Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data.

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset.

Accelerometer-Derived "Weekend Warrior" Physical Activity and Incident Cardiovascular Disease.

Importance: Guidelines recommend 150 minutes or more of moderate to vigorous physical activity (MVPA) per week for overall health benefit, but the relative effects of concentrated vs more evenly distributed activity are unclear. Objective: To examine associations between an accelerometer-derived "weekend warrior" pattern (ie, most MVPA achieved over 1-2 days) vs MVPA spread more evenly with risk of incident cardiovascular events. Design, Setting, and Participants: Retrospective analysis of UK Biobank cohort study participants providing a full week of accelerometer-based physical activity data between June 8, 2013, and December 30, 2015. Exposures: Three MVPA patterns were compared: active weekend warrior (active WW, ≥150 minutes with ≥50% of total MVPA achieved in 1-2 days), active regular (≥150 minutes and not meeting active WW status), and inactive (<150 minutes). The same patterns were assessed using the sample median threshold of 230.4 minutes or more of MVPA per week. Main Outcomes and Measures: Associations between activity pattern and incident atrial fibrillation, myocardial infarction, heart failure, and stroke were assessed using Cox proportional hazards regression, adjusted for age, sex, racial and ethnic background, tobacco use, alcohol intake, Townsend Deprivation Index, employment status, self-reported health, and diet quality. Results: A total of 89 573 individuals (mean [SD] age, 62 [7.8] years; 56% women) who underwent accelerometry were included. When stratified at the threshold of 150 minutes or more of MVPA per week, a total of 37 872 were in the active WW group (42.2%), 21 473 were in the active regular group (24.0%), and 30 228 were in the inactive group (33.7%). In multivariable-adjusted models, both activity patterns were associated with similarly lower risks of incident atrial fibrillation (active WW: hazard ratio [HR], 0.78 [95% CI, 0.74-0.83]; active regular: 0.81 [95% CI, 0.74-0.88; inactive: HR, 1.00 [95% CI, 0.94-1.07]), myocardial infarction (active WW: 0.73 [95% CI, 0.67-0.80]; active regular: 0.65 [95% CI, 0.57-0.74]; and inactive: 1.00 [95% CI, 0.91-1.10]), heart failure (active WW: 0.62 [95% CI, 0.56-0.68]; active regular: 0.64 [95% CI, 0.56-0.73]; and inactive: 1.00 [95% CI, 0.92-1.09]), and stroke (active WW: 0.79 [95% CI, 0.71-0.88]; active regular: 0.83 [95% CI, 0.72-0.97]; and inactive: 1.00 [95% CI, 0.90-1.11]). Findings were consistent at the median threshold of 230.4 minutes or more of MVPA per week, although associations with stroke were no longer significant (active WW: 0.89 [95% CI, 0.79-1.02]; active regular: 0.87 [95% CI, 0.74-1.02]; and inactive: 1.00 [95% CI, 0.90-1.11]). Conclusions and Relevance: Physical activity concentrated within 1 to 2 days was associated with similarly lower risk of cardiovascular outcomes to more evenly distributed activity.

Comparison of Accelerometry-Based Measures of Physical Activity: Retrospective Observational Data Analysis Study.

