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.

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

BackgroundPrevention and management of work-related stress and related mental problems is a great challenge. Mobile applications are a promising way to integrate prevention strategies into the everyday lives of citizens.ObjectiveThe objectives of this study was to study the usage, acceptance, and usefulness of a mobile mental wellness training application among working-age individuals, and to derive preliminary design implications for mobile apps for stress management.MethodsOiva, a mobile app based on acceptance and commitment therapy (ACT), was designed to support active learning of skills related to mental wellness through brief ACT-based exercises in the daily life. A one-month field study with 15 working-age participants was organized to study the usage, acceptance, and usefulness of Oiva. The usage of Oiva was studied based on the usage log files of the application. Changes in wellness were measured by three validated questionnaires on stress, satisfaction with life (SWLS), and psychological flexibility (AAQ-II) at the beginning and at end of the study and by user experience questionnaires after one week’s and one month’s use. In-depth user experience interviews were conducted after one month’s use to study the acceptance and user experiences of Oiva.ResultsOiva was used actively throughout the study. The average number of usage sessions was 16.8 (SD 2.4) and the total usage time per participant was 3 hours 12 minutes (SD 99 minutes). Significant pre-post improvements were obtained in stress ratings (mean 3.1 SD 0.2 vs mean 2.5 SD 0.1, P=.003) and satisfaction with life scores (mean 23.1 SD 1.3 vs mean 25.9 SD 0.8, P=.02), but not in psychological flexibility. Oiva was perceived easy to use, acceptable, and useful by the participants. A randomized controlled trial is ongoing to evaluate the effectiveness of Oiva on working-age individuals with stress problems.ConclusionsA feasibility study of Oiva mobile mental wellness training app showed good acceptability, usefulness, and engagement among the working-age participants, and provided increased understanding on the essential features of mobile apps for stress management. Five design implications were derived based on the qualitative findings: (1) provide exercises for everyday life, (2) find proper place and time for challenging content, (3) focus on self-improvement and learning instead of external rewards, (4) guide gently but do not restrict choice, and (5) provide an easy and flexible tool for self-reflection.

BackgroundInternal motivation and good psychological capabilities are important factors in successful eating-related behavior change. Thus, we investigated whether general acceptance and commitment therapy (ACT) affects reported eating behavior and diet quality and whether baseline perceived stress moderates the intervention effects.MethodsSecondary analysis of unblinded randomized controlled trial in three Finnish cities. Working-aged adults with psychological distress and overweight or obesity in three parallel groups: (1) ACT-based Face-to-face (n = 70; six group sessions led by a psychologist), (2) ACT-based Mobile (n = 78; one group session and mobile app), and (3) Control (n = 71; only the measurements). At baseline, the participants’ (n = 219, 85% females) mean body mass index was 31.3 kg/m2 (SD = 2.9), and mean age was 49.5 years (SD = 7.4). The measurements conducted before the 8-week intervention period (baseline), 10 weeks after the baseline (post-intervention), and 36 weeks after the baseline (follow-up) included clinical measurements, questionnaires of eating behavior (IES-1, TFEQ-R18, HTAS, ecSI 2.0, REBS), diet quality (IDQ), alcohol consumption (AUDIT-C), perceived stress (PSS), and 48-h dietary recall. Hierarchical linear modeling (Wald test) was used to analyze the differences in changes between groups.ResultsGroup x time interactions showed that the subcomponent of intuitive eating (IES-1), i.e., Eating for physical rather than emotional reasons, increased in both ACT-based groups (p = .019); the subcomponent of TFEQ-R18, i.e., Uncontrolled eating, decreased in the Face-to-face group (p = .020); the subcomponent of health and taste attitudes (HTAS), i.e., Using food as a reward, decreased in the Mobile group (p = .048); and both subcomponent of eating competence (ecSI 2.0), i.e., Food acceptance (p = .048), and two subcomponents of regulation of eating behavior (REBS), i.e., Integrated and Identified regulation (p = .003, p = .023, respectively), increased in the Face-to-face group. Baseline perceived stress did not moderate effects on these particular features of eating behavior from baseline to follow-up. No statistically significant effects were found for dietary measures.ConclusionsACT-based interventions, delivered in group sessions or by mobile app, showed beneficial effects on reported eating behavior. Beneficial effects on eating behavior were, however, not accompanied by parallel changes in diet, which suggests that ACT-based interventions should include nutritional counseling if changes in diet are targeted.Trial registrationClinicalTrials.gov (NCT01738256), registered 17 August, 2012.Electronic supplementary materialThe online version of this article (10.1186/s12966-018-0654-8) contains supplementary material, which is available to authorized users.

b s t r a c tStress-related eating may be a potential factor in the obesity epidemic. Rather little is known about how stress associates with eating behavior and food intake in overweight individuals in a free-living situation. Thus, the present study aims to investigate this question in psychologically distressed overweight and obese working-aged Finns. The study is a cross-sectional baseline analysis of a randomized controlled trial. Of the 339 study participants, those with all the needed data available (n ¼ 297, 84% females) were included. The mean age was 48.9 y (SD ¼ 7.6) and mean body mass index 31.3 kg/m 2 (SD ¼ 3.0). Perceived stress and eating behavior were assessed by self-reported questionnaires Perceived Stress Scale (PSS), Intuitive Eating Scale, the Three-Factor Eating Questionnaire, Health and Taste Attitude Scales and ecSatter Inventory. Diet and alcohol consumption were assessed by 48-h dietary recall, Index of Diet Quality, and AUDIT-C. Individuals reporting most perceived stress (i.e. in the highest PSS tertile) had less intuitive eating, more uncontrolled eating, and more emotional eating compared to those reporting less perceived stress (p < 0.05). Moreover, individuals in the highest PSS tertile reported less cognitive restraint and less eating competence than those in the lowest tertile (p < 0.05). Intake of whole grain products was the lowest among those in the highest PSS tertile (p < 0.05). Otherwise the quality of diet and alcohol consumption did not differ among the PSS tertiles.In conclusion, high perceived stress was associated with the features of eating behavior that could in turn contribute to difficulties in weight management. Stress-related way of eating could thus form a potential risk factor for obesity. More research is needed to develop efficient methods for clinicians to assist in handling stress-related eating in the treatment of obese people.