Serious Gaming for Chemotherapy-Induced Nausea and Vomiting in Older Adults With Cancer: Protocol for a Randomized Clinical Trial
Bibliographic record
Abstract
Background Older adults are at high risk for toxicity due to cancer treatment and increased risk for adverse events related to chemotherapy-induced nausea and vomiting (CINV). Unfortunately, older adults report multiple treatment-related symptoms but use few strategies to self-manage these symptoms due to erroneous beliefs related to the effectiveness of commonly taught self-management strategies. We developed a novel serious game, Managing at Home (MAH), to help older adults learn how to effectively self-manage CINV at home. Objective This study has 2 aims. Aim 1 is to examine changes in CINV severity, self-management behaviors, functioning, quality of life, cognitive representation, and health care use within the intervention group from baseline (T1) to completion of the study (T6). Aim 2 is to determine the efficacy of the MAH intervention by comparing differences in primary outcomes (CINV severity and health care use) and secondary outcomes (self-management behaviors, functioning, and quality of life) between the intervention and control groups at each follow-up visit (T2-T6) and completion of the study (T6). Methods This is a longitudinal randomized clinical trial. We will collect data from 500 older adults receiving cancer-related chemotherapy at baseline (T1) and at each treatment cycle until cycle 6 (T6). Participants will be enrolled if they are 60 years or older of age, are newly diagnosed with cancer, being treated with any chemotherapy agent with moderate or high emetic potential, are on a 2-, 3-, or 4-week treatment cycle, are proficient in English, and have a telephone. Previous diagnosis or treatment for cancer, end-stage disease with less than 6 months to live, and uncorrected visual or hearing impairment are exclusion criteria. Results This study was funded in September 2022 and received institutional review board approval in October 2022. As of July 2023, the enrollment of participants is ongoing and currently has 130 enrolled participants. Data collection and analysis will be complete in 2027. Conclusions This study addresses self-management of CINV in older adults using an innovative serious game. The MAH intervention uses simulation and gaming technology to engage older adults in active learning in order to reframe erroneous perceptions about symptom self-management. If shown to be effective, it can easily be adapted to include other cancer-related symptoms or other chronic illnesses. Trial Registration ClinicalTrials.gov NCT05838638; https://clinicaltrials.gov/study/NCT05838638 International Registered Report Identifier (IRRID) DERR1-10.2196/64673
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".