Rachel M. Brian

Mobile health (mHealth) for mental health in Asia: Objectives, strategies, and limitations

Mobile technologies are transforming the way in which we interact with one another, access resources, find information, and conduct business around the world. Harnessing the capabilities of mobile technologies to support health care initiatives worldwide has developed into a new interdisciplinary field called mobile health (mHealth). In the current paper, we review the penetration of mobile technology in Asia, and consider the integration of mobile technologies into the study, diagnoses, and treatment of mental disorders in the region. We outline how mHealth programs could improve mental health literacy, provide greater access to mental health services, extend community-based outreach and engagement, support self-management of illness, and regulate medication distribution. We end with a consideration of the potential barriers and limitations of mHealth for mental health, including funding, language and literacy barriers, power supply considerations, data security, and privacy issues.

CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia

Early detection of mental health changes in individuals with serious mental illness is critical for effective intervention. CrossCheck is the first step towards the passive monitoring of mental health indicators in patients with schizophrenia and paves the way towards relapse prediction and early intervention. In this paper, we present initial results from an ongoing randomized control trial, where passive smartphone sensor data is collected from 21 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-8.5 months. Our results indicate that there are statistically significant associations between automatically tracked behavioral features related to sleep, mobility, conversations, smart-phone usage and self-reported indicators of mental health in schizophrenia. Using these features we build inference models capable of accurately predicting aggregated scores of mental health indicators in schizophrenia with a mean error of 7.6% of the score range. Finally, we discuss results on the level of personalization that is needed to account for the known variations within people. We show that by leveraging knowledge from a population with schizophrenia, it is possible to train accurate personalized models that require fewer individual-specific data to quickly adapt to new users.

CrossCheck: Integrating self-report, behavioral sensing, and smartphone use to identify digital indicators of psychotic relapse.

Objective: This purpose of this study was to describe and demonstrate CrossCheck, a multimodal data collection system designed to aid in continuous remote monitoring and identification of subjective and objective indicators of psychotic relapse. Method: Individuals with schizophrenia-spectrum disorders received a smartphone with the monitoring system installed along with unlimited data plan for 12 months. Participants were instructed to carry the device with them and to complete brief self-reports multiple times a week. Multimodal behavioral sensing (i.e., physical activity, geospatials activity, speech frequency, and duration) and device use data (i.e., call and text activity, app use) were captured automatically. Five individuals who experienced psychiatric hospitalization were selected and described for instructive purposes. Results: Participants had unique digital indicators of their psychotic relapse. For some, self-reports provided clear and potentially actionable description of symptom exacerbation prior to hospitalization. Others had behavioral sensing data trends (e.g., shifts in geolocation patterns, declines in physical activity) or device use patterns (e.g., increased nighttime app use, discontinuation of all smartphone use) that reflected the changes they experienced more effectively. Conclusion: Advancements in mobile technology are enabling collection of an abundance of information that until recently was largely inaccessible to clinical research and practice. However, remote monitoring and relapse detection is in its nascence. Development and evaluation of innovative data management, modeling, and signal-detection techniques that can identify changes within an individual over time (i.e., unique relapse signatures) will be essential if we are to capitalize on these data to improve treatment and prevention.

Using Facebook to Reach People Who Experience Auditory Hallucinations

Background Auditory hallucinations (eg, hearing voices) are relatively common and underreported false sensory experiences that may produce distress and impairment. A large proportion of those who experience auditory hallucinations go unidentified and untreated. Traditional engagement methods oftentimes fall short in reaching the diverse population of people who experience auditory hallucinations. Objective The objective of this proof-of-concept study was to examine the viability of leveraging Web-based social media as a method of engaging people who experience auditory hallucinations and to evaluate their attitudes toward using social media platforms as a resource for Web-based support and technology-based treatment. Methods We used Facebook advertisements to recruit individuals who experience auditory hallucinations to complete an 18-item Web-based survey focused on issues related to auditory hallucinations and technology use in American adults. We systematically tested multiple elements of the advertisement and survey layout including image selection, survey pagination, question ordering, and advertising targeting strategy. Each element was evaluated sequentially and the most cost-effective strategy was implemented in the subsequent steps, eventually deriving an optimized approach. Three open-ended question responses were analyzed using conventional inductive content analysis. Coded responses were quantified into binary codes, and frequencies were then calculated. Results Recruitment netted N=264 total sample over a 6-week period. Ninety-seven participants fully completed all measures at a total cost of

Video-Based Mobile Health Interventions for People With Schizophrenia: Bringing the "Pocket Therapist" to Life.

8.14 per participant across testing phases. Systematic adjustments to advertisement design, survey layout, and targeting strategies improved data quality and cost efficiency. People were willing to provide information on what triggered their auditory hallucinations along with strategies they use to cope, as well as provide suggestions to others who experience auditory hallucinations. Women, people who use mobile phones, and those experiencing more distress, were reportedly more open to using Facebook as a support and/or therapeutic tool in the future. Conclusions Facebook advertisements can be used to recruit research participants who experience auditory hallucinations quickly and in a cost-effective manner. Most (58%) Web-based respondents are open to Facebook-based support and treatment and are willing to describe their subjective experiences with auditory hallucinations.

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Objective: To examine whether video-based mobile health (mHealth) interventions are feasible, acceptable, understandable, and engaging to people with schizophrenia. Method: This study used a mixed-methods design. Ten individuals with schizophrenia spectrum disorders were recruited for a month-long trial in which they used FOCUS-Audio/Video (FOCUS–AV), a smartphone system that offers video and written intervention options. Participants completed posttrial measures and engaged in semistructured interviews. Findings: One participant dropped out. The remaining 9 participants used intervention videos successfully. Participants responded to 67% of system-delivered prompts to engage FOCUS–AV, and 52% of FOCUS–AV use was initiated by the users. On average, participants used interventions 6 days a week, 4 times daily. Participants used video functions an average of 28 times. They chose video over written interventions on 67% of the times they used on-demand functions but opted for written content 78% of the times they responded to prescheduled prompts. Clinician videos were rated as more personal, engaging, and helpful than written interventions. Video and written interventions were rated as equally usable and understandable. Written interventions were rated as more favorable in letting users proceed at their own pace. Similarly to what is seen in live therapy, the communication style and demeanor of clinicians depicted in intervention videos reportedly affected participants’ experience with treatment. Conclusions and Implications for Practice: Video-based mHealth may be a feasible, usable, acceptable, and highly engaging method for flexible delivery of interventions to people with schizophrenia using mobile technology. Producing intervention videos is more time-, labor-, and cost-intensive than generating written content, but participant feedback suggests that there may be added value in this approach. Additional research will determine whether video-based mHealth interventions lead to better, faster, or more sustainable clinical gains.

Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia

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.