Chronic diseases, such as type II diabetes, are on the rise worldwide. There is consistent evidence that physical activity and healthy eating are important lifestyle factors which affect the risk for chronic diseases. Community-based interventions are of particular public health interest as they reach target groups in their natural living environment and may thus achieve high population-level impacts. We conducted a systematic literature search to assess the effectiveness of community-based interventions to promote physical activity and healthy eating. Specifically, we searched for promising intervention strategies in this setting. We narratively summarized the results of 18 systematic reviews. Among children and adolescents, we found moderate evidence for effects on weight change in primary school-aged children for interventions containing a school component. The evidence for interventions aimed at general adult populations was inconclusive. Self-monitoring, group-based components, and motivational signs to encourage stair use were identified as promising strategies to increase physical activity. Among adults at risk for type II diabetes, evidence was found for beneficial effects on weight change and diabetes incidence. However, interventions for this group were not integrated in more comprehensive community-based approaches.

BackgroundThe uptake, implementation, and maintenance of effective interventions promoting physical activity (PA) and a healthy diet and the implementation of policies targeting these behaviors are processes not well understood. We aimed to gain a better understanding of what health promotion professionals and policy makers think are important factors facilitating adoption, implementation, and maintenance of multi-level interventions and policies promoting healthy eating and PA in Belgium, Germany, Ireland, Norway, and Poland.MethodsSix interventions and six policies were identified based on pre-defined criteria. Forty semi-structured interviews were conducted with stakeholders from various sectors to elicit information on factors impacting adoption, implementation, and maintenance of these interventions and policies. All interview transcripts were coded in NVivo, using a common categorization matrix. Coding in the respective countries was done by one researcher and validated by a second researcher.ResultsActive involvement of relevant stakeholders and good communication between coordinating organizations were described as important factors contributing to successful adoption and implementation of both interventions and policies. Additional facilitating factors included sufficient training of staff and tailoring of materials to match needs of various target groups. The respondents indicated that maintenance of implemented interventions/policies depended on whether they were embedded in existing or newly created organizational structures in different settings and whether continued funding was secured.ConclusionsDespite considerable heterogeneity of interventions and health policies in the five countries, stakeholders across these countries identify similar factors facilitating adoption, implementation, and maintenance of these interventions and policies.Electronic supplementary materialThe online version of this article (10.1186/s12889-017-4929-9) contains supplementary material, which is available to authorized users.

Study purposes were to develop energy expenditure (EE) prediction models from raw accelerometer data and to investigate the performance of three different accelerometers on five different wear positions in preschoolers. MethodsFourty-one children (54% boys; 3-6.3 years) wore two Actigraph GT3X (left and right hip), three GENEActiv (right hip, left and right wrist), and one activPAL (right thigh) while completing a semi-structured protocol of 10 age-appropriate activities. Participants wore a portable indirect calorimeter to estimate EE. Utilized models to estimate EE included a linear model (LM), a mixed linear model (MLM), a random forest model (RF), and an artificial neural network model (ANN). For each accelerometer, model and wear position, we assessed prediction accuracy via leave-one-out cross-validation and calculated the root-mean-squarederror (RMSE). ResultsMean RMSE ranged from 2.56-2.76 kJ/min for the RF, from 2.72-3.08 kJ/min for the ANN, from 2.83-2.94 kJ/min for the LM, and from 2.81-2.92 kJ/min for the MLM. The GENEActive obtained mean RMSE of 2.56 kJ/min (left and right wrist) and 2.73 kJ/min (right hip). Predicting EE using the GT3X on the left and right hip obtained mean RMSE of 2.60 and 2.74 kJ/min.The activPAL obtained a mean RMSE of 2.76 kJ/min. ConclusionThese results demonstrate good prediction accuracy for recent accelerometers on different wear positions in preschoolers. The RF and ANN were equally accurate in EE prediction compared with (mixed) linear models. The RF seems to be a viable alternative to linear and ANN models for EE prediction in young children in a semi-structured setting.

BackgroundRegular moderate to vigorous physical activity is essential for maintaining health and preventing the onset of chronic diseases. Both global rates of smartphone ownership and the market for physical activity and fitness apps have grown rapidly in recent years. The use of physical activity and fitness apps may assist the general population in reaching evidence-based physical activity recommendations. However, it remains unclear whether there are evidence-informed physical activity apps and whether behavior change techniques (BCTs) previously identified as effective for physical activity promotion are used in these apps.ObjectiveThis study aimed to identify English and German evidence-informed physical activity apps and BCT employment in those apps.MethodsWe identified apps in a systematic search using 25 predefined search terms in the Google Play Store. Two reviewers independently screened the descriptions of apps and screenshots applying predefined inclusion and exclusion criteria. Apps were included if (1) their description contained information about physical activity promotion; (2) they were in English or German; (3) physical activity recommendations of the World Health Organization or the American College of Sports Medicine were mentioned; and (4) any kind of objective physical activity measurement was included. Two researchers downloaded and tested apps matching the inclusion criteria for 2 weeks and coded their content using the Behavioral Change Technique Taxonomy v1 (BCTTv1).ResultsThe initial screening in the Google Play Store yielded 6018 apps, 4108 of which were not focused on physical activity and were not in German or English. The descriptions of 1216 apps were further screened for eligibility. Duplicate apps and light versions (n=694) and those with no objective measurement of physical activity, requiring additional equipment, or not outlining any physical activity guideline in their description (n=1184) were excluded. Of the remaining 32 apps, 4 were no longer available at the time of the download. Hence, 28 apps were downloaded and tested; of these apps, 14 did not contain any physical activity guideline as an app feature, despite mentioning it in the description, 5 had technical problems, and 3 did not provide objective physical activity measurement. Thus, 6 were included in the final analyses. Of 93 individual BCTs of the BCTTv1, on average, 9 (SD 5) were identified in these apps. Of 16 hierarchical clusters, on average, 5 (SD 3) were addressed. Only BCTs of the 2 hierarchical clusters “goals and planning” and “feedback and monitoring” were identified in all apps.ConclusionsDespite the availability of several thousand physical activity and fitness apps for Android platforms, very few addressed evidence-based physical activity guidelines and provided objective physical activity measurement. Furthermore, available descriptions did not accurately reflect the app content and only a few evidence-informed physical activity apps incorporated several BCTs. Future apps should address evidence-ba...