BACKGROUND: Given the evolution of processing and analysis methods for accelerometry data over the past decade, it is important to understand how newer summary measures of physical activity compare with established measures. OBJECTIVE: We aimed to compare objective measures of physical activity to increase the generalizability and translation of findings of studies that use accelerometry-based data. METHODS: High-resolution accelerometry data from the Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data from 655 participants who used a wrist-worn ActiGraph GT9X device continuously for a week were summarized at the minute level as ActiGraph activity count, monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity. We calculated these measures using open-source packages in R. Pearson correlations between activity count and each measure were quantified both marginally and conditionally on age, sex, and BMI. Each measures pair was harmonized using nonparametric regression of minute-level data. RESULTS: Data were from a sample (N=655; male: n=298, 45.5%; female: n=357, 54.5%) with a mean age of 69.8 years (SD 14.2) and mean BMI of 27.3 kg/m2 (SD 5.0). The mean marginal participant-specific correlations between activity count and monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity were r=0.988 (SE 0.0002324), r=0.867 (SE 0.001841), r=0.913 (SE 0.00132), and r=0.970 (SE 0.0006868), respectively. After harmonization, mean absolute percentage errors of predicting total activity count from monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 2.5, 14.3, 11.3, and 6.3, respectively. The accuracies for predicting sedentary minutes for an activity count cut-off of 1853 using monitor-independent movement summary, Euclidean norm minus one, mean amplitude deviation, and activity intensity were 0.981, 0.928, 0.904, and 0.960, respectively. An R software package called SummarizedActigraphy, with a unified interface for computation of the measures from raw accelerometry data, was developed and published. CONCLUSIONS: The findings from this comparison of accelerometry-based measures of physical activity can be used by researchers and facilitate the extension of knowledge from existing literature by demonstrating the high correlation between activity count and monitor-independent movement summary (and other measures) and by providing harmonization mapping.

Accelerometer-Measured, Habitual Physical Activity and Circulating Brain-Derived Neurotrophic Factor: A Cross-Sectional Study.

BACKGROUND: One of the mechanisms suggested to link physical activity (PA) to favorable brain health is through stimulation of neural growth factors such as brain-derived neurotrophic factor (BDNF). Acute bouts of PA stimulate circulating BDNF levels. OBJECTIVE: In this investigation, we assessed whether habitual, accelerometer-measured PA levels were related to circulating BDNF levels in a middle-aged cohort. METHODS: In the Framingham Heart Study Third Generation cohort, 1,769 participants provided reliable accelerometry data and were not missing BDNF measurement or platelet counts. In a cross-sectional analysis, using multivariable regression, we related PA measures to serum BDNF levels, adjusting for age, sex, smoking status, platelet count, depression status, and accelerometer wear time. RESULTS: Our study participants (mean age 47±9 years, 50.8% women) spent an average of 22.3 mins/day in moderate-to-vigorous (MV)PA. Most PA variables (steps, MVPA, light activity, and sedentary time) were not related to BDNF levels (p > 0.05). We observed a non-linear trend, where 15-50 mins/week vigorous activity was associated with lower BDNF compared to those with 0 min vigorous activity (ß= -0.049±0.024, p = 0.05), but with no significant associations at lower or higher vigorous activity levels. In smokers, MVPA was also associated with lower BDNF levels (ß= -0.216±0.079, p = 0.01). CONCLUSION: Our study reveals that circulating BDNF is not chronically elevated in individuals with higher levels of habitual PA in middle-aged adults from the community and may even be chronically suppressed with higher PA in subgroups, including current smokers. These results do not contradict previous studies demonstrating that circulating BDNF rises acutely after PA.

Steps per Day and All-Cause Mortality in Middle-aged Adults in the Coronary Artery Risk Development in Young Adults Study.

