Health Information Technology Commons^™

Familybloom: Examining Ecologies Of Collaboration In Family-Centered Health Tracking, Lucas M. Silva, Aehong Min, Evropi Stefanidi, Franceli L. Cibrian, Jesus A. Beltran, Cassie Zeiler, Sabrina E. B. Schuck, Kimberley D. Lakes, Gillian R. Hayes, Daniel A. Epstein

Engineering Faculty Articles and Research

Family health informatics tools can help support well-being with shared data tracking. Prior work typically focused on shared data review, but often in specific moments, like bedtime, or centered on caregiving of children or elderly members. To investigate how tracking can support mutual health collaboration between family members pervasively across daily contexts, we designed and deployed FamilyBloom, a glanceable smartwatch and home display system for mood and goal tracking. Twelve families with both neurotypical and ADHD members used FamilyBloom for three months on average. Our findings reveal how family-centered tracking created collaboration opportunities and tensions across multiple ecological systems: individual …

Parentcoach: Designing An Mhealth Parenting App To Enhance Parental Involvement In Adhd Support, Franceli L. Cibrian, Nancy Herrera, Jesus A. Beltran, Lucas M. Silva, Mikaela Pulse, Kayla Anderson, Cassie Zeiler, Luc Rieffel, Daniel I. Lee, Sabrina E. B. Schuck, Kimberley D. Lakes

Engineering Faculty Articles and Research

Introduction: Parents play a vital role in supporting self-regulation and managing behaviors in children with Attention-Deficit/Hyperactivity Disorder (ADHD). However, many face barriers to accessing consistent, evidence-based support. Mobile health (mHealth) technologies offer a promising way to deliver flexible, low-burden guidance for parents on best practices and strategies to support their children's self-regulation. However, designing them is non-trivial.

Objective: This paper introduces ParentCoach, a mobile application designed to support parents of children with ADHD through brief daily lessons, reflection prompts, and skill-building activities.

Methods: ParentCoach was developed in two phases: (1) secondary analysis of qualitative data from over 30 families …

Telerehabilitation For Aphasia (Terra) Phase Ii Trial Design, Christy Cassarly, Alexandra Basilakos, Lisa Johnson, Janina Wilmskoetter, Jordan Elm, Argye E. Hillis, Leonardo Bonilha, Chris Rorden, Gregory Hickok, Dirk B. Den Ouden, Julius Fridriksson

Communication Sciences and Disorders Faculty Articles and Research

Background and purpose

Methods

Digital Health Intervention For Children With Adhd To Improve Mental Health Intervention, Patient Experiences, And Outcomes: A Study Protocol, Nancy Herrera, Franceli L. Cibrian, Lucas M. Silva, Jesus Armando Beltran, Sabrina E. B. Schuck, Gillian R. Hayes, Kimberley D. Lakes

Engineering Faculty Articles and Research

Background

Attention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition with profound public health, personal, and family consequences. ADHD requires comprehensive treatment; however, lack of communication and integration across multiple points of care is a substantial barrier to progress. Given the chronic and pervasive challenges associated with ADHD, innovative approaches are crucial. We developed the digital health intervention (DHI)—CoolTaCo [Cool Technology Assisting Co-regulation] to address these critical barriers. CoolTaCo uses Patient-Centered Digital Healthcare Technologies (PC-DHT) to promote co-regulation (child/parent), capture patient data, support efficient healthcare delivery, enhance patient engagement, and facilitate shared decision-making, thereby improving access to …

Digital Assessments For Children And Adolescents With Adhd: A Scoping Review, Franceli L. Cibrian, Elissa M. Monteiro, Kimberley D. Lakes

Engineering Faculty Articles and Research

Introduction: In spite of rapid advances in evidence-based treatments for attention deficit hyperactivity disorder (ADHD), community access to rigorous gold-standard diagnostic assessments has lagged far behind due to barriers such as the costs and limited availability of comprehensive diagnostic evaluations. Digital assessment of attention and behavior has the potential to lead to scalable approaches that could be used to screen large numbers of children and/or increase access to high-quality, scalable diagnostic evaluations, especially if designed using user-centered participatory and ability-based frameworks. Current research on assessment has begun to take a user-centered approach by actively involving participants to ensure the development …

Review Of Data Bias In Healthcare Applications, Atharva Prakash Parate, Aditya Ajay Iyer, Kanav Gupta, Harsh Porwal, P. C. Kishoreraja, R. Sivakumar, Rahul Soangra

