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      Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review

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          Abstract

          Ecological momentary assessment (EMA) and ecological momentary intervention (EMI) are alternative approaches to retrospective self-reports and face-to-face treatments, and they make it possible to repeatedly assess patients in naturalistic settings and extend psychological support into real life. The increase in smartphone applications and the availability of low-cost wearable biosensors have further improved the potential of EMA and EMI, which, however, have not yet been applied in clinical practice. Here, we conducted a systematic review, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to explore the state of the art of technology-based EMA and EMI for major depressive disorder (MDD). A total of 33 articles were included (EMA = 26; EMI = 7). First, we provide a detailed analysis of the included studies from technical (sampling methods, duration, prompts), clinical (fields of application, adherence rates, dropouts, intervention effectiveness), and technological (adopted devices) perspectives. Then, we identify the advantages of using information and communications technologies (ICTs) to extend the potential of these approaches to the understanding, assessment, and intervention in depression. Furthermore, we point out the relevant issues that still need to be addressed within this field, and we discuss how EMA and EMI could benefit from the use of sensors and biosensors, along with recent advances in machine learning for affective modelling.

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          Most cited references79

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          Cognition and depression: current status and future directions.

          Cognitive theories of depression posit that people's thoughts, inferences, attitudes, and interpretations, and the way in which they attend to and recall information, can increase their risk for depression. Three mechanisms have been implicated in the relation between biased cognitive processing and the dysregulation of emotion in depression: inhibitory processes and deficits in working memory, ruminative responses to negative mood states and negative life events, and the inability to use positive and rewarding stimuli to regulate negative mood. In this review, we present a contemporary characterization of depressive cognition and discuss how different cognitive processes are related not only to each other, but also to emotion dysregulation, the hallmark feature of depression. We conclude that depression is characterized by increased elaboration of negative information, by difficulties disengaging from negative material, and by deficits in cognitive control when processing negative information. We discuss treatment implications of these conclusions and argue that the study of cognitive aspects of depression must be broadened by investigating neural and genetic factors that are related to cognitive dysfunction in this disorder. Such integrative investigations should help us gain a more comprehensive understanding of how cognitive and biological factors interact to affect the onset, maintenance, and course of depression.
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            Wearable Sensors for Remote Health Monitoring

            Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.
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              Mental illness stigma, help seeking, and public health programs.

              Globally, more than 70% of people with mental illness receive no treatment from health care staff. Evidence suggests that factors increasing the likelihood of treatment avoidance or delay before presenting for care include (1) lack of knowledge to identify features of mental illnesses, (2) ignorance about how to access treatment, (3) prejudice against people who have mental illness, and (4) expectation of discrimination against people diagnosed with mental illness. In this article, we reviewed the evidence on whether large-scale anti-stigma campaigns could lead to increased levels of help seeking.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                05 April 2019
                April 2019
                : 8
                : 4
                : 465
                Affiliations
                [1 ]Department of Basic Psychology, Clinic and Psychobiology, Universitat Jaume I, Av. Sos Baynat, s/n, 12071 Castellón, Spain; azucena@ 123456uji.es (A.G.-P.); botella@ 123456uji.es (C.B.)
                [2 ]Department of Psychology, Università Cattolica del Sacro Cuore, Largo Gemelli, 1, 20100 Milan, Italy; javier.fernandezkirszman@ 123456unicatt.it (J.F.-Á.); pietro.cipresso@ 123456unicatt.it (P.C.); giuseppe.riva@ 123456unicatt.it (G.R.)
                [3 ]Department of Computer Science, University of Oxford, Wolfson Building, Parks Rd, Oxford OX1 3QD, UK; andrea.patane@ 123456cs.ox.ac.uk (A.P.); marta.kwiatkowska@ 123456cs.ox.ac.uk (M.K.)
                [4 ]Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, 20149 Milan, Italy; semonellamichelle@ 123456gmail.com
                [5 ]CIBER Fisiopatología Obesidad y Nutrición (CIBERobn), Instituto Salud Carlos III, 28029 Madrid, Spain
                Author notes
                [* ]Correspondence: dcolombo@ 123456uji.es ; Tel.: +34-964-387-644
                Author information
                https://orcid.org/0000-0003-0333-2003
                https://orcid.org/0000-0002-0662-7678
                https://orcid.org/0000-0003-3657-106X
                https://orcid.org/0000-0001-8783-6959
                Article
                jcm-08-00465
                10.3390/jcm8040465
                6518287
                30959828
                53bd45f6-9ee0-4ca1-8950-4c393a71962a
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 06 March 2019
                : 01 April 2019
                Categories
                Review

                major depressive disorder,ecological momentary assessment,ecological momentary intervention

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