Accelerometer-derived physical activity in those with cardio-metabolic disease compared to healthy adults: a UK Biobank study of 52,556 participants

Abstract

AimCardio-metabolic disease and physical activity are closely related but large-scale objective studies which measure physical activity are lacking. Using the largest accelerometer cohort to date, we aimed to investigate whether there is an association between disease status and accelerometer variables after a 5-year follow-up.Methods106,053 UK Biobank participants wore a wrist-worn GENEactiv monitor. Those with acceptable wear time (> 3 days) were split into 4 cardio-metabolic disease groups based on self-report disease status which was collected 5 ± 1 years prior. Multiple linear regression models were used to investigate associations, controlling for confounders and stratified for gender.ResultsAverage daily acceleration was lower in men (‘healthy’-42 ± 15 mg v ‘Type 2 diabetes + cardiovascular disease (CVD)’-31 ± 12 mg) and women (‘healthy’-44 ± 13 mg v ‘Type 2 diabetes + CVD’-31 ± 11 mg) with cardio-metabolic disease and this was consistent across both week and weekend days. Men and women with the worst cardio-metabolic disease perform around half of moderate to vigorous physical activity on a daily basis compared to healthy individuals, and spend almost 7 h per day in 30 min inactivity bouts. Significant associations were seen between cardio-metabolic disease and accelerometer variables 5 years on when controlling for confounders.ConclusionIn the largest accelerometer cohort to date, there are significant associations between cardio-metabolic disease and physical activity variables after 5 years of follow-up. Triaxial accelerometers provide enhanced measurement opportunities for measuring lifestyle behaviours in chronic disease.

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

“…Analyses were completed in SPSS (version 25), except for confirmatory and exploratory factor analyses, which were completed in MPlus 7.3. Participants’ outcome data were analysed descriptively and compared to population norms to determine whether scores differed significantly to comparable samples’ scores on wellbeing [10], depression, anxiety, and stress [30], quality of life [31], subjective physical activity [32], objective physical activity [33], and sleep duration [34].…”

GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data

“…Results from the Whitehall II study showed 1) the association between physical activity and body mass index was much stronger when using accelerometer data rather than questionnaire data (Sabia et al, 2015); 2) obese people with an unfavorable metabolic profile had a lower level of physical activity than those with a healthy metabolic profile, which was not evident when using questionnaire data (Bell et al, 2015); 3) the association between moderate and vigorous physical activity and healthy aging was evident whether the activity was performed in short (<10 minutes) or long (≥10 minutes) bouts (Menai et al, 2017). Results from the UK Biobank also showed 1) people with cardiometabolic disease are less physically active and tend to engage more in sedentary activities that last more than 30 minutes (Cassidy et al, 2018), and 2) short bursts of very high intensity physical activity are associated with better bone health in pre-and postmenopausal women (Stiles, Metcalf, Knapp, & Rowlands, 2017).…”

Physical activity and sedentary activity: population epidemiology and concordance in Australian children aged 11–12 years and their parents

“…Recently, Rowlands et al 58 reported average MVPA durations of 92 min/day for a small adult sample, using the GeneActiv. A recent study using the large (n=22 978) Biobank dataset reported 106 min/day MVPA in healthy adults 59. In children, Telford et al 60 reported averages of 43 and 31 min/day of MVPA for boys and girls aged 8–12 years, respectively.…”