Development of a Mobile App to Support Head and Neck Cancer Caregiving: Mixed Methods Study
Bibliographic record
Abstract
Background: Survivors with head and neck cancer (HNC) face challenging treatment consequences that can lead to severe disruptions in swallowing and result in weight loss, malnutrition, and feeding tube dependence. Caregivers (family or friends who provide support), therefore, often encounter distressing nutritional caregiving burdens and feel unprepared to provide adequate support at home. Objective: The purpose of this mixed methods study was to develop a mobile support app to support HNC caregiving with an emphasis on nutritional support following treatment. Methods: We assessed perspectives on nutritional recovery challenges and mobile support app preferences in (1) a national panel of oncology dietitians using a web-based cross-sectional survey and (2) survivors with HNC completing treatment within the past 24 months and their nominated caregivers using dyadic semistructured interviews. Descriptive statistics for survey data were synthesized with thematic analysis of interview data to characterize nutrition-related perceptions and intervention preferences; results were integrated, and themes were translated to high-priority main menu domains and subdomains for a mobile app for caregivers. Results: Surveys were completed by dietitians (n=116, 100%; female n=87, 50%, with >10 years practice experience). Interviews included survivors with HNC (n=15; 12/15, 80% male, and 6/15, 40% with oropharynx cancer) and their caregivers (n=13; 11/13, 85% female, and 10/13, 77% spouses). Dietitians, survivors, and caregivers perceived that the majority of nutritional concerns assessed (eg, swallowing, feeding tube management, weight maintenance, and caregiver distress about nutrition) were very or extremely important to caregiving in the 6 months following treatment conclusion. The caregiving tasks rated highest in importance by dietitians included tracking nutritional concerns (n=113, 97%), working together as a team on nutritional concerns (n=104, 90%), and making care decisions (n=102, 88%). Five themes emerged from dyadic interviews, including types of nutritional challenges faced, that competing symptoms were difficult to separate from nutritional challenges, the emotional challenges related to nutrition and recovery, the diverse set of medical and support tasks taken on by caregivers, and information and resource needs in caregivers. Qualitative interview and survey themes guided the content of the Healthy Eating and Recovery Together (HEART) app with an intake tracker and sections for nutrition recovery support, other competing caregiving tips, peer support, and caregiver self-care. Conclusions: Results pinpointed optimal content for a mobile app for caregivers of individuals with HNC and support the acceptability of implementing the HEART app following HNC treatment.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".