Experiences of Using Digital Mindfulness-Based Interventions: Rapid Scoping Review and Thematic Synthesis (Preprint)
Open accessPreprint — published as 10.2196/44220 · Paper page
Abstract
BACKGROUND: Digital mindfulness-based interventions (MBIs) are a promising approach to deliver accessible and scalable mindfulness training and have been shown to improve a range of health outcomes. However, the success of digital MBIs is reliant on adequate engagement, which remains a crucial challenge. Understanding people’s experiences of using digital MBIs and identifying the core factors that facilitate or act as barriers to engagement is essential to inform intervention development and maximize engagement and outcomes. OBJECTIVE: This study aims to systematically map the literature on people’s experiences of using digital MBIs that target psychosocial variables (eg, anxiety, depression, distress, and well-being) and identify key barriers to and facilitators of engagement. METHODS: We conducted a scoping review to synthesize empirical qualitative research on people’s experiences of using digital MBIs. We adopted a streamlined approach to ensure that the evidence could be incorporated into the early stages of intervention development. The search strategy identified articles with at least one keyword related to mindfulness, digital, user experience, and psychosocial variables in their title or abstract. Inclusion criteria specified that articles must have a qualitative component, report on participants’ experiences of using a digital MBI designed to improve psychosocial variables, and have a sample age range that at least partially overlapped with 16 to 35 years. Qualitative data on user experience were charted and analyzed using inductive thematic synthesis to generate understandings that go beyond the content of the original studies. We used the Quality of Reporting Tool to critically appraise the included sources of evidence. RESULTS: The search identified 510 studies, 22 (4.3%) of which met the inclusion criteria. Overall, the samples were approximately 78% female and 79% White; participants were aged between 16 and 69 years; and the most used measures in intervention studies were mindfulness, psychological flexibility, and variables related to mental health (including depression, anxiety, stress, and well-being). All studies were judged to be adequately reported. We identified 3 themes characterizing barriers to and facilitators of engagement: responses to own practice (ie, negative reactions to one’s own practice are common and can deplete motivation), making mindfulness a habit (ie, creating a consistent training routine is essential yet challenging), and leaning on others (ie, those engaging depend on someone else for support). CONCLUSIONS: The themes identified in this review provide crucial insights as to why people frequently stop engaging with digital MBIs. Researchers and developers should consider using person-based coparticipatory methods to improve acceptability of and engagement with digital MBIs, increase their effectiveness, and support their translation to real-world use. Such strategies must be grounded in relevant literature and meet the priorities and needs of the individuals who will use the interventions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mechanisms of mindfulness training: Monitor and Acceptance Theory (MAT
Lindsay, E. K., & Creswell, J. D. (2017) · Clinical Psychology Review · doi:10.1016/j.cpr.2016.10.011 · Cited by 919 · see citing works
Flagged first, then by citations
- Authors
- Osborne, E. L., Ainsworth, B., Hooper, N., & Atkinson, M. J.
- Affiliations
- University of Bath (GB)
- University of Southampton (GB)
- Cardiff University (GB)
- Corresponding author
- Osborne, Emma Louise
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-11-14
- Volume
- 25
- Pages
- e44220-e44220
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC10570895
- OpenAlex
- W4385468433
- Cited by
- 37
- References
- 1
- Citations by year
- 2026: 17 · 2025: 15 · 2024: 5
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion, cc-by)
- Funders
- Economic and Social Research Council (2232546, ES/P000630/1)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Clinical Psychology › Mindfulness and Compassion Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- user experience, habit formation, social support, mental health, anxiety, depression, well-being, psychological flexibility, young adults, intervention development, acceptability, mental health apps, user-centered design, participatory design, co-design, user satisfaction
- MeSH terms
- Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Qualitative Research, Young Adult, Mindfulness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review