Evaluating the Effects of a Mobile Health App on Reducing Patient Care Needs and Improving Quality of Life After Oral Cancer Surgery: Quasiexperimental Study
Bibliographic record
Abstract
BACKGROUND: Intervention with a mobile Health (mHealth) app can improve the efficacy of early detection of oral cancer and the outcomes for patients taking oral anticancer medications. The quality of life of oral cancer patients is significantly reduced within three months after surgery; also, their needs for nursing care and health information increase, mainly due to side effects and associated psychological problems. OBJECTIVE: This study aimed to evaluate changes in the care needs and quality of life of patients with oral cancer after receiving the intervention of a newly developed mHealth app. METHODS: After surgery, oral cancer patients were divided into an experimental group (n=50) who received the mHealth app intervention and a control group (n=50) who received routine health care and instruction. After 3 months of intervention, survey questionnaires were used to assess the patients' quality of life, nursing care needs, and acceptance of the mHealth app. RESULTS: The physiological care needs were significantly decreased in the experimental group compared with the control group (P<.05). Although the differences were not statistically significant, the psychological needs, communication needs, and care support needs all improved after the mHealth app intervention. The overall improvement in quality of life was higher in the experimental group than in the control group (-7.24 vs -4.36). In terms of intention to use, perceived usefulness, and perceived ease of use, the acceptability scores of the mHealth app were significantly increased after 3 months of intervention (P<.05). CONCLUSIONS: Compared with routine health care and instruction, for patients after surgery, the education/information intervention using the mHealth app significantly reduced their nursing care needs, improved their quality of life, and increased their acceptance of using an mHealth app on a mobile device. These findings can provide a theoretical basis for future health care app design and improvement. This study suggests that an mHealth app should be incorporated into the routine care of oral cancer patients to provide medical information quickly and improve their self-management abilities, thereby reducing the patients' need for physiological care and improving their quality of life. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049968; https://www.clinicaltrials.gov/ct2/show/NCT04049968.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".