Surveillance of Physical Activity and Sedentary Behavior Among Youth and Adults in the United States: History and Opportunities

Analysis of Physical Activity Using Wearable Health Technology in US Adults Enrolled in the All of Us Research Program: Multiyear Observational Study

BACKGROUND

To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by using commercial activity monitoring (Fitbit) data from the All of Us dataset.

OBJECTIVE

The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated sociodemographic determinants. Additionally, we compared 3 distinct methods of processing physical activity (PA) data to estimate adherence to the 2008 PAGA.

METHODS

We used the National Institutes of Health's All of Us dataset, which contains minute-level Fitbit data for 13,947 US adults over a 7-year time span (2015-2022), to estimate adherence to PAGA. A published step-based method was used to estimate metabolic equivalents and assess adherence to the 2018 PAGA (ie, ≥150 minutes of moderate- to vigorous-intensity PA per week). We compared the step-based method, the heart rate-based method, and the proprietary Fitbit-developed algorithm to estimate adherence to the 2008 PAGA.

RESULTS

The average overall adherence to the 2018 PAGA was 21.6% (3006/13,947; SE 0.4%). Factors associated with lower adherence in multivariate logistic regression analysis included female sex (relative to male sex; adjusted odds ratio [AOR] 0.66, 95% CI 0.60-0.72; P<.001); BMI of 25.0-29.9 kg/m2 (AOR 0.53, 95% CI 0.46-0.60; P<.001), 30-34.9 kg/m2 (AOR 0.30, 95% CI 0.25-0.36; P<.001), or ≥35 kg/m2 (AOR 0.13, 95% CI 0.10-0.16; P<.001; relative to a BMI of 18.5-24.9 kg/m2); being aged 30-39 years (AOR 0.66, 95% CI 0.56-0.77; P<.001), 40-49 years (AOR 0.79, 95% CI 0.68-0.93; P=.005), or ≥70 years (AOR 0.74, 95% CI 0.62-0.87; P<.001; relative to being 18-29 years); and non-Hispanic Black race or ethnicity (AOR 0.63, 95% CI 0.50-0.79; P<.001; relative to non-Hispanic White race or ethnicity). The Fitbit algorithm estimated that a larger percentage of the sample (10,307/13,947, 73.9%; 95% CI 71.2-76.6) adhered to the 2008 PAGA compared to the heart rate method estimate (4740/13,947, 34%; 95% CI 32.8-35.2) and the step-based method (1401/13,947, 10%; 95% CI 9.4-10.6).

CONCLUSIONS

Our results show significant sociodemographic differences in PAGA adherence and notably different estimates of adherence depending on the algorithm used. These findings warrant the need to account for these disparities when implementing PA interventions and the need to establish an accurate and reliable method of using commercial accelerometers to examine PA, particularly as health care systems begin integrating wearable device data into patient health records.

Racial, Ethnic, and Socioeconomic Disparities in Meeting Physical Activity Guidelines among Female Breast Cancer Survivors in the United States

BACKGROUND

Cancer survivors show low physical activity participation rates in the United States. However, there are limited national-level data on disparities in the prevalence of meeting physical activity guidelines among women with and without breast cancer. We aimed to evaluate national-level trends in meeting physical activity guidelines across demographic and socioeconomic characteristics of breast cancer survivors and women without cancer.

METHODS

Data for women ≥35 years of age with and without breast cancer were obtained from the 2004 to 2018 National Health Interview Survey. We used National Health Interview Survey sample weights to generate national-level prevalence estimates and calculate absolute and relative indices of disparity for breast cancer survivors and women without cancer meeting aerobic (150 minutes/week) and muscle-strengthening guidelines (2 sessions/week) stratified by demographic (e.g., race/ethnicity) and socioeconomic (e.g., homeownership) characteristics.

