Technological advances have seen a burgeoning industry for accelerometer-based wearable activity monitors targeted at the consumer market. The purpose of this study was to determine the convergent validity of a selection of consumer-level accelerometer-based activity monitors. 21 healthy adults wore seven consumer-level activity monitors (Fitbit One, Fitbit Zip, Jawbone UP, Misfit Shine, Nike Fuelband, Striiv Smart Pedometer and Withings Pulse) and two research-grade accelerometers/multi-sensor devices (BodyMedia SenseWear, and ActiGraph GT3X+) for 48-hours. Participants went about their daily life in free-living conditions during data collection. The validity of the consumer-level activity monitors relative to the research devices for step count, moderate to vigorous physical activity (MVPA), sleep and total daily energy expenditure (TDEE) was quantified using Bland-Altman analysis, median absolute difference and Pearson’s correlation. All consumer-level activity monitors correlated strongly (r > 0.8) with research-grade devices for step count and sleep time, but only moderately-to-strongly for TDEE (r = 0.74-0.81) and MVPA (r = 0.52-0.91). Median absolute differences were generally modest for sleep and steps (<10% of research device mean values for the majority of devices) moderate for TDEE (<30% of research device mean values), and large for MVPA (26-298%). Across the constructs examined, the Fitbit One, Fitbit Zip and Withings Pulse performed most strongly. In free-living conditions, the consumer-level activity monitors showed strong validity for the measurement of steps and sleep duration, and moderate valid for measurement of TDEE and MVPA. Validity for each construct ranged widely between devices, with the Fitbit One, Fitbit Zip and Withings Pulse being the strongest performers.

Multimorbidity is an emerging public health priority. Physical activity (PA) is recommended as one of the main lifestyle behaviours, yet the benefits of PA for people with multimorbidity are unclear. We assessed the benefits of PA on mortality and life expectancy in people with and without multimorbidity. Using the UK Biobank dataset, we extracted data on 36 chronic conditions and defined multimorbidity as (a) 2 or more conditions, (b) 2 or more conditions combined with self-reported overall health, and (c) 2 or more top-10 most common comorbidities. Leisure-time PA (LTPA) and total PA (TPA) were measured by questionnaire and categorised as low (< 600 metabolic equivalent (MET)-min/week), moderate (600 to < 3000 MET-min/week), and high (≥ 3000 MET-min/week), while objectively assessed PA was assessed by wrist-worn accelerometer and categorised as low (4 min/day), moderate (10 min/day), and high (22 min/day) walking at brisk pace. Survival models were applied to calculate adjusted hazard ratios (HRs) and predict life expectancy differences. 491,939 individuals (96,622 with 2 or more conditions) had a median follow-up of 7.0 (IQR 6.3–7.6) years. Compared to low LTPA, for participants with multimorbidity, HR for mortality was 0.75 (95% CI 0.70–0.80) and 0.65 (0.56–0.75) in moderate and high LTPA groups, respectively. This finding was consistent when using TPA measures. Using objective PA, HRs were 0.49 (0.29–0.80) and 0.29 (0.13–0.61) in the moderate and high PA groups, respectively. These findings were similar for participants without multimorbidity. In participants with multimorbidity, at the age of 45 years, moderate and high LTPA were associated with an average of 3.12 (95% CI 2.53, 3.71) and 3.55 (2.34, 4.77) additional life years, respectively, compared to low LTPA; in participants without multimorbidity, corresponding figures were 1.95 (1.59, 2.31) and 1.85 (1.19, 2.50). Similar results were found with TPA. For objective PA, moderate and high levels were associated with 3.60 (− 0.60, 7.79) and 5.32 (− 0.47, 11.11) life years gained compared to low PA for those with multimorbidity and 3.88 (1.79, 6.00) and 4.51 (2.15, 6.88) life years gained in those without. Results were consistent when using other definitions of multimorbidity. There was an inverse dose-response association between PA and mortality. A moderate exercise is associated with a longer life expectancy, also in individuals with multimorbidity.

