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Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

ABSTRACT: Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology.The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness.A panel of 40 experts in digital behavioral health who met the participation criteria completed a 19-question survey, rating predefined responses on a 9-point Likert scale. Consensus was determined using a chi-square test of score distributions across three ranges (1-3, 4-6, 7-9). Categorical ratings of first, second, or third line were designated based on the lowest category into which the CI of the mean ratings fell, with a boundary >6.5 for first line. Here, we report experts' responses to nine questions (265 options) that focused on (1) user characteristics that would promote or hinder the use of digital health tools, (2) potential benefits or motivators and barriers or unintended consequences of digital health tool use, and (3) support and training for patients and health care professionals.Among patient characteristics most likely to promote use of digital health tools, experts endorsed interest in using state-of-the-art technology, availability of necessary resources, good occupational functioning, and perception of the tool as beneficial. Certain disease-associated signs and symptoms (eg, more severe symptoms, substance abuse problems, and a chaotic living situation) were considered likely to make it difficult for patients to use digital health tools. Enthusiasm among health care professionals for digital health tools and availability of staff and equipment to support their use were identified as variables to enable health care professionals to successfully incorporate digital health tools into their practices. The experts identified a number of potential benefits of and barriers to use of digital health tools by patients and health care professionals. Experts agreed that both health care professionals and patients would need to be trained in the use of these new technologies.These results provide guidance to the mental health field on how to optimize the development and deployment of digital health tools for patients with serious mental illness.

SUBMITTER: Hatch A

PROVIDER: S-EPMC6019847 | biostudies-other | 2018 Jun

REPOSITORIES: biostudies-other

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Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

Hatch Ainslie A Hoffman Julia E JE Ross Ruth R Docherty John P JP

JMIR mental health 20180612 2

<h4>Background</h4>Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology.<h4>Objective</h4>The aim of this study was to conduct a quantitative expert consensus ...[more]

PMID: 29895514

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Project description:BACKGROUND:Peer support is recognized globally as an essential recovery service for people with mental health conditions. With the influx of digital mental health services changing the way mental health care is delivered, peer supporters are increasingly using technology to deliver peer support. In light of these technological advances, there is a need to review and synthesize the emergent evidence for peer-supported digital health interventions for adults with mental health conditions. OBJECTIVE:The aim of this study was to identify and review the evidence of digital peer support interventions for people with a lived experience of a serious mental illness. METHODS:This systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) procedures. The PubMed, Embase, Web of Science, Cochrane Central, CINAHL, and PsycINFO databases were searched for peer-reviewed articles published between 1946 and December 2018 that examined digital peer support interventions for people with a lived experience of a serious mental illness. Additional articles were found by searching the reference lists from the 27 articles that met the inclusion criteria and a Google Scholar search in June 2019. Participants, interventions, comparisons, outcomes, and study design (PICOS) criteria were used to assess study eligibility. Two authors independently screened titles and abstracts, and reviewed all full-text articles meeting the inclusion criteria. Discrepancies were discussed and resolved. All included studies were assessed for methodological quality using the Methodological Quality Rating Scale. RESULTS:A total of 30 studies (11 randomized controlled trials, 2 quasiexperimental, 15 pre-post designs, and 2 qualitative studies) were included that reported on 24 interventions. Most of the studies demonstrated feasibility, acceptability, and preliminary effectiveness of peer-to-peer networks, peer-delivered interventions supported with technology, and use of asynchronous and synchronous technologies. CONCLUSIONS:Digital peer support interventions appear to be feasible and acceptable, with strong potential for clinical effectiveness. However, the field is in the early stages of development and requires well-powered efficacy and clinical effectiveness trials. TRIAL REGISTRATION:PROSPERO CRD42020139037; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID= 139037.

