Effects and Processes of an mHealth Intervention for the Management of Chronic Diseases: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) interventions for self-management are a promising way to meet the needs of patients with chronic diseases in primary care practices. Therefore, an mHealth intervention, TelePraCMan, was developed and evaluated for patients with type 2 diabetes mellitus, chronic obstructive pulmonary disease, high blood pressure, or heart failure in a German primary care setting. TelePraCMan entails a symptom diary, an appointment manager, a manager to document goals, and a warning system. The app should foster the self-management of participating patients. OBJECTIVE: We aimed to examine the effects of TelePraCMan on patient activation and quality of life and explored the underlying contextual factors, impacts, and degree of implementation. METHODS: In a prospective observational study design, we collected data by using interviews and written questionnaires from participating patients (intervention and control groups) and primary care workers (physicians and practice assistants). The primary outcomes of interest were patient-reported quality of life (12-Item Short Form Survey) and patient activation (patient activation measure). The quantitative analysis focused on differences between patients in the intervention and control groups, as well as before (T0) and after (T1) the intervention. Interviews were analyzed by using qualitative content analysis via MAXQDA (VERBI GmbH). RESULTS: At baseline, 25 patients and 24 primary care workers completed the questionnaire, and 18 patients and 21 primary care workers completed the follow-up survey. The patients were predominantly male and, on average, aged 64 (SD 11) years (T0). The primary care workers were mostly female (62%) and, on average, aged 47 (SD 10) years (T0). No differences were observed in the outcomes before and after the intervention or between the intervention and control groups. In the additional interviews, 4 patients and 11 primary care workers were included. The interviewees perceived that the intervention was useful for some patients. However, contextual factors and problems with implementation activities negatively affected the use of the app with patients. The main reasons for the low participation were the COVID-19 pandemic and the target group, which seemed to have less interest in mHealth; the interviewees attributed this to the older age of patients. However, the respondents felt that the app would be better accepted in 5 or 10 years. CONCLUSIONS: Although the TelePraCMan app was rated as very good and important by the participants, few patients used it. The digital intervention was hardly implemented and had limited impact in the current setting of German primary care. TRIAL REGISTRATION: German Clinical Trials Register DRKS00017320; https://tinyurl.com/4uwrzu85.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".