Most longitudinal, correlational studies on health-behaviour change examine effects of Time1 social-cognitive predictors on subsequent behaviour. In contrast, our research focusses on associations between changes in predictors with change in behaviour. The Health Action Process Approach (HAPA) distinguishes between motivational predictors for intention formation and volitional predictors for behavioural change and served as theoretical basis. Two online-studies were launched targeting different behaviours (low-fat diet, smoking), different samples (Study 1: N = 469; Study 2: N = 441) and different time spans (Study 1: 3 months, Study 2: 4 weeks). Data were analysed by means of structural equation modelling with latent difference scores. Both studies resulted in almost parallel prediction patterns. Change in risk awareness and change in outcome expectancies did not result in change in intentions, whereas change in self-efficacy was of crucial importance. Change in behaviour was associated with change in action planning and action control over and above the effects of intentions. In one study, increases in self-efficacy yielded increases in behaviour change. Results demonstrate that change in action planning and especially action control was of great importance for behaviour change across two different behaviours. Analysing change in social-cognitive predictors allows drawing precise conclusions for interventions.

Many teachers experience high levels of work-related strain due to time pressure, which over time can lead to various health problems, such as emotional exhaustion. However, there is growing evidence that this could be a reciprocal effect. Moreover, it is known that perceived social support can buffer the negative effects of stress, such as time pressure, on health outcomes. Less is known about buffering effects of received social support. Based on longitudinal data of n = 1071 Swiss primary and secondary school teachers over the course of one school year, the present study examined the reciprocal relationship between teachers’ perceived time pressure and emotional exhaustion and whether received social support from the school principal buffers this relationship. Results of a random intercept cross-lagged panel model show a strong relationship between teachers’ perceived time pressure and emotional exhaustion at the between-person level, but no effects at the within-person level. Further, received social support was directly related to less perceived time pressure and less emotional exhaustion. The results showed neither evidence for reciprocal effects between perceived time pressure and emotional exhaustion nor for a buffering effect of received social support from the school principal. Concluding, present findings indicate that the receipt of social support from the school principal is a central job resource that beneficially relates to teachers’ experience of time pressure and emotional exhaustion.

Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring. Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. Conclusions: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. Trial Registration: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d) International Registered Report Identifier (IRRID): DERR1-10.2196/11540