Factors Associated with Use of the LETSGO mHealth App in Routine Follow-Up After Gynecologic Cancer Treatment: An Observational Study (Preprint)
Bibliographic record
Abstract
Background: Mobile health (mHealth) apps offer new opportunities to support cancer survivors in managing their health, adopting healthier lifestyles, and increasing awareness of recurrence symptoms. However, despite their potential, little is known about survivors' use of such tools in routine follow-up care, or which factors are associated with their engagement. Objective: The aim of the present study was to identify survivor characteristics associated with the use of the Lifestyle and Empowerment Techniques in Survivorship of Gynecologic Oncology (LETSGO) app. In addition, we aimed to describe engagement patterns among gynecologic cancer survivors during the first year of follow-up after treatment. Methods: App data from 378 participants in the intervention group of the LETSGO multicenter clinical trial were included in the analysis. Using multiple logistic regression, we analyzed app data from the first year of LETSGO app use to identify participant characteristics associated with use. In addition, we described the frequency of user interactions with app features and identified the most frequently used features during the first year of the intervention in a subset of 49 participants with complete app log data. Results: Of the 378 participants in the intervention group, 267 (70.6%) used the LETSGO app at least twice and were defined as app users. The median age of app users was 63 (53-70) years vs 70 (60-78) years among app nonusers (P<.001). App use was associated with younger age (odds ratio [OR] 0.96 per year, 95% CI 0.93-0.99; P=.003), higher education (OR 2.2, 95% CI 1.1-4.6; P=.03), and having ovarian cancer (OR 2.9; 95% CI 1.1-7.5; P=.03). The LETSGO app included a monthly reminder for symptom self-registration, and monthly peaks in the symptom monitoring feature corresponded with the timing of this reminder. App log data from the full first year of the intervention were available for 49 participants. Among these, user engagement was highest for the physical activity and activity goal-setting features, whereas the disease information feature showed steady but less frequent use. Conclusions: Our findings indicate that LETSGO app use was associated with survivor characteristics such as age, education level, and cancer type. Considering user characteristics when tailoring mHealth apps may support user engagement in digital follow-up care and inform the development of mHealth tools that better align with survivors' needs and preferences. Engagement with the LETSGO app during the first year of follow-up among a subset of participants with complete app log data was highest for the physical activity and activity goal-setting features. The pattern of symptom registration following reminders indicates that scheduled prompts may support regular app engagement, while steady use of the disease information feature suggests an ongoing need for accessible health information during follow-up.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".