Dataset Information

Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., ) Project.

ABSTRACT: In this study, the Search Your Mind (S.Y.M., ) project aimed to collect prospective digital phenotypic data centered on mood and anxiety symptoms across psychiatric disorders through a smartphone application (app) platform while using both centralized and decentralized research designs: the centralized research design is a hybrid of a general prospective observational study and a digital platform-based study, and it includes face-to-face research such as informed written consent, clinical evaluation, and blood sampling. It also includes digital phenotypic assessment through an application-based platform using wearable devices. Meanwhile, the decentralized research design is a non-face-to-face study in which anonymous participants agree to electronic informed consent forms on the app. It also exclusively uses an application-based platform to acquire individualized digital phenotypic data. We expect to collect clinical, biological, and digital phenotypic data centered on mood and anxiety symptoms, and we propose a possible model of centralized and decentralized research design.

SUBMITTER: Kang YW

PROVIDER: S-EPMC9334802 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., ) Project.

Kang Ye-Won YW Sun Tai Hui TH Kim Ga-Yeong GY Jung Ho-Young HY Kim Hyun-Jin HJ Lee Seulki S Park Yu Rang YR Tu Jaiden J Lee Jae-Hon JH Choi Kwang-Yeon KY Cho Chul-Hyun CH

Psychiatry investigation 20220721 7

In this study, the Search Your Mind (S.Y.M., ) project aimed to collect prospective digital phenotypic data centered on mood and anxiety symptoms across psychiatric disorders through a smartphone application (app) platform while using both centralized and decentralized research designs: the centralized research design is a hybrid of a general prospective observational study and a digital platform-based study, and it includes face-to-face research such as informed written consent, clinical evalua ...[more]

PMID: 35903061

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Project description:BackgroundMental health is integral to our salubrity, but mental disorders are very debilitating and common. Therefore, it is critical to provide accessible, timely, and inexpensive mental care. This can be done through mobile health (mHealth), namely, mobile medical apps, which are gaining popularity among clinicians and patients. mHealth is a fast-paced field, and there is significant variation in the number of installs among psychiatry apps. However, the factors that influence psychiatry app installs have yet to be studied.ObjectiveThis study aimed to identify predictors of the number of app installs in psychiatry.MethodsA literature review identified which factors influence app installs. Psychiatry apps available in the Google Play Store were reviewed, and publicly available data were collected. A multivariate ordinal logistic regression analysis was performed to evaluate the effect of said factors on the number of installs.ResultsOur search identified 128 psychiatry apps: 2.3% (3/128) had never been installed, approximately half (53.1%, 68/128) had less than 500 installs, and only 0.8% (1/128) had over 10,000,000 installs. A multivariate logistic regression analysis identified that apps with a lower price (P<.001), a higher rating (P<.001), optional in-app purchases (P<.001), and age restriction (P=.04) had a higher number of installs. The involvement of a psychiatrist or other health care professional (HCP) had no statistically significant influence on the number of installs. Only data from the Google Play Store and the developers' websites were available for analysis, and the depth of involvement of HCPs was impossible to document.ConclusionsPsychiatry apps with a lower price, optional in-app purchases, age restriction, and a higher rating are expected to have a higher number of installs. Unlike other medical fields, in this study, the explicit participation of psychiatrists in app development was not a significant predictor of the number of installs. Research is needed to identify other factors that may influence the number of installs, as that can help mHealth app development.

Project description:BackgroundKnowledge of the quantity and quality of apps related to coronavirus disease (COVID-19) is lacking. In addition, no directory has been established listing all the apps developed to address the COVID-19 pandemic.ObjectiveThe aim of this study was to identify smartphone apps designed to address the COVID-19 pandemic and to analyze their characteristics.MethodsWe performed an observational, cross-sectional, descriptive study of all smartphone apps associated with COVID-19. Between April 27 and May 2, 2020, we searched the App Store (iOS) and Google Play Store (Android) for COVID-19 apps. The search terms used were coronavirus, COVID-19, and SARS-COV-2. The apps were downloaded and evaluated. The variables analyzed were name, platform, country, language, category, cost, update date, size, version, number of downloads, developer, and purpose. Purpose was further classified into the following categories: news, general information, self-diagnosis, contact tracing, notices to contacts, notification of close cases, awareness, helplines, monitoring of clinical parameters, recording of symptoms and treatment, and messaging with health care professionals.ResultsWe identified 114 apps on the investigated platforms. Of these, 62/114 (54.4%) were on Android and 52/114 (45.6%) were on iOS. Of the 114 apps, 37 (32.5%) were developed in Europe, 32 (28.1%) in Asia, and 30 (26.3%) in North America. The most frequent languages were English (65/114, 57.0%), Spanish (34/114, 29.8%), and Chinese (14/114, 12.3%). The most common categories were health and well-being/fitness apps (41/114, 41.2%) and medicine apps (43/114, 37.7%). Of the 114 apps, 113 (99.1%) were free. The mean time between the date of the analysis and the date of the last update was 11.1 days (SD 11.0). Overall, 95 of the 114 apps (83.3%) were intended for the general population, 99 apps (7.9%) were intended for health professionals, and 3 apps (2.6%) were intended for both. Regarding the type of developer, 64/114 apps (56.1%) were developed by governments; 42/114 (64.1%) were developed by national governments, and 23/114 (35.9%) were developed by regional governments. The apps with the highest number of downloads (100,000+) were developed by governments (P=.13), except for the World Health Organization app (500,000+). The purposes of the apps available in Western languages (107/114, 93.9%) were determined; the most common purposes were general information about COVID-19 (66, 64.0%), COVID-19 news (53, 51.0%), recording of symptoms (53, 51.0%), and contact tracing (51, 47.7%). More than one purpose was identified for 99/107 apps (92.5%).ConclusionsThis paper offers a comprehensive and unique review of all available COVID-19 apps. Governments have adopted these tools during the pandemic, and more than half of the apps were developed by government agencies. The most common purposes of the apps are providing information on the numbers of infected, recovered, and deceased patients, recording of symptoms, and contact tracing.

