Introduction: Chronic pelvic pain disorders (CPPDs) affect up to one in four reproductive-age women and are characterized by fluctuating, heterogeneous symptoms that complicate engagement in regular physical exercise. Standard programs rarely adjust to daily symptom variability. WorkoutCPP is a pilot series of N-of-1 trials evaluating the feasibility of an adaptive mobile-health (mHealth) intervention using reinforcement learning (RL) to personalize exercise for individuals with CPPDs. Methods and Analysis: This pilot study consists of a series of N-of-1 trials employing a randomized ABAB or BABA design, in which 45 participants (aged 18-55 years) with CPPDs each serve as their own control. Each participant completes one baseline week followed by four alternating 2-week intervention phases (A = generic exercise guidance; B = adaptive RL-generated recommendations), totaling 9 weeks. The RL agent integrated into the ehive app suggests daily exercise recommendations based on participant-reported pain and prior exercise behavior. Primary outcomes are exercise recommendation adherence rate and participant retention rate; safety and additional feasibility indicators are also monitored. Secondary outcomes assess algorithm interpretability. Exploratory outcomes assess contextual sensitivity of the RL algorithm and changes in pain and behavioral engagement. Ethics and Dissemination: Ethical approval was granted by the Icahn School of Medicine IRB (Protocol # STUDY-23-00721). Electronic informed consent will be obtained. Findings will be shared via peer-reviewed publications, conferences, and de-identified data summaries. Registration: This study protocol was publicly registered on the Open Science Framework (OSF) Registries (URL: https://osf.io/d9f36) prior to participant enrollment, to support international open-science data transparency and reproducibility standards for mobile health adaptive intervention trials. The study was subsequently registered on ClinicalTrials.gov ( NCT07810218 ), consistent with ICMJE recommendations for trials informing future confirmatory research. All documented study procedures match the baseline human subjects tracking mechanisms approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (Protocol # STUDY-23-00721).
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.
Carmine Giardino, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 175 citations· ⚡19
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Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
It is found that roles of MVPs in startups were not fully aware by entrepreneurs, and entrepreneurs should consider a systematic approach to fully explore the value of MVP, as a multiple facet product (MFP).
Anh Nguyen-Duc, P. Abrahamsson· International Conference on...· 93 citations· ⚡9
It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.
Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al.· International Conference on...· 62 citations· ⚡6
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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