The Use and Effect of the Health Storylines mHealth App on Female Childhood Cancer Survivors’ Self-efficacy, Health-Related Quality of Life and Perceived Illness
Bibliographic record
Abstract
BACKGROUND: mHealth apps have been not been well tested among childhood cancer survivors (CCSs) to track physical and psychosocial functioning for improved self-management of post-treatment needs. OBJECTIVES: This pilot study had 3 aims: (1) assess the usage of the Health Storylines mHealth app; (2) examine its effect in improving self-efficacy in managing survivorship healthcare needs, health-related quality of life, and perceived illness; and (3) determine if app usage moderated the effects on the above patient-reported outcome measures among female CCSs. METHODS: Study participants accessed the Health Storylines mHealth app on their own personal device. This single-group, pilot study included 3 measurement points: baseline and 3 and 6 months after initiation of using the app. RESULTS: Use of the mHealth app ranged from 0 times to 902 times. Every study participant who used the app (n = 26) also used the mental health app component of the Health Storylines app. Generalized estimating equations were fit to examine the effect of the mHealth app use on self-efficacy, perceived illness, and health-related quality of life, between baseline, 3-month follow-up, and 6-month follow-up. No statistically significant changes were evident, on average, from baseline to 3- or 6-month follow-up on any outcome. Subsequent testing of effect moderation showed differential trends for high versus low users. CONCLUSIONS: Studies are needed among this clinical population to determine who will benefit and who will perceive the app as a useful aspect of their survivorship care. IMPLICATIONS FOR PRACTICE: Sharing mental health functioning tracked on mhealth apps with healthcare providers may inform needed interventions for young adult female CCSs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".