Leveraging Personal Technologies in the Treatment of Schizophrenia Spectrum Disorders: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital mental health is a rapidly growing field with an increasing evidence base due to its potential scalability and impacts on access to mental health care. Further, within underfunded service systems, leveraging personal technologies to deliver or support specialized service delivery has garnered attention as a feasible and cost-effective means of improving access. Digital health relevance has also improved as technology ownership in individuals with schizophrenia has improved and is comparable to that of the general population. However, less digital health research has been conducted in groups with schizophrenia spectrum disorders compared to other mental health conditions, and overall feasibility, efficacy, and clinical integration remain largely unknown. OBJECTIVE: This review aims to describe the available literature investigating the use of personal technologies (ie, phone, computer, tablet, and wearables) to deliver or support specialized care for schizophrenia and examine opportunities and barriers to integrating this technology into care. METHODS: Given the size of this review, we used scoping review methods. We searched 3 major databases with search teams related to schizophrenia spectrum disorders, various personal technologies, and intervention outcomes related to recovery. We included studies from the full spectrum of methodologies, from development papers to implementation trials. Methods and reporting follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: This search resulted in 999 studies, which, through review by at least 2 reviewers, included 92 publications. Included studies were published from 2010 to 2023. Most studies examined multitechnology interventions (40/92, 43%) or smartphone apps (25/92, 27%), followed by SMS text messaging (16/92, 17%) and internet-based interventions (11/92, 12%). No studies used wearable technology on its own to deliver an intervention. Regarding the stage of research in the field, the largest number of publications were pilot studies (32/92, 35%), followed by randomized control trials (RCTs; 20/92, 22%), secondary analyses (16/92, 17%), RCT protocols (16/92, 17%), development papers (5/92, 5%), and nonrandomized or quasi-experimental trials (3/92, 3%). Most studies did not report on safety indices (55/92, 60%) or privacy precautions (64/92, 70%). Included studies tend to report consistent positive user feedback regarding the usability, acceptability, and satisfaction with technology; however, engagement metrics are highly variable and report mixed outcomes. Furthermore, efficacy at both the pilot and RCT levels report mixed findings on primary outcomes. CONCLUSIONS: Overall, the findings of this review highlight the discrepancy between the high levels of acceptability and usability of these digital interventions, mixed efficacy results, and difficulties with sustained engagement. The discussion highlights common patterns that may underscore this observation in the field; however, as this was a scoping review, a more in-depth systematic review or meta-analysis may be required to better understand the trends outlined in this review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.022 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".