Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials

Jefferson Digital Commons quarterly report: January-March 2020

This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common

The validity of smartphone data and its relationship to clinical symptomatology and brain biology: an exploratory analysis

BACKGROUND: Presently, there is very little research on the clinical validity of mental health smartphone application data, its relationship to brain biology, and its ability to inform clinical decisions. This paper seeks to explore these relationships within a sample of schizophrenic patients through the analysis of data collected on the mental health smartphone application Biewe. OBJECTIVES: To validate mental health smartphone applications and support their potential to augment clinical practice. METHODS: The application involved a series of 21 questions from several questionnaires including Patient Health Questionnaire-8 (PHQ-8), Generalized Anxiety Disorder-7 (GAD-7), Warning Signals Scale (WSS), Pittsburgh Sleep Quality Index, and the psychosis subscale of the Mini Mental State Examination. Data was collected over a period of 3 months, and patients attended a total of 4 clinic visits during this timeframe. Seven study participants also had brain scan data available from the BSNIP, PARDIP and Biceps studies currently in progress at MMHC which has been used for analysis. The structural MPRAGE T1 scans were processed using Free Surfer 6 in which thickness and volume measures were extracted. All statistical analyses on the data were carried out using R statistics software. RESULTS: Clinic and application responses within the same week were not significantly different from each other. The application answers, however, appeared to be more sensitive to structural abnormalities in the brain. Symptoms defined as a lack of normal emotional responses (i.e. negative symptoms of schizophrenia) were negatively correlated to home time and positively correlated to distance travelled, which was a counterintuitive result. CONCLUSIONS: The results show that mobile monitoring has the potential to be a valid and reliable method of data collection and that it may be able to augment clinical decision making

Developing Student, Family, and School Constructs From NLTS2 Data

The purpose of this study was to use data from the National Longitudinal Transition Study–2 (NLTS2) to (a) conceptually identify and empirically establish student, family, and school constructs; (b) explore the degree to which the constructs can be measured equivalently across disability groups; and (c) examine latent differences (means, variances, and correlations) in the constructs across disability groups. Conceptual analysis of NLTS2 individual survey items yielded 21 student, family, and school constructs, and 16 were empirically supported. Partial strong metric invariance was established across disability groups, and in the latent space, a complex pattern of mean and variance differences across disability groups was found. Disability group moderated the correlational relationships between multiple predictor constructs, suggesting the key role of disability-related characteristics in understanding the experiences of youth with disabilities. Implications for future research and practice are discussed

Dissertation: Examining and investigating home modifications and smart home technologies to reduce fall injury among older adults.

Nearly one in six U.S. residents are over the age of 65. The proportion of older adults in the U.S. is anticipated to grow to 22.1% of the total population by 2050. The cost of treating age related conditions and injuries is expensive, government programs including Medicaid paid over $550 Billion in 2017, and makes up between 14-16% of the federal budget each year. With the high cost of treating age related conditions and injuries, and the proportion of older adults continuing to increase every year, it is imperative that researchers and government entities find and invest in preventative measures in order to reduce injury and related healthcare costs. Among the many age-related injuries older adults suffer, falls are arguably the most important to address. It is estimated that one in three older adults has a fall every year. In 2016, falls were the seventh leading cause of death among older adults. Approximately one third of all fallers require medical attention after experiencing a fall. Over 800,000 older adults are hospitalized each year due to fall related injuries. Injuries sustained as a result of a serious fall include various fractures, traumatic brain injuries, and other cuts and bruises.Home modifications, and more recently smart home technologies, can help increase the safety of older adults living in the community. With older adults wanting to “age in place”, installing these modifications and technologies before an accident happens may lower rates of injury. Today, dozens of companies sell various smart home devices for the consumer market. Bud despite the high demand for these technologies by the American consumer, the ability of these devices to keep older adults safe, and how older adults value these technologies, remains uncertain. These home technologies may be particularly beneficial to older adults living in rural areas due to the increased isolation and limited access to healthcare resources. Previous research indicates rural populations have a greater proportion of older adults compared to urban areas, yet lack the infrastructure to provide specialty care to this population. It is estimated that more than 60 million family members provide some sort of informal care to an older adult relative. Of all of these family members, nearly 40% report spending 20 or more hours a week providing this unpaid care. Previous research has failed to examine how these family members feel about home modifications and technologies for their older adult relative. Finding ways to ease the burden of caring for older family members will significantly better the situations of many family relatives.This dissertation aims to cover three areas. 1. Identify people at risk of suffering subsequent fall injuries. Find the average time between an initial fall injury and a subsequent fall injury, and find average time between an initial fall injury and death.2. Examine the preferences of older adults living in a rural area towards various smart home technologies and home modifications.3. Examine the preferences of family members of older adults regarding smart home technologies and home modifications

Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time