Video-based mobile health interventions for people with schizophrenia: Bringing the “pocket therapist” to life.
Open accessAbstract
OBJECTIVE: To examine whether video-based mobile health (mHealth) interventions are feasible, acceptable, understandable, and engaging to people with schizophrenia. METHOD: This study used a mixed-methods design. Ten individuals with schizophrenia spectrum disorders were recruited for a month-long trial in which they used FOCUS-Audio/Video (FOCUS-AV), a smartphone system that offers video and written intervention options. Participants completed posttrial measures and engaged in semistructured interviews. FINDINGS: One participant dropped out. The remaining 9 participants used intervention videos successfully. Participants responded to 67% of system-delivered prompts to engage FOCUS-AV, and 52% of FOCUS-AV use was initiated by the users. On average, participants used interventions 6 days a week, 4 times daily. Participants used video functions an average of 28 times. They chose video over written interventions on 67% of the times they used on-demand functions but opted for written content 78% of the times they responded to prescheduled prompts. Clinician videos were rated as more personal, engaging, and helpful than written interventions. Video and written interventions were rated as equally usable and understandable. Written interventions were rated as more favorable in letting users proceed at their own pace. Similarly to what is seen in live therapy, the communication style and demeanor of clinicians depicted in intervention videos reportedly affected participants' experience with treatment. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Video-based mHealth may be a feasible, usable, acceptable, and highly engaging method for flexible delivery of interventions to people with schizophrenia using mobile technology. Producing intervention videos is more time-, labor-, and cost-intensive than generating written content, but participant feedback suggests that there may be added value in this approach. Additional research will determine whether video-based mHealth interventions lead to better, faster, or more sustainable clinical gains. (PsycINFO Database Record
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ben-Zeev et al measured the number of times users chose the video feature over the written content feature in an app for schizophrenia self-management.
“Ben-Zeev et al measured the number of times users chose the video feature over the written content feature [36].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [36]
Reviewed studies assessed the number of videos watched from a playlist, educational modules completed, or self-care advice accessed.
“Others assessed the number of videos watched from a playlist [36,55], educational modules completed [52], or self-care advice accessed [69].”
From A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review (2019) · cites this paper as [36]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 24 · Cited by 50
Research works printed but not linked: 1
- Wilkinson GS Robertson GJ 2006 Wide Range Achievement Test - Fourth Edition Lutz, FL Psychological Assessment Resources
- Authors
- Ben-Zeev, D., Brian, R. M., Aschbrenner, K. A., Jonathan, G., & Steingard, S.
- Affiliations
- Dartmouth College (US)
- Venue
- Psychiatric Rehabilitation Journal
- Published
- 2018-03
- Volume
- 41
- Issue
- 1
- Pages
- 39-45
- ISSN
- 1095-158X (ISSN-L)
- 1559-3126
- PMC
- PMC5154775
- OpenAlex
- W2433115544
- Cited by
- 50
- References
- 24
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 11 · 2018: 9 · 2017: 2
- FWCI
- 11.24
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Psychiatric Rehabilitation Journal (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R34MH100195)
- Dartmouth SYNERGY Clinical and Translational Science Institute
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- schizophrenia, smartphone-based intervention, feasibility, acceptability, user engagement, usability, schizophrenia spectrum disorders, digital mental health
- MeSH terms
- Adult, Feasibility Studies, Female, Humans, Male, Middle Aged, Patient Acceptance of Health Care, Psychotic Disorders, Schizophrenia, Video Recording, Telemedicine
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