Using a lifestyle management application for women with prediabetes to assist with behaviour change: A qualitative exploration
Bibliographic record
Abstract
Lifestyle behaviour change is challenging; however, technology can positively assist in this area. There is an increased interest from researchers and clinicians to utilize smartphone applications (apps) to deliver health-related interventions or assist with behaviour change. As six million people in Canada are living with prediabetes, lifestyle adjustments are needed to reduce risk of developing type 2 diabetes. Lifestyle interventions involving exercise and diet can reduce this progression. Due to resource challenges, most lifestyle inverventions are short-lived. As such, utilizing effective accessible and user-friendly technological tools can help individuals beyond the intervention. The purpose of this study was to explore users' experiences with using a lifestyle management app (HealthWatch 360) for women with prediabetes. Use of this app was one component of a 3-week behaviour change program. Participants were guided in how to use the app at program commencement. After program completion, participants were encouraged to continue to use the app to assist with behaviour changes related to exercise and diet. Fourteen women (Mage=60.07, SD=5.05) were interviewed at two time points (post-intervention, 3-month follow-up; Mlength=49 min) to understand experiences with using HealthWatch 360 to aid in prediabetes management. Interviews were conducted as limited qualitative research exists in this area and less on understanding the effectiveness of apps over time. An inductive thematic analysis revealed three themes related to app use: facilitators, barriers, and recommendations. Findings provide insight into opportunities and challenges in utilizing an app as part of health-related behaviour change and can inform evidence-based interventions that integrate lifestyle apps.Acknowledgments: Michael Smith Foundation for Health Research
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".