This study aimed to describe the scope of accelerometry data collected internationally in adults and to obtain a consensus from measurement experts regarding the optimal strategies to harmonize international accelerometry data. In March 2014, a comprehensive review was undertaken to identify studies that collected accelerometry data in adults (sample size, n ≥ 400). In addition, 20 physical activity experts were invited to participate in a two-phase Delphi process to obtain consensus on the following: unique research opportunities available with such data, additional data required to address these opportunities, strategies for enabling comparisons between studies/countries, requirements for implementing/progressing such strategies, and value of a global repository of accelerometry data. The review identified accelerometry data from more than 275,000 adults from 76 studies across 36 countries. Consensus was achieved after two rounds of the Delphi process; 18 experts participated in one or both rounds. The key opportunities highlighted were the ability for cross-country/cross-population comparisons and the analytic options available with the larger heterogeneity and greater statistical power. Basic sociodemographic and anthropometric data were considered a prerequisite for this. Disclosure of monitor specifications and protocols for data collection and processing were deemed essential to enable comparison and data harmonization. There was strong consensus that standardization of data collection, processing, and analytical procedures was needed. To implement these strategies, communication and consensus among researchers, development of an online infrastructure, and methodological comparison work were required. There was consensus that a global accelerometry data repository would be beneficial and worthwhile. This foundational resource can lead to implementation of key priority areas and identification of future directions in physical activity epidemiology, population monitoring, and burden of disease estimates.

Hods One hundred and twenty-nine 9- to 10-yr-old children wore a wrist-mounted GENEActiv accelerometer (GAwrist) and a hip-mounted ActiGraph GT3X+ accelerometer (AGhip) for 7 d. Both devices measured raw accelerations, and the AGhip also provided count-based data. Results More children wore the GAwrist than those from the AGhip regardless of wear time criteria applied (P < 0.001–0.035). Raw data signal vector magnitude (r = 0.68), moderate PA (MPA) (r = 0.81), vigorous PA (VPA) (r = 0.85), and moderate-to-vigorous PA (MVPA) (r = 0.83) were strongly associated between devices (P < 0.001). GAwrist signal vector magnitude (P = 0.001), MPA (P = 0.037), VPA (P = 0.002), and MVPA (P = 0.016) were significantly greater than those from the AGhip. According to GAwrist raw data, 86.9% of children engaged in at least 60 min·d−1 of MVPA, compared with 19% for AGhip. ActiGraph MPA (raw) was 42.00 ± 1.61 min·d−1 compared with 35.05 ± 0.99 min·d−1 (counts) (P = 0.02). ActiGraph VPA was 7.59 ± 0.46 min·d−1 (raw) and 37.06 ± 1.85 min·d−1 (counts; P = 0.19). Conclusions In children, accelerometer wrist placement promotes superior compliance than the hip. Raw accelerations were significantly higher for GAwrist compared with those for AGhip possibly because of placement location and technical differences between devices. AGhip PA calculated from raw accelerations and counts differed substantially, demonstrating that PA outcomes derived from cut points for raw output and counts cannot be directly compared....

Activity measurement specialist, although the end user who is conducting studies with these devices also may benefit from knowing about this topic. Best Practices Initially, wearable physical activity monitors should undergo unit calibration to ensure interinstrument reliability. The next step is to simultaneously collect both raw signal data (e.g., acceleration) from the wearable monitors and rates of energy expenditure, so that algorithms can be developed to convert the direct signals into energy expenditure. This process should use multiple wearable monitors and a large and diverse subject group and should include a wide range of physical activities commonly performed in daily life (from sedentary to vigorous). Future Directions New methods of calibration now use “pattern recognition” approaches to train the algorithms on various activities, and they provide estimates of energy expenditure that are much better than those previously available with the single-regression approach. Once a method of predicting energy expenditure has been established, the next step is to examine its predictive accuracy by cross-validating it in other populations. In this article, we attempt to summarize the best practices for calibration and validation of wearable physical activity monitors. Finally, we conclude with some ideas for future research ideas that will move the field of physical activity measurement forward....