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      Mobile Health for Obsessive-Compulsive Disorder: Patients' Preferences and Perception of Patient-Centeredness

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          Abstract

          Background:

          The increasingly fast development of mobile health technologies holds significant value for individuals dealing with mental health conditions. However, inadequate consideration of patients' preferences and expectations undermines real-world outcomes, including sustained adherence. Driven by the belief that specific characteristics, such as youth and higher education, of individuals with obsessive-compulsive disorder make them suitable for digital adoption, we investigated mHealth-related desirability factors within this patient group.

          Methods:

          Fifty-one conveniently selected adults with obsessive-compulsive disorder filled in a self-report questionnaire about symptom self-management preferences, with an emphasis on assessing mobile health options and perceptions of patient-centeredness.

          Results:

          The smartphone phone app emerged as the top choice of most of the sample for receiving information about symptom status (82.4%), obtaining general information about obsessive-compulsive disorder (74.5%), and symptom self-registration (66.7%), with no significant effect of sex or living location. Although only 23.5% of participants were using a health-related app, most expressed interest in using it for receiving symptom management tips (98.1%), medical advice (94.2%), symptom evolution updates (90.2%), lifestyle information (92.2%), medication tracking (88.2%) and short symptom self-reports (90.2%). Median expectations regarding mobile health's impact on patient-centeredness, satisfaction, and adherence were positive or very positive.

          Conclusions:

          Our data confirm that individuals with obsessive-compulsive disorder exhibit strong inclinations and optimistic expectations toward technology-based solutions. We highlight some of the preferences within this patient group, which can inform the design of practical, real-world applications.

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          Most cited references40

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          Scaling-up treatment of depression and anxiety: a global return on investment analysis.

          Depression and anxiety disorders are highly prevalent and disabling disorders, which result not only in an enormous amount of human misery and lost health, but also lost economic output. Here we propose a global investment case for a scaled-up response to the public health and economic burden of depression and anxiety disorders.
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            Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials.

            Various psychological interventions are effective for reducing symptoms of anxiety when used alone, or as an adjunct to anti-anxiety medications. Recent studies have further indicated that smartphone-supported psychological interventions may also reduce anxiety, although the role of mobile devices in the treatment and management of anxiety disorders has yet to be established.
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              New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research

              Background A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health.
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                Author and article information

                Journal
                Actas Esp Psiquiatr
                Actas Esp Psiquiatr
                stme
                Actas Españolas de Psiquiatría
                Actas Españolas de Psiquiatría
                1139-9287
                1578-2735
                5 January 2025
                Jan-Feb 2025
                : 53
                : 1
                : 100-109
                Affiliations
                [1] 1Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
                [2] 2Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, 3000-548 Coimbra, Portugal
                [3] 3Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal
                [4] 4Department of Psychiatry, Coimbra Hospital and University Centre, 3004-561 Coimbra, Portugal
                [5] 5NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisboa, Portugal
                Author notes
                *Corresponding author details: Ana Isabel Araújo, Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal; Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, 3000-548 Coimbra, Portugal; Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, 3000-548 Coimbra, Portugal; Department of Psychiatry, Coimbra Hospital and University Centre, 3004-561 Coimbra, Portugal. Email: araujo.ana90@ 123456gmail.com
                Author information
                https://orcid.org/0000-0002-8407-5821
                Article
                10.62641/aep.v53i1.1715
                10.62641/aep.v53i1.1715
                11726213
                39801413
                10ed4be0-2e50-4cd4-8a4c-542654fda73f
                Copyright: © 2025 The Author(s).

                This is an open access article under the CC BY 4.0 license .

                History
                : 11 June 2024
                : 31 July 2024
                : 9 August 2024
                Funding
                Funded by: Portuguese Foundation for Science and Technology
                Award ID: 2020.08114.BD
                This study was funded by the Portuguese Foundation for Science and Technology (fund number: 2020.08114.BD/ https://doi.org/10.54499/2020.08114. BD), as this entity is responsible for funding the research activity of the principal investigator.
                Categories
                Article

                obsessive-compulsive disorder,mhealth,patient preferences,patient-centered care

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