Identifying research priorities for digital technology in mental health care: results of the James Lind Alliance Priority Setting Partnership
- PMID: 30170964
- DOI: 10.1016/S2215-0366(18)30296-7
Identifying research priorities for digital technology in mental health care: results of the James Lind Alliance Priority Setting Partnership
Abstract
Digital technology, including the internet, smartphones, and wearables, provides the possibility to bridge the mental health treatment gap by offering flexible and tailored approaches to mental health care that are more accessible and potentially less stigmatising than those currently available. However, the evidence base for digital mental health interventions, including demonstration of clinical effectiveness and cost-effectiveness in real-world settings, remains inadequate. The James Lind Alliance Priority Setting Partnership for digital technology in mental health care was established to identify research priorities that reflect the perspectives and unmet needs of people with lived experience of mental health problems and use of mental health services, their carers, and health-care practitioners. 644 participants contributed 1369 separate questions, which were reduced by qualitative thematic analysis into six overarching themes. Following removal of out-of-scope questions and a comprehensive search of existing evidence, 134 questions were verified as uncertainties suitable for research. These questions were then ranked online and in workshops by 628 participants to produce a shortlist of 26. The top ten research priorities, which were identified by consensus at a stakeholder workshop, should inform research policy and funding in this field. Identified priorities primarily relate to the safety and efficacy of digital technology interventions in comparison with face-to-face interventions, evidence of population reach, mechanisms of therapeutic change, and the ways in which the effectiveness of digital interventions in combination with human support might be optimised.
Copyright © 2018 Elsevier Ltd. All rights reserved.
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