Wearable Technologies in Head and Neck Oncology: Scoping Review
Bibliographic record
Abstract
Background: Head and neck cancer (HNC) survivors face profound functional and quality-of-life deficits due to disease- and treatment-related sequelae, ranging from mild fatigue to debilitating dysphagia. Wearable technology, by monitoring biometric data such as step counts or providing swallowing biofeedback, offers a unique method for tracking and monitoring the negative effects of HNC. Objective: The aim of this study was to explore the current applications of wearable technology in HNC. Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. A search strategy was built, and a literature search was performed across 5 databases. The initial search yielded 5256 studies, which underwent a 2-phase screening process: title and abstract review followed by full-text review. Inclusion criteria included peer-reviewed, English-language articles published between January 2002 and April 2024 that used wearable technology in HNC care. After full-text review, 9 studies met the inclusion criteria. Data were manually extracted and synthesized narratively. Results: The included studies examined 3 main types of wearable devices: radioactivity (2 studies), physical activity (4 studies), and throat physiology monitors (3 studies). Radioactivity monitors detected residual radioactivity and thyroidal radioiodine uptake. They demonstrated potential to reduce radioactivity exposure risk and personalize radiation doses for patients with thyroid cancer. Physical activity monitors tracked step counts, heart rate, and sleep habits. Low step counts were significantly associated with increased anxiety, radiation-related toxicity, hospital admission rates, and feeding tube placement. One study also linked poor sleep patterns to declines in quality of life. Throat physiology monitors measured pharyngeal electromyography data as well as extrinsic laryngeal muscle movements. Throat sensors achieved high accuracy in classifying swallowing events and translating muscle movements into speech. While earliest in the development continuum, they are promising tools for swallowing and vocal rehabilitation therapy. Barriers to wearable adoption included wearable discomfort, technical difficulties, and patient withdrawal due to treatment side effects. As the definition of wearable adherence varied widely, we propose that future studies report wearable adherence as "percentage of prescribed wear time achieved" to facilitate cross-study comparisons. Conclusions: Wearable technology may enhance treatment monitoring, prognostication, and rehabilitation in head and neck oncology. Radioactivity and physical activity monitors provide actionable insights for clinical decision-making, while throat physiology monitors offer innovative solutions for speech and swallowing therapy. However, challenges such as device adherence, data integration, and patient comfort must be addressed to realize their full potential. Future research should prioritize larger, longitudinal studies, standardized adherence metrics, and consider the integration of artificial intelligence to refine predictive capabilities. By overcoming these barriers, wearable technology could transform survivorship care, improving functional outcomes and quality of life for patients with HNC.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".