Importance: Steps per day is a meaningful metric for physical activity promotion in clinical and population settings. To guide promotion strategies of step goals, it is important to understand the association of steps with clinical end points, including mortality. Objective: To estimate the association of steps per day with premature (age 41-65 years) all-cause mortality among Black and White men and women. Design, Setting, and Participants: This prospective cohort study was part of the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants were aged 38 to 50 years and wore an accelerometer from 2005 to 2006. Participants were followed for a mean (SD) of 10.8 (0.9) years. Data were analyzed in 2020 and 2021. Exposure: Daily steps volume, classified as low (<7000 steps/d), moderate (7000-9999 steps/d), and high (≥10 000 steps/d) and stepping intensity, classified as peak 30-minute stepping rate and time spent at 100 steps/min or more. Main Outcomes and Measures: All-cause mortality. Results: A total of 2110 participants from the CARDIA study were included, with a mean (SD) age of 45.2 (3.6) years, 1205 (57.1%) women, 888 (42.1%) Black participants, and a median (interquartile range [IQR]) of 9146 (7307-11 162) steps/d. During 22 845 person years of follow-up, 72 participants (3.4%) died. Using multivariable adjusted Cox proportional hazards models, compared with participants in the low step group, there was significantly lower risk of mortality in the moderate (hazard ratio [HR], 0.28 [95% CI, 0.15-0.54]; risk difference [RD], 53 [95% CI, 27-78] events per 1000 people) and high (HR, 0.45 [95% CI, 0.25-0.81]; RD, 41 [95% CI, 15-68] events per 1000 people) step groups. Compared with the low step group, moderate/high step rate was associated with reduced risk of mortality in Black participants (HR, 0.30 [95% CI, 0.14-0.63]) and in White participants (HR, 0.37 [95% CI, 0.17-0.81]). Similarly, compared with the low step group, moderate/high step rate was associated with reduce risk of mortality in women (HR, 0.28 [95% CI, 0.12-0.63]) and men (HR, 0.42 [95% CI, 0.20-0.88]). There was no significant association between peak 30-minute intensity (lowest vs highest tertile: HR, 0.98 [95% CI, 0.54-1.77]) or time at 100 steps/min or more (lowest vs highest tertile: HR, 1.38 [95% CI, 0.73-2.61]) with risk of mortality. Conclusions and Relevance: This cohort study found that among Black and White men and women in middle adulthood, participants who took approximately 7000 steps/d or more experienced lower mortality rates compared with participants taking fewer than 7000 steps/d. There was no association of step intensity with mortality.

Estimating changes in physical behavior during lockdowns using accelerometry-based simulations in a large UK cohort.

To contain the recent COVID-19 outbreak, restrictions have been imposed, which has limited outdoor activity. These physical behavior changes can have serious health implications, but there is little objective information quantifying these changes. This study aimed to estimate the change in physical behavior levels during full lockdown conditions using objective data collected from a thigh-worn activity monitor. Data used were from 6492 individuals in the 1970 British Cohort Study, collected between 2016 and 2018. Using walking bout characteristics, days were classified as either "indoor only" (n = 861), "indoor and exercise" (n = 167), and "outdoor active" (n = 31 934). When compared to "outdoor active" days, "indoor only" days had 6590 fewer steps per day (2320 vs 8876, p < 0.001), a longer sedentary time (1.5 h, p < 0.001), longer lying time (1.4 h, p < 0.001) and shorter standing (1.9 h, p < 0.001) and stepping (1.3 h, p < 0.001) times. The "indoor and exercise" days had a smaller number of steps compared to "outdoor active" (7932 vs 8876, p < 0.05). There is a strong relationship between reduced daily stepping, and increased sedentary time, with a range of poor health outcomes. This has important implications for public health policy and messaging during pandemics.

Exposure to prolonged sedentary behavior on weekdays rather than weekends in white-collar workers in comparison with blue-collar workers.

OBJECTIVES: Objectively measured sedentary behavior (SB) on weekdays and weekends has been mainly assessed in white-collar workers, while data in blue-collar workers are sparse. Therefore, this study presented the difference in accelerometer-measured SB levels between weekdays and weekends, stratified by white- and blue-collar occupations. METHODS: This study was a sub-analysis of accelerometer data from 73 workers (31 blue-collar and 42 white-collar) at a Japanese manufacturing plant. SB was defined as ≤1.5 metabolic equivalents estimated using an accelerometer, and compared between weekdays and weekends by using mixed models adjusted for confounders. The proportion of workers who sat for ≤8 h/day on weekdays and weekends were compared using McNemar's test. RESULTS: In white-collar workers, SB time on weekdays was significantly longer than that on weekends (598 vs 479 min/day, P < .001). In blue-collar workers, there was no significant difference in SB time between weekdays and weekends (462 vs 485 min/day, P = .43). The proportion of workers who achieved the recommended SB levels (≤8 h) was only 4.8% for white-collar workers on weekdays and 54.8% on weekends (P = .04), while that of blue-collar workers was 45.2% and 58.1% respectively (P > .99). CONCLUSIONS: White-collar workers were exposed to significantly longer SB time on weekdays than on weekends, which was not the case in blue-collar workers. It may be rather challenging for white-collar workers to limit their SB time to the level recommended by the latest guidelines for better health, especially on weekdays.

