Identifying Time-Variant Predictors of Interest in Completing Brief Digital Mental Health Interventions Among Adult Survivors of Cancer: Ecological Momentary Assessment Study
Bibliographic record
Abstract
BACKGROUND: Digital microinterventions have strong potential to improve the lives of adults diagnosed with cancer. However, little is known about which types of digital microinterventions are most desired and how contextual factors may influence those preferences. This potentially limits guidance for personalized and timely digital microintervention delivery. OBJECTIVE: This study aims to identify time-varying and person-level predictors of relative digital microintervention interest among adult survivors of cancer. METHODS: We enrolled US adults within 5 years of a cancer diagnosis in a 5-week observational study using ecological momentary assessment. Participants (N=407) were asked 3 times a day for 5 weeks which of 9 brief, mobile-delivered interventions, if any, they would have been interested in completing within the past hour. Intervention options were (1) reducing worry, (2) reducing negative thoughts, (3) problem solving, (4) increasing positive emotions, (5) connecting with values, (6) guided relaxation, (7) getting support from others, (8) setting goals, and (9) something else. Multinomial models were used to identify demographic (ie, age), cancer-related (ie, treatment status), and psychological (ie, depression symptom severity, anxiety symptom severity, history of major depressive diagnosis, history of anxiety disorder diagnosis, and psychotherapy status) predictors of individual differences in modal intervention preference. Multilevel logistic and multilevel multinomial models were used to identify momentary negative affect, positive affect, and pain predictors of relative intervention interest. RESULTS: =23.0; P=.006) were less likely to modally endorse guided relaxation compared to other intervention options like increasing positive emotions, reducing negative thoughts, and getting support from others. Higher momentary negative affect and pain and lower momentary positive affect each predicted a greater likelihood to endorse interest in completing an intervention (vs not completing an intervention; P<.05) and to endorse interest in completing multiple interventions (vs only one; P<.001). Finally, higher momentary negative affect generally predicted greater interest in completing an intervention other than guided relaxation, whereas higher momentary pain generally predicted greater relative interest in guided relaxation. CONCLUSIONS: Adult survivors of cancer differ in their digital microintervention preferences between and within persons. Guided relaxation alone is less appealing to survivors of cancer when they are in greater emotional distress but may be more appealing in response to instances of increased pain.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".