Getting enough sleep, exercising, and limiting sedentary activities can greatly contribute to disease prevention and overall health and longevity. Measuring the full 24-h activity cycle-sleep, ...sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous physical activity (MVPA)-may now be feasible using small wearable devices.
This study compared nine devices for accuracy in a 24-h activity measurement.
Adults (n = 40, 47% male) wore nine devices for 24 h: ActiGraph GT3X+, activPAL, Fitbit One, GENEactiv, Jawbone Up, LUMOback, Nike Fuelband, Omron pedometer, and Z-Machine. Comparisons (with standards) were made for total sleep time (Z-machine), time spent in SED (activPAL), LPA (GT3X+), MVPA (GT3X+), and steps (Omron). Analysis included mean absolute percent error, equivalence testing, and Bland-Altman plots.
Error rates ranged from 8.1% to 16.9% for sleep, 9.5% to 65.8% for SED, 19.7% to 28.0% for LPA, 51.8% to 92% for MVPA, and 14.1% to 29.9% for steps. Equivalence testing indicated that only two comparisons were significantly equivalent to standards: the LUMOback for SED and the GT3X+ for sleep. Bland-Altman plots indicated GT3X+ had the closest measurement for sleep, LUMOback for SED, GENEactiv for LPA, Fitbit for MVPA, and GT3X+ for steps.
Currently, no device accurately captures activity data across the entire 24-h day, but the future of activity measurement should aim for accurate 24-h measurement as a goal. Researchers should continue to select measurement devices on the basis of their primary outcomes of interest.
The activPAL monitor, often worn 24 h d−1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to participants and researchers-are ...commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18-40 years) then evaluated in AusDiab 2011/12 participants (n = 741, 44% men, aged >35 years, mean ± SD 58.5 ± 10.4 years) who wore the activPAL3™ (7 d, 24 h d−1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ > 0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p < 0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d−1 higher than by the diary method, with 95% limits of agreement of approximately this amount ±2 h d−1. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.
High levels of occupational sitting is an emerging health concern. As working from home has become a common practice as a result of COVID-19, it is imperative to validate an appropriate self-report ...measure to assess sitting in this setting. This secondary analysis study aimed to validate the occupational sitting and physical activity questionnaire (OSPAQ) against an activPAL4™ in full-time home-based ‘office’ workers (n = 148; mean age = 44.90). Participants completed a modified version of the OSPAQ and wore an activPAL4™ for a full work week. The findings suggest that the modified OSPAQ has fair levels of validity in terms of correlation for sitting and standing (ρ = 0.35–0.43, all p < 0.05) and agreement (bias = 2–12%) at the group level; however, estimates were poor at an individual level, as suggested by wide limits of agreement (±22–30%). Overall, the OSPAQ showed to be an easily administered and valid questionnaire to measure group level sitting and standing in this sample of adults.
Different approaches have been implemented to calculate stepping cadence (steps/min) that vary in the time demominator used. Given the differences in how stepping intensity are calculated, it is ...unclear if they are more so associated with total step counts.
This study compared three methods of calculating stepping cadence and determined their relationship with total step counts.
132 participants (74♀; 35 ± 20 years; body mass index: 24.9 ± 4.0 kg•m-2) wore an activPAL monitor 24-hr/day for up to 8-d (total: 869-d). The total steps/day, time spent stepping (0.1 s resolution; to calculate bout stepping rate), time spent stepping in 60 s epochs (step accumulation), and awake time (awake cadence) were determined. Each cadence method (in steps/min) were compared via Spearman’s rank correlation. The relationships versus total step count were determined, and the strength of these relationships compared between cadence measures (95% confidence interval of correlation differences).
Bout stepping rate (85 ± 14 steps/min) was larger than step accumulation (34 ± 12 steps/min) and awake cadence (10 ± 5 steps/min, both: P < 0.001). Step accumulation was positively strongly related to bout stepping rate (ρ = 0.813; P < 0.001) whereas awake cadence was weakly related to bout stepping rate (ρ = 0.496; P < 0.001). Step accumulation (ρ = 0.634; P < 0.001) and awake cadence (ρ = 0.964; P < 0.001) were more related to step counts than bout stepping rate (ρ = 0.497; P < 0.001; 95% confidence intervals of correlation differences: step accumulation=0.10–0.17, awake cadence: 0.42–0.52).