High-intensity activity is more strongly associated with metabolic health in children compared to sedentary time: a cross-sectional study of the I.Family cohort.

BACKGROUND: Physical activity (PA) during childhood is important for preventing future metabolic syndrome (MetS). To examine the relationship between PA and MetS in more detail, accurate measures of PA are needed. Previous studies have only utilized a small part of the information available from accelerometer measured PA. This study investigated the association between measured PA and MetS in children with a new method for data processing and analyses that enable more detailed interpretation of PA intensity level. METHODS: The association between PA pattern and risk factors related to MetS was investigated in a cross- sectional sample of children (n = 2592, mean age 10.9 years, 49.4% male) participating in the European multicenter I. Family study. The risk factors examined include body mass index, blood pressure, high-density lipoprotein cholesterol, insulin resistance and a combined risk factor score (MetS score). PA was measured by triaxial accelerometers and raw data was processed using the 10 Hz frequency extended method (FEM). The PA output was divided into an intensity spectrum and the association with MetS risk factors was analyzed by partial least squares regression. RESULTS: PA patterns differed between the European countries investigated, with Swedish children being most active and Italian children least active. Moderate intensity physical activity was associated with lower insulin resistance (R2 = 2.8%), while vigorous intensity physical activity was associated with lower body mass index (R2 = 3.6%), MetS score (R2 = 3.1%) and higher high-density lipoprotein cholesterol (R2 = 2.3%). PA of all intensities was associated with lower systolic- and diastolic blood pressure, although the associations were weaker than for the other risk factors (R2 = 1.5% and R2 = 1.4%). However, the multivariate analysis implies that the entire PA pattern must be considered. The main difference in PA was observed between normal weight and overweight children. CONCLUSIONS: The present study suggests a greater importance of more PA corresponding to an intensity of at least brisk walking with inclusion of high-intense exercise, rather than a limited time spent sedentary, in the association to metabolic health in children. The methods of data processing and statistical analysis enabled accurate analysis and interpretation of the health benefits of high intensity PA that have not been shown previously.

Relationship between internal and external load in elite female youth basketball players

Introduction: Monitoring load has been a key point in team sports during last years. This study aimed to determine the relationship between the external and internal training load during full basketball practices in elite female youth basketball players.Material and methodsThirteen elite female youth basketball players (age 16.3±1 years, height 181.7±5.8cm and body mass 71.2±9.6kg) had physical and physiological monitored over seven weeks. Players’ internal load was assessed using the session-rating of perceived exertion (sRPE), the Edward's summated heart rate zones model (SHRZ) and, the Banister's training impulse (TRIMPB). The external load was determined through: 1) total accelerations (TA); the sum of all accelerations and decelerations; 2) maximal accelerations and decelerations (TAMax); 3) total accelerations per minutes (TA·min−1); 4) accelerations per minute (Acc·min−1); and 5) decelerations per minute (Dec·min−1). Heart rate-based and accelerometry models were assessed via Polar Pro technology.ResultsCorrelations between variables revealed different magnitudes. SHRZ model showed a positive correlation with TA (0.63); the TRIMPB model showed a high degree of correlation with TA (0.78); the sRPE model also presented a high correlation with TA (0.62).ConclusionOur study establishes different levels of association between external and internal load models in elite female youth basketball players. However, we cannot assume that a high relationship between internal and external loads exists, as both models should be considered as different constructs. (AU)

Bidirectional associations of accelerometer-derived physical activity and stationary behavior with self-reported mental and physical health during midlife.

