A Health App Platform Providing a Budget to Purchase Preselected Apps as an Innovative Way to Support Public Health: Qualitative Study With End Users and Other Stakeholders
Bibliographic record
Abstract
BACKGROUND: eHealth has the potential to improve health outcomes. However, this potential is largely untapped. Individuals face an overload of apps and have difficulties choosing suitable apps for themselves. In the FitKnip experiment, individuals were given access to a health app platform, where they could purchase reliable preselected health apps with a personal budget of €100 (US $107.35). By conducting a prospective study, we aimed to scientifically evaluate the FitKnip experiment as an innovative way to improve population health. OBJECTIVE: The aim of the experiment was to scientifically evaluate the FitKnip experiment as an innovative way to improve population health. More specifically, we conducted an in-depth qualitative evaluation of the concept and acceptability of FitKnip, its perceived impact on health empowerment, as well as the roles of stakeholders for the future implementation of a health app platform through focus group interviews. METHODS: This study followed a phenomenological research design and included 7 focus group interviews with end users and 1 with stakeholders, held between July and December 2020. End users were recruited through various institutions in the Netherlands, for example, insurance companies and local governments. All focus groups were semistructured using interview guides and were held via videoconferencing due to the COVID-19 pandemic measures. Each participant received access to a health app platform where they were enabled to purchase reliable, preselected health apps with a budget of €100 (US $107.35). The budget was valid for the entire research period. The health app platform offered 38 apps. A third party, a health care coalition, selected the apps to be included in FitKnip. The analyses were conducted according to the principles of the Framework Method. RESULTS: A priori formulated themes were concept, acceptability, health empowerment, and outcomes, and the roles of stakeholders for the future implementation of a health app platform. Both end users (n=31) and stakeholders (n=5) were enthusiastic about the concept of a health app platform. End users indicated missing apps regarding physical health and lifestyle and needing more guidance toward suitable apps. End users saw health empowerment as a precondition to using a health app platform and achieving health outcomes depending on the purchased mobile apps. End users and stakeholders identified potential providers and financing parties of FitKnip. Stakeholders recommended the establishment of a reputable national or international quality guidelines or certification for health and wellbeing apps, that can demonstrate the quality and reliability of mobile health applications. CONCLUSIONS: This study showed the need for a personalized and flexible platform. Next to this, a deeper understanding of the roles of stakeholders in such initiatives is needed especially on financing and reimbursement of health promotion and digital health services. A personalized, flexible health app platform is a promising initiative to support individuals in their health.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".