Dataset Information

Physical activity levels in adults and elderly from triaxial and uniaxial accelerometry. The Tromso Study.

ABSTRACT:

Introduction

Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences these estimates, in a large population-based cohort of Norwegian adults.

Methods

This cross-sectional study included 5918 women and men aged 40-84 years who participated in the seventh wave of the Tromsø Study (2015-16). The participants wore an ActiGraph wGT3X-BT accelerometer attached to the hip for 24 hours per day over seven consecutive days. Accelerometry variables were expressed as volume (counts·minute-1 and steps·day-1) and as minutes per day in sedentary, light physical activity and moderate and vigorous physical activity (MVPA).

Results

From triaxial accelerometry data, 22% (95% confidence interval (CI): 21-23%) of the participants fulfilled the current global recommendations for physical activity (?150 minutes of MVPA per week in ?10-minute bouts), while 70% (95% CI: 69-71%) accumulated ?150 minutes of non-bouted MVPA per week. When analysing uniaxial data, 18% fulfilled the current recommendations (i.e. 20% difference compared with triaxial data), and 55% (95% CI: 53-56%) accumulated ?150 minutes of non-bouted MVPA per week. We observed approximately 100 less minutes of sedentary time and 90 minutes more of light physical activity from triaxial data compared with uniaxial data (p<0.001).

Conclusion

The prevalence estimates of sufficiently active adults and elderly are more than three times higher (22% vs. 70%) when comparing triaxial bouted and non-bouted MVPA. Physical activity estimates are highly dependent on accelerometry data processing criteria and on different definitions of physical activity recommendations, which may influence prevalence estimates and tracking of physical activity patterns over time.

SUBMITTER: Sagelv EH

PROVIDER: S-EPMC6890242 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Physical activity levels in adults and elderly from triaxial and uniaxial accelerometry. The Tromsø Study.

Sagelv Edvard H EH Ekelund Ulf U Pedersen Sigurd S Brage Søren S Hansen Bjørge H BH Johansson Jonas J Grimsgaard Sameline S Nordström Anna A Horsch Alexander A Hopstock Laila A LA Morseth Bente B

PloS one 20191203 12

<h4>Introduction</h4>Surveillance of physical activity at the population level increases the knowledge on levels and trends of physical activity, which may support public health initiatives to promote physical activity. Physical activity assessed by accelerometry is challenged by varying data processing procedures, which influences the outcome. We aimed to describe the levels and prevalence estimates of physical activity, and to examine how triaxial and uniaxial accelerometry data influences the ...[more]

PMID: 31794552

Similar Datasets

Project description:Background In a hospital setting and among older patients, inactivity and bedrest are associated with a wide range of negative outcomes such as functional decline, increased risk of falls, longer hospitalization and institutionalization. Our aim was to assess the distribution, determinants and predictors of physical activity (PA) levels using wrist-worn accelerometers in older patients hospitalized with acute medical illness. Methods Observational study conducted from February to November 2018 at an acute internal medicine unit in the University hospital of Lausanne, Switzerland. We enrolled 177 patients aged ?65?years, able to walk prior to admission. PA during acute hospital stay was continuously recorded via a 3D wrist accelerometer. Clinical data was collected from medical records or by interview. Autonomy level prior to inclusion was assessed using Barthel Index score. PA levels were defined as <?30?mg for inactivity, 30–99?mg for light and???100 for moderate PA. Physically active patients were defined as 1) being in the highest quartile of time spent in light and moderate PA or 2) spending ?20?min/day in moderate PA. Results Median [interquartile range - IQR] age was 83 [74–87] years and 60% of participants were male. The median [IQR] time spent inactive and in light PA was 613 [518–663] and 63 [30–97] minutes/day, respectively. PA peaked between 8 and 10?am, at 12?am and at 6?pm. Less than 10% of patients were considered physically active according to definition 2. For both definitions, active patients had a lower prevalence of walking aids and a lower dependency level according to Barthel Index score. For definition 1, use of medical equipment was associated with a 70% reduction in the likelihood of being active: odds ratio (OR) 0.30 [0.10–0.92] p =?0.034; for definition 2, use of walking aids was associated with a 75% reduction in the likelihood of being active: OR?=?0.24 [0.06–0.89], p =?0.032. Conclusion Older hospitalized patients are physically active only 10% of daily time and concentrate their PA around eating periods. Whether a Barthel Index below 95 prior to admission may be used to identify patients at risk of inactivity during hospital stay remains to be proven.

Project description:BACKGROUND:24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to estimate habitual PA, and reliability are unclear. METHODS:We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients. RESULTS:Between- and within-person variance accounted for 34.4-45.5% and 54.5-65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p?=?0.03), time in inactivity (p?=?0.003), in low intensity PA (p?=?0.001), in moderate PA (p?=?0.02), and in vigorous PA (p?=?0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for <?19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68-0.82). CONCLUSIONS:Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.