Feasibility of the aktivplan Digital Health Intervention for Supporting Regular Physical Activity Following Phase II Rehabilitation: a Randomized Controlled Pilot Feasibility Study (ACTIVE-CaRe Pilot) (Preprint)
Bibliographic record
Abstract
Background: Patients with cardiovascular disease (CVD) often struggle to develop and maintain heart-healthy physical activity habits, even after completing a cardiac rehabilitation program. Digital tools offer opportunities to support long-term behavior change in secondary prevention. The aktivplan digital health intervention (DHI) was designed to help patients establish heart-healthy physical activity routines. Objective: This study aimed to evaluate the feasibility of a randomized controlled trial design to assess the effectiveness of the aktivplan DHI for patients with CVD or increased CVD risk. Methods: This was a pilot feasibility study with a 2-arm, parallel-group, nonblinded randomized controlled design. Patients admitted to 2 phase II rehabilitation centers in Austria were screened. Eligible participants were patients admitted for cardiac rehabilitation and those admitted for noncardiac rehabilitation who had an increased cardiovascular risk. Recruited patients were randomly assigned to the aktivplan intervention or standard care without digital support. Before discharge, the intervention group was given access to the aktivplan app and a personalized prospective physical activity plan. Data collected at baseline, discharge from the rehabilitation center, and 10-week follow-up included clinical assessments, patient-reported outcomes, and wearables (accelerometry, heart rate). All patients participated in qualitative interviews. Data on intervention implementation were gathered from all health care professionals (HCPs) through questionnaires and a focus group. The qualitative data analysis was conducted according to the framework analysis method. Feasibility outcomes included recruitment rate, attrition, data completeness, adverse events, patient adherence to the aktivplan intervention, fidelity of intervention delivery regarding shared decision-making by HCPs, and both patients' and HCPs' experiences of the intervention and study procedures. All analyses were descriptive. Results: From October 2023 to August 2024, a total of 34 participants (men: n=21, 62%, mean age 57, SD 14 y) were recruited. Sixteen (47%) participants were allocated to the intervention group, and 18 (53%) to the control group. At study sites 1 and 2, the recruitment rate was 1.0 and 0.6 patients per week, respectively. Randomization resulted in equal groups with respect to sex and physical fitness. Attrition was 18%, and data completeness was 97.4%. There were no adverse events related to the intervention or study procedures. Patient adherence was high, with 71% of planned physical activities completed and a mean of 39 (SD 38) additional physical activities per participant. Participants and HCPs were generally supportive of the intervention and the study procedures, providing positive feedback and helpful suggestions for the improvement of design and intervention. HCPs felt that the intervention was implemented effectively (Normalization Measure Development Questionnaire: mean score 3.75, SD 0.50; scale 1-5, with a higher score indicating better implementation). Conclusions: This pilot study generated data to inform the design of a future definitive effectiveness trial of the aktivplan DHI, including strategies for optimizing recruitment and the acceptability of the intervention.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".