Evaluating the Feasibility of a Digital Therapeutic Program for Patients With Cancer During Active Treatment: Pre-Post Interventional Study
Bibliographic record
Abstract
BACKGROUND: Increasing evidence shows that lifestyle interventions can improve the symptoms, quality of life (QoL), and even overall survival of patients with cancer. Digital therapeutics (DTx) can help implement behavioral modifications and empower patients through education, lifestyle support, and remote symptom monitoring. OBJECTIVE: We aimed to test the feasibility of a DTx program for patients with cancer, as measured by engagement, retention, and acceptability. In addition, we explored the effects of the program on cancer-related QoL. METHODS: We conducted a 4-week single-arm trial in Iceland, where DTx was delivered through a smartphone app. The intervention consisted of patient education about mindfulness, sleep, stress, and nutrition; lifestyle coaching; and the completion of daily missions for tracking physical activity and exercise, reporting patient-reported outcomes (PROs), practicing mindfulness, and logging healthy food intake. Information on program engagement and retention, step goal attainment, as well as PROs were collected throughout the study. QoL was measured using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C30 at baseline and follow-up. RESULTS: In total, 30 patients with cancer undergoing active therapy were enrolled, and 29 registered in the app (23 female, 18 with breast cancer; mean age 52.6, SD 11.5 years). Overall, 97% (28/29) of participants were active in 3 of the 4 weeks and completed the pre- and postprogram questionnaires. The weekly active days (median) were 6.8 (IQR 5.8-6.8), and 72% (21/29) of participants were active at least 5 days a week. Users interacted with the app on average 7.7 (SD 1.9) times per day. On week 1, all 29 participants used the step counter and logged an average of 20,306 steps; 21 (72%) participants reached their step goals of at least 3000 steps per day. On week 4, of the 28 active users, 27 (96%) were still logging their steps, with 19 (68%) reaching their step goals. Of the 28 participants who completed the satisfaction questionnaire, 25 (89%) were likely to recommend the program, 23 (82%) said the program helped them deal with the disease, and 24 (86%) said it helped them remember their medication. QoL assessment showed that the average global health status, functioning, and symptom burden remained stable from baseline to follow-up. In all, 50% (14/28) of participants reported less pain, and the average pain score decreased from 31 (SD 20.1) to 22.6 (SD 23.2; P=.16). There was no significant change in PROs on the quality of sleep, energy, and stress levels from the first to the last week. CONCLUSIONS: The high retention, engagement, and acceptability found in this study demonstrate that multidisciplinary DTx is feasible for patients with cancer. A longer, full-scale randomized controlled trial is currently being planned to evaluate the efficacy of the intervention.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".