Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012–2019)

To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population. These results were obtained from the 2003-2004 National Health and Nutritional Examination Survey (NHANES), a cross-sectional study of a complex, multistage probability sample of the civilian, noninstitutionalized U.S. population in the United States. Data are described from 6329 participants who provided at least 1 d of accelerometer data and from 4867 participants who provided four or more days of accelerometer data. Males are more physically active than females. Physical activity declines dramatically across age groups between childhood and adolescence and continues to decline with age. For example, 42% of children ages 6-11 yr obtain the recommended 60 min x d(-1) of physical activity, whereas only 8% of adolescents achieve this goal. Among adults, adherence to the recommendation to obtain 30 min x d(-1) of physical activity is less than 5%. Objective and subjective measures of physical activity give qualitatively similar results regarding gender and age patterns of activity. However, adherence to physical activity recommendations according to accelerometer-measured activity is substantially lower than according to self-report. Great care must be taken when interpreting self-reported physical activity in clinical practice, public health program design and evaluation, and epidemiological research.

Background Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. Methods Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. Results 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5–7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen’s d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. Conclusions It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.

Understanding the basic principles that govern physical activity is needed to curb the global pandemic of physical inactivity 1–7 and the 5.3 million deaths per year associated with in-activity 2 . Our knowledge, however, remains limited owing to the lack of large-scale measurements of physical activity patterns across free-living populations worldwide 1, 6 . Here, we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at planetary scale. We study a dataset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, were associated with less gender gap in activity and activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment for improving physical activity and health.