Dataset Information

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

ABSTRACT:

Background

Today, 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.

Objective

The 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.

Methods

We 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.

Results

From 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.

Conclusions

Although 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.

SUBMITTER: Meng J

PROVIDER: S-EPMC6068382 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

Meng Jingbo J Hussain Syed Ali SA Mohr David C DC Czerwinski Mary M Zhang Mi M

Journal of medical Internet research 20180717 7

<h4>Background</h4>Today, 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.<h4>Objective</h4>The main object ...[more]

PMID: 30021710

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Project description:BackgroundAlthough effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care.ObjectiveThis study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity.MethodsA total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9).ResultsA total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079).ConclusionsThis study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

Project description:BACKGROUND:Hundreds of mental health apps are available to the general public. With increasing pressures on health care systems, they offer a potential way for people to support their mental health and well-being. However, although many are highly rated by users, few are evidence-based. Equally, our understanding of what makes apps engaging and valuable to users is limited. OBJECTIVE:The aim of this paper was to analyze functionality and user opinions of mobile apps purporting to support cognitive behavioral therapy for depression and to explore key factors that have an impact on user experience and support engagement. METHODS:We systematically identified apps described as being based on cognitive behavioral therapy for depression. We then conducted 2 studies. In the first, we analyzed the therapeutic functionality of apps. This corroborated existing work on apps' fidelity to cognitive behavioral therapy theory, but we also extended prior work by examining features designed to support user engagement. Engagement features found in cognitive behavioral therapy apps for depression were compared with those found in a larger group of apps that support mental well-being in a more general sense. Our second study involved a more detailed examination of user experience, through a thematic analysis of publicly available user reviews of cognitive behavioral therapy apps for depression. RESULTS:We identified 31 apps that purport to be based on cognitive behavioral therapy for depression. Functionality analysis (study 1) showed that they offered an eclectic mix of features, including many not based on cognitive behavioral therapy practice. Cognitive behavioral therapy apps used less varied engagement features compared with 253 other mental well-being apps. The analysis of 1287 user reviews of cognitive behavioral therapy apps for depression (study 2) showed that apps are used in a wide range of contexts, both replacing and augmenting therapy, and allowing users to play an active role in supporting their mental health and well-being. Users, including health professionals, valued and used apps that incorporated both core cognitive behavioral therapy and non-cognitive behavioral therapy elements, but concerns were also expressed regarding the unsupervised use of apps. Positivity was seen as important to engagement, for example, in the context of automatic thoughts, users expressed a preference to capture not just negative but also positive ones. Privacy, security, and trust were crucial to the user experience. CONCLUSIONS:Cognitive behavioral therapy apps for depression need to improve with respect to incorporating evidence-based cognitive behavioral therapy elements. Equally, a positive user experience is dependent on other design factors, including consideration of varying contexts of use. App designers should be able to clearly identify the therapeutic basis of their apps, but they should also draw on evidence-based strategies to support a positive and engaging user experience. The most effective apps are likely to strike a balance between evidence-based cognitive behavioral therapy strategies and evidence-based design strategies, including the possibility of eclectic therapeutic techniques.

Project description:BackgroundThe growing number of patients with complex chronic conditions presents an urgent challenge across the Canadian health care system. Current care delivery models are overburdened, struggling to monitor and stabilize the complex needs of this growing patient population.ObjectiveThis qualitative study aimed to explore the needs and perspectives of patients and members of the care team to inform the development of an innovative integrated model of care and the needs of telemonitoring (TM) for patients with complex chronic conditions. Furthermore, we explored how these needs could be successfully embedded to support this novel model of complex chronic care.MethodsA qualitative description design was utilized to conduct and analyze 29 semistructured interviews with patients (n=16) and care team members (CTM) (n=13) involved in developing the model of care in an ambulatory care facility in Southern Ontario. Participants were identified through purposive sampling. Two researchers performed an iterative thematic analysis using NVivo 12 (QSR International; Melbourne, Australia) to gain insights from examining multiple perspectives of different participants on complex chronic care needs.ResultsThe analysis revealed 3 themes and 13 subthemes, including the following: (1) adequate health care delivery remains challenging for patients with complex care needs, (2) insights into how to structure an integrated care model, and (3) opportunities for TM in an integrated model of care. Participants not only identified continued challenges in accessing and navigating care in a fragmented and disconnected delivery system but also identified the need for more self-management support. Patients and CTM described the structure of an integrated model of care, including the need for a clear referral and triage processes and composing a tight-knit circle of collaborating interdisciplinary providers led by a nurse practitioner (NP). Finally, opportunities for TM in an integrated model of care were identified, including increasing access and communication, the ability to monitor specific signs and symptoms, and building a clinical workflow around TM-enabled care.ConclusionsDespite entrenched health care service delivery models, a new model of care is acutely needed to care for patients with complex chronic needs (CCN). NPs are in a unique position to lead TM-enabled integrated models of care. TM can facilitate frequent and necessary monitoring of patients with CCN with more than one condition in integrated models of care.

Dataset Information

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

Background

Objective

Methods

Results

Conclusions

Publications

Exploring User Needs for a Mobile Behavioral-Sensing Technology for Depression Management: Qualitative Study.

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