CrossCheck

Wang, Rui; Aung, Min S. H.; Abdullah, Saeed; Brian, Rachel; Campbell, Andrew T.; Choudhury, Tanzeem; Hauser, Marta; Kane, John M.; Merrill, Michael; Scherer, Emily A.; Tseng, Vincent W.-S.; Ben‐Zeev, Dror

doi:10.1145/2971648.2971740

Cited by 214 publications

(89 citation statements)

References 49 publications

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“…But as Spencer’s case shows, sometimes a smartphone app is not the solution. There is also growing excitement about the potential of big data in mental health and using smartphones and sensors to gather tremendous amounts of data from those with mental illness, in order to uncover new insights [7]. Passive data, sensor data gathered without active user engagement (e.g., step count collected automatically by a fitness tracker versus active button presses on a tally counter), is an accelerating area of health research now.…”

Section: Brief Discussionmentioning

confidence: 99%

Patient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in Schizophrenia

Torous¹,

Roux²

2017

JMIR Ment Health

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This patient perspective piece presents an important case at the intersection of mobile health technology, mental health, and innovation. The potential of digital technologies to advance mental health is well known, although the challenges are being increasingly recognized. Making mobile health work for mental health will require broad collaborations. We already know that those who experience mental illness are excited by the potential technology, with many actively engaged in research, fundraising, advocacy, and entrepreneurial ventures. But we don’t always hear their voice as often as others. There is a clear advantage for their voice to be heard: so we can all learn from their experiences at the direct intersection of mental health and technology innovation. The case is cowritten with an individual with schizophrenia, who openly shares his name and personal experience with mental health technology in order to educate and inspire others. This paper is the first in JMIR Mental Health’s patient perspective series, and we welcome future contributions from those with lived experience.

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Section: Brief Discussionmentioning

confidence: 99%

Patient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in Schizophrenia

Torous¹,

Roux²

2017

JMIR Ment Health

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“…Clinicians need to track schizophrenia patients' symptom states to identify risks and adjust treatment as necessary. At the CrossCheck study [1,2,3] partner hospital, Zucker Hillside Hospital, in New York City, schizophrenia outpatients regularly schedule clinical visits with their clinicians. The time between visits varies from once a week to once a month, depending on the patients' symptom severity and risk.…”

Section: [Highlights]mentioning

confidence: 99%

“…Th e CrossCheck study is an ongoing randomized controlled trial [6] conducted in collaboration with a large psychiatric hospital, Zucker Hillside Hospital, in New York City [3]. Th e study aims to recruit 150 participants for 12 months using rolling enrollment.…”

Section: Crosscheck Studymentioning

confidence: 99%

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Wang

Aung

et al. 2017

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.

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“…The StudentLife project used mobile sensing technology to monitor the daily behavior of college students and found that the tracked behaviors were associated with students’ mental states, such as stress [12]. The CrossCheck project used data collected from phone sensors and ecological momentary assessment to build models to predict mental health indicators in schizophrenic patients based on phone sensor data [8]. In addition, mobile technologies have been shown to have advantages in delivering mental health therapies [13,14].…”

Section: Introductionmentioning

confidence: 99%

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

Meng¹,

Hussain²,

Mohr³

et al. 2018

J Med Internet Res

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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.

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CrossCheck

Cited by 214 publications

References 49 publications

Patient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in Schizophrenia

Patient-Driven Innovation for Mobile Mental Health Technology: Case Report of Symptom Tracking in Schizophrenia

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

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

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