Assessing the Content Validity, Acceptability, and Feasibility of the Hypo-METRICS App: Survey and Interview Study
Bibliographic record
Abstract
BACKGROUND: The Hypoglycaemia - MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone app was developed to investigate the impact of hypoglycemia on daily functioning in adults with type 1 diabetes mellitus or insulin-treated type 2 diabetes mellitus. The app uses ecological momentary assessments, thereby minimizing recall bias and maximizing ecological validity. It was used in the Hypo-METRICS study, a European multicenter observational study wherein participants wore a blinded continuous glucose monitoring device and completed the app assessments 3 times daily for 70 days. OBJECTIVE: The 3 aims of the study were to explore the content validity of the app, the acceptability and feasibility of using the app for the duration of the Hypo-METRICS study, and suggestions for future versions of the app. METHODS: Participants who had completed the 70-day Hypo-METRICS study in the United Kingdom were invited to participate in a brief web-based survey and an interview (approximately 1h) to explore their experiences with the app during the Hypo-METRICS study. Thematic analysis of the qualitative data was conducted using both deductive and inductive methods. RESULTS: A total of 18 adults with diabetes (type 1 diabetes: n=10, 56%; 5/10, 50% female; mean age 47, SD 16 years; type 2 diabetes: n=8, 44%; 2/8, 25% female; mean age 61, SD 9 years) filled out the survey and were interviewed. In exploring content validity, participants overall described the Hypo-METRICS app as relevant, understandable, and comprehensive. In total, 3 themes were derived: hypoglycemia symptoms and experiences are idiosyncratic; it was easy to select ratings on the app, but day-to-day changes were perceived as minimal; and instructions could be improved. Participants offered suggestions for changes or additional questions and functions that could increase engagement and improve content (such as providing more examples with the questions). In exploring acceptability and feasibility, 5 themes were derived: helping science and people with diabetes; easy to fit in, but more flexibility wanted; hypoglycemia delaying responses and increasing completion time; design, functionality, and customizability of the app; and limited change in awareness of symptoms and impact. Participants described using the app as a positive experience overall and as having a possible, although limited, intervention effect in terms of both hypoglycemia awareness and personal impact. CONCLUSIONS: The Hypo-METRICS app shows promise as a new research tool to assess the impact of hypoglycemia on an individual's daily functioning. Despite suggested improvements, participants' responses indicated that the app has satisfactory content validity, overall fits in with everyday life, and is suitable for a 10-week research study. Although developed for research purposes, real-time assessments may have clinical value for monitoring and reviewing hypoglycemia symptom awareness and personal impact.
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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 | 0.082 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".