Project description:BackgroundSymptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.AimsIn this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.MethodSelf-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.ResultsThe three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.ConclusionsUsing a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Project description:BackgroundPeer support provides the opportunity for peers with experiential knowledge of a mental illness to give emotional, appraisal and informational assistance to current service users, and is becoming an important recovery-oriented approach in healthcare for people with mental illness.ObjectivesTo assess the effects of peer-support interventions for people with schizophrenia or other serious mental disorders, compared to standard care or other supportive or psychosocial interventions not from peers.Search methodsWe searched the Cochrane Schizophrenia Group's Study-Based Register of Trials on 27 July 2016 and 4 July 2017. There were no limitations regarding language, date, document type or publication status.Selection criteriaWe selected all randomised controlled clinical studies involving people diagnosed with schizophrenia or other related serious mental illness that compared peer support to standard care or other psychosocial interventions and that did not involve 'peer' individual/group(s). We included studies that met our inclusion criteria and reported useable data. Our primary outcomes were service use and global state (relapse).Data collection and analysisThe authors of this review complied with the Cochrane recommended standard of conduct for data screening and collection. Two review authors independently screened the studies, extracted data and assessed the risk of bias of the included studies. Any disagreement was resolved by discussion until the authors reached a consensus. We calculated the risk ratio (RR) and 95% confidence interval (CI) for binary data, and the mean difference and its 95% CI for continuous data. We used a random-effects model for analyses. We assessed the quality of evidence and created a 'Summary of findings' table using the GRADE approach.Main resultsThis review included 13 studies with 2479 participants. All included studies compared peer support in addition to standard care with standard care alone. We had significant concern regarding risk of bias of included studies as over half had an unclear risk of bias for the majority of the risk domains (i.e. random sequence generation, allocation concealment, blinding, attrition and selective reporting). Additional concerns regarding blinding of participants and outcome assessment, attrition and selective reporting were especially serious, as about a quarter of the included studies were at high risk of bias for these domains.All included studies provided useable data for analyses but only two trials provided useable data for two of our main outcomes of interest, and there were no data for one of our primary outcomes, relapse. Peer support appeared to have little or no effect on hospital admission at medium term (RR 0.44, 95% CI 0.11 to 1.75; participants = 19; studies = 1, very low-quality evidence) or all-cause death in the long term (RR 1.52, 95% CI 0.43 to 5.31; participants = 555; studies = 1, very low-quality evidence). There were no useable data for our other prespecified important outcomes: days in hospital, clinically important change in global state (improvement), clinically important change in quality of life for peer supporter and service user, or increased cost to society.One trial compared peer support with clinician-led support but did not report any useable data for the above main outcomes.Authors' conclusionsCurrently, very limited data are available for the effects of peer support for people with schizophrenia. The risk of bias within trials is of concern and we were unable to use the majority of data reported in the included trials. In addition, the few that were available, were of very low quality. The current body of evidence is insufficient to either refute or support the use of peer-support interventions for people with schizophrenia and other mental illness.

Project description:BACKGROUND:Smoking rates in the United States have been reduced in the past decades to 15% of the general population. However, up to 88% of people with psychiatric symptoms still smoke, leading to high rates of disease and mortality. Therefore, there is a great need to develop smoking cessation interventions that have adequate levels of usability and can reach this population. OBJECTIVE:The objective of this study was to report the rationale, ideation, design, user research, and final specifications of a novel smoking cessation app for people with serious mental illness (SMI) that will be tested in a feasibility trial. METHODS:We used a variety of user-centered design methods and materials to develop the tailored smoking cessation app. This included expert panel guidance, a set of design principles and theory-based smoking cessation content, development of personas and paper prototyping, usability testing of the app prototype, establishment of app's core vision and design specification, and collaboration with a software development company. RESULTS:We developed Learn to Quit, a smoking cessation app designed and tailored to individuals with SMI that incorporates the following: (1) evidence-based smoking cessation content from Acceptance and Commitment Therapy and US Clinical Practice Guidelines for smoking cessation aimed at providing skills for quitting while addressing mental health symptoms, (2) a set of behavioral principles to increase retention and comprehension of smoking cessation content, (3) a gamification component to encourage and sustain app engagement during a 14-day period, (4) an app structure and layout designed to minimize usability errors in people with SMI, and (5) a set of stories and visuals that communicate smoking cessation concepts and skills in simple terms. CONCLUSIONS:Despite its increasing importance, the design and development of mHealth technology is typically underreported, hampering scientific innovation. This report describes the systematic development of the first smoking cessation app tailored to people with SMI, a population with very high rates of nicotine addiction, and offers new design strategies to engage this population. mHealth developers in smoking cessation and related fields could benefit from a design strategy that capitalizes on the role visual engagement, storytelling, and the systematic application of behavior analytic principles to deliver evidence-based content.

Project description:BackgroundSerious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms.ObjectiveThe aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms.MethodsA mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms.ResultsThe study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021.ConclusionsFindings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes.Trial registrationClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252.International registered report identifier (irrid)DERR1-10.2196/39010.

Project description:OBJECTIVE:Community Partners in Care (CPIC) was a group-randomized study of two approaches to implementing expanded collaborative depression care: Community Engagement and Planning (CEP), a coalition approach, and Resources for Services (RS), a technical assistance approach. Collaborative care networks in both arms involved health care and other agencies in five service sectors. This study examined six- and 12-month outcomes for CPIC participants with serious mental illness. METHODS:This secondary analysis focused on low-income CPIC participants from racial-ethnic minority groups with serious mental illness in underresourced Los Angeles communities (N=504). Serious mental illness was defined as self-reported severe depression (?20 on the Patient Health Questionnaire-8) at baseline or a lifetime history of bipolar disorder or psychosis. Logistic and Poisson regression with multiple imputation and response weights, controlling for covariates, was used to model intervention effects. RESULTS:Among CPIC participants, 50% had serious mental illness. Among those with serious mental illness, CEP relative to RS reduced the likelihood of poor mental health-related quality of life (OR=.62, 95% CI=.41-.95) but not depression (primary outcomes); reduced the likelihood of having homelessness risk factors and behavioral health hospitalizations; increased the likelihood of mental wellness; reduced specialty mental health medication and counseling visits; and increased faith-based depression visits (each p<.05) at six months. There were no statistically significant 12-month effects. CONCLUSIONS:Findings suggest that a coalition approach to implementing expanded collaborative depression care, compared with technical assistance to individual programs, may reduce short-term behavioral health hospitalizations and improve mental health-related quality of life and some social outcomes for adults with serious mental illness, although no evidence was found for long-term effects in this subsample.

Dataset Information

Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

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Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

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