Bedtime App–Guided Mindfulness Meditation in Patients With Insomnia: Mixed Methods Feasibility and Acceptability Pilot Study
Bibliographic record
Abstract
Background: While mindfulness meditation (MM) apps have gained popularity as a tool for promoting sleep, research focusing on bedtime mindfulness practice and app usage is limited. Objective: As the first step toward understanding the efficacy and mechanisms of such bedtime practice and to inform future investigations, the goal of this pilot study was to explore the feasibility of app-guided bedtime MM practice with both in-lab and at-home physiological and self-report sleep remote assessments. Methods: We conducted a single-arm, prospective mixed methods pilot study that included both standard in-lab sleep studies and remote at-home assessments of individuals with insomnia disorder with self-reported difficulty falling asleep. Participants practiced MM guided by a commercially available smartphone app at bedtime for 4 weeks. Pre-post assessments included a battery of sleep-related and psychological health questionnaires, objective physiological sleep measures (polysomnography and actigraphy), and daily sleep logs. We also conducted qualitative exit interviews to further assess feasibility and acceptability. Transcripts were analyzed for dominant themes using inductive and deductive qualitative methods. Results: We recruited 13 participants with chronic insomnia (symptoms ≥3 nights weekly for ≥3 months) to complete the study protocol within 8 months (retention rate 77%). We were able to collect analyzable physiological and psychometric data with overall completion rates of more than 90%. The study was deemed feasible, meeting a priori benchmarks including recruitment, retention, completion, and adherence. The 10 participants retained in the program had excellent engagement (95% completion of in-lab studies, 100% completion of questionnaires, and 91% compliance with use of the app). Our preliminary analysis of subjective measures indicated improvement in sleep quality, insomnia severity, and presleep arousal, including Pittsburgh Sleep Quality Index change of -3.7 (95% CI -6.7 to -0.7), Insomnia Severity Index change of -4.5 (95% CI -7.7 to -1.4), Pre-Sleep Arousal Scale change of -7.7 (95% CI -13.1 to -2.3), and trend toward improvement in the Ford Insomnia Response to Stress Test indicated by a change of -2.5 (95% CI -5.9 to 0.9). From qualitative data, we identified domains that inform the feasibility and acceptability of the study, including (1) barriers to sleep prior to the study, (2) benefits and skills imparted by mindfulness, and (3) feedback on app use. Benefits and skills imparted by mindfulness included decreased catastrophizing, acceptance and nonreactivity, body awareness and relaxation, self-kindness, awareness of sleep hygiene and bedtime routine, earlier defusing of stress, increased focus and presence, and calm throughout the day. Conclusions: Bedtime app-guided MM as an intervention in patients with insomnia and the hybrid study design with in-lab and at-home assessments are feasible and acceptable. This study informs the design of future clinical and mechanistic research examining app-guided MM to impact insomnia severity and presleep arousal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".