RESULTS

We included 5,845 breast cancer survivors and 160,162 women without cancer. The weighted percentage of breast cancer survivors meeting aerobic guidelines was 37.7% compared with 40.9% of women without cancer. Fewer women met muscle-strengthening guidelines. There were lower proportions of women who were younger (<50-years), were non-Hispanic Black, were Hispanic, worked 35+ hours/week, or rented their home among breast cancer survivors meeting aerobic guidelines compared with women without cancer meeting aerobic guidelines.

CONCLUSIONS

Breast cancer survivors were less likely to meet physical activity guidelines compared with women without cancer. Demographic and socioeconomic disparities may exist among breast cancer survivors and women without cancer meeting physical activity guidelines.

IMPACT

Targeted interventions may be necessary to address low physical activity participation among breast cancer survivors.

Impact of Replacing Sedentary Behavior with Physical Activity and Sleep on Stroke Risk: A Prospective Cohort Study

Objective

Our research explores how leisure-time sedentary behavior (SB) correlates with stroke risk. Additionally, we utilize the isotemporal substitution model (ISM) to examine how replacing brief durations of leisure-time SB with light physical activity (LPA), moderate physical activity (MPA), vigorous physical activity (VPA), and sleep might influence the risk of stroke.

Methods

This investigation tracked 478,198 participants from the UK Biobank. Data regarding individual leisure-time SB and PA were collected through a standardized questionnaire. A Cox proportional hazards model, alongside an isotemporal substitution model (ISM), was utilized.

Results

We identified 10,003 cases of incident stroke over 12.7 years. When compared to participants who engaged in leisure-time SB for less than 4 hours per day, the hazard ratios (HRs) for stroke incidence increased with more prolonged leisure-time SB: HRs were 1.06 (95% CI: 1.01 to 1.11) for 4-6 h/d, 1.16 (95% CI: 1.10 to 1.23) for 6-8 h/d, and 1.24 (95% CI: 1.15 to 1.33) for over 8 h/d. According to the ISM analysis, substituting leisure-time SB with various forms of PA could markedly reduce stroke risk. For individuals sleeping ≤8h/d, replacing one hour of leisure-time SB with an equivalent duration of LPA, VPA, or sleep corresponded to a 3.0%, 7.0%, and 22.0% decrease in stroke risk, respectively. Meanwhile, for those already sleeping more than 8h/d, substituting one hour of leisure-time SB with an equivalent duration of LPA or VPA resulted in a notable decrease in the risk of stroke by 6.0% and 18.0%, respectively.

Conclusion

The findings demonstrate that leisure-time SB and unhealthy sleep durations are confirmed risk factors for stroke. For individuals sleeping 8 hours or less per day, and for those who sleep more than 8 hours, substituting SB with an adequate amount of sleep or engaging in VPA, respectively, emerges as an effective strategy for reducing stroke risk.

Meeting the Aerobic and Muscle-Strengthening Physical Activity Guidelines Among Older US Adults, National Health Interview Survey 1998-2018

The Physical Activity Guidelines for Americans, second edition, recommends older adults participate in ≥150 minutes per week of moderate-intensity equivalent aerobic activity and ≥2 days per week of muscle-strengthening activity. We estimated prevalence and trends of meeting the guidelines among US adults aged ≥65 years from 1998 to 2018. Using the 1998-2018 National Health Interview Survey, we estimated the prevalence of meeting aerobic, muscle-strengthening, and combined physical activity guidelines stratified by age group, sex, race and ethnicity, and education level. Within age groups, we calculated prevalence differences by sociodemographic categories. Prevalence of meeting each guideline increased for all age groups and most sociodemographic subgroups. The increased magnitude of meeting the combined guideline from 1998-2000 to 2016-2018 differed across levels of educational attainment for most age groups. Despite increasing over time, the prevalence among older adults of meeting physical activity guidelines remains low (range for combined guideline: 7.2%-17.2%).

Time Playing Outdoors Among Children Aged 3-5 Years: National Survey of Children's Health, 2021

INTRODUCTION

Federal guidelines recommend physical activity throughout the day for preschool-aged children. Time playing outdoors can support physical activity participation, health, and development. Estimates of time playing outdoors among U.S. children aged 3-5 years have not been published.