BACKGROUND: Moderate-to-vigorous intensity physical activity (MVPA) is associated with favorable self-rated mental and physical health. Conversely, poor self-rated health in these domains could precede unfavorable shifts in activity. We evaluated bidirectional associations of accelerometer-estimated time spent in stationary behavior (SB), light intensity physical activity (LPA), and MVPA with self-rated health over 10 years in in the CARDIA longitudinal cohort study. METHODS: Participants (n = 894, age: 45.1 ± 3.5; 63% female; 38% black) with valid accelerometry wear and self-rated health at baseline (2005-6) and 10-year follow-up (2015-6) were included. Accelerometry data were harmonized between exams and measured mean total activity and duration (min/day) in SB, LPA, and MVPA; duration (min/day) in long-bout and short-bout SB (≥30 min vs. < 30 min) and MVPA (≥10 min vs. < 10 min) were also quantified. The Short-Form 12 Questionnaire measured both a mental component score (MCS) and physical component score (PCS) of self-rated health (points). Multivariable linear regression associated baseline accelerometry variables with 10-year changes in MCS and PCS. Similar models associated baseline MCS and PCS with 10-year changes in accelerometry measures. RESULTS: Over 10-years, average (SD) MCS increased 1.05 (9.07) points, PCS decreased by 1.54 (7.30) points, and activity shifted toward greater SB and less mean total activity, LPA, and MVPA (all p < 0.001). Only baseline short-bout MVPA was associated with greater 10-year increases in MCS (+ 0.92 points, p = 0.021), while baseline mean total activity, MVPA, and long-bout MVPA were associated with greater 10-year changes in PCS (+ 0.53 to + 1.47 points, all p < 0.005). In the reverse direction, higher baseline MCS and PCS were associated with favorable 10-year changes in mean total activity (+ 9.75 cpm, p = 0.040, and + 15.66 cpm, p < 0.001, respectively) and other accelerometry measures; for example, higher baseline MCS was associated with - 13.57 min/day of long-bout SB (p < 0.001) and higher baseline PCS was associated with + 2.83 min/day of MVPA (p < 0.001) in fully adjusted models. CONCLUSIONS: The presence of bidirectional associations between SB and activity with self-rated health suggests that individuals with low overall activity levels and poor self-rated health are at high risk for further declines and supports intervention programming that aims to dually increase activity levels and improve self-rated health.

Lockdown, slow down: impact of the COVID-19 pandemic on physical activity-an observational study.

AIMS: In response to the COVID-19 pandemic, the UK was placed under strict lockdown measures on 23 March 2020. The aim of this study was to quantify the effects on physical activity (PA) levels using data from the prospective Triage-HF Plus Evaluation study. METHODS: This study represents a cohort of adult patients with implanted cardiac devices capable of measuring activity by embedded accelerometery via a remote monitoring platform. Activity data were available for the 4 weeks pre-implementation and post implementation of 'stay at home' lockdown measures in the form of 'minutes active per day' (min/day). RESULTS: Data were analysed for 311 patients (77.2% men, mean age 68.8, frailty 55.9%. 92.2% established heart failure (HF) diagnosis, of these 51.2% New York Heart Association II), with comorbidities representative of a real-world cohort.Post-lockdown, a significant reduction in median PA equating to 20.8 active min/day was seen. The reduction was uniform with a slightly more pronounced drop in PA for women, but no statistically significant difference with respect to age, body mass index, frailty or device type. Activity dropped in the immediate 2-week period post-lockdown, but steadily returned thereafter. Median activity week 4 weeks post-lockdown remained significantly lower than 4 weeks pre-lockdown (p≤0.001). CONCLUSIONS: In a population of predominantly HF patients with cardiac devices, activity reduced by approximately 20 min active per day in the immediate aftermath of strict COVID-19 lockdown measures. TRIAL REGISTRATION NUMBER: NCT04177199.

The relationship of self-reported and device-based measures of physical activity and health-related quality of life in adolescents.

