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Abstract Background Connected health devices and applications (referred to hereafter as “SDApps” - Smart devices and applications) are being portrayed as a new way for prevention, with the promise of accessibility, effectiveness and personalization. Many effectiveness evaluations (experimental designs) with strong internal validity exist. While effectiveness does appear to vary, the mechanisms used by these devices have not yet been thoroughly investigated. This article seeks to unpack this black box, and describes the process of elaboration of an intervention theory for healthy eating and physical activity SDApps. It includes a set of requirements relative to their impact on social health inequalities. Methods To build this theory, we drew on theory-driven approaches and in particular on the theory of change (ToC) method. To this end, we developed a cumulative and iterative process combining scientific data from the literature with knowledge from experts (researchers and practitioners) and from patients or users. It was a 3-step process, as follows: 1 - identifying the evidence base; 2 - developing the theory through design intervention and creating realistic expectations, including in our case specific work on social health inequalities (SHIs); 3 - modeling process and outcome. Results We produced an evidence-based theory according to the ToC model, based on scientific evidence and knowledge from experts and users. It sets out a causal pathway leveraging 11 key mechanisms - theoretical domains - with which 50 behavior change techniques can be used towards 3 ultimate goals: Capacity, Opportunity, Motivation – Behavior (COM-B). Furthermore, the theory specifically integrates requirements relative to the impact on SHIs. Conclusions This theory is an aid to SDAapp design and evaluation and it can be used to consider the question of the possible impact of SDApps on the increase in inequalities. Firstly, it enables developers to adopt a more overarching and thorough approach to supporting behavior change, and secondly it encourages comprehensive and contributive evaluations of existing SDApps. Lastly, it allows health inequalities to be fully considered.

Abstract Background Many effective physical activity (PA) interventions have focused on individual factors or a single theoretical model, limiting our understanding of the determinants of PA practice and their interactions in the cancer trajectory. The present mixed-method study aims to capture social and psychological determinants of PA practice from diagnosis to remission among cancer patients, and to identify key levers for PA practice. Methods/design A nested sequential mixed-method design QUAN (QUAL+QUAL) will be used, with qualitative studies embedded in the quantitative study to broaden our understanding of the determinants of PA practice. The design is sequential, since qualitative data on medical staff will be collected before patient inclusion (Phase 1), followed by quantitative patient data collection lasting one year (Phase 2) and a final qualitative data collection one year after inclusion (Phase 3). Phase 1 will be a case study in the two hospitals involved in the study, exploring knowledge of and support for PA practice among medical staff. Through interviews and documental analyses, the PA support dynamic will be evaluated with regard to PA prescription. Phase 2 will be a one-year observational study among 693 cancer patients. Quantitative medical, social, dispositional and psychological data, PA practices and preferences, will be collected at diagnosis, and six months and one year thereafter. Phase 3 will be a retrospective study, evaluating societal and policy factors, as well as unexpected factors playing a role in PA levels and preferences among cancer patients. For this phase thirty patients will be identified six months after inclusion on the basis of their PA profiles. Quantitative data will provide the main dataset, whilst qualitative data will complete the picture, enabling determinants of PA practice and their interactions to be captured throughout the cancer trajectory. Discussion The present study aims to identify key levers and typical trajectories for PA practice among cancer patients, adapted to different times in the course of cancer and taking into account “what works”, “for whom”, “where” and “how”. The challenge is the tailoring of PA interventions to patients at different times in their cancer trajectory, and the implication of medical staff support. Trial registration Clinical Trial NCT03919149, 18 April 2019. Prospectively registered.

Abstract Background This follow-up study investigated the year-round effects of a four-week randomized controlled trial using different types of feedback on employees’ physical activity, including a need-supportive coach intervention. Methods Participants (n = 227) were randomly assigned to a Minimal Intervention Group (MIG; no feedback), a Pedometer Group (PG; feedback on daily steps only), a Display Group (DG; feedback on daily steps, on daily moderate-to-vigorous physical activity [MVPA] and on total energy expenditure [EE]), or a Coaching Group (CoachG; same as DG with need supportive coaching). Daily physical activity level (PAL; Metabolic Equivalent of Task [MET]), number of daily steps, daily minutes of moderate to vigorous physical activity (MVPA), active daily EE (EE > 3 METs) and total daily EE were measured at five time points: before the start of the 4-week intervention, one week after the intervention, and 3, 6, and 12 months after the intervention. Results For minutes of MVPA, MIG showed higher mean change scores compared with the DG. For steps and daily minutes of MVPA, significantly lower mean change scores emerged for MIG compared with the PG. Participants of the CoachG showed significantly higher change scores in PAL, steps, minutes of MVPA, active EE, total EE compared with the MIG. As hypothesized, participants of the CoachG had significantly higher mean change scores in PAL and total EE compared with groups that only received feedback. However, no significant differences were found for steps, minutes of MVPA and active EE between CoachG and PG. Conclusions Receiving additional need-supportive coaching resulted in a higher PAL and active EE compared with measurement (display) feedback only. These findings suggest to combine feedback on physical activity with personal coaching in order to facilitate long-term behavioral change. When it comes to increasing steps, minutes of MVPA or active EE, a pedometer constitutes a sufficient tool. Trial registration Clinical Trails.gov NCT01432327. Date registered: 12 September 2011.