Exploring the Effects of Technology-Enabled Mindfulness and Meditation on Stress Management
Bibliographic record
Abstract
Background: Studies suggest that certain technologies may help regulate emotional states and reduce stress. Spire is a wearable device that measures breathing patterns to identify distinct emotional states—calm, tense or focus—to send feedback to the user. Muse is a meditation device that gives real time user audio-feedback based on the EEG wave patterns. Objective: The objective of our study was to test the effect of each device on managing stress. Methods: There were 126 participants recruited remotely and randomized to one of two interventions evaluating the Spire (group 1) and Muse (group 2) devices. Each study involved a 2-week baseline period followed by a 6-week intervention. All participants wore the Spire device in the baseline period where it collected data on the frequency and duration of calm, focus, or tense breathing patterns. Then, in group 1, feedback from the Spire device was turned on during the intervention period alerting participants of unfavorable breathing patterns on a companion app. In group 2, feedback from the Spire device remained off for the 6-week intervention period. Additionally, participants in group 2 meditated with the Muse device for 3 to 5 times per week during the intervention period. The Perceived Stress Survey (PSS-14) and Connor-Davidson Stress Resilience Scale (CD-Risc) were administered to all participants at enrollment (Week 1) and closeout (Week 8). Questionnaire and device data from group 1 and 2 were analyzed to determine the effect of each device on mental health outcomes from enrollment to closeout, and baseline to intervention, respectively. Results: In group 1, perceived stress significantly decreased from 23.59 to 20.24 (P=.001), but there was no significant change in stress resilience [(68.25 to 69.5 (P=.44)]. In group 2, perceived stress significantly decreased from 22.49 to 19.15 (P<.001), and stress resilience scores significantly increased from 70.00 to 73.44 (P=.014). Average calm minutes per day using Spire did not significantly change for Group 1 or 2 [61.75 (SD 27.25) to 59.22 (SD 35.24) (P=.69) and 69.72 (SD 29.41) to 63.89 (SD 29.71) (P=.13), respectively]. In Group 1, average focus minutes per day using Spire significantly decreased from 64.15 (SD 31.2) to 50.79 (SD 28.73) (P=.001) minutes, but in group 2, the average focus minutes per day as measured Spire did not significantly change [82.38 (SD) to 71.29 (SD 46.55) (P=.08)]. In Group 1, average tense streaks did not significantly decrease [17.63 (SD 16.99) to 14.08 (SD 10.63) (P=.15)]. However, in Group 2, average tense streaks significantly decreased from 18.67 (SD 16.38) to 13.78 (SD 11.71) (P=.004). After controlling for age, minutes of Spire use, baseline depression, and baseline anxiety in group 1 participants, each day of Spire use was significantly associated with 53.04 (P=.02) more minutes of calm and 62.69 (P=.04) more minutes of focus mind-states. Group 2 participants had 2.9 more tense mind-state minutes (P=.02) with each day of using the Muse device after also controlling for minutes of Muse use, baseline depression, baseline anxiety and age. Conclusions: Emotion sensing technologies may have a positive role in improving stress management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".