BACKGROUND: Physical activity (PA) has beneficial effects on health and health-related quality of life (HRQoL), which is a protective factor of illness and mortality. The purpose of this examination was to investigate if self-reported and device-based measures of PA were related to HRQoL in adolescents. METHODS: Participants (N = 1565; 54.3% female; Mage = 14.37 years, SDage = 1.99) were recruited from 167 sample points across Germany. Adolescents self-reported their PA, supplemented by a 1-week examination of device-based PA using accelerometry. Additionally, they completed the multidimensional KIDSCREEN-27 to assess HRQoL. RESULTS: Results showed that self-reported PA was correlated with overall HRQoL, Physical Well-Being, Psychological Well-Being, Social Support & Peers, and School Environment, whereas device-based PA was only correlated with Physical as well as Psychological Well-Being. Further, self-reported PA significantly predicted all facets of HRQoL except for Autonomy and Parent Relations, whereas device-based PA solely heightened the amount of explained variance in the Physical Well-Being subscale. CONCLUSIONS: Findings demonstrate the importance of self-reported PA as it is related to almost all facets of HRQoL. Both measures of PA are not congruent in their relationship with HRQoL and thus implications have to be carefully considered. Future studies should investigate the direct effect of PA on HRQoL and health in a longitudinal approach to account for the causality of effects.

Reporting physical activity in minutes not bouts: findings from a survey in Australia.

OBJECTIVE: Physical activity has been commonly measured as time spent in bouts of at least 10 minutes, however, each minute of physical activity achieved in a day is now regarded as beneficial. This study aims to compare differences in total time spent walking in a week to time spent walking in 10-minute bouts over the same seven-day period. METHODS: Two self-reported measures of time spent walking were compared: one using time-constrained bouts and the other using total minutes. The first measure comes from the validated Active Australia Survey and reports walking in bouts of 10min or more, the second measure reports total estimated time spent walking. RESULTS: Data from 4,582 university participants in a census-styled survey were analysed. When comparing the reported minutes of walking in either 10-minute bouts or total minutes, more walking occurs in short bursts with a difference of 117min/week (p<0.001). CONCLUSIONS: This result shows more walking is reported across a week in short bursts of less than 10 minutes. Implications for public health: To monitor trends and evaluate physical activity interventions, reliable and valid measures of physical activity are necessary. Our result questions the usefulness of reporting bouted-walking as a measure which accurately captures population behaviour.

Relationships between serum omentin-1 concentration, body composition and physical activity levels in older women.

ABSTRACT: This study aimed to investigate the relationships between omentin-1, body composition and physical activity (PA) levels in older women.Eighty-one older women (age = 64 ±â€Š6years; body mass index = 24.2 ±â€Š3.2 kg/m2; body fat percentage = 36.1 ±â€Š5.7%) participated in this study. We divided the subjects into overweight/obesity and normal weight group. Body composition was measured by dual energy X-ray absorptiometry. Serum omentin-1 concentration was measured using enzyme linked immunosorbent assay. PA levels were obtained by using accelerometers. In addition, anthropometric and insulin resistance values were determined.Omentin-1 level in overweight/obesity group was significantly lower than in the normal weight group (P < .01). Analysis of all subjects showed that serum omentin-1 was negatively correlated with body weight, BMI (body mass index), waist circumference (WC), WHR (waist-to-hip ratio), percentage of body fat, total body fat mass (FM), fat-free mass (FFM) (r = -.571, -0.569, -0.546, -0.382, -0.394, -0.484, -0.524, all P < .01), respectively. We also found a negative correlation between moderate-to-vigorous physical activity (MVPA) and total body FM (r = -.233, P < .05). However, no significant correlation was found between omentin-1 and sedentary behavior and MVPA (both P > .05). Moreover, the relationship between omentin-1, body composition and PA was analyzed by using multiple linear stepwise regressions. The results showed that serum omentin-1 concentration was inversely correlated with total body FM (ß = -0.334, P = .004) in multiple linear stepwise regression analysis.We found that total body FM was inversely related to serum omentin-1 concentration and PA levels, but there was no correlation between omentin-1 and PA levels. These results showed that PA may participate in the regulation of body composition, which may be also affected by serum omentin-1. However, the mechanism by which PA affects body composition may not be through omentin-1 and was more likely through other metabolic pathways.

