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BackgroundDespite the theoretical potential of mHealth solutions in the treatment of patients with schizophrenia, there remains a lack of technological tools in clinical practice. ObjectiveThe aim of this study was to measure the receptivity of patients, informal carers, and clinicians to a European integral intervention model focused on patients with persistent positive symptoms: Mobile Therapeutic Attention for Patients with Treatment-Resistant Schizophrenia (m-RESIST). MethodsBefore defining the system requirements, a qualitative study of the needs of outpatients with treatment-resistant schizophrenia was carried out in Spain, Israel, and Hungary. We analyzed the opinions of patients, informal carers, and clinicians concerning the services originally intended to be part of the solution. A total of 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries, using discourse analysis as the framework. ResultsA webpage and an online forum were perceived as suitable to get both reliable information on the disease and support. Data transmission by a smart watch (monitoring), Web-based visits, and instant messages (clinical treatment) were valued as ways to improve contact with clinicians. Alerts were appreciated as reminders of daily tasks and appointments. Avoiding stressful situations for outpatients, promoting an active role in the management of the disease, and maintaining human contact with clinicians were the main suggestions provided for improving the effectiveness of the solution. ConclusionsPositive receptivity toward m-RESIST services is related to its usefulness in meeting user needs, its capacity to empower them, and the possibility of maintaining human contact.

In the European Union, it is estimated that around 5 million people (0.2-2.6%) suffer from psychotic disorders. Between 30-50% can be considered resistant to treatment, and 10-20% ultra-resistant (Juarez-Reyes et al., 1995; Essock et al., 1996). The standard intervention in patients with resistant schizophrenia is complex, mainly for the presence of persistent positive symptomatology, extensive periods of hospital care and greater risk of multimorbidity, that leads to a scenario with a high degree of suffering for the patients, family and social environment, and a high proportion of costs to the healthcare system (Kennedy et al., 2014). To date, the standard treatment is not enough to achieve remission in resistant schizophrenia patients. Therefore, an improved understanding of treatment refractory schizophrenia and the development of innovative evidence-based interventions adjunctive to pharmacological and psychosocial treatment are necessary. ICT-based programmes are a novel possibility to improve the outcomes of schizophrenia since previous studies have indexed the suitability and promising results of interventions based on m-Health techniques (Granholm et al., 2012; Ben-Zeev et al., 2013). However, new proposals adapted to the specific nature of well-identified clinical subgroups, such as resistant patients, are needed. Intervention in resistant schizophrenia patients should include psychiatric, psychological and medical treatment. Moreover the role of the caregivers in the prognosis of the illness is a key aspect, improving adherence to treatment and identifying early signs of relapse or physical problems. Bearing all these aspects in mind, we propose m-RESIST, as an innovative, secure and sustainable project, which offers an integrated care addressed to empower patients suffering from resistant schizophrenia. m-RESIST intervention, although it is still under development, will integrate three main components: 1) a sensor data analysis module, which will provide passive information about the patients in terms of movement activity, social activity and physiological status, processing data coming from smartphone and wearable devices; 2) a predictive modelling engine, which will enable prediction of clinically significant events, such as hospitalization, risk behaviours and social isolation; and 3) a clinical decision support system (CDSS), which will provide users with necessary information to support health-related and clinical decisionmaking. This CDSS will include psychiatric intervention in three main areas, clinical symptoms management, healthy lifestyle habits and treatment adherence, as well as psychological intervention based in the cognitive behaviour therapy for psychosis. This project highlights the importance of the involvement and participation of the patients and their caregivers in the therapeutic process, whose will have an active and collaborative role with the medical team in the treatment decision-making procedure. m-RESIST system, in order to reduce the severity of episodes and further complications, will ensure the continuation of health care services and also will provide a new tool for a better monitorization of the patients through a personalised and optimised integrated therapeutic process. m-RESIST project is founded by Horizon 2020 Framework Programme of the European Union. PHC26 2014 m-RESIST "Mobile Therapeutic Attention for patients with Treatment Resistant Schizophrenia". The authors listed in this abstract are only a part of the m-RESIST Group. [ABSTRACT FROM AUTHOR]

Mild Cognitive Impairment (MCI) causes slight but noticeable disruption in cognitive systems, primarily executive and memory functions. However, it is not clear if the development of sequence learning is affected by an impaired cognitive system and, if so, how. The goal of our study was to investigate the development of probabilistic sequence learning, from the initial acquisition to consolidation, in MCI and healthy elderly control groups.We used the Alternating Serial Reaction Time task (ASRT) to measure probabilistic sequence learning. Individuals with MCI showed weaker learning performance than the healthy elderly group. However, using the reaction times only from the second half of each learning block-after the reactivation phase-we found intact learning in MCI. Based on the assumption that the first part of each learning block is related to reactivation/recall processes, we suggest that these processes are affected in MCI. The 24-h offline period showed no effect on sequence-specific learning in either group but did on general skill learning: the healthy elderly group showed offline improvement in general reaction times while individuals with MCI did not. Our findings deepen our understanding regarding the underlying mechanisms and time course of sequence acquisition and consolidation. [ABSTRACT FROM AUTHOR]

In European Union, between 0.2 and 2.6% of the population suffer from psychotic disorders (Wittchen et al., 2011). The largest group is patients with schizophrenia, and around 40% can be considered resistant to pharmacological treatment (Essock et al., 1996; Kennedy et al., 2014). Standard intervention in patients with treatment-resistant schizophrenia (TRS) is difficult due to the persistence of positive symptoms, extensive periods of hospitalization, and higher risk of comorbidities. The full scope of this scenario generates a financial burden on society because of frequent emergency visits, hospitalizations and chronic use of polytherapy (de Silva et al., 2012), as well as a huge impact in the humanistic burden. This concerns patients and caregivers, and involves several dimensions such as quality of life, treatment side effects, caregiver burden, social impairment, suicide, violence, and healthy lifestyle among the most important (Millier et al., 2014 ) In this sense, we have developed the m-RESIST, an intervention programme based on m-Health that offer to TRS patients an holistic approach to: i) integrate psychiatric and psychosocial assistance with other medical health-carers; ii) better monitorization of symptoms through a personalised and optimised therapeutic process; iii) promote acceptance and self-management of disease and co-morbidities; and iv) potentiate involvement of patients and their caregivers in the therapeutic process. Moreover, there are no studies in TRS patients that collect real-time information from patients that allow monitoring, evaluation and intervention in the main areas of the disease, as well as in situations of risk. In this regard, m-RESIST brings an excellent opportunity to access this information, as well as permit an immediate and tailored intervention. This program will have integrated a mobile application that will deliver three possible intervention packages: treatment adherence, healthy lifestyle and symptoms management that include training strategies based on cognitive behaviour therapy for psychosis. Clinical algorithms have been created for each intervention package in order to deliver tailored recommendations to patients, caregivers and clinicians, which will be based on clinical and real-time sensor data. Moreover, a risk level ranking system will be used in order to prioritize actions in risk situations. On-going challenges include a pilot test to assess acceptability, usability and satisfaction of patients, caregivers and clinicians with the m-RESIST intervention program. [ABSTRACT FROM AUTHOR]