mHealth based interventions for the assessment and treatment of psychotic disorders: a systematic review
Bibliographic record
Abstract
The relative burden of mental health disorders is increasing globally, in terms of prevalence and disability. There is limited data available to guide treatment choices for clinicians in low resourced settings, with mHealth technologies being a potentially beneficial avenue to bridging the large mental health treatment gap globally. The aim of the review was to search the literature systematically for studies of mHealth interventions for psychosis globally, and to examine whether mHealth for psychosis has been investigated. A systematic literature search was completed in Embase, Medline, PsychINFO and Evidence Based Medicine Reviews databases from inception to May 2016. Only studies with a randomised controlled trial design that investigated an mHealth intervention for psychosis were included. A total of 5690 records were identified with 7 studies meeting the inclusion criteria. The majority of included studies, were conducted across Europe and the United Sates with one being conducted in China. The 7 included studies examined different parameters, such as Experiential Sampling Methodology (ESM), medication adherence, cognitive impairment, social functioning and suicidal ideation in veterans with schizophrenia. Considering the increasing access to mobile devices globally, mHealth may potentially increase access to appropriate mental health care. The results of this review show promise in bridging the global mental health treatment gap, by enabling individuals to receive treatment via their mobile phones, particularly for those individuals who live in remote or rural areas, areas of high deprivation and for those from low resourced settings.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".