'Snacktivity™' to increase physical activity: Time to try something different?

Evidence demonstrates that participation in regular physical activity (PA) reduces the risk of morbidity and mortality. However, current PA guidelines are focused on weekly accumulation of 150 min of moderate intensity PA as a threshold. Although recent developments of this guidance have discussed the merits of short bouts of physical activity, guidance that sets large behavioural goals for PA has not been successful in supporting the public to become sufficiently physically active and a 'one-size fits all' approach to PA guidelines may not be optimal. A complementary 'whole day' approach to PA promotion (i.e. incorporating PA throughout the day) that could motivate the population to be more physically active, is a concept we have called 'Snacktivity™'. The Snacktivity™ approach promotes small or 'bite' size bouts (e.g. 2-5 min) of PA accumulated throughout the whole day. Snacktivity™ is consistent with the small change approach which suggest that behaviour change and habit formation are best achieved through gradual building of task self-efficacy, celebrating small successes. Snacktivity™ also offers opportunities to "piggyback" on to existing behaviours/habits, using them as prompts for Snacktivity™. Moreover, small behaviour changes are easier to initiate and maintain than larger ones. A plethora of evidence supports the hypothesis that Snacktivity may be a more acceptable and effective way to help the public reach, or exceed current PA guidelines. This paper outlines the evidence to support the Snacktivity™ approach and the mechanisms by which it may increase population levels of physical activity. Future research directions for Snacktivity™ are also outlined.

Measuring Caenorhabditis elegans Sleep During the Transition to Adulthood Using a Microfluidics-based System.

C. elegans sleep during development is regulated by genes and cellular mechanisms that are conserved across the animal kingdom (Singh et al., 2014; Trojanowski & Raizen, 2016). C. elegans developmental sleep is usually assessed during the transition to adulthood, a 2.6 h time interval called lethargus (Raizen et al., 2008; Singh et al., 2011). During lethargus, animals cycle between periods of immobility (sleep bouts) and periods of active locomotion (motion bouts). Sleep bouts resemble sleep in other species based on behavioral criteria, including cessation of feeding and locomotion, increased arousal threshold for response to sensory stimulation, rapid reversibility, and homeostatic response to sleep loss. Several assays have been developed to study sleep in C. elegans (Belfer et al., 2013; Bringmann, 2011; Nelson et al., 2013; Raizen et al., 2008). Here, we contribute a detailed protocol for assessment of C. elegans sleep during lethargus, which has been used successfully by many research groups, incorporating simple microfluidic chambers, a low cost camera with lighting system, and computational analysis based on image subtraction. We note that this system could be easily adapted to assess sleep in any small animal.

Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements.

The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman's rho 0.43-0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.

SegWay: A simple framework for unsupervised sleep segmentation in experimental EEG recordings.

Sleep analysis in animal models typically involves recording an electroencephalogram (EEG) and electromyogram (EMG) and scoring vigilance state in brief epochs of data as Wake, REM (rapid eye movement sleep) or NREM (non-REM) either manually or using a computer algorithm. Computerized methods usually estimate features from each epoch like the spectral power associated with distinctive cortical rhythms and dissect the feature space into regions associated with different states by applying thresholds, or by using supervised/unsupervised statistical classifiers; but there are some factors to consider when using them:•Most classifiers require scored sample data, elaborate heuristics or computational steps not easily reproduced by the average sleep researcher, who is the targeted end user.•Even when prediction is reasonably accurate, small errors can lead to large discrepancies in estimates of important sleep metrics such as the number of bouts or their duration.•As we show here, besides partitioning the feature space by vigilance state, modeling transitions between the states can give more accurate scores and metrics. An unsupervised sleep segmentation framework, "SegWay", is demonstrated by applying the algorithm step-by-step to unlabeled EEG recordings in mice. The accuracy of sleep scoring and estimation of sleep metrics is validated against manual scores.

Effect of number of touches and exercise duration on the kinematic profile and heart rate response during small-sided games in soccer.

This study aimed to examine the effect of exercise duration and the number of touches allowed during possession on time-motion characteristics and the physiological responses of soccer players in 6 vs. 6 small-sided games (SSGs) lasting 12 minutes. The analysis divided each game into two 6-min periods and we compared two formats: free play (SSGFP) vs. a maximum of two touches per individual possession (SSG2T). Participants were 12 semi-professional players (age: 22.7±4.3 years; body height: 177.5±4.9 cm; body mass: 74.9±6.3 kg) and the following variables were measured by means of heart rate monitors and GPS devices: mean heart rate (HRmean), time spent in each exercise intensity zone, total distance covered, total distance covered in different speed zones, number of accelerations at different intensities, maximum speed reached, player load, and the work-to-rest ratio. The results showed that in SSGFP there was a decrease in the intensity of physical parameters during the second 6-min period (6-12 min), whereas this decrease was not observed when a maximum of two touches per individual possession was allowed. During the second period (6-12 min) of SSG2T there was an increase in HRmean and in the time spent in high exercise intensity zones, but these differences were not observed in SSGFP. The value of these findings for soccer coaches is that they illustrate how different technical, tactical or conditioning objectives could be addressed by altering the length and format of the SSG used in training.