Within the past several years, various consumer-level devices have been brought to market to provide real-time feedback about changes in patients’ status, including key vital signs that can impact clinical decision-making. Utilization of these technologies is critical to create new patient care efficiencies given ever rising healthcare costs and limited resources. To this end, we are validating a remote monitoring system for febrile neutropenia that includes a thermometer, blood pressure (BP) cuff, and smartwatch with intermittent (hourly) heartrate (HR) measurement. Upon measurement, each device wirelessly transmits data to a central hub via Bluetooth. This remote monitoring system is being validated in an inpatient setting against conventional vital signs measurements acquired by healthcare staff. Once the system accuracy has been validated, we plan to assess this approach for safety and efficiency of remote monitoring in lower-risk outpatient febrile neutropenia patients. Ideally, the system will decrease healthcare utilization by encouraging outpatient management of such low-risk patients with febrile neutropenia via improved monitoring while simultaneously supporting early intervention by providers and caregivers.
Multiple physical and disease morbidities are known to confer risk for early mortality, greater symptom burden, and higher healthcare costs. Innovative health information technologies can improve quality of life, decision-making, monitoring, and outcomes in the ambulatory home context. Using SensePRO, an innovative mobile technology platform (smartwatch + beacons) developed at the UCLA Center for SMART Health, physical and functional data collected from a combination of wearable and home sensors are continuously combined with contextual data such as patient-reported outcomes (PROs). The goal of the project is to risk-stratify, monitor, communicate daily and weekly wellness status, and to inform efforts to reduce the need for acute, costly, and distressing health services (emergency room visits and hospitalization). This population-based study is being conducted in conjunction with UCLA Health. Our system collects PROs derived using the NIH PROMIS question bank, capturing measures related to physical function, anxiety, depression, fatigue, sleep, social activities, and pain. These data are then automatically combined with context-aware system measures: energy, activity, position (lying, sitting, standing), mobility (walking, steps, walking speed), indoor localization, and key daily activities. Based on this comprehensive view, daily and weekly summaries of the data are being developed to improve the management of patients through communication with case workers, caregivers, and providers.
The prevalence of obesity and its consequent comorbidities continues to rise globally. Markedly, in the United States it is a major cause of preventable chronic disease and thus a source of significant healthcare costs. mHealth technologies have the potential to promote healthier behaviors in individuals, including improvements in diet and exercise. This project is an exploratory study using wearable technologies as an adjunct to an existing weight loss intervention trial, combining cognitive behavioral therapy (CBT) with a gut-targeted (hypocaloric) diet. We are investigating if wearable technologies can enhance adherence to treatment, providing a low-cost and unobtrusive means to improve and maintain weight loss outcomes. Insights from this effort will help us better understand the strengths and weaknesses of mHealth to train individuals over time, and its potential for helping people prevent obesity
The Center for SMART Health is harnessing a wide range of wearable technologies to provide sophisticated, continuous physical activity tracking methods and objective assessment, while still being unobtrusive and easy to use in daily activities. Our Sensing in At-Risk Populations (SARP) is enabling healthcare providers to gain a detailed understanding of an individual’s functional, mental, and psychological well-being, particularly among frail patients with serious illnesses or high-risk comorbidities. Combining wearable and environmental sensors, wireless devices, and a HIPAA-compliant, cloud-based analytic engine, SARP provides real-time display of a user’s status over time, providing healthcare providers and caregivers a daily storyline. SARP’s algorithms provide for early prediction of rehabilitation failure or success, enabling staff to efficiently triage, admit, and discharge patients.
With increased COVID-19 testing and the detection of more positive cases (COVID+), many “at-risk” individuals who appear asymptomatic or only with mild symptoms are being told to self-quarantine at home. Unfortunately, there are situations where a sudden worsening of the disease occurs and symptoms progress quickly, usually within 24-48 hours. This rapid health decline often goes unreported until too late. More proactive surveillance of these COVID+ individuals could provide an alert to healthcare providers to initiate earlier intervention and save lives.
