-h day, but the future of activity measurement should aim for accurate 24-h measurement as a goal. Researchers should continue to select measurement devices on the basis of their primary outcomes of interest.

SUBMITTER: Rosenberger ME

PROVIDER: S-EPMC4760880 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices.

Rosenberger Mary E ME Buman Matthew P MP Haskell William L WL McConnell Michael V MV Carstensen Laura L LL

Medicine and science in sports and exercise 20160301 3

<h4>Unlabelled</h4>Getting enough sleep, exercising, and limiting sedentary activities can greatly contribute to disease prevention and overall health and longevity. Measuring the full 24-h activity cycle-sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA)-may now be feasible using small wearable devices.<h4>Purpose</h4>This study compared nine devices for accuracy in a 24-h activity measurement.<h4>Methods</h4>Adults (n = 4 ...[more]

PMID: 26484953

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Project description:BackgroundAdolescence is a critical phase of human development that lays the foundation for health in later life. Of the 1.8 billion adolescents in the world, roughly 90% live in low and middle-income countries. Yet most longitudinal studies of adolescent physical activity, sedentary behavior, and sleep come from high-income countries. There is a need for a better understanding of these behaviors to inform obesity and chronic disease prevention strategies.AimsThe aim of this study is to identify longitudinal patterns and associations between physical activity, sedentary behavior and sleep in urban South African adolescents.MethodsWe analyzed data from the Birth-to-Twenty Plus Cohort (Bt20+), a longitudinal study of children in Soweto, Johannesburg, South Africa. Behaviors were self-reported annually between ages 12 and 17 y. We used Latent Class Growth Analysis to group participants into classes based on common longitudinal trajectories of time spent in informal physical activity, organized sports, walking to and from school, sedentary behavior, and school-night and weekend sleep, respectively. We performed group-based multi-trajectory modeling to identify latent clusters of individuals who followed similar trajectories of informal physical activity, organized sports and walking to and from school, and who followed similar trajectories of these three domains together with sedentary behavior and sleep.ResultsThe large majority of males (82%) and all females failed to meet the World Health Organization (WHO) physical activity recommendation for adolescents of 60 min of moderate-vigorous intensity physical activity per day. The physical activity domains clustered together in three multi-trajectory groups that define individuals' overall physical activity pattern. While two patterns indicated decreases in physical activity throughout adolescence, one pattern, including 29% of the sample in males and 17% of the sample in females, indicated higher levels of activity throughout adolescence. Sedentary behavior and sleep trajectories did not cluster together with the physical activity domains.ConclusionMost adolescents in this South African population did not meet WHO recommendations for physical activity. In this population, trajectories of sedentary behavior and sleep were independent of physical activity.

Project description:BackgroundSeveral systematic reviews (SRs), with and without meta-analyses, have investigated the use of wearable devices to improve physical activity, and there is a need for frequent and updated syntheses on the topic.ObjectiveWe aimed to evaluate whether using wearable devices increased physical activity and reduced sedentary behaviour in adults.MethodsWe conducted an umbrella review searching PubMed, Cumulative Index to Nursing and Allied Health Literature, the Cochrane Library, MedRxiv, Rxiv and bioRxiv databases up to February 5th, 2023. We included all SRs that evaluated the efficacy of interventions when wearable devices were used to measure physical activity in adults aged over 18 years. The primary outcomes were physical activity and sedentary behaviour measured as the number of steps per day, minutes of moderate to vigorous physical activity (MVPA) per week, and minutes of sedentary behaviour (SB) per day. We assessed the methodological quality of each SR using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR 2) and the certainty of evidence of each outcome measure using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations). We interpreted the results using a decision-making framework examining the clinical relevance and the concordances or discordances of the SR effect size.ResultsFifty-one SRs were included, of which 38 included meta-analyses (302 unique primary studies). Of the included SRs, 72.5% were rated as 'critically low methodological quality'. Overall, with a slight overlap of primary studies (corrected cover area: 3.87% for steps per day, 3.12% for MVPA, 4.06% for SB) and low-to-moderate certainty of the evidence, the use of WDs may increase PA by a median of 1,312.23 (IQR 627-1854) steps per day and 57.8 (IQR 37.7 to 107.3) minutes per week of MVPA. Uncertainty is present for PA in pathologies and older adults subgroups and for SB in mixed and older adults subgroups (large confidence intervals).ConclusionsOur findings suggest that the use of WDs may increase physical activity in middle-aged adults. Further studies are needed to investigate the effects of using WDs on specific subgroups (such as pathologies and older adults) in different follow-up lengths, and the role of other intervention components.