Validity of Wrist-Wearable Activity Devices for Estimating Physical Activity in Adolescents: Comparative Study.

BACKGROUND: The rapid advancements in science and technology of wrist-wearable activity devices offer considerable potential for clinical applications. Self-monitoring of physical activity (PA) with activity devices is helpful to improve the PA levels of adolescents. However, knowing the accuracy of activity devices in adolescents is necessary to identify current levels of PA and assess the effectiveness of intervention programs designed to increase PA. OBJECTIVE: The study aimed to determine the validity of the 11 commercially available wrist-wearable activity devices for monitoring total steps and total 24-hour total energy expenditure (TEE) in healthy adolescents under simulated free-living conditions. METHODS: Nineteen (10 male and 9 female) participants aged 14 to 18 years performed a 24-hour activity cycle in a metabolic chamber. Each participant simultaneously wore 11 commercial wrist-wearable activity devices (Mi Band 2 [XiaoMi], B2 [Huawei], Bong 2s [Meizu], Amazfit [Huamei], Flex [Fitbit], UP3 [Jawbone], Shine 2 [Misfit], GOLiFE Care-X [GoYourLife], Pulse O2 [Withings], Vivofit [Garmin], and Loop [Polar Electro]) and one research-based triaxial accelerometer (GT3X+ [ActiGraph]). Criterion measures were total EE from the metabolic chamber (mcTEE) and total steps from the GT3X+ (AGsteps). RESULTS: Pearson correlation coefficients r for 24-hour TEE ranged from .78 (Shine 2, Amazfit) to .96 (Loop) and for steps ranged from 0.20 (GOLiFE) to 0.57 (Vivofit). Mean absolute percent error (MAPE) for TEE ranged from 5.7% (Mi Band 2) to 26.4% (Amazfit) and for steps ranged from 14.2% (Bong 2s) to 27.6% (Loop). TEE estimates from the Mi Band 2, UP3, Vivofit, and Bong 2s were equivalent to mcTEE. Total steps from the Bong 2s were equivalent to AGsteps. CONCLUSIONS: Overall, the Bong 2s had the best accuracy for estimating TEE and total steps under simulated free-living conditions. Further research is needed to examine the validity of these devices in different types of physical activities under real-world conditions.

Sleep classification from wrist-worn accelerometer data using random forests.

Accurate and low-cost sleep measurement tools are needed in both clinical and epidemiological research. To this end, wearable accelerometers are widely used as they are both low in price and provide reasonably accurate estimates of movement. Techniques to classify sleep from the high-resolution accelerometer data primarily rely on heuristic algorithms. In this paper, we explore the potential of detecting sleep using Random forests. Models were trained using data from three different studies where 134 adult participants (70 with sleep disorder and 64 good healthy sleepers) wore an accelerometer on their wrist during a one-night polysomnography recording in the clinic. The Random forests were able to distinguish sleep-wake states with an F1 score of 73.93% on a previously unseen test set of 24 participants. Detecting when the accelerometer is not worn was also successful using machine learning ([Formula: see text]), and when combined with our sleep detection models on day-time data provide a sleep estimate that is correlated with self-reported habitual nap behaviour ([Formula: see text]). These Random forest models have been made open-source to aid further research. In line with literature, sleep stage classification turned out to be difficult using only accelerometer data.

A comparison of the utility of different step-indices to translate the physical activity recommendation in adolescents.

