Enhancing Engagement, Practice Integration, and Skill Learning in Mobile Technology–Delivered Interventions Using Human Support: Randomized Controlled Trial With Depressed College Students
Bibliographic record
Abstract
Background Among evidence-based mobile technology–delivered interventions (mTDIs), mindfulness apps such as Headspace have demonstrated numerous benefits. These benefits are particularly important for college students, who continue to face high rates of depression and psychological distress that are paired with insufficient mental health services to meet these needs. mTDIs offer scalable solutions to ameliorating mental health symptoms and may be able to help address this gap in limited access to mental health services for all populations. While mTDIs have great promise for maximizing reach, their utility can be hamstrung by low rates of user engagement and uptake. Thus, this study implemented 2 human support enhancements designed to boost user in-app engagement, practice integration into daily life (ie, sustainability), and app-related skill learning (ie, perceived benefits) in a sample of college students with depression who were granted full access to an mTDI (Headspace). Objective This randomized controlled trial evaluated the impact of two human support enhancements—(1) a one-time face-to-face orientation with or without (2) placement in a peer supportive accountability group—on self-reported and objectively captured mTDI engagement, practice integration, and skill learning among a sample of college students with depression. Methods Participants (n=123) authorized access to their recorded app use data, provided by Headspace. In addition, at the midpoint (1 mo), postintervention (2 mo), and follow-up (3 mo) assessments, participants self-reported on the extent to which they had used the app, how likely they were to continue using the app and related skills in the future, and the extent to which they learned skills and practiced these skills in their daily lives. Results Compared to participants who were simply given access to the app (37/123, 30.1%) without these enhancements, those who attended the orientation (86/123, 69.9%), regardless of additional random allocation to the peer supportive accountability group (48/123, 39%), demonstrated significantly greater mTDI engagement (ie, more minutes meditated [F2,117=11.20; P<.001] and more sessions overall [F2,117=15.00; P<.001]) and rated more favorably multiple aspects of practice integration (ie, more everyday mindfulness practice [F2,72=6.20; P=.003] and greater likelihood of future mindfulness [F2,71=7.42; P<.001]) and skill learning (ie, learning about mindfulness [F2,73=6.02; P=.004], learning mindfulness skills [F2,72=11.01; P<.001], and an increased awareness of thoughts and feelings [F2,73=6.05; P=.004]), indicating potential implications for amplifying the benefits of mTDIs through increased user engagement. Conclusions The results of this study illustrate that an initial face-to-face orientation boosts mTDI engagement, enhances the integration of intervention skills into everyday life, and increases learning. Future work is needed to determine the active ingredients of the orientation as well as to narrow in on the optimal implementation of supportive accountability that might drive increased levels of engagement and the associated positive intervention benefits. Trial Registration Open Science Foundation (OSF) 3trzk; https://osf.io/3trzk
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".