The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005–6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.

BackgroundActivity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions.MethodHealthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots.ResultsTest-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition.ConclusionThe reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

Marlies van Nimwegen AbstractObjective To evaluate whether a multifaceted behavioural change programme increases physical activities in patients with Parkinson's disease.

BackgroundSedentary 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.MethodsCross 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.ResultsAll 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.ConclusionSelf-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.Electronic supplementary materialThe online version of this article (10.1186/s12966-018-0652-x) contains supplementary material, which is available to authorized users.

Background

Gain in adiposity after renal transplantation is related to lifestyle factors such as high consumption of energy-rich drinks, high intake of mono- and disaccharides and low daily physical activity. RCTs are needed to investigate potential benefits of lifestyle intervention on long-term morbidity and mortality.

The Seniors USP study measured sedentary behaviour (activPAL3, 9 day wear) in older adults. The measurement protocol had three key characteristics: enabling 24-hour wear (monitor location, waterproofing); minimising data loss (reducing monitor failure, staff training, communication); and quality assurance (removal by researcher, confidence about wear). Two monitors were not returned; 91% (n=700) of returned monitors had 7 valid days of data. Sources of data loss included monitor failure (n=11), exclusion after quality assurance (n=5), early removal for skin irritation (n=8) or procedural errors (n=10). Objective measurement of physical activity and sedentary behaviour in large studies requires decisional trade-offs between data quantity (collecting representative data) and utility (derived outcomes that reflect actual behaviour).

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