Heuristic Evaluation of Ehealth Interventions: Establishing Standards That Relate to the Therapeutic Process Perspective
Bibliographic record
Abstract
In recent years, the number of available eHealth interventions aimed at treating behavioral and mental health challenges has been growing. From the perspective of health care providers, there is a need for eHealth interventions to be evaluated prior to clinical trials and for the limited resources allocated to empirical research to be invested in the most promising products. Following a literature review, a gap was found in the availability of eHealth interventions evaluation principles related to the patient experience of the therapeutic process. This paper introduces principles and concepts for the evaluation of eHealth interventions developed as a first step in a process to outline general evaluation guidelines that relate to the clinical context from health care providers' perspective. Our approach was to conduct a review of literature that relates to the examination of eHealth interventions. We identified the literature that was most relevant to our study and used it to define guidelines that relate to the clinical context. We then compiled a list of heuristics we found to be useful for the evaluation of eHealth intervention products' suitability for empirical examination. Four heuristics were identified with respect to the therapeutic process: (1) the product's ease of use (ie, usability), (2) the eHealth intervention's compatibility with the clinical setting, (3) the presence of tools that make it easier for the user to engage in therapeutic activities, and (4) the provision of a feasible therapeutic pathway to growth. We then used this set of heuristics to conduct a detailed examination of MyFitnessPal. This line of work could help to set the bar higher for product developers and to inform health care providers about preferred eHealth intervention designs.
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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 | 0.322 | 0.488 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".