Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study.

BACKGROUND: Serious mental illnesses (SMI) are common, disabling, and challenging to treat, requiring years of monitoring and treatment adjustments. Stress or reduced medication adherence can lead to rapid worsening of symptoms and behaviors. Illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving limited opportunity to modify treatments. Previous research suggests that passive mobile sensing may be beneficial for individuals with SMI by helping them monitor mental health status and behaviors, and quickly detect worsening mental health for prompt assessment and intervention. However, there is too little research on its feasibility and acceptability and the extent to which passive data can predict changes in behaviors or symptoms. OBJECTIVE: The aim of this research is to study the feasibility, acceptability, and safety of passive mobile sensing for tracking behaviors and symptoms of patients in treatment for SMI, as well as developing analytics that use passive data to predict changes in behaviors and symptoms. METHODS: A mobile app monitors and transmits passive mobile sensor and phone utilization data, which is used to track activity, sociability, and sleep in patients with SMI. The study consists of a user-centered design phase and a mobile sensing phase. In the design phase, focus groups, interviews, and usability testing inform further app development. In the mobile sensing phase, passive mobile sensing occurs with participants engaging in weekly assessments for 9 months. Three- and nine-month interviews study the perceptions of passive mobile sensing and ease of app use. Clinician interviews before and after the mobile sensing phase study the usefulness and feasibility of app utilization in clinical care. Predictive analytic models are built, trained, and selected, and make use of machine learning methods. Models use sensor and phone utilization data to predict behavioral changes and symptoms. RESULTS: The study started in October 2020. It has received institutional review board approval. The user-centered design phase, consisting of focus groups, usability testing, and preintervention clinician interviews, was completed in June 2021. Recruitment and enrollment for the mobile sensing phase began in October 2021. CONCLUSIONS: Findings may inform the development of passive sensing apps and self-tracking in patients with SMI, and integration into care to improve assessment, treatment, and patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05023252; https://clinicaltrials.gov/ct2/show/NCT05023252. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39010.

Posttraumatic stress disorder and cardiometabolic disease.

The need for addressing posttraumatic stress disorder (PTSD) among combat veterans returning from Afghanistan and Iraq is a growing public health concern. Current PTSD management addresses psychiatric parameters of this condition. However, PTSD is not simply a psychiatric disorder. Traumatic stress increases the risk for inflammation-related somatic diseases and early mortality. The metabolic syndrome reflects the increased health risk associated with combat stress and PTSD. Obesity, dyslipidemia, hypertension, diabetes mellitus, and cardiovascular disease are prevalent among PTSD patients. However, there has been little appreciation for the need to address these somatic PTSD comorbidities. Medical professionals treating this vulnerable population should screen patients for cardiometabolic risk factors and avail themselves of existing preventive diet, exercise, and pharmacologic modalities that will reduce such risk factors and improve overall long-term health outcomes and quality of life. There is the promise that cardiometabolic preventive therapy complementing psychiatric intervention may, in turn, help improve the posttraumatic stress system dysregulation and favorably impact psychiatric and neurologic function. © 2013 S. Karger AG, Basel.

Driving sandpiles to criticality and beyond.

A popular theory of self-organized criticality relates driven dissipative systems to systems with conservation. This theory predicts that the stationary density of the Abelian sandpile model equals the threshold density of the fixed-energy sandpile. We refute this prediction for a wide variety of underlying graphs, including the square grid. Driven dissipative sandpiles continue to evolve even after reaching criticality. This result casts doubt on the validity of using fixed-energy sandpiles to explore the critical behavior of the Abelian sandpile model at stationarity.

Approach to criticality in sandpiles.

A popular theory of self-organized criticality predicts that the stationary density of the Abelian sandpile model equals the threshold density of the corresponding fixed-energy sandpile. We recently announced that this "density conjecture" is false when the underlying graph is any of Z2, the complete graph K(n), the Cayley tree, the ladder graph, the bracelet graph, or the flower graph. In this paper, we substantiate this claim by rigorous proof and extensive simulations. We show that driven-dissipative sandpiles continue to evolve even after a constant fraction of the sand has been lost at the sink. Nevertheless, we do find (and prove) a relationship between the two models: the threshold density of the fixed-energy sandpile is the point at which the driven-dissipative sandpile begins to lose a macroscopic amount of sand to the sink.