Dataset Information

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.

SUBMITTER: Jaeschke L

PROVIDER: S-EPMC6972881 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Jaeschke Lina L Steinbrecher Astrid A Boeing Heiner H Gastell Sylvia S Ahrens Wolfgang W Berger Klaus K Brenner Hermann H Ebert Nina N Fischer Beate B Greiser Karin Halina KH Hoffmann Wolfgang W Jöckel Karl-Heinz KH Kaaks Rudolf R Keil Thomas T Kemmling Yvonne Y Kluttig Alexander A Krist Lilian L Leitzmann Michael M Lieb Wolfgang W Linseisen Jakob J Löffler Markus M Michels Karin B KB Obi Nadia N Peters Annette A Schipf Sabine S Schmidt Börge B Zinkhan Melanie M Pischon Tobias T

Scientific reports 20200121 1

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, mari ...[more]

PMID: 31964962

Similar Datasets

Project description:Positive associations have been identified between bone outcomes and accelerometer-derived moderate (MPA) and vigorous (VPA) physical activity (PA) in youth; however, it remains unclear which intensity is most beneficial. This systematic review aimed to summarise accelerometer-derived methods used to estimate habitual PA in children and adolescents and determine whether the magnitude of association was consistently stronger for a particular intensity (MPA/MVPA/VPA). Observational studies assessing associations between accelerometer-derived MPA and/or MVPA and VPA with bone outcomes in children and adolescents (≤ 18 years) were identified in MEDLINE, EMBASE, Web of Science, SPORTDiscus and the Cochrane Central Register of Controlled Trials. Thirty articles were included (total n = 20,613 (10,077 males), 4-18 years). Chi-square tests determined whether the proportion of significant associations and strongest within-study associations differed significantly between intensities. Results demonstrated that accelerometer methods were highly variable between studies. Of the 570 associations analysed, 186 were significant (p < 0.05). The proportion of within-study strongest associations differed by PA intensity (3 × 2 χ2 = 86.6, p < 0.001) and was significantly higher for VPA (39%) compared to MVPA (5%; 2 × 2 χ2 = 55.3, p < 0.001) and MPA (9%, 2 × 2 χ2 = 49.1, p < 0.001). Results indicated a greater benefit of VPA over MPA/MVPA; however, variability in accelerometer-derived methods used prevents the precise bone-benefitting amount of VPA from being identified. Long epochs and numerous intensity cut-point definitions mean that bone-relevant PA has likely been missed or misclassified in this population. Future research should explore the use of shorter epochs (1 s) and identify bone-specific activity intensities, rather than using pre-defined activity classifications more relevant to cardiovascular health.

Project description:BackgroundThis research compared accelerometry (ACC)-derived and muscle electromyography (EMG)-based estimates of physical activity (PA) and sedentary time in typical PA tasks and during the daily lives of children.MethodsData was included from two exploratory studies. In Study I, 6-7-year-old children (n = 11, 64% girls) were assessed for eight PA tasks (walking, stair negotiation, climbing, crawling, swinging, balancing, trampoline jumping and a game of tag). In Study II, 7-9-year-old children (n = 14, 38% girls) were assessed for six PA tasks (walking, sitting, static squat, single leg hops, jump for height and standing long jump), and daily PA during one day with and one day without structured exercise. Quadriceps and hamstring muscle activity and inactivity using EMG shorts and acceleration by waist-mounted accelerometer were simultaneously measured and classified as sedentary, light, moderate and vigorous activity. Data from ACC was further analyzed using five different published cut-off points and varying time windows (1-60 s) for comparison with EMG.ResultsIn the PA tasks ACC counts and EMG amplitude showed marked differences in swinging, trampoline jumping, crawling, static squat, single leg hops, standing long jump and jump for height, the difference being over 170% when signals were normalized to that during walking. Furthermore, in walking, swinging, trampoline jumping, stair negotiation and crawling ACC classified over 60% of the time as vigorous-intensity activity, while EMG indicated primarily light- and moderate-intensity activities. During both days with and without exercise, ACC resulted in greater proportion of light activity (p < 0.01) and smaller proportion of moderate activity compared to EMG (p < 0.05). The choice of cut-off points and epoch length in ACC analysis influenced the classification of PA level and sedentary time. In the analysis of daily activities the cut-off points by Evenson et al. (2008) with epochs of 7.5 s and 15 s yielded the smallest difference (less than 10% of recording time at each intensity) against EMG-derived PA levels.DiscussionThis research provides novel insight on muscle activity and thereby on neuromuscular loading of major locomotor muscles during normal daily activities of children. While EMG and ACC provided similar estimates of sedentary time in 13 typical PA tasks, duration of light, moderate and vigorous PA varied considerably between the methods especially during walking, stair negotiation, crawling, swinging and trampoline jumping. Evenson et al.'s (2008) cut-off points with ≤15 s epoch provided similar classification of PA than EMG during daily life. Compared to impacts recorded using ACC, EMG can provide understanding on children's neuromuscular loading during motor tasks that is useful when studying effects of PA interventions on, and development of, motor competence and coordination.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets