Developing a novel mobile application for cognitive behavioral therapy for insomnia for people with schizophrenia: integration of wearable and environmental sleep sensors.

BACKGROUND: People with serious mental illnesses (SMIs) have three-fold higher rates of comorbid insomnia than the general population, which has downstream effects on cognitive, mental, and physical health. Cognitive Behavioral Therapy for Insomnia (CBT-i) is a safe and effective first-line treatment for insomnia, though the therapy's effectiveness relies on completing nightly sleep diaries which can be challenging for some people with SMI and comorbid cognitive deficits. Supportive technologies such as mobile applications and sleep sensors may aid with completing sleep diaries. However, commercially available CBT-i apps are not designed for individuals with cognitive deficits. To aid with this challenge, we have developed an integrated mobile application, named "Sleep Catcher," that will automatically incorporate data from a wearable fitness tracker and a bed sensor to track nightly sleep duration, overnight awakenings, bed-times, and wake-times to generate nightly sleep diaries for CBT-i. METHODS: The application development process will be described-writing algorithms to generating useful data, creating a clinician web portal to oversee patients and the mobile application, and integrating sleep data from device platforms and user input. RESULTS: The mobile and web applications were developed using Flutter, IBM Code Engine, and IBM Cloudant database. The mobile application was developed with a user-centered approach and incremental changes informed by a series of beta tests. Special user-interface features were considered to address the challenges of developing a simple and effective mobile application targeting people with SMI. CONCLUSION: There is strong potential for synergy between engineering and mental health expertise to develop technologies for specific clinical populations. Digital health technologies allow for the development of multi-disciplinary solutions to existing health disparities in vulnerable populations, particularly in people with SMI.

Differences in metabolic biomarkers in people with schizophrenia who are of Mexican descent compared to non-Hispanic whites.

Metabolic dysfunction is highly prevalent and contributes to premature mortality among people with schizophrenia (PwS), especially in Hispanic/Latino/a/x/e PwS, compared to non-Hispanic White (NHW) PwS. This study evaluated the relative contributions of Mexican descent and schizophrenia diagnosis to metabolic biomarker levels. This cross-sectional study included 115 PwS and 102 non-psychiatric comparison (NC) participants - English-speakers aged 26-66 years, 27% Mexican descent, and 52% women across both groups. Assessments included evaluations of BMI, psychopathology, and fasting metabolic biomarkers. We used ANOVA analyses to compare metabolic outcomes between diagnostic and ethnic subgroups, linear regression models to examine associations between Mexican descent and metabolic outcomes, and Spearman's correlations to examine relationships between metabolic outcomes and illness-related variables in PwS. Mexican PwS had higher hemoglobin A1c levels, insulin resistance, and body mass index than NHW PwS. Mexican descent was associated with higher hemoglobin A1c levels, insulin resistance, body mass index, and leptin levels, controlling for age, sex, depression, education, and smoking. Among Mexican PwS, worse negative symptoms were associated with greater insulin resistance. These findings support the possibility of ethnicity-based differences in metabolic dysregulation, though further investigation is warranted to create targeted health interventions for Hispanic PwS.