Feasibility of the aktivplan Digital Health Intervention for Regular Physical Activity Following Phase II Rehabilitation: Protocol for a Mixed Method Randomized Controlled Pilot Study (ACTIVE-CaRe Pilot)
Bibliographic record
Abstract
BACKGROUND: Patients with cardiovascular disease (CVD) often encounter challenges in establishing and maintaining heart-healthy physical activity habits, even after successfully completing a cardiac rehabilitation program. Digital health technologies hold promise to support long-term habit formation in the secondary prevention of CVD. The aktivplan digital health intervention has been developed to support patients with CVD in establishing long-term heart-healthy physical activity habits. OBJECTIVE: The primary study aim is to pilot and assess the feasibility of a future randomized controlled trial design to investigate the effectiveness of the aktivplan intervention and to assess the usability, user experience, and acceptance of the aktivplan app. The secondary objective is to collect clinical and safety outcomes. METHODS: This multicenter, mixed method, randomized controlled pilot study aims to recruit 40 patients with an established diagnosis of CVD or with increased risk of CVD (physically inactive along with 1 further CVD risk factor) who are undergoing phase II rehabilitation at 2 rehabilitation centers in Austria. Participants will be allocated to the intervention or standard care control group by stratified randomization and will be monitored for 10 weeks after discharge from phase II rehabilitation. Participants, health care professionals, and outcome assessors are not masked (blinded) to group allocation. Data collection will include recruitment and drop-out rate; data completeness; adherence to the intervention; usability, user experience, and user acceptance questionnaires; technical reliability of the intervention; clinical assessments (exercise capacity, physical activity behavior, and CVD risk factors); adverse events; self-reported outcome measures (health-related quality of life, exercise self-efficacy, depression and anxiety, and kinesiophobia); patient interviews, and focus groups with health care professionals. Quantitative data will be analyzed descriptively, and 95% CIs will be calculated for recruitment and drop-out rates and for data completeness. No confirmatory inferential statistical analysis or hypothesis testing will be conducted. Qualitative data will be analyzed thematically by framework analysis. RESULTS: A total of 34 participants were recruited between October 2023 and May 2024. Data collection was completed in August 2024. Currently, the data are being analyzed and prepared for publication. The first publication of feasibility results is expected by summer 2025. CONCLUSIONS: This pilot study is expected to generate valuable and comprehensive insights to inform the study design of a future definitive effectiveness trial of the aktivplan intervention, guide the need for further iteration of the aktivplan app before entering a definitive trial, and inform future implementation strategies for the intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT06025526; https://clinicaltrials.gov/study/NCT06025526. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/73704.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".