METHODS

Parent/caregiver-reported data on children aged 3-5 years from the 2021 National Survey of Children's Health were analyzed in 2022-23. Chi-square tests were used to identify differences in time playing outdoors by sociodemographic and neighborhood characteristics. Multiple logistic regression analyses were conducted with significant characteristics for weekdays and weekend days.

RESULTS

Among 11,743 children aged 3-5 years, 37% played outdoors for ≤1 hour on weekdays, and 24% played outdoors for ≤1 hour on weekend days. In 9 states, ≥40% of children played outdoors for ≤1 hour on weekdays. Adjusted models for weekdays and weekend days showed a greater likelihood of ≤1 hour playing outdoors among those in all racial/ethnic groups compared to non-Hispanic White, those who lived in metropolitan statistical areas, those who did not participate in child care, and those whose adult proxy disagreed with "we watch out for each other's children in this neighborhood." The weekday model showed additional differences by sex, with girls more likely to have ≤1 hour of time playing outdoors.

CONCLUSIONS

Nearly 40% of preschool-aged children play outdoors for ≤1 hour per day on weekdays, with differences by sociodemographic and neighborhood characteristics. Further study and interventions focused on building supportive, equitable communities might increase the amount of time preschool-aged children spend playing outdoors.

Levels of actigraphy-derived physical activity among Polish nurses: factors associated with the prevalence of selected metabolic disorders

Numerous studies demonstrate a relationship between physical activity and the development of non-communicable diseases. Nurses play a crucial role in the healthcare system, and their demanding work can have an impact on their health. The objective of this cross-sectional study was to assess physical activity in relation to factors predisposing to the occurrence of specific metabolic disorders among Polish nurses. The measurements included physical activity level using ActiGraph GT3X, body weight composition using Tanita MC-980, body mass index, waist circumference, blood pressure using Welch Allyn 4200B, lipid profile, and fasting blood glucose using CardioChek PA. The results indicate that nearly one-third (31.75%) of the total sample of studied nurses do not meet the criteria for the minimum amount of physical activity of at least moderate intensity. Furthermore, over half of the surveyed nurses (55.5%) were classified as overweight or obese based on BMI, and almost half (42.86%) had abdominal obesity. The regression model, employing linear regression, revealed that factors predisposing to selected metabolic disorders were age, engaging in multiple jobs, and the number of steps per day. There is a pressing need to implement comprehensive and supportive initiatives to improve the overall health condition of nurses in Poland through increased physical activity. Activating and supporting this professional group is an investment that benefits not only the nurses themselves but also the healthcare system and the entire nation.

Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study

BACKGROUND

Despite apparent shortcomings such as measurement error and low precision, self-reported sedentary time is still widely used in surveillance and research. The aim of this study was threefold; (i) to examine the agreement between self-reported and device-measured sitting time in a general adult population; (ii), to examine to what extent demographics, lifestyle factors, long-term health conditions, physical work demands, and educational level is associated with measurement bias; and (iii), to explore whether correcting for factors associated with bias improves the prediction of device-measured sitting time based on self-reported sitting time.

METHODS

A statistical validation model study based on data from 23 993 adults in the Trøndelag Health Study (HUNT4), Norway. Participants reported usual sitting time on weekdays using a single-item questionnaire and wore two AX3 tri-axial accelerometers on the thigh and low back for an average of 3.8 (standard deviation [SD] 0.7, range 1-5) weekdays to determine their sitting time. Statistical validation was performed by iteratively adding all possible combinations of factors associated with bias between self-reported and device-measured sitting time in a multivariate linear regression. We randomly selected 2/3 of the data (n = 15 995) for model development and used the remaining 1/3 (n = 7 998) to evaluate the model.

RESULTS

Mean (SD) self-reported and device-measured sitting time were 6.8 (2.9) h/day and 8.6 (2.2) h/day, respectively, corresponding to a mean difference of 1.8 (3.1) h/day. Limits of agreement ranged from - 8.0 h/day to 4.4 h/day. The discrepancy between the measurements was characterized by a proportional bias with participants device-measured to sit less overestimating their sitting time and participants device-measured to sit more underestimating their sitting time. The crude explained variance of device-measured sitting time based on self-reported sitting time was 10%. This improved to 24% when adding age, body mass index and physical work demands to the model. Adding sex, lifestyle factors, educational level, and long-term health conditions to the model did not improve the explained variance.

