Feasibility and Acceptability of a Smartphone-Delivered Mindfulness Intervention for Stress Reduction in Adult Singaporeans: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background Fully decentralized self-administered mindfulness interventions show promise for stress reduction, but rigorous evaluations of their feasibility, acceptability, and effectiveness using both self-report and physiological measures remain limited. In Singapore, where mental health concerns rank as the top health care priority (46%), ahead of cancer (38%) and stress-related issues (35%), accessible and scalable interventions are urgently needed to address the significant economic burden of mental health conditions. Objective This study aimed to evaluate a decentralized, 3-day self-administered mindfulness intervention with minimal supervision, compared with a sham control in Singaporean adults, examining effects on self-reported and physiologically measured stress responses. The inclusion of a sham control condition was intended to disentangle mindfulness-specific effects from demand characteristics and expectation effects, addressing a critical methodological gap in digital mindfulness research. Methods This was a purely smartphone-based, decentralized pilot trial with no face-to-face components. Participants were recruited using web-based methods and, after providing informed consent, 60 adults were randomized to either a mindfulness intervention or a structurally matched sham control. The daily 10-minute mindfulness sessions guided participants to focus on present-moment breath and body sensations, while the sham control omitted a focus on present-moment awareness. Outcomes were assessed remotely, using self-report questionnaires (State-Trait Anxiety Inventory-6) and physiological data from smartphone-based photoplethysmography (heart rate variability; HRV). The study incorporated three methodological innovations: (1) a structurally equivalent sham control to match expectancy and credibility, (2) remote collection of HRV as an objective physiological biomarker, and (3) full decentralization allowing unsupervised multiplatform delivery. Feasibility was evaluated through recruitment, retention rates, and data quality. Acceptability was assessed through quantitative ratings and qualitative feedback. Results The study demonstrated excellent feasibility, with near-perfect retention (59/60, 98.3%) and moderate HRV data quality (231/331, 69.8% valid signals). Acceptability ratings were high (mean 4.17, SD 0.53), with comfort/engagement receiving the highest scores (mean 4.27, SD 0.57) on a 5-point scale, exceeding established usability benchmarks for digital health interventions. Qualitative feedback identified technical challenges (HRV instability and device overheating) and scheduling difficulties. While Bayesian analyses did not detect significant group differences in stress reduction (Bayes factor10 [BF10]=0.03) or HRV improvement (BF10=0.2), both groups showed significant stress reductions (BF10=3.01×106), suggesting that observed benefits may stem from nonspecific factors common to both interventions. Conclusions This study demonstrates (1) the feasibility of conducting fully decentralized mindfulness trials with multimodal assessment, (2) the value of a mixed methods acceptability evaluation, and (3) the identification of key technical and control condition refinements necessary for future trials. By addressing methodological limitations through improved control conditions and objective measures, this study provides a foundation for more rigorous investigation of mindfulness-specific effects. Trial Registration ClinicalTrials.gov NCT06765889; https://clinicaltrials.gov/study/NCT06765889
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".