A5030351053- Physical Activity and Health
- Context-Aware Activity Recognition Systems
- Mobile Health and mHealth Applications
- Functional Brain Connectivity Studies
- Balance, Gait, and Falls Prevention
- Health, Environment, Cognitive Aging
- Amyotrophic Lateral Sclerosis Research
- Cardiovascular and exercise physiology
- Lower Extremity Biomechanics and Pathologies
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Neurogenetic and Muscular Disorders Research
- Face and Expression Recognition
- Gait Recognition and Analysis
- Cell Image Analysis Techniques
- Children's Physical and Motor Development
- Statistical Methods and Inference
- MRI in cancer diagnosis
- Sleep and related disorders
- Non-Invasive Vital Sign Monitoring
- Time Series Analysis and Forecasting
- Energy, Environment, Agriculture Analysis
- Parkinson's Disease Mechanisms and Treatments
- Delphi Technique in Research
- Sports Performance and Training
- Data Analysis with R
Harvard University
2022-2024
Metropolitan University
2024
Takeda (United States)
2023-2024
Harvard University Press
2022-2023
Massachusetts General Hospital
2022
Johns Hopkins University
2017-2022
Clinical Research Institute
2022
Evidation Health (United States)
2020
Indiana University Bloomington
2017
Cleveland State University
1998-2002
Abstract Digital clinical measures based on data collected by wearable devices have seen rapid growth in both trials and healthcare. The widely-used wearables are epoch-based physical activity counts using accelerometer data. Even though been the backbone of thousands epidemiological studies, there large variations algorithms that compute their associated parameters—many which often kept proprietary device providers. This lack transparency has hindered comparability between studies different...
Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought if mobile applications (apps) and wearable devices can be used quantify ALS disease progression through active (surveys) passive (sensors) data collection. Forty ambulatory adults with were followed for 6-months. The Beiwe app was administer the self-entry scale-revised (ALSFRS-RSE) Rasch Overall Disability Scale (ROADS)...
Background Given the evolution of processing and analysis methods for accelerometry data over past decade, it is important to understand how newer summary measures physical activity compare with established measures. Objective We aimed objective increase generalizability translation findings studies that use accelerometry-based data. Methods High-resolution from Baltimore Longitudinal Study on Aging were retrospectively analyzed. Data 655 participants who used a wrist-worn ActiGraph GT9X...
Passively collected smartphone sensor data provide an opportunity to study physical activity and mobility unobtrusively over long periods of time may enable disease monitoring in people with amyotrophic lateral sclerosis (PALS).
BackgroundObjective evaluation of people with amyotrophic lateral sclerosis (PALS) in free-living settings is challenging. The introduction portable digital devices, such as wearables and smartphones, may improve quantifying disease progression hasten therapeutic development. However, there a need for tools to characterize upper limb movements neurologic disability.MethodsTwenty PALS wore wearable accelerometer, ActiGraph Insight Watch, on their wrist six months. They also used Beiwe,...
Abstract Background Wearable devices have become widespread in research applications, yet evidence on whether they are superior to structured clinic-based assessments is sparse. In this manuscript, we compare traditional, laboratory-based metrics of mobility with a novel accelerometry-based measure free-living gait cadence for predicting fall rates. Methods Using negative binomial regression, compared traditional in-clinic measures (6-minute cadence, speed, and distance, 4-m speed) from...
The purpose of this article is to review the functional biomechanics ankle foot complex. In addition, assessment and orthotic design implications for individuals in many diagnostic groups who present with limited talocrural dorsiflexion secondary soft-tissue limitations will be discussed. Adequate necessary activities, especially walking. When range unavailable, compensatory strategies are used. A common compensation supplement motion utilize midtarsal joint that occurs conjunction subtalar...
A major challenge in the monitoring of rehabilitation is lack long-term individual baseline data which would enable accurate and objective assessment functional recovery. Consumer-grade wearable devices tracking everyday functioning prior to illness or other medical events necessitate recovery trajectories.For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device steps, heart rate, sleep from 26 weeks before after self-reported date. We identified...
