Dataset Information

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

ABSTRACT:

Background

There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable.

Objective

The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform.

Methods

A total of 73 participants (67% [49/73] male, 48% [35/73] non-Hispanic white, 33% [24/73] veteran status) who reported at least one symptom of PTSD or depression completed a 12-week field trial. Behavioral indicators were collected through the noninvasive mobile sensing platform on participants' mobile phones. Clinical symptoms were measured through validated clinical interviews with a licensed clinical social worker. A combination hypothesis and data-driven approach was used to derive key features for modeling symptoms, including the sum of outgoing calls, count of unique numbers texted, absolute distance traveled, dynamic variation of the voice, speaking rate, and voice quality. Participants also reported ease of use and data sharing concerns.

Results

Behavioral indicators predicted clinically assessed symptoms of depression and PTSD (cross-validated area under the curve [AUC] for depressed mood=.74, fatigue=.56, interest in activities=.75, and social connectedness=.83). Participants reported comfort sharing individual data with physicians (Mean 3.08, SD 1.22), mental health providers (Mean 3.25, SD 1.39), and medical researchers (Mean 3.03, SD 1.36).

Conclusions

Behavioral indicators passively collected through a mobile sensing platform predicted symptoms of depression and PTSD. The use of mobile sensing platforms can provide clinically validated behavioral indicators in real time; however, further validation of these models and this platform in large clinical samples is needed.

SUBMITTER: Place S

PROVIDER: S-EPMC5374272 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

Place Skyler S Blanch-Hartigan Danielle D Rubin Channah C Gorrostieta Cristina C Mead Caroline C Kane John J Marx Brian P BP Feast Joshua J Deckersbach Thilo T Pentland Alex Sandy AS Nierenberg Andrew A Azarbayejani Ali A

Journal of medical Internet research 20170316 3

<h4>Background</h4>There is a critical need for real-time tracking of behavioral indicators of mental disorders. Mobile sensing platforms that objectively and noninvasively collect, store, and analyze behavioral indicators have not yet been clinically validated or scalable.<h4>Objective</h4>The aim of our study was to report on models of clinical symptoms for post-traumatic stress disorder (PTSD) and depression derived from a scalable mobile sensing platform.<h4>Methods</h4>A total of 73 partici ...[more]

PMID: 28302595

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Project description:BackgroundMood disorders are dynamic disorders characterized by multimodal symptoms. Clinical assessment of symptoms is currently limited to relatively sparse, routine clinic visits, requiring retrospective recollection of symptoms present in the weeks preceding the visit. Novel advances in mobile tools now support ecological momentary assessment of mood, conducted frequently using mobile devices, outside the clinical setting. Such mood assessment may help circumvent problems associated with infrequent reporting and better characterize the dynamic presentation of mood symptoms, informing the delivery of novel treatment options.ObjectivesThe aim of our study was to validate the Immediate Mood Scaler (IMS), a newly developed, iPad-deliverable 22-item self-report tool designed to capture current mood states.MethodsA total of 110 individuals completed standardized questionnaires (Patient Health Questionnaire, 9-item [PHQ-9]; generalized anxiety disorder, 7-Item [GAD-7]; and rumination scale) and IMS at baseline. Of the total, 56 completed at least one additional session of IMS, and 17 completed one additional administration of PHQ-9 and GAD-7. We conducted exploratory Principal Axis Factor Analysis to assess dimensionality of IMS, and computed zero-order correlations to investigate associations between IMS and standardized scales. Linear Mixed Model (LMM) was used to assess IMS stability across time and to test predictability of PHQ-9 and GAD-7 score by IMS.ResultsStrong correlations were found between standard mood scales and the IMS at baseline (r=.57-.59, P<.001). A factor analysis revealed a 12-item IMS ("IMS-12") with two factors: a "depression" factor and an "anxiety" factor. IMS-12 depression subscale was more strongly correlated with PHQ-9 than with GAD-7 (z=1.88, P=.03), but the reverse pattern was not found for IMS-12 anxiety subscale. IMS-12 showed less stability over time compared with PHQ-9 and GAD-7 (.65 vs .91), potentially reflecting more sensitivity to mood dynamics. In addition, IMS-12 ratings indicated that individuals with mild to moderate depression had greater mood fluctuations compared with individuals with severe depression (.42 vs .79; P=.04). Finally, IMS-12 significantly contributed to the prediction of subsequent PHQ-9 (beta=1.03, P=.02) and GAD-7 scores (beta =.93, P=.01).ConclusionsCollectively, these data suggest that the 12-item IMS (IMS-12) is a valid tool to assess momentary mood symptoms related to anxiety and depression. Although IMS-12 shows good correlation with standardized scales, it further captures mood fluctuations better and significantly adds to the prediction of the scales. Results are discussed in the context of providing continuous symptom quantification that may inform novel treatment options and support personalized treatment plans.

