Katya Rubinstein

Background: Treatment-resistant schizophrenia (TRS) is a severe form of schizophrenia associated with low adherence to treatment and poor outcomes. Mobile health (mHealth) interventions may be effective in preventing relapses, increasing treatment adherence, and managing some of the symptoms of schizophrenia. Mobile therapeutic attention for treatment-resistant schizophrenia (m-RESIST) is an innovative mHealth developed specifically for TRS. Objective: We aim to evaluate the effects of m-RESIST on the clinical and functional outcomes and on the perceived quality of life in people with TRS. Methods: A feasibility study without a control group was performed to test the m-RESIST solution on patients with TRS. Participants were recruited from Spain, Israel, and Hungary. This study’s population (N=31) followed 3 months of intervention. The m-RESIST was configured by an app, a wearable, and a web-based platform. The severity of symptoms was evaluated by using the Positive and Negative Syndrome Scale (PANSS) and the Clinical Global Impression-Schizophrenia (CGI-SCH) scale. Functionality was assessed by the Global Assessment of Functioning and perceived quality of life was evaluated by the EuroQol visual analogue scale (EQ-VAS). Results: Significant reductions were found in symptoms from pretrial to posttrial on the PANSS total (mean difference −7.2, 95% CI −11.1 to −3.4; P=.001), the PANSS positive (mean difference −1.36, 95% CI −2.6 to −0.1; P=.04), the PANSS negative (mean difference −2.1, 95% CI −3.1 to −1.1; P<.001), and the PANSS general symptoms (mean difference −3.8, 95% CI −6.8 to −0.8; P=.02). In almost one-fifth of the participants (6/31), the overall score for the PANSS decreased by more than 20%, which may be considered a clinically significant change. On the CGI-SCH scale, the sum of total severity of illness decreased significantly (P=.03). A decrease in the sum of positive and negative symptoms of the CGI-SCH score was also found (P=.04 and P=.03, respectively). The sum of depressive or cognitive symptoms did not change. The functionality of participants increased significantly on the Global Assessment of Functioning (P≤.001). The perceived quality of life on the EQ-VAS also improved (mean difference 6.7, 95% CI 0.5 to 12.9; P=.04). Conclusions: To our best knowledge, this was the first study to address the efficacy of the mHealth app m-RESIST on the symptoms and functional capacity and on the quality of life for people with TRS. Our preliminary findings showed that implementing the m-RESIST solution decreased the symptoms and severity of disease, and improved the functionality and perceived quality of life among those with TRS. The change of symptoms on the PANSS total may be clinically significant. Modern technologies such as mHealth interventions may be useful in treating symptoms and functionality even in TRS, which is a major clinical challenge, with usually poor outcomes. These results should be corroborated by performing a controlled trial.

Introduction: Community services are gaining ground when it comes to attention to patients with psychiatric diseases. Regarding patients with treatment-resistant schizophrenia (TRS), the use of information and communication technology (ICT) could help to shift the focus from hospital-centered attention to community services. This study compares the differences in mental health services provided for patients with TRS in Budapest (Hungary), Tel-Aviv (Israel) and Catalonia (Spain) by means of a method for the quick appraisal of gaps among the three places, for a potential implementation of the same ICT tool in these regions. Methods: An adapted version of the Description and Standardised Evaluation of Services and Directories in Europe for Long Term Care (DESDE-LTC) instrument was made by researchers in Semmelweis University (Budapest, Hungary), Gertner Institute (Tel-Aviv, Israel) and Hospital de la Santa Creu I Sant Pau and Parc Sanitari Sant Joan de Déu (Catalonia, Spain). Results: Two types of outpatient care services were available in the three regions. Only one type of day-care facility was common in the whole study area. Two residential care services, one for acute and the other for non-acute patients were available in every region. Finally, two self-care and volunteer-care facilities were available in the three places. Conclusion: Although the availability of services was different in each region, most of the services provided were sufficiently similar to allow the implementation of the same ICT solution in the three places.

Background: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration.Objective: To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy.Methods: A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied.Results: Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression.Conclusions: Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.

BACKGROUND Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST) is an EU Horizon 2020-funded project aimed at designing and validating an innovative therapeutic program for treatment-resistant schizophrenia. The program exploits information from mobile phones and wearable sensors for behavioral tracking to support intervention administration. OBJECTIVE To systematically review original studies on sensor-based mHealth apps aimed at uncovering associations between sensor data and symptoms of psychiatric disorders in order to support the m-RESIST approach to assess effectiveness of behavioral monitoring in therapy. METHODS A systematic review of the English-language literature, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed through Scopus, PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases. Studies published between September 1, 2009, and September 30, 2018, were selected. Boolean search operators with an iterative combination of search terms were applied. RESULTS Studies reporting quantitative information on data collected from mobile use and/or wearable sensors, and where that information was associated with clinical outcomes, were included. A total of 35 studies were identified; most of them investigated bipolar disorders, depression, depression symptoms, stress, and symptoms of stress, while only a few studies addressed persons with schizophrenia. The data from sensors were associated with symptoms of schizophrenia, bipolar disorders, and depression. CONCLUSIONS Although the data from sensors demonstrated an association with the symptoms of schizophrenia, bipolar disorders, and depression, their usability in clinical settings to support therapeutic intervention is not yet fully assessed and needs to be scrutinized more thoroughly.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.