<b><i>Background:</i></b> Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer potential objectively measure functional but are limited by inaccuracy with poor capacity. Open-source methods exist analyze accelerometer data estimate gait cadence (steps/min), which directly associated activity intensity. Here, we...
Background Previous studies investigating environmental and behavioral drivers of chronic disease have often had limited temporal spatial data coverage. Smartphone-based digital phenotyping mitigates the limitations these by using intensive collection schemes that take advantage widespread use smartphones while allowing for less burdensome longer follow-up periods. In addition, smartphone apps can be programmed to conduct daily or intraday surveys on health behaviors psychological...
Quantifying gait parameters and ambulatory monitoring of changes in these have become increasingly important epidemiological clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, accurate method for segmentation individual walking strides. ADEPT computes the covariance between scaled translated function data, an idea similar to continuous wavelet transform. The difference is that uses data-based...
Modern wearable monitors and laboratory equipment allow the recording of high-frequency data that can be used to quantify human movement. However, currently, analysis approaches in these domains remain limited. This article proposes a new framework analyze biomechanical patterns sport training recorded across multiple sessions using multilevel functional models. We apply methods subsecond-level knee location trajectories collected 19 recreational runners during medium-intensity continuous...
Abstract A quantitatively‐driven evaluation of existing clinical data and associated knowledge to accelerate drug discovery development is a highly valuable approach across therapeutic areas, but remains underutilized. This especially the case for rare diseases which particularly challenging. The current work outlines an organizational framework support quantitatively‐based reverse translation development. was applied characterize predictors trajectory cognition in Hunter syndrome...
Objective. We evaluate the stride segmentation performance of Adaptive Empirical Pattern Transformation (ADEPT) for subsecond-level accelerometry data collected in free-living environment using a wrist-worn sensor.Approach. substantially expand scope existing ADEPT pattern-matching algorithm. Methods are applied to continuously 4 weeks 45 participants, including 30 arthritis and 15 control patients. estimate daily walking cadence each participant quantify its association with SF-36 quality...
Abstract Background Digital clinical measures based on data collected by wearable devices have seen rapid growth in both trials and healthcare. The widely-used wearables are epoch-based physical activity counts using accelerometer data. Even though been the backbone of thousands epidemiological studies, there large variations algorithms that compute their associated parameters – many which often kept proprietary device providers. This lack transparency has hindered comparability between...
Abstract Wearable accelerometers provide detailed, objective, and continu-ous measurements of physical activity (PA). Recent advances in technology the decreasing cost wearable devices led to an explosion popula-rity health research. An ever increasing number studies collect high-throughput, sub-second level raw acceleration data. In this paper we discuss problems related collection analysis acce-lerometry data insights into potential solutions. particular, describe size complexity data,...
Abstract Background Given the evolution of processing and analyzing accelerometry data over past decade, it is utmost importance that we as a field understand how newer (e.g., MIMS) summary measures compare to long-established ones ActiGraph activity counts). Objective Our study aims harmonize accelerometry-based physical (PA) increase comparability, generalizability, translation findings across studies using objective PA. Methods High resolution were collected from 655 participants in...
Abstract Step count is one of the most used real-world (RW) outcomes for understanding physical functioning, activity, and overall quality life. In current investigation, we systematically evaluated performances modern wrist-accelerometry-based algorithms based on peak detection, autocorrelation, template matching, movement frequency machine learning a common dataset that included continuous walking trials varying speeds regularities. The accuracies were computed with respect to ground truth...
Abstract Background Smartphone-based monitoring in natural settings provides opportunities to monitor mental health behaviors, including suicidal thoughts and behaviors. To date, most behaviors research using smartphones has primarily relied on collecting so-called “active” data, requiring participants engage by completing surveys. Data collected passively from smartphone sensors logs may offer an objectively measured representation of individual’s behavior, screen time. Objective This study...
Abstract Reduced daily movement, particularly in the mornings, is linked with elevated Alzheimer’s disease risk, yet prior studies have consisted of primarily non-Hispanic White participants. Whether this association exists Black older adults, who often are underrepresented research and experience delays cognitive impairment diagnosis, remains unexplored. Wrist-worn accelerometry was analyzed from 21 participants (median age: 73 years, 83% women) referred Johns Hopkins Disease Research...