Without a precise measure of time spent stepping, stepping cadence is lower using the step accumulation and awake cadence methods. Step accumulation and awake cadence are more related to total step counts than bout stepping rate. Bout stepping rate outcomes reflect continuous stepping rate, does not rely on a preset epoch, and may have less overlap with step counts, which may have implications for determining the unique contributions of step count versus stepping cadence on health outcomes.
•Stepping cadence provides important information about activity intensity patterns.•Different analytical approaches have been used to determine stepping cadence.•Without a true measure of stepping time, cadence is underestimated.•Cadences that do not measure stepping time are more related to total step counts.•Cadence outcomes vary greatly depending on the analytical approach implemented.
Sedentary behaviour is a public health concern that requires surveillance and epidemiological research. For such large scale studies, self-report tools are a pragmatic measurement solution. A large ...number of self-report tools are currently in use, but few have been validated against an objective measure of sedentary time and there is no comparative information between tools to guide choice or to enable comparison between studies. The aim of this study was to provide a systematic comparison, generalisable to all tools, of the validity of self-report measures of sedentary time against a gold standard sedentary time objective monitor.
Cross sectional data from three cohorts (N = 700) were used in this validation study. Eighteen self-report measures of sedentary time, based on the TAxonomy of Self-report SB Tools (TASST) framework, were compared against an objective measure of postural sitting (activPAL) to provide information, generalizable to all existing tools, on agreement and precision using Bland-Altman statistics, on criterion validity using Pearson correlation, and on data loss.
All self-report measures showed poor accuracy compared with the objective measure of sedentary time, with very wide limits of agreement and poor precision (random error > 2.5 h). Most tools under-reported total sedentary time and demonstrated low correlations with objective data. The type of assessment used by the tool, whether direct, proxy, or a composite measure, influenced the measurement characteristics. Proxy measures (TV time) and single item direct measures using a visual analogue scale to assess the proportion of the day spent sitting, showed the best combination of precision and data loss. The recall period (e.g. previous week) had little influence on measurement characteristics.
Self-report measures of sedentary time result in large bias, poor precision and low correlation with an objective measure of sedentary time. Choice of tool depends on the research context, design and question. Choice can be guided by this systematic comparative validation and, in the case of population surveillance, it recommends to use a visual analog scale and a 7 day recall period. Comparison between studies and improving population estimates of average sedentary time, is possible with the comparative correction factors provided.
Investigations using wearable monitors have begun to examine how sedentary time behaviors influence health.
The objective of this study is to demonstrate the use of a measure of sedentary behavior ...and to validate the activPAL (PAL Technologies Ltd., Glasgow, Scotland) and ActiGraph GT3X (Actigraph, Pensacola, FL) for estimating measures of sedentary behavior: absolute number of breaks and break rate.
Thirteen participants completed two 10-h conditions. During the baseline condition, participants performed normal daily activity, and during the treatment condition, participants were asked to reduce and break up their sedentary time. In each condition, participants wore two ActiGraph GT3X monitors and one activPAL. The ActiGraph was tested using the low-frequency extension filter (AG-LFE) and the normal filter (AG-Norm). For both ActiGraph monitors, two count cut points to estimate sedentary time were examined: 100 and 150 counts per minute. Direct observation served as the criterion measure of total sedentary time, absolute number of breaks from sedentary time, and break rate (number of breaks per sedentary hour (brk·sed-h)).
Break rate was the only metric sensitive to changes in behavior between baseline (5.1 3.3-6.8 brk·sed-h) and treatment conditions (7.3 4.7-9.8 brk·sed-h) (mean (95% confidence interval)). The activPAL produced valid estimates of all sedentary behavior measures and was sensitive to changes in break rate between conditions (baseline, 5.1 2.8-7.1 brk·sed-h; treatment, 8.0 5.8-10.2 brk·sed-h). In general, the AG-LFE and AG-Norm were not accurate in estimating break rate or the absolute number of breaks and were not sensitive to changes between conditions.
