221Association of physical activity bout length and changing intensity with mortality in UK Biobank

Background Spending more time active (and less time sedentary) is associated with many health benefits but it is unclear whether these associations differ depending on whether time spent sedentary or in moderate-vigorous physical activity (MVPA) is accumulated in long or short bouts. We used a novel analytical approach to examine whether length of sedentary and MVPA bouts associates with all-cause mortality. Methods We used data on 79,507 participants from UK Biobank. We derived the total time participants spent in activity categories (sleep, sedentary, light activity and MVPA) and in sedentary and MVPA bouts of short (1-15 minutes), medium (16-40 minutes) and long (41+ minutes) duration, on average per day. We used Cox proportion hazards regression to estimate the association of spending 10 minutes more average daily time in one activity or bout length category, coupled with spending 10 minutes less time in another, with all-cause mortality. Results Those spending more time in MVPA had lower mortality risk, irrespective of whether this replaced time spent sleeping, sedentary or in light activity. We found little evidence to suggest that mortality risk differed depending on the length of sedentary or MVPA bouts. Conclusions We uniquely show that higher total MVPA improves health irrespective of whether it is obtained from several short bouts or fewer longer bouts, supporting recent policy changes in some countries. Key messages Our results suggest that time spent in MVPA associates with lower mortality risk irrespective of whether it is obtained from several short bouts or fewer longer bouts.

The Experimental Effect of Parent Versus Peer Influence on Children’s Physical Activity and Sedentary Behavior

Purpose: To assess children’s physical activity, sedentary behavior, liking, and motivation during 3 separate simulated recess conditions: playing alone, with their parent participating, and with their peer participating. Methods: Children participated in the 3 separate conditions. During each condition, the children had access to an outdoor playground and sedentary activity options for 30 minutes. Accelerometry recorded the physical activity. Time allocated to sedentary options was monitored via a stopwatch. A visual analog scale was used to assess liking, and motivation was assessed as the children’s willingness to participate in an additional 10 minutes of each condition. Results: The children sat 88% less and were 33% more physically active with their peer versus playing alone. The children also sat 65% less during the parent condition than alone. Lastly, the children reported ≥34% liking and were ≥2-fold more likely to participate in the additional 10-minute activity bout during the parent and peer conditions than alone. The differences were significant (P ≤ .05) except for the children’s decision to participate in the additional 10 minutes in the parent versus the alone condition (P = .058). Conclusions: Relative to the alone condition, the presence of a peer or parent reduced sedentary behavior and increased liking and the motivation to participate in that condition. However, only the presence of a peer increased physical activity versus alone.

A plausible mechanism for Drosophila larva intermittent behavior

The behavior of many living organisms is not continuous. Rather, activity emerges in bouts that are separated by epochs of rest, a phenomenon known as intermittent behavior. Although intermittency is ubiquitous across phyla, empirical studies are scarce and the underlying neural mechanisms remain unknown. Here we present the first empirical evidence of intermittency during Drosophila larva free exploration. We report power-law distributed rest-bout and log-normal distributed activity-bout durations. We show that a stochastic network model can transition between power-law and non-power-law distributed states and we suggest a plausible neural mechanism for the alternating rest and activity in the larva. Finally, we discuss possible implementations in behavioral simulations extending spatial Levy-walk or coupled-oscillator models with temporal intermittency.

Waveform Plasticity under Entrainment to 12-h T-cycles in Drosophila melanogaster: Behavior, Neuronal Network, and Evolution

A crucial property of circadian clocks is the ability to regulate the shape of an oscillation over its cycle length (waveform) appropriately, thus enhancing Darwinian fitness. Many studies over the past decade have revealed interesting ways in which the waveform of rodent behavior could be manipulated, one of which is that the activity bout bifurcates under environments that have 2 light/dark cycles within one 24-h day (LDLD). It has been observed that such unique, although unnatural, environments reveal acute changes in the circadian clock network. However, although adaptation of waveforms to different photoperiods is well studied, modulation of waveforms under LDLD has received relatively less attention in research on insect rhythms. Therefore, we undertook this study to ask the following questions: what is the extent of waveform plasticity that Drosophila melanogaster exhibits, and what are the neuronal underpinnings of such plasticity under LDLD? We found that the activity/rest rhythms of wild-type flies do not bifurcate under LDLD. Instead, they show similar but significantly different behavior from that under a long-day LD cycle. This behavior is accompanied by differences in the organization of the circadian neuronal network, which include changes in waveforms of a core clock component and an output molecule. In addition, to understand the functional significance of such variations in the waveform, we examined laboratory selected populations that exhibit divergent eclosion chronotypes (and therefore, waveforms). We found that populations selected for predominant eclosion in an evening window ( late chronotypes) showed reduced amplitude plasticity and increased phase plasticity of activity/rest rhythms. This, we argue, is reflective of divergent evolution of circadian neuronal network organization in our laboratory selected flies.

