A Mobile Patient-Facing App for Tracking Patient-Reported Outcomes in Head and Neck Cancer Survivors: Single-Arm Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patients with head and neck cancer (HNC) frequently experience disease-related symptoms and treatment adverse effects that impact their overall quality of life. Cancer-specific mobile health apps for patient-related outcomes allow patients to communicate with their clinicians and proactively track their symptoms, which have been shown to improve clinical management and disease outcomes. OBJECTIVE: The purpose of this study was to evaluate the feasibility of LogPAL, a novel iPhone-based mobile health app designed to help HNC survivors track and manage their posttreatment symptoms. METHODS: Patients who completed curative treatment for HNC in the preceding 24 months were recruited from 2 clinical sites within a single institution. Upon enrollment, participants completed a brief sociodemographic survey, downloaded the app onto their iPhone devices, and were asked to complete a series of biweekly questionnaires (based on the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events) via the app for an 8-week study period. The primary feasibility endpoints included retention (retaining >80% of the enrolled participants for the duration of the study period), adherence (>50% of the participants completing 100% of the questionnaires over the study period), and usability (a mean system usability scale [SUS] score >68). Additional postintervention questions were collected to assess perceived usefulness, acceptance, and overall satisfaction. RESULTS: Between January and October 2019, 38 participants were enrolled in the study. Three participants dropped out, and 3 were classified as nonusers. The remaining 32 (87%) were eligible for analysis. Their mean age was 57.8 (SD 12.3) years (range 24-77 years, 81% [26/32] male). Overall, 375 of 512 (73.2%) questionnaires were completed, with 17 (53%) of the 32 participants adherent. Participant-reported usability was acceptable; the mean SUS score was 71.9 (95% CI 64.3-79.5) with high satisfaction of LogPAL usefulness and likelihood to recommend to other cancer survivors. CONCLUSIONS: This single-arm prospective pilot study showed that LogPAL is a feasible, regularly used, accepted app for HNC survivors, justifying a full-scale pilot. Based on the findings from this study, future iterations will aim to improve usability and test intervention efficacy.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".