Project description:BackgroundThe researchers have used mobile phones to assist in monitoring, analyzing, and managing moods to acquire insight into mood patterns. There is a lack of evidence in their use as clinical tools and interventions, which necessitates a comprehensive review and quality assessment to understand barriers and facilitators for app implementation as an impactful clinical intervention.ObjectiveThis review aims to (1) provide an overview of the recent evidence on mobile mood-monitoring apps that are intended for facilitating self-management and support of mental health in children, adolescents, and young people; and (2) investigate the quality of publicly available apps.MethodsThe study will first involve a scoping review of the literature on mood-monitoring apps for children, adolescents, and young people followed by an evaluation of features of the apps available in the marketplace. The scoping review will follow the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and search 6 databases- Embase, CINAHL, PubMed, ACM Digital Library, Scopus, and Springer LNCS-for relevant studies and reviews published in the last 3 years. The author will then screen the references, extract data from the included studies, and analyze them to synthesize the evidence on mood apps. Next, the Apple App Store and Google Play Store will be searched for mood apps. A total of 2 independent reviewers will screen the apps based on eligibility criteria, and disagreements will be resolved through consensus. The features of the selected apps will then be evaluated using the Mobile Health Index and Navigation framework, and descriptive analysis will be used to synthesize the findings.ResultsLiterature search and screening began soon after submission of the protocol and is expected to be completed by September 2024. The app evaluation will be completed by October 2024.ConclusionsCombined, the scoping literature review and app evaluation will provide an in-depth overview of the most recent scientific evidence related to mood apps and the quality of apps actually available for use.International registered report identifier (irrid)PRR1-10.2196/56400.

Project description:BackgroundSmartphones and wearable devices can be used to obtain diverse daily log data related to circadian rhythms. For patients with mood disorders, giving feedback via a smartphone app with appropriate behavioral correction guides could play an important therapeutic role in the real world.ObjectiveWe aimed to evaluate the effectiveness of a smartphone app named Circadian Rhythm for Mood (CRM), which was developed to prevent mood episodes based on a machine learning algorithm that uses passive digital phenotype data of circadian rhythm behaviors obtained with a wearable activity tracker. The feedback intervention for the CRM app consisted of a trend report of mood prediction, H-score feedback with behavioral guidance, and an alert system triggered when trending toward a high-risk state.MethodsIn total, 73 patients with a major mood disorder were recruited and allocated in a nonrandomized fashion into 2 groups: the CRM group (14 patients) and the non-CRM group (59 patients). After the data qualification process, 10 subjects in the CRM group and 33 subjects in the non-CRM group were evaluated over 12 months. Both groups were treated in a similar manner. Patients took their usual medications, wore a wrist-worn activity tracker, and checked their eMoodChart daily. Patients in the CRM group were provided with daily feedback on their mood prediction and health scores based on the algorithm. For the CRM group, warning alerts were given when irregular life patterns were observed. However, these alerts were not given to patients in the non-CRM group. Every 3 months, mood episodes that had occurred in the previous 3 months were assessed based on the completed daily eMoodChart for both groups. The clinical course and prognosis, including mood episodes, were evaluated via face-to-face interviews based on the completed daily eMoodChart. For a 1-year prospective period, the number and duration of mood episodes were compared between the CRM and non-CRM groups using a generalized linear model.ResultsThe CRM group had 96.7% fewer total depressive episodes (n/year; exp β=0.033, P=.03), 99.5% shorter depressive episodes (total; exp β=0.005, P<.001), 96.1% shorter manic or hypomanic episodes (exp β=0.039, P<.001), 97.4% fewer total mood episodes (exp β=0.026, P=.008), and 98.9% shorter mood episodes (total; exp β=0.011, P<.001) than the non-CRM group. Positive changes in health behaviors due to the alerts and in wearable device adherence rates were observed in the CRM group.ConclusionsThe CRM app with a wearable activity tracker was found to be effective in preventing and reducing the recurrence of mood disorders, improving prognosis, and promoting better health behaviors. Patients appeared to develop a regular habit of using the CRM app.Trial registrationClinicalTrials.gov NCT03088657; https://clinicaltrials.gov/ct2/show/NCT03088657.

Dataset Information

Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., ) Project.

Publications

Design and Methods of a Prospective Smartphone App-Based Study for Digital Phenotyping of Mood and Anxiety Symptoms Mixed With Centralized and Decentralized Research Form: The Search Your Mind (S.Y.M., ) Project.

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