Factors associated with habitual time spent in different physical activity intensities using multiday accelerometry

Abstract

To investigate factors associated with time in physical activity intensities, we assessed physical activity of 249 men and women (mean age 51.3 years) by 7-day 24h-accelerometry (ActiGraph GT3X+). Triaxial vector magnitude counts/minute were extracted to determine time in inactivity, in low-intensity, moderate, and vigorous-to-very-vigorous activity. Cross-sectional associations with sex, age, body mass index, waist circumference, smoking, alcohol consumption, education, employment, income, marital status, diabetes, and dyslipidaemia were investigated in multivariable regression analyses. Higher age was associated with more time in low-intensity (mean difference, 7.3 min/d per 5 years; 95% confidence interval 2.0,12.7) and less time in vigorous-to-very-vigorous activity (−0.8 min/d; −1.4, −0.2), while higher BMI was related to less time in low-intensity activity (−3.7 min/d; −6.3, −1.2). Current versus never smoking was associated with more time in low-intensity (29.2 min/d; 7.5, 50.9) and less time in vigorous-to-very-vigorous activity (−3.9 min/d; −6.3, −1.5). Finally, having versus not having a university entrance qualification and being not versus full time employed were associated with more inactivity time (35.9 min/d; 13.0, 58.8, and 66.2 min/d; 34.7, 97.7, respectively) and less time in low-intensity activity (−31.7 min/d; −49.9, −13.4, and −50.7; −76.6, −24.8, respectively). The assessed factors show distinct associations with activity intensities, providing targets for public health measures aiming to increase activity.

Activity Energy Expenditure Predicts Clinical Average Levels of Physical Activity in Older Population: Results from Salus in Apulia Study

“…Interestingly, age was not associated to time inactivity, suggesting that while higher intense activity may diminish with age, accelerometer-based inactivity time may not differ significantly. These findings were consistent with previous investigations from cross-sectional and longitudinal studies that reported higher age related to less time in VV activity and more time in low-intensity activity, including 24 h-accelerometry-based data [ 13 , 41 ]. Although age was related to the intensity of PA (ACC) and to activity-related energy expenditure (AEE in kJ day −1 kg −1 ), we did not observe an influence of age on the linear regression model.…”

“…We will be then able to provide for a clearer understanding of PA determinants, as well as the dynamic interaction between PA and health effects in our community. As previously reported, using multiday 24 h-accelerometry to assess habitual PA in a population-based study highlighted distinct associations with biological, behavioral, socioeconomic, and sociocultural factors [ 13 ], thus setting clear objectives for public health interventions aiming to increase activity.…”

“…Higher PA levels could therefore prevent chronic diseases and mobility limitations and improve the quality and length of life in the older population [ 9 ]. Furthermore, there is considerable evidence linking physical activity to successful ageing in cross-sectional [ 10 , 11 , 12 , 13 ] and longitudinal studies [ 14 , 15 ]. Self-reporting methods, including questionnaires, have provided a powerful tool of PA assessment that can include useful and reliable knowledge on several health domains.…”

“…Menai and colleagues [ 16 ] showed that accelerometer data from a large sample of UK older adults suggest that successful agers practiced moderate to vigorous physical activity (MVPA) for longer duration and at higher intensity. Jaeschke and colleagues [ 13 ] investigated factors associated with habitual time spent in different activity intensities in the general German adult population (NAKO Study) using multiday 24 h-accelerometry. They showed that time spent in low-intensity activity was associated to both age and BMI with an inverse relationship: older adults spent more time in low-intensity while overweight–obese subjects less.…”

A study of the relationship between habitual recreational sporting activity and bone health in adolescents and young adults

“…Furthermore, some activities could be either competitive or non-competitive such as running, walking, dance and resistance exercise. Factors such as time spent inactive or in education, time and type of paid work activity and socio-economic status of the participants were not assessed accurately, although these factors may affect the level and time spent in habitual PA (Ahrens et al, 2020). Defining and measuring any form of PA or sedentary behaviour is not simple (Higgins et al, 2019) and our study was completed without access to a validated accelerometer.…”