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.

Activity 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. Healthy 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. Test-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. The 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.

Objective To evaluate whether a multifaceted behavioural change programme increases physical activities in patients with Parkinson’s disease.Design Multicentre randomised controlled trial.Setting 32 community hospitals in the Netherlands, collaborating in a nationwide network (ParkinsonNet).Participants 586 sedentary patients with idiopathic Parkinson’s disease aged between 40 and 75 years with mild to moderate disease severity (Hoehn and Yahr stage ≤3).Intervention Patients were randomly assigned to the ParkFit programme or a matched general physiotherapy intervention. ParkFit is a multifaceted behavioural change programme, designed specifically to achieve an enduring increase in the level of physical activity (coaches using motivational strategies; ambulatory feedback).Main outcome measures The primary endpoint was the level of physical activity, measured every six months with a standardised seven day recall (LASA physical activity questionnaire—LAPAQ). Secondary endpoints included two other measures of physical activity (activity diary and ambulatory activity monitor), quality of life (Parkinson’s disease questionnaire—PDQ-39), and fitness (six minute walk test).Results 540 (92.2%) patients completed the primary outcome. During follow-up, overall time spent on physical activities (LAPAQ) was comparable between the groups (adjusted group difference 7%, 95% confidence interval −3 to 17%; P=0.19). Analyses of three secondary outcomes indicated increased physical activity in ParkFit patients, as suggested by the activity diary (difference 30%; P<0.001), the activity monitor (difference 12%; P<0.001), and the six minute walk test (difference 4.8 m; P=0.05). PDQ-39 did not differ between ParkFit patients and controls (difference −0.9 points; P=0.14). The number of fallers was comparable between ParkFit patients (184/299; 62%) and controls (191/287; 67%).Conclusions The ParkFit behavioural change programme did not increase overall physical activity, as measured with the LAPAQ. The analysis of the secondary endpoints justifies further work into the possible merits of behavioural change programmes to increase physical activities in daily life in Parkinson’s disease.Trial registration Clinical trials NCT00748488.

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.

Background: Physical activity is the only nonpharmacological therapy that is proven to be effective in heart failure (HF) patients in reducing morbidity. To date, little is known about the levels of daily physical activity in HF patients and about related factors. Objective: The objectives of this study were to (a) describe performance-based daily physical activity in HF patients, (b) compare it with physical activity guidelines, and (c) identify related factors of daily physical activity. Methods: The daily physical activity of 68 HF patients was measured using an accelerometer (SenseWear) for 48 hours. Psychological characteristics (self-efficacy, motivation, and depression) were measured using questionnaires. To have an indication how to interpret daily physical activity levels of the study sample, time spent on moderate- to vigorous-intensity physical activities was compared with the 30-minute activity guideline. Steps per day was compared with the criteria for healthy adults, in the absence of HF-specific criteria. Linear regression analyses were used to identify related factors of daily physical activity. Results: Forty-four percent were active for less than 30 min/d, whereas 56% were active for more than 30 min/d. Fifty percent took fewer than 5000 steps per day, 35% took 5000 to 10 000 steps per day, and 15% took more than 10 000 steps per day. Linear regression models showed that New York Heart Association classification and self-efficacy were the most important factors explaining variance in daily physical activity. Conclusions: The variance in daily physical activity in HF patients is considerable. Approximately half of the patients had a sedentary lifestyle. Higher New York Heart Association classification and lower self-efficacy are associated with less daily physical activity. These findings contribute to the understanding of daily physical activity behavior of HF patients and can help healthcare providers to promote daily physical activity in sedentary HF patients.