Jussi Seppälä

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.

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 mobile health (mHealth) solution based on novel technology offering high modular and flexible functioning developed specifically for TRS. To our best knowledge no studies have addressed the efficacy of mHealth applications on the symptoms and functional capacity, and on the quality of life in subjects having TRS. OBJECTIVE To evaluate the effects of m-RESIST on the clinical and functional outcomes, and on the perceived quality of life among participants having TRS. METHODS A feasibility study without a control group was performed to test the m-RESIST solution in TRS patients. Participants were recruited from Spain, Israel and Hungary. Study population (N=31) followed the 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 Clinical Global Impression Schizophrenia (CGI-SCH). Functionality was assessed by the Global Assessment of Functioning (GAF) and perceived quality of life was evaluated by the Analogue Visual Scale of EuroQol 5 dimensions 5 levels questionnaire (EQ-VAS). RESULTS Significant reductions were found in symptoms from pretrial to post trial on the PANSS total; mean difference -7.2 (P=.001), on the PANSS positive; -1.36 (P =.036) and PANSS negative; -2.1 (P <.001), and also on the PANSS general symptoms; -3.8 (P =.015). In almost one-fifth of the participants (6/31), the overall score for PANSS decreased by more than 20% which may be considered a clinically significant change. On CGI-SCH the sum of total severity of illness decreased significantly (P=.027). A decrease in the sum of positive and negative symptoms of the CGI-SCH-score was also found (P=.037 and P=.03, respectively). The sum of depressive or cognitive symptoms did not change. The functionality of participants increased significantly on the GAF (P =<.001). The perceived quality of life on EQ-VAS improved also (mean difference 6.7, P=.035). CONCLUSIONS To our best knowledge, this was the first study collecting data on the symptoms, functional capacity, and quality of life in TRS by using the mHealth platform (m-RESIST solution). Our novel findings showed that implementing m-RESIS-solution decreased the symptoms and severity of illness, and improved the functionality and perceived quality of life among those having TRS. The change of symptoms on the PANSS total may be clinically significant. Modern technologies like mHealth interventions seem to be useful in treating symptoms and functionality even in TRS which is a major clinical challenge having usually poor outcomes. These results should be corroborated by performing a controlled trial. CLINICALTRIAL ClinicalTrials.gov NCT03064776; https://clinicaltrials.gov/ct2/show/record/NCT03064776 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2017-021346

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.: We studied the cumulative incidence of physical illnesses, and the effect of early environmental factors (EEFs) on somatic comorbidity in schizophrenia, in nonschizophrenic psychosis and among nonpsychotic controls from birth up to the age of 50 years. Methods.: The sample included 10,933 members of the Northern Finland Birth Cohort 1966, of whom, 227 had schizophrenia and 205 had nonschizophrenic psychosis. Diagnoses concerning physical illnesses were based on nationwide registers followed up to the end of 2016 and classified into 13 illness categories. Maternal education and age, family type at birth and paternal socioeconomic status were studied as EEFs of somatic illnesses. Results.: When adjusted by gender and education, individuals and especially women with nonschizophrenic psychosis had higher risk of morbidity in almost all somatic illness categories compared to controls, and in some categories, compared to individuals with schizophrenia. The statistically significant adjusted hazard ratios varied from 1.27 to 2.42 in nonschizophrenic psychosis. Regarding EEFs, single-parent family as the family type at birth was a risk factor for a higher somatic score among men with schizophrenia and women with nonschizophrenic psychosis. Maternal age over 35 years was associated with lower somatic score among women with nonschizophrenic psychosis. Conclusions.: Persons with nonschizophrenic psychoses have higher incidence of somatic diseases compared to people with schizophrenia and nonpsychotic controls, and this should be noted in clinical work. EEFs have mostly weak association with somatic comorbidity in our study.

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.

Especially low vitamin B12 or folate and low intake of omega-3-fatty acids, but also low vitamin D may associate with increased risk of depression. B12 and folate may also be useful in the treatment of depression. The importance of individual fatty acids is unclear. The causal relationship between depression and diet, the efficacy of vitamins or dietary supplements in the treatment of depression, or the impact of diet compared with other treatment options need to be scrutinized. An overall healthy diet rich in vitamin B12, D or folate and fish oils may have positive effect also on depression.

An association between vitamin B₁₂ levels and depressive symptoms (DS) has been reported in several epidemiological studies. The purpose of this study was to evaluate vitamin B₁₂ levels in population-based samples with melancholic or non-melancholic DS as the relationship between vitamin B₁₂ levels and different subtypes of DS has not been evaluated in previous studies. Subjects without previously known type 2 diabetes, aged 45–74 years were randomly selected from the National Population Register as a part of the Finnish diabetes prevention programme (FIN-D2D). The study population (N = 2806, participation rate 62%) consisted of 1328 men and 1478 women. The health examinations were carried out between October and December 2007 according to the WHO MONICA protocol. The assessment of DS was based on the Beck Depression Inventory (BDI, cut-off ≥10 points). A DSM-IV- criteria based summary score of melancholic items in the BDI was used in dividing the participants with DS (N = 429) into melancholic (N = 138) and non-melancholic DS (N = 291) subgroups. In the statistical analysis we used chi-squared test, t-test, permutation test, analysis of covariance, multivariate logistic regression analysis and multinomial regression model. The mean vitamin B₁₂ level was 331±176 pmol/L in those without DS while the subjects with non-melancholic DS had a mean vitamin B₁₂ level of 324 ± 135 pmol/L, and those with melancholic DS had the lowest mean vitamin B₁₂ level of 292±112 pmol/L (p < 0.001 after adjusted for age, sex, use of antidepressive medication and chronic diseases sum index). The adjusted difference of vitamin B₁₂ levels between the non-melancholic and the melancholic group was 33 pmol/L (95%CI 8 to 57, p = 0.008). Melancholic DS and vitamin B₁₂ levels showed an independent linearly inverse association. The relative risk ratio (RRR) for melancholic DS was 2.75 (95%CI 1.66 to 4.56) in the lowest vitamin B₁₂ level tertile versus the highest (p for linearity 12 level was associated with melancholic DS but not with non-melancholic DS.