Engagement With an Internet-Administered, Guided, Low-Intensity Cognitive Behavioral Therapy Intervention for Parents of Children Treated for Cancer: Analysis of Log-Data From the ENGAGE Feasibility Trial
Bibliographic record
Abstract
BACKGROUND: Parents of children treated for cancer may experience psychological difficulties including depression, anxiety, and posttraumatic stress. Digital interventions, such as internet-administered cognitive behavioral therapy, offer an accessible and flexible means to support parents. However, engagement with and adherence to digital interventions remain a significant challenge, potentially limiting efficacy. Understanding factors influencing user engagement and adherence is crucial for enhancing the acceptability, feasibility, and efficacy of these interventions. We developed an internet-administered, guided, low-intensity cognitive behavioral therapy (LICBT)-based self-help intervention for parents of children treated for cancer, (EJDeR [internetbaserad självhjälp för föräldrar till barn som avslutat en behandling mot cancer or internet-based self-help for parents of children who have completed cancer treatment]). EJDeR included 2 LICBT techniques-behavioral activation and worry management. Subsequently, we conducted the ENGAGE feasibility trial and EJDeR was found to be acceptable and feasible. However, intervention adherence rates were marginally under progression criteria. OBJECTIVE: This study aimed to (1) describe user engagement with the EJDeR intervention and examine whether (2) sociodemographic characteristics differed between adherers and nonadherers, (3) depression and anxiety scores differed between adherers and nonadherers at baseline, (4) user engagement differed between adherers and nonadherers, and (5) user engagement differed between fathers and mothers. METHODS: We performed a secondary analysis of ENGAGE data, including 71 participants. User engagement data were collected through log-data tracking, for example, communication with e-therapists, homework submissions, log-ins, minutes working with EJDeR, and modules completed. Chi-square tests examined differences between adherers and nonadherers and fathers and mothers concerning categorical data. Independent-samples t tests examined differences regarding continuous variables. RESULTS: Module completion rates were higher among those who worked with behavioral activation as their first LICBT module versus worry management. Of the 20 nonadherers who opened the first LICBT module allocated, 30% (n=6) opened behavioral activation and 70% (n=14) opened worry management. No significant differences in sociodemographic characteristics were found. Nonadherers who opened behavioral activation as the first LICBT module allocated had a significantly higher level of depression symptoms at baseline than adherers. No other differences in depression and anxiety scores between adherers and nonadherers were found. Minutes working with EJDeR, number of log-ins, days using EJDeR, number of written messages sent to e-therapists, number of written messages sent to participants, and total number of homework exercises submitted were significantly higher among adherers than among nonadherers. There were no significant differences between fathers and mothers regarding user engagement variables. CONCLUSIONS: Straightforward techniques, such as behavioral activation, may be well-suited for digital delivery, and more complex techniques, such as worry management, may require modifications to improve user engagement. User engagement was measured behaviorally, for example, through log-data tracking, and future research should measure emotional and cognitive components of engagement. TRIAL REGISTRATION: ISRCTN Registry 57233429; https://doi.org/10.1186/ISRCTN57233429.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".