Application and Effectiveness of Telehealth to Support Severe Mental Illness Management: Systematic Review
Bibliographic record
Abstract
BACKGROUND: People with severe mental illness (SMI) must receive early interventions to prevent mental health deterioration or relapse. Telecommunications and other technologies are increasingly being used to assist in health care delivery using "telehealth," which includes telephones and mobile phones, computers, remote sensors, the internet, and other devices, to provide immediate real-time information to service users to improve the management of chronic health conditions. Some initial findings have suggested that technology could improve the quality of life of people with SMI. OBJECTIVE: In this systematic review, we aimed to identify the various uses and efficacy of telehealth technology for SMI. METHODS: We systematically searched electronic databases from inception to March 2016 (MEDLINE, EMBASE, PsycINFO, Cochrane Central Register of Controlled Trials, Allied and Complementary Medicine Database, Health Technology Assessment, CINAHL Plus, and NHS Economic Evaluations Database) to identify randomized controlled trials evaluating telehealth for adults with SMI published in English. Additional literature was identified through searching reference lists of key articles. The articles meeting the inclusion criteria were systematically reviewed and assessed for quality and risk of bias. RESULTS: Our search identified 31 articles describing 29 trials as eligible for the review. The included studies evaluated the use of computers to deliver cognitive rehabilitation (15 trials), patient education (3 trials), and Web-based self-management interventions (2 trials) and to support consultations (1 trial). Virtual reality was used to simulate work and social situations (2 trials) and to deliver cognitive training (1 trial). Telephones were used to prompt service users to take medications (3 trials) and to report symptoms to their health care team (1 trial). Remote sensors were used to monitor medication use (1 trial). Telephone support was found effective in improving medication adherence and reducing the severity of symptoms and inpatient days. Computer-assisted cognitive rehabilitation was effective in improving cognitive function. The impact of telehealth on other outcomes was inconsistent. The results of this review should be taken in the context of varied quality in study design, with only 5 studies demonstrating a low risk of bias. CONCLUSIONS: A growing variety of telehealth technologies are being used to support the management of SMI. Specific technology types have been found to be effective for some outcomes (eg, telephone and remote medication monitoring for adherence to treatment), while other types of telehealth technologies (eg, delivery of patient education using computers) had no benefit over traditional nurse-based methods and were less acceptable to patients. Further research is warranted to establish the full potential benefits of telehealth for improving the quality of life in people with SMI, acceptability from the service user perspective, and cost-effectiveness. The findings of this review are limited by the poor quality of many of the studies reviewed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".