Project description:BackgroundAdolescence is marked by a decline in physical activity, rapid physical growth and changes in body composition, which have been linked to body composition. Prospective data on these associations are rare, particularly in Africa.AimThe aim of this study was to examine the association of longitudinal patterns across adolescent in physical activity, sedentary behavior and sleep, with anthropometry and body composition at age 18 years in urban South Africa.MethodsWe analyzed data from the Birth-to-Twenty Plus Cohort (Bt20+), a longitudinal study of children born in 1990 in Soweto-Johannesburg, South Africa. We used general linear models to investigate the association of adolescent (ages 12 to 18 years) longitudinal trends in physical activity, sedentary behavior and schoolnight sleep and overall physical activity patterns, with body mass index (BMI), waist circumference, fat mass index (FMI), lean mass index (LMI) and percent body fat at age 18 years.ResultsThe final study sample included 1337 participants with anthropometric measurements (52% female) and 958 participants with body composition measurements (53% female). Males who were consistently more active and consistently walked to school over adolescence had lower waist circumference (B = - 2.0, 95% CI: - 3.9 to - 0.2), FMI (B = - 0.8, 95%: CI: - 1.2 to - 0.1) and percent body fat (B = -2.9, 95% CI: - 4.9 to - 0.9) at age 18 years than those who decreased activity and did not walk to school. Consistently-sedentary females had higher waist circumference than those whose sedentary behavior increased over adolescence (B = 5.4, 95% CI: 0.2 to 10.6). Males who reported sleeping 9 h or more per night on schoolnights had significantly lower BMI (B = -1.0, 95% CI: - 1.4 to- 0.5), and percent body fat (B = -1.5, 95%CI - 2.8 to - 0.1) than those who reported sleeping 8 h or less per night.ConclusionPatterns of adolescent physical activity, sedentary behavior and sleep are related to young-adult body composition in urban South Africa. These modifiable behaviors may be paths for public health interventions to curb overweight and obesity in many low- or middle-income countries.

Project description:BackgroundThe constructs and interdependency of physical behaviors are not well described and the complexity of physical activity (PA) data analysis remains unexplored in COPD. This study examined the interrelationships of 24-hour physical behaviors and investigated their associations with participant characteristics for individuals with mild-moderate airflow obstruction and healthy control subjects.Patients and methodsVigorous PA (VPA), moderate-to-vigorous PA (MVPA), light PA (LPA), stationary time (ST), average movement intensity (vector magnitude counts per minute), and sleep duration for 109 individuals with COPD and 135 healthy controls were obtained by wrist-worn accelerometry. Principal components analysis (PCA) examined interrelationships of physical behaviors to identify distinct behavioral constructs. Using the PCA component loadings, linear regressions examined associations with participant (+, positive correlation; -, negative correlation), and were compared between COPD and healthy control groups.ResultsFor both groups PCA revealed ST, LPA, and average movement intensity as distinct behavioral constructs to MVPA and VPA, labeled "low-intensity movement" and "high-intensity movement," respectively. Sleep was also found to be its own distinct behavioral construct. Results from linear regressions supported the identification of distinct behavioral constructs from PCA. In COPD, low-intensity movement was associated with limitations with mobility (-), daily activities (-), health status (+), and body mass index (BMI) (-) independent of high-intensity movement and sleep. High-intensity movement was associated with age (-) and self-care limitations (-) independent of low-intensity movement and sleep. Sleep was associated with gender (0= female, 1= male; [-]), lung function (-), and percentage body fat (+) independent of low-intensity and high-intensity movement.ConclusionDistinct behavioral constructs comprising the 24-hour day were identified as "low-intensity movement," "high-intensity movement," and "sleep" with each construct independently associated with different participant characteristics. Future research should determine whether modifying these behaviors improves health outcomes in COPD.

Dataset Information

Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices.

Publications

Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices.

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