CONCLUSIONS

Self-reported sitting time had low validity and including a range of factors associated with bias in self-reported sitting time only marginally improved the prediction of device-measured sitting time.

Association between passively collected walking and bicycling data and purposefully collected active commuting survey data—United States, 2019

Commercially-available location-based services (LBS) data derived primarily from mobile devices may provide an alternative to surveys for monitoring physically-active transportation. Using Spearman correlation, we compared county-level metrics of walking and bicycling from StreetLight with metrics of physically-active commuting among U.S. workers from the American Community Survey. Our strongest pair of metrics ranked counties (n = 298) similarly for walking (rho = 0.53 [95% CI: 0.44-0.61]) and bicycling (rho = 0.61 [0.53-0.67]). Correlations were higher for denser and more urban counties. LBS data may offer public health and transportation professionals timely information on walking and bicycling behavior at finer geographic scales than some existing surveys.

Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep-wake cycles

Background

Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days.

Methods

MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application.

Results

MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes.

Conclusions

Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.

Physical Inactivity, Metabolic Syndrome and Prostate Cancer Diagnosis: Development of a Predicting Nomogram

Insufficient physical activity (PA) may be a shared risk factor for the development of both metabolic syndrome (MetS) and prostate cancer (PCa). To investigate this correlation and to develop a nomogram able to predict tumor diagnosis. Between 2016 and 2018, a consecutive series of men who underwent prostate biopsy at three institutions were prospectively enrolled. PA was self-assessed by patients through the Physical Activity Scale for the Elderly (PASE) questionnaire; MetS was assessed according to Adult Treatment Panel III criteria. A logistic regression analyses was used to identify predictors of PCa diagnosis and high-grade disease (defined as International Society of Uro-Pathology grade >2 tumors). A nomogram was then computed to estimate the risk of tumor diagnosis. A total of 291 patients were enrolled; 17.5% of them (n = 51) presented with MetS. PCa was diagnosed in 110 (38%) patients overall while 51 presented high-grade disease. At multivariable analysis, age (OR 1.04; 95%CI: 1.00−1.08; p = 0.048), prostate volume (PV) (OR 0.98; 95%CI: 0.79−0.99; p = 0.004), suspicious digital rectal examination (OR 2.35; 95%CI: 1.11−4.98; p = 0.02), total PSA value (OR 1.12; 95%CI: 1.05−1.2; p < 0.001), and PASE score (OR 0.99; 95%CI: 0.98−0.99; p = 0.01) were independent predictors of tumor diagnosis. The latter two also predicted high-grade PCa. MetS was not associated with PCa diagnosis and aggressiveness. The novel nomogram displayed fair discrimination for PCa diagnosis (AUC = 0.76), adequate calibration (p > 0.05) and provided a net benefit in the range of probabilities between 20% and 90%. reduced PA was associated with an increased risk of PCa diagnosis and high-grade disease. Our nomogram could improve the selection of patients scheduled for prostate biopsy at increased risk of PCa.

A Summary of One Research Team's Contributions to Understanding Physical Activity Behavior in Children and Youth

Schools are well-positioned to provide physical activity opportunities to help youth achieve the recommended 60 or more daily minutes of moderate-to-vigorous physical activity. The Children's Physical Activity Research Group (CPARG) at the University of South Carolina has focused on understanding physical activity in school-aged youth for 30+ years. The purpose of this article was to critically review (CPARG) contributions to the field in school settings and school-age youth. We reviewed 127 published CPARG articles from six research projects conducted between 1993-2019. The review was guided by questions in five categories: measurement of physical activity and its determinants, characteristics of physical activity behavior, correlates/determinants of physical activity, physical activity interventions, and race/ethnicity and physical activity. Results were summarized by question and synthesized across categories. CPARG contributions included assessing physical activity levels, patterns, forms, and contexts; identifying and measuring physical activity correlates/determinants; and conducting school-based physical activity interventions. Identifying multiple domains of physical activity determinants enables researchers and practitioners to select/design age-appropriate, valid, and reliable instruments to assess determinants. Focusing on determinants enables them to create effective physical activity interventions, environments, programs, and policies in schools. These efforts must address race/ethnicity differences, ensuring that measurement instruments and intervention strategies are culturally appropriate.

Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study

This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age ( Collapse

Key Words

• Accelerometer
• Elderly
• Movement
• Steps
• Time-use

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Affiliation(s)

Erin E. Dooley The University of Alabama at Birmingham, Birmingham, AL, USA
Lisa A. Pompeii Baylor College of Medicine, Houston, TX, USA
Priya Palta Columbia University Irving Medical Center, New York, NY, USA
Pablo Martinez-Amezcua Columbia University Irving Medical Center, New York, NY, USA
Bjoern Hornikel The University of Alabama at Birmingham, Birmingham, AL, USA
Kelly R. Evenson Gillings School of Global Public Health, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jennifer A. Schrack Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Kelley Pettee Gabriel The University of Alabama at Birmingham, Birmingham, AL, USA

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Physical activity and sedentary behaviour of primary school learners in the Eastern Cape province of South Africa

Background

This study was designed to assess physical activity (PA) levels and sedentary behaviour amongst primary school learners in the Eastern Cape province of South Africa.

Methods

A cross-sectional study was adopted to assess the patterns and levels of PA and sedentary behaviour using the Physical Activity Questionnaire for Older Children (PAQ-C). The sample consisted of primary school learners, both boys and girls, aged 9–14 years. The learners were randomly selected from rural, urban and peri-urban areas in the Eastern Cape province of South Africa.

Results

Using a complete case analysis, 870 randomly selected participants (boys = 351 and girls = 519) aged 9–14 years were retained. Overall, the sample had a low mean PAQ-C score of 2.33 ± 0.43. The mean of PA in boys was significantly higher (p = 0.003) in comparison with the girls. The 13- to 14-age group had significantly higher PA levels (p = 0.014). Learners from urban areas (n = 136; 77.3%) engaged more in sedentary behaviour compared to those from rural areas (n = 252; 54.9%).

Conclusion

The findings demonstrated low levels of PA and high engagement of sedentary behaviour across the combined gender groups, which have negative implications on health, growth and development of children. The study, therefore, recommends relevant stakeholders to implement interventions aimed at promoting an increase in PA and a reduction in sedentary behaviours for primary school learners in the Eastern Cape province of South Africa.

Trends in Self-Reported Sitting Time by Physical Activity Levels Among US Adults, NHANES 2007/2008-2017/2018

BACKGROUND

High levels of sedentary behavior and physical inactivity increase the risk of premature mortality and several chronic diseases. Monitoring national trends and correlates of sedentary behavior and physical inactivity can help identify patterns of risk in the population over time.

METHODS

The authors used self-reported data from the National Health and Nutrition Examination Surveys (2007/2008-2017/2018) to estimate trends in US adults' mean daily sitting time, overall, and stratified by levels of leisure-time and multidomain physical activity, and in the joint prevalence of high sitting time (>8 h/d) and physical inactivity. Trends were tested using orthogonal polynomial contrasts.

RESULTS

Overall, mean daily sitting time increased by 19 minutes from 2007/2008 (332 min/d) to 2017/2018 (351 min/d) (Plinear < .05; Pquadratic < .05). The highest point estimate occurred in 2013/2014 (426 min/d), with a decreasing trend observed after this point (Plinear < .05). Similar trends were observed across physical activity levels and domains, with one exception: an overall linear increase was not observed among sufficiently active adults. The mean daily sitting time was lowest among highly active adults compared with less active adults when using the multidomain physical activity measure.

CONCLUSIONS

Sitting time among adults increased over the study period but decreased in recent years.