Characteristics Associated With the Use of the Mindfulness Meditation App Headspace in a Large Public Health Deployment: Cross-Sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Mindfulness-based apps can be an effective and accessible resource for mental health support. However, little is known about their use outside of research settings and what user characteristics relate to app use. OBJECTIVE: This study aimed to examine the characteristics of people who decided to use, not use, or stop using Headspace within the context of a large-scale public deployment, which offered the mindfulness meditation app Headspace as a free mental health resource to community members. METHODS: Nearly 100,000 community members received Headspace. All members (N=92,311) received an email inviting them to complete a voluntary and uncompensated survey. In total, 2725 participants completed the survey. The 20-minute survey asked about the use of Headspace, user experience, mental health problems, mental health stigma, and mental health use. Logistic regression models were used to examine relationships between predictors and nonuse, past use, or current use of Headspace. RESULTS: Participants who were still using Headspace at the time of completing the survey (2076/2725, 76.18%) were more likely to experience mental health challenges and distress and make more use of other digital mental health resources (ie, online tools and connecting with people online) than people who were not using Headspace. In addition, current users of Headspace rated the app higher on user experience compared with past users. The most common reasons for abandoning Headspace were that people were already using other strategies to support their mental health (198/570, 34.7%), no longer needed Headspace (73/570, 12.8%), or did not think Headspace was useful (46/570, 8.1%). CONCLUSIONS: Results indicate that a person's mental health challenges, a perceived need for support, and familiarity with digital resources were associated with continued use of Headspace. While the most common reason for not using Headspace was that people were already using other resources, it is important to consider the continuity of mental health support beyond these free programs for those who may not have easy access to other resources. We discuss potential implications of our findings for offering and using apps such as Headspace as a mental health resource, along with factors that influence engagement with this app.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".