This study demonstrates the use of expressing breaks from sedentary time as a rate per sedentary hour, a metric specifically relevant to free-living behavior, and provides further evidence that the activPAL is a valid tool to measure components of sedentary behavior in free-living environments.
Abstract Objectives To determine the ActiGraph GT3X+ cut-points with the highest accuracy for estimating time spent in sedentary behaviour in older adults in free-living environments. ActivPAL3 ™ was ...used as the reference standard. Design Cross-sectional study. Methods 37 participants (13 males and 24 females, 73.5 ± 7.3 years old) wore an ActiGraph GT3X+ and an ActivPAL3 ™ for 7 consecutive days. For ActivPAL3 ™, variables were created based on posture. For ActiGraph GT3X+, sedentary behaviour was defined as (1) vector magnitude and (2) vertical axis counts for 1-s, 15-s and 1-min epochs, with cut-points for 1-s epochs of <1 to <10 counts, for 15-s epochs of <1 to <100 counts and for 1-min epochs of <1 to <400 counts. For each of the ActiGraph GT3X+ cut-points, area under the receiver operating characteristic curve (area under the curve), sensitivity, specificity, and percentage correctly classified were calculated. Bias and 95% limits of agreement were calculated using the Bland-Altman method. Results The highest areas under the curve were obtained for the vector magnitude cut-points: <1 count/s, <70 counts/15-s, and <200 counts/min; and for the vertical axis cut-points: <1 count/s, <10 counts/15-s and <25 counts/min. Mean biases ranged from −4.29 to 124.28 min/day. The 95% limits of agreement for these cut-points were ±2 h suggesting great inter-individual variation. Conclusions The results suggest that cut-points are dependent on unit of analyses (i.e. epoch length and axes); cut-points for a given epoch length and axis cannot simply be extrapolated to other epoch lengths. Limitations regarding inter-individual variability and misclassification of standing activity as sitting/lying must be considered.
•Sitting is a ubiquitous health-related behaviour•Assessment of sitting has been challenging and nuances in the length of sitting are often missed in studies•Using wearable devices, we assessed ...different types of sitting time in over 1500 adults•We calculated associations of sitting with anxiety, depression, and health-related quality of life (HRQoL).•Device-based measures of both total and prolonged sitting time were associated with depression and health-related quality of life, but not anxiety.
Assessment of sitting has been challenging and nuances in the length of sitting are often missed.
The present study assessed total, short and prolonged sitting time, and number of breaks from sitting, and their association with anxiety, depression, and health-related quality of life (HRQoL). Adults (M=59.1 years) in three studies (n=1,574) wore the activPAL accelerometer (thigh) to obtain a measure of sitting, and the Actigraph accelerometer (hip) for estimating moderate-to-vigorous physical activity (MVPA). Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale, and HRQoL using the EQ-5D-5L (for health state and utility scores). Generalised linear modelling tested associations.
Total and prolonged sitting were associated with higher depression total: β = 0.132 (0.010, 0.254); prolonged: β = 0.178 (0.053, 0.304) and worse HRQoL health state scores (total: β = -0.985 (-1.471, -0.499); prolonged: β = -0.834 (-1.301, -0.367) and utility scores (total: β = -0.008 (-0.012, -0.003); prolonged: β = -0.008 (-0.012, -0.004), after controlling for covariates. MVPA was associated with better HRQoL health state and utility scores health state: β =0.554 (0.187, 0.922); utility: β = 0.001 (0.001, 0.002). Total and prolonged sitting were associated with a 14% increased odds of being in the borderline/abnormal category for depression. No interactions were observed between MVPA status (active vs. inactive) and total or prolonged sitting. Anxiety was unrelated to any sitting variable.
Device-based measures of both total and prolonged sitting time were associated with depression and health-related quality of life, but not anxiety.
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