Bibliographic record
Abstract
BACKGROUND: There is increasing interest in using mobile technologies such as smartphones for improving the care of patients with schizophrenia. However, less is known about the current clinical evidence for the feasibility and effectiveness of smartphone apps in this population. OBJECTIVE: To review the published literature of smartphone apps applied for the care of patients with schizophrenia and other psychotic disorders. METHODS: An electronic database search of Ovid MEDLINE, the Cochrane Central Register of Controlled Trials, Health Technology Assessment Database, Allied and Complementary Medicine, Health and Psychosocial Instruments, PsycINFO, and Embase was conducted on May 24, 2015. All eligible studies were systematically reviewed, and proportional meta-analyses were applied to pooled data on recruitment, retention, and adherence to examine the overall feasibility of smartphone interventions for schizophrenia. RESULTS: Our search produced 226 results from which 7 eligible articles were identified, reporting on 5 studies of smartphone apps for patients with schizophrenia. All examined feasibility, and one assessed the preliminary efficacy of a smartphone intervention for schizophrenia. Study lengths varied between 6 and 130 days. Overall retention was 92% (95% CI 82-98%). Participants consistently used the smartphone apps on more than 85% of days during the study period, averaging 3.95 interactions per person per day. Furthermore, participants responded to 71.9% of automated prompts (95% CI 65.7-77.8%). Participants reported a range of potential benefits from the various interventions, and user experience was largely positive. CONCLUSIONS: Although small, the current published literature demonstrates strong evidence for the feasibility of using smartphones to enhance the care of people with schizophrenia. High rates of engagement and satisfaction with a broad range of apps suggest the nascent potential of this mobile technology. However, there remains limited data on the efficacy of such interventions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".