Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies.

Digital footprints, the automatically accumulated by-products of our technology-saturated lives, offer an exciting opportunity for psychiatric research. The commercial sector has already embraced the electronic trails of customers as an enabling tool for guiding consumer behaviour, and analogous efforts are ongoing to monitor and improve the mental health of psychiatric patients. The untargeted collection of digital footprints that may or may not be health orientated comprises a large untapped information resource for epidemiological scale research into psychiatric disorders. Real-time monitoring of mood, sleep and physical and social activity in a substantial portion of the affected population in a naturalistic setting is unprecedented in psychiatry. We propose that digital footprints can provide these measurements from real world setting unobtrusively and in a longitudinal fashion. In this perspective article, we outline the concept of digital footprints and the services and devices that create them, and present examples where digital footprints have been successfully used in research. We then critically discuss the opportunities and fundamental challenges associated digital footprints in psychiatric research, such as collecting data from different sources, analysis, ethical and research design challenges.

Physical activity, heart rate variability-based stress and recovery, and subjective stress during a 9-month study period.

The aim of this study was to investigate the association between physical activity (PA) and objective heart rate variability (HRV)-based stress and recovery with subjective stress in a longitudinal setting. Working-age participants (n = 221; 185 women, 36 men) were overweight (body mass index, 25.3-40.1 kg/m2 ) and psychologically distressed (≥3/12 points on the General Health Questionnaire). Objective stress and recovery were based on HRV recordings over 1-3 work days. Subjective stress was assessed with the Perceived Stress Scale and PA level with a questionnaire. Data were collected at three time points: baseline, 10 weeks post intervention, and at the 36-week follow-up. We adopted a latent growth model to investigate the initial level and change in PA, objective stress and recovery, and subjective stress at the three measurement time points. The results showed that initial levels of PA (P < 0.001) and objective stress (P = 0.001) and recovery (P < 0.01) were associated with the change in subjective stress. The results persisted after adjustment for intervention group. The present results suggest that high PA and objectively assessed low stress and good recovery have positive effects on changes in subjective stress in the long-term.

Perceptual and Biochemical Responses in Relation to Different Match-Day +2 Training Interventions in Soccer Players.

The aim of this study was to examine the impact of two different post-match training interventions on the subsequent recovery of perceptual and biochemical parameters after the game. In a crossover design, eight sub-elite players underwent a soccer-specific training (SST) and an active recovery (AR) regimen on the second day after a match (+48 h). Muscle soreness as well as muscle damage (creatine kinase, CK), inflammatory (C-reactive protein and interleukin 6), immunological (e.g., lymphocytes, neutrophils, and monocytes), and endocrine (cortisol) markers were obtained at baseline (-72 h), immediately after (0 h), and 72 h post-match (+72 h). AR promoted a higher restoration of muscle soreness values (P = 0.004, η2 p = 0.49) together with a better restoration of CK within 72 h post-match compared with SST (P = 0.04, η2 p = 0.36). Conversely, no significant (P > 0.05, η2 p < 0.91) differences were observed in the recovery timeframe of inflammatory, immunological, and endocrine responses between SST and AR. Overall, AR elicited a quicker muscle soreness and CK restoration compared to SST intervention at 72 h post-match. Such information provides novel evidence-based findings on the appropriateness of different recovery strategies and may aid to improve the practitioners' decision-making process when two consecutive games are played within 3 days.

Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions.

Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.

Estimating intensity of physical activity: a comparison of wearable accelerometer and gyro sensors and 3 sensor locations.

Automatic estimation of physical activity using wearable sensors can be used for promotion of a healthier lifestyle. In this study, accelerometers and gyroscopes attached to ankle, wrist and hip were used to estimate intensity of physical activity. The estimates are compared to metabolic equivalent (MET) obtained from a portable cardiopulmonary exercise testing system. Data from common everyday tasks and exercise were collected with 11 subjects. The tasks include, e.g., ironing, vacuuming, walking, running and cycling on exercise bicycle (ergometer). The strongest linear correlation with metabolic equivalent was obtained with the tri-axial accelerometer attached to the ankle (r=0.86).