Our project adapts our current mHealth framework to the immediate needs of the COVID-19 crisis. Unlike other mHealth efforts that are using apps for contact tracing, self-reporting of symptoms, or new sensors for detecting disease onset, we focus on using mHealth to enable (safe) remote monitoring of known COVID+ patients. We originally created the Sensing for At-Risk Patients (SARP) platform to automatically collect information on older, frail patients with different conditions. SARP uses a smartwatch and different sensors to capture this information, automatically and securely sending the data to UCLA Health for review by healthcare staff. We are extending SARP by adding several FDA-approved devices (thermometer, pulse oximeter, respiratory monitor) to augment its present capabilities. SARP will be used to gather objective observations for at-home COVID+ cases, with the data reviewed by healthcare providers to assess patient health.
Capturing this data will further elucidate the myriad presentation of symptoms in COVID+ patients in a natural environment, helping establish a better understanding of this disease and the evolving pandemic. As more insight is gained about these patients and the presentation of the disease, we will explore the application of machine learning methods on this data to detect early patterns of worsening symptoms, leading to potential clinical decision support tools for physicians.
Pediatric asthma presently affects over 6 million children in the United States. As the most common chronic childhood disease, uncontrolled asthma results in numerous hospitalizations annually and is a significant burden on both the child and caregivers resulting in a lower quality of life (e.g., decreased playtime and physical activity), days lost from school, and decreased work productivity. The management of childhood asthma is complex, as each individual’s health and triggers differ. Until recently, healthcare providers have not had a way of fully understanding a given child’s asthma in real-life activities and environments. Mobile health (mHealth) technologies now provide a more complete and accurate view of this condition and its ongoing management.
As part of a National Institutes of Health (NIH) research project, we developed Biomedical REAl-Time Health Evaluation (BREATHE), providing a smartwatch/smartphone informatics platform for integrating sensor data from multiple sources. BREATHE combines this information with data from the patient’s electronic health record (EHR) and other online sources to establish a complete, contextualized view of the individual within his/her environment. Personal physiological and local data (e.g., personal air quality monitors, daily spirometry, inhaler usage, etc.) are automatically captured and relayed securely to our cloud-based infrastructure. Combined in real-time with geospatial and other information (e.g., weather, local traffic patterns, regional air quality measures, proximity to parks, etc.), we then begin to establish individually-tailored models of a child’s asthma triggers.
You can find more information on BREATHE here.
Getting a good night’s sleep is essential for both physical and mental health, but it is hard to quantify the quality of sleep. Many studies that have shown that non-electroencephalography techniques are poor indicators of sleep/wakefulness to specific sleep staging (there is usually <70% agreement with polysomnography data). We are working with a novel sensor developed by Circadia to evaluate a new approach to non-contact sleep monitoring and sleep disorder assessment. This device sits beside the bed and uses sonar technology to track body movements, including respiratory motion, and estimates sleep/wake cycles. The validation of this innovative technology would permit remote, non-contact monitoring of individuals’ sleep and associated disorders in lieu of time-consuming sleep diaries and overnight outpatient studies. It would also allow data to be collected on patients using positive airway pressure therapy to gain more insight on non-adherence due to nocturia.
Depression affects an astounding 300 million people worldwide and one of society’s biggest problems – yet it is poorly understood. In 2018, UCLA launched the Depression Grand Challenge (DGC) with the ambitious goal of cutting in half the worldwide burden of depression by 2050. New diagnostic and treatment methods are being explored, including mHealth. The DGC, in a partnership with Apple Inc., launched a major study to provide greater insights into how we diagnose and treat anxiety and depression. Using devices connected to a smartphone/smartwatch, we are obtaining objective measures around sleep, physical activity, heartrate and daily routines to illuminate the relationship between these factors and symptoms of depression and anxiety. This joint effort has the very real potential to transform behavioral health research and clinical care by enabling healthcare providers to note warning signs before severe mental health crises occur. The study is also an important step toward greater understanding of the different types of depression, and which treatments work best for each. As part of this effort, we are helping with subject recruitment, as well as coordinating secure sensor data collection and integrated analysis.