Ultradian regulation of rest and activity bouts in mice

The suprachiasmatic nucleus (SCN), which serves as the central pacemaker in mammals, regulates the 24-hour rhythm in behavioral activity. However, it is currently unclear whether and how bouts of activity and rest are regulated within the 24-hour cycle (i.e., over ultradian time scales). Therefore, we used passive infrared sensors to measure behavior in mice housed under either a light-dark (LD) cycle or continuous darkness (DD). We found that a probabilistic Markov model captures the ultradian changes in the behavioral state over a 24-hour cycle. In this model, the animal’s behavioral state in the next time interval is determined solely by the animal’s current behavioral state and by the “toss” of a proverbial “biased coin”. We found that the bias of this “coin” is regulated by light input and by the phase of the clock. Moreover, the bias of this “coin” for an animal is related to the average length of rest and activity bouts in that animal. In LD conditions, the average length of rest bouts was greater during the day compared to during the night, whereas the average length of activity bouts was greater during the night compared to during the day. Importantly, we also found that day-night changes in the rest bout lengths were significantly greater than day-night changes in the activity bout lengths. Finally, in DD conditions, the activity and rest bouts also differed between subjective night and subjective day, albeit to a lesser extent compared to LD conditions. The persistent differences in bout length over the circadian cycle following loss of the external LD cycle indicate that the central pacemaker plays a role in regulating rest and activity bouts on an ultradian time scale.

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data (Preprint)

BACKGROUND Time-resolved quantification of physical activity can contribute to both personalized medicine and epidemiological research studies, for example, managing and identifying triggers of asthma exacerbations. A growing number of reportedly accurate machine learning algorithms for human activity recognition (HAR) have been developed using data from wearable devices (eg, smartwatch and smartphone). However, many HAR algorithms depend on fixed-size sampling windows that may poorly adapt to real-world conditions in which activity bouts are of unequal duration. A small sliding window can produce noisy predictions under stable conditions, whereas a large sliding window may miss brief bursts of intense activity. OBJECTIVE We aimed to create an HAR framework adapted to variable duration activity bouts by (1) detecting the change points of activity bouts in a multivariate time series and (2) predicting activity for each homogeneous window defined by these change points. METHODS We applied standard fixed-width sliding windows (4-6 different sizes) or greedy Gaussian segmentation (GGS) to identify break points in filtered triaxial accelerometer and gyroscope data. After standard feature engineering, we applied an Xgboost model to predict physical activity within each window and then converted windowed predictions to instantaneous predictions to facilitate comparison across segmentation methods. We applied these methods in 2 datasets: the human activity recognition using smartphones (HARuS) dataset where a total of 30 adults performed activities of approximately equal duration (approximately 20 seconds each) while wearing a waist-worn smartphone, and the Biomedical REAl-Time Health Evaluation for Pediatric Asthma (BREATHE) dataset where a total of 14 children performed 6 activities for approximately 10 min each while wearing a smartwatch. To mimic a real-world scenario, we generated artificial unequal activity bout durations in the BREATHE data by randomly subdividing each activity bout into 10 segments and randomly concatenating the 60 activity bouts. Each dataset was divided into ~90% training and ~10% holdout testing. RESULTS In the HARuS data, GGS produced the least noisy predictions of 6 physical activities and had the second highest accuracy rate of 91.06% (the highest accuracy rate was 91.79% for the sliding window of size 0.8 second). In the BREATHE data, GGS again produced the least noisy predictions and had the highest accuracy rate of 79.4% of predictions for 6 physical activities. CONCLUSIONS In a scenario with variable duration activity bouts, GGS multivariate segmentation produced smart-sized windows with more stable predictions and a higher accuracy rate than traditional fixed-size sliding window approaches. Overall, accuracy was good in both datasets but, as expected, it was slightly lower in the more real-world study using wrist-worn smartwatches in children (BREATHE) than in the more tightly controlled study using waist-worn smartphones in adults (HARuS). We implemented GGS in an offline setting, but it could be adapted for real-time prediction with streaming data.

Validity of Multisensor Array for Measuring Energy Expenditure of an Activity Bout in Early Stroke Survivors

Introduction. Stroke survivors use more energy than healthy people during activities such as walking, which has consequences for the way exercise is prescribed for stroke survivors. There is a need for wearable device that can validly measure energy expenditure (EE) of activity to inform exercise prescription early after stroke. We aimed to determine the validity and reliability of the SenseWear-Armband (SWA) to measure EE and step-counts during activity <1 month after stroke. Materials and Methods. EE was measured using the SWA and metabolic cart and steps-counts were measured using the SWA and direct observation. Based on walking ability, participants performed 2x six-minute walks or repeated sit-to-stands. Concurrent validity and test-retest reliability were determined by calculating intraclass and concordance correlation coefficients. Results and Discussion. Thirteen participants walked; nine performed sit-to-stands. Validity of the SWA measuring EE for both activities was poor (ICC/CCC < 0.40). The SWA overestimates EE during walking and underestimated EE during sit-to-stands. Test-retest agreement showed an ICC/CCC of <0.40 and >0.75 for walking and sit-to-stand, respectively. However, agreement levels changed with increasing EE levels (i.e., proportional bias). The SWA did not accurately measure step-counts. Conclusion. The SWA should be used with caution to measure EE of activity of mild to moderate stroke survivors <1 month after stroke.