Project description:BACKGROUND:As part of the response plans for the current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), authorities are drafting and implementing containment measures across jurisdictions worldwide in the effort to slow down transmission and reduce the infection rate. A solid communication strategy is needed to increase the reach of valid information to health professionals, reduce misinformation, and efficiently implement recommended measures. OBJECTIVE:The aim of this paper is to describe the utilization of a dedicated mobile health (mHealth) platform to disseminate up-to-date and validated information about SARS-CoV-2 to all medical staff of the Children's Hospital at the University Hospitals of Geneva. METHODS:Three documents containing institutional information concerning screening, local containment procedures, and frequently asked questions and answers for parents were made available to the staff through a mobile app developed in the University of Geneva, Switzerland. Using a third-party statistics tool, we anonymously monitored user activity as well as content utilization patterns since the diagnosis of the first case of SARS-CoV-2 in Switzerland on February 25, 2020. RESULTS:From February 25, 2020, to March 13, 2020 (18 days), information documents on SARS-CoV-2 were viewed 859 times, which accounted for 35.6% of the total content views (total views=332). User activity increased significantly with 50.8 (SD 14.4) users per day in this period as compared to the previous weeks (mean 26.4, SD 9.8; P<.001). In addition, session numbers per day more than doubled during the aforementioned period (P<.001). In a survey, medical staff found the information easy to find within the app. On a 10-point Likert scale, the ability of the app to reassure staff in clinical practice was rated as 7.6 (SD 2.1), time-saving ability was rated as 8.5 (SD 2.1), and the need to look for information from other sources was rated as 5.9 (SD 3.3). CONCLUSIONS:The use of an mHealth solution to disseminate novel coronavirus-related information seemed to be an effective and time-saving communication channel within our institution during the SARS-CoV-2 outbreak. Medical staff felt reassured and informed in daily practice. More research should be done on the clinical impact and outcomes of the integration of mHealth solutions as a communication channel of validated information within health institutions.

Project description:BackgroundParental history of mood or anxiety disorders is one of the strongest and most consistent risk factors for the development of these disorders in offspring. Gaps remain however in our knowledge of whether maternal or paternal disorders are more strongly associated with offspring disorders, and whether the association exists in non-clinical samples. This study uses a large population-based sample to test if maternal or paternal history of mood and/or anxiety disorders increases the risk of mood and/or anxiety disorders, or symptoms of specific anxiety disorders, in offspring.MethodsData were drawn from the Nicotine Dependence in Teens Study, a prospective cohort investigation of 1293 grade 7 students. Data on mental health outcomes were collected in mailed self-report questionnaires when participants were aged 20.4 (0.7) years on average. Parental data were collected in mailed self-report questionnaires. This current analysis pertains to 564 participants with maternal and/or paternal data. The association between maternal and paternal history and each of diagnosed anxiety disorder, diagnosed mood disorder, and symptoms of specific anxiety disorders in offspring was studied in multivariate logistic regression.ResultsA higher proportion of mothers than fathers had a diagnosed mood/anxiety disorder (23% versus 12%). Similarly, 14% of female offspring had a diagnosed mood/anxiety disorder, compared to 6% of male offspring. The adjusted odds ratio (95% confidence interval) for maternal history was 2.2 (1.1, 4.5) for diagnosed mood disorders, 4.0 (2.1, 7.8) for diagnosed anxiety disorders, and 2.2 (1.2, 4.0) for social phobia symptoms. Paternal history was not associated with any of the mental health outcomes in offspring.ConclusionMaternal, but not paternal mood/anxiety disorders were associated with diagnosed psychiatric disorders, as well as symptoms of specific anxiety disorders, in offspring. Efforts to detect mood and anxiety disorders in offspring with a maternal history should be encouraged.

Project description:BackgroundToday, college students are dealing with depression at some of the highest rates in decades. As the primary mental health service provider, university counseling centers are limited in their capacity and efficiency to provide mental health care due to time constraints and reliance on students' self-reports. A mobile behavioral-sensing platform may serve as a solution to enhance the efficiency and accessibility of university counseling services.ObjectiveThe main objectives of this study are to (1) understand the usefulness of a mobile sensing platform (ie, iSee) in improving counseling services and assisting students' self-management of their depression conditions, and (2) explore what types of behavioral targets (ie, meaningful information extracted from raw sensor data) and feedback to deliver from both clinician and students' perspectives.MethodsWe conducted semistructured interviews with 9 clinicians and 12 students with depression recruited from a counseling center at a large Midwestern university. The interviews were 40-50 minutes long and were audio recorded and transcribed. The interview data were analyzed using thematic analysis with an inductive approach. Clinician and student interviews were analyzed separately for comparison. The process of extracting themes involved iterative coding, memo writing, theme revisits, and refinement.ResultsFrom the clinician perspective, the mobile sensing platform helps to improve counseling service by providing objective evidence for clinicians and filling gaps in clinician-patient communication. Clinicians suggested providing students with their sensed behavioral targets organized around personalized goals. Clinicians also recommended delivering therapeutic feedback to students based on their sensed behavioral targets, including positive reinforcement, reflection reminders, and challenging negative thoughts. From the student perspective, the mobile sensing platform helps to ease continued self-tracking practices. Students expressed their need for integrated behavioral targets to understand correlations between behaviors and depression. They also pointed out that they would prefer to avoid seeing negative feedback.ConclusionsAlthough clinician and student participants shared views on the advantages of iSee in supporting university counseling, they had divergent opinions on the types of behavioral targets and feedback to be provided via iSee. This exploratory work gained initial insights into the design of a mobile sensing platform for depression management and informed a more conclusive research project for the future.

Project description:The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage. CCS Concepts:• Human-centered computing ? Ubiquitous and mobile computing; Ubiquitous and mobile computing systems and tools; • Computer systems organization ? Embedded and cyber-physical systems. ACM Reference format:Syed Monowar Hossain, Timothy Hnat, Nazir Saleheen, Nusrat Jahan Nasrin, Joseph Noor, Bo-Jhang Ho, Tyson Condie, Mani Srivastava, and Santosh Kumar. 2017. mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions. In Proceedings of SenSys '17, Delft, Netherlands, November 6-8, 2017, 14 pages.

Dataset Information

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

Background

Objective

Methods

Results

Conclusions

Publications

Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders.

Similar Datasets

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