Previous studies translating the daily moderate-to-vigorous physical activity (MVPA) recommendation of total steps/day among adolescents are inconsistent, and those with cadence-based steps are scarce. The main purpose was to compare the accuracy of different daily steps index-based cut-points related to the daily 60 minutes of MVPA recommendation measured by a waist-worn accelerometer for adolescents. Following a cross-sectional design, 428 Spanish adolescents (final sample 351, 50.4% males), aged 13-16 years old, wore an ActiGraph GT3X/+ accelerometer (reference standard = MVPA; index tests = total steps/day, average steps/min and peak 1-min cadence) on the right hip for eight consecutive days. 32.5% of the adolescents met the daily MVPA recommendation. The multiple ROC curve comparisons showed that the accuracy of the daily total step-based recommendation (AUC = 0.97) was statistically higher than for those with the steps/min (AUC = 0.90) and peak 1-min cadence (AUC = 0.58) (p < 0.001). The 10,000-step-per-day cut-point (k= 0.59-0.83) showed highest accuracy values than the 12,000 steps/day (k= 0.20-0.32). Daily total step-based recommendations are more accurate than those with steps/min and peak 1-min cadence for classifying adolescents as being physically active or inactive. A 10,000-step-per-day target is simple and accurate for both male and female adolescents.

Quantifying the Predictive Performance of Objectively Measured Physical Activity on Mortality in the UK Biobank.

BACKGROUND: Objective measures of physical activity (PA) derived from wrist-worn accelerometers are compared with traditional risk factors in terms of mortality prediction performance in the UK Biobank. METHOD: A subset of participants in the UK Biobank study wore a tri-axial wrist-worn accelerometer in a free-living environment for up to 7 days. A total of 82 304 individuals over the age of 50 (439 707 person-years of follow-up, 1959 deaths) had both accelerometry data that met specified quality criteria and complete data on a set of traditional mortality risk factors. Predictive performance was assessed using cross-validated Concordance (C) for Cox regression models. Forward selection was used to obtain a set of best predictors of mortality. RESULTS: In univariate Cox regression, age was the best predictor of all-cause mortality (C = 0.681) followed by 12 PA predictors, led by minutes of moderate-to-vigorous PA (C = 0.661) and total acceleration (C = 0.661). Overall, 16 of the top 20 predictors were objective PA measures (C = 0.578-0.661). Using a threshold of 0.001 improvement in Concordance, the Concordance for the best model that did not include PA measures was 0.735 (9 covariates) compared with 0.748 (12 covariates) for the best model with PA variables (p-value < .001). CONCLUSIONS: Objective measures of PA derived from accelerometry outperform traditional predictors of all-cause mortality in the UK Biobank except age and substantially improve the prediction performance of mortality models based on traditional risk factors. Results confirm and complement previous findings in the National Health and Nutrition Examination Survey (NHANES).

Feasibility Study Comparing Physical Activity Classifications from Accelerometers with Wearable Camera Data.

Device-based assessments are frequently used to measure physical activity (PA) but contextual measures are often lacking. There is a need for new methods, and one under-explored option is the use of wearable cameras. This study tested the use of wearable cameras in PA measurement by comparing intensity classifications from accelerometers with wearable camera data. Seventy-eight 18-30-year-olds wore an Actigraph GT9X link accelerometer and Autographer wearable camera for three consecutive days. An image coding schedule was designed to assess activity categories and activity sub-categories defined by the 2011 Compendium of Physical Activities (Compendium). Accelerometer hourly detailed files processed using the Montoye (2020) cut-points were linked to camera data using date and time stamps. Agreement was examined using equivalence testing, intraclass correlation coefficient (ICC) and Spearman's correlation coefficient (rho). Fifty-three participants contributing 636 person-hours were included. Reliability was moderate to good for sedentary behavior (rho = 0.77), light intensity activities (rho = 0.59) and moderate-to-vigorous physical activity (MVPA) (rho = 0.51). The estimates of sedentary behavior, light activity and MVPA from the two methods were similar, but not equivalent. Wearable cameras are a potential complementary tool for PA measurement, but practical challenges and limitations exist. While wearable cameras may not be feasible for use in large scale studies, they may be feasible in small scale studies where context is important.