Physical Therapy Faculty Articles and Research

In the area of medical artificial intelligence (AI), data bias is a major difficulty that affects several phases of data collection, processing, and model building. The many forms of data bias that are common in AI in healthcare are thoroughly examined in this review study, encompassing biases related to socioeconomic status, race, and ethnicity as well as biases in machine learning models and datasets. We examine how data bias affects the provision of healthcare, emphasizing how it might worsen health inequalities and jeopardize the accuracy of AI-driven clinical tools. We address methods for reducing data bias in AI and focus …

Multimodal Mixing Convolutional Neural Network And Transformer For Alzheimer’S Disease Recognition, Junde Chen, Yun Wang, Adnan Zeb, M. D. Suzauddola, Yuxin Wen

Engineering Faculty Articles and Research

Early recognition of Alzheimer’s disease (AD) and its precursor state, mild cognitive impairment (MCI), is pivotal in interrupting the progression of the disease and providing suitable treatment. Recent development in deep learning techniques has drawn great research attention for improving the efficacy of AD recognition. However, numerous current methods solely utilize data from a single auxiliary domain, limiting their ability to harness valuable intrinsic insights from multiple domains. To cope with the challenge, this paper is devoted to establishing an innovative multimodal medical data fusion model, termed as MMDF, to perform Alzheimer’s disease recognition. Multimodal data including clinical records and …

Evidence-Based Prostate Cancer Screening Interventions For Black Men: A Systematic Review, Abigail Lopez, Jared T. Bailey, Dorothy Galloway, Leanne Woods-Burnham, Susanne B. Montgomery, Rick Kittles, Dede K. Teteh-Brooks

Health Sciences and Kinesiology Faculty Articles

Abstract

Prostate cancer is the second leading cause of death for men in the U.S. and Black men are twice as likely to die from the disease. However, prostate cancer, if diagnosed at an earlier stage, is curable. The purpose of this review is to identify prostate cancer screening clinical trials that evaluate screening decision-making processes of Black men.

Methods

The databases PubMed, Ovid MEDLINE, CINAHL Plus, and PsychInfo were utilized to examine peer-reviewed publications between 2017 and 2023. Data extracted included implementation plans, outcome measures, intervention details, and results of the study. The Critical Appraisal Skills Programme was used …

The Sensory Accommodation Framework For Technology: Bridging Sensory Processing To Social Cognition, Louanne Boyd

Engineering Faculty Books and Book Chapters

This book provides a thorough introduction to the many facets of designing technologies for autism, with a particular focus on optimizing visual attention frameworks. This book is designed to provide a detailed overview of several aspects of technology for autism. Each Chapter illustrates different parts of the Sensory Accommodation Framework and provides examples of relevant available technologies. The books first discusses a variety of skills that make up human development as well as a history of autism as a diagnosis and the birth of the neurodiversity movement. It goes on to detail individual types of therapy and how they interact …

Investigating Agreement And Reliability Of Mobile Health App For Continuous Walking On A Figure-Eight Path With Periodic Vision Interruptions, Brent A. Harper, Michael Shiraishi, Rahul Soangra

Physical Therapy Faculty Articles and Research

Smartphone applications already provide accessible and cost-effective objective metrics in healthcare. The purpose of this study was to assess the agreement and reliability of an app to a manual stopwatch during a figure-of-eight walking course both with and without visual perturbation. A laboratory-based observation study with twelve healthy adults completed the course under two different visual conditions, one without visual obstruction and the other with visual disruption induced by wearing stroboscopic glasses. Results indicate that each measurement metric is similar but should not be used interchangeably. Bland-Altman plots suggest reliability under each condition. The ICC of each device during both …

Towards A Virtual Reality Visualization Of Hand-Object Interactions To Support Remote Physical Therapy, Trudi Di Qi, Louanne Boyd, Scott Fitzpatrick, Meghna Raswan, Franceli L. Cibrian

Engineering Faculty Articles and Research

Improving object manipulation skills through hand-object interaction exercises is crucial for rehabilitation. Despite limited healthcare resources, physical therapists propose remote exercise routines followed up by remote monitoring. However, remote motor skills assessment remains challenging due to the lack of effective motion visualizations. Therefore, exploring innovative ways of visualization is crucial, and virtual reality (VR) has shown the potential to address this limitation. However, it is unclear how VR visualization can represent understandable hand-object interactions. To address this gap, in this paper, we present VRMoVi, a VR visualization system that incorporates multiple levels of 3D visualization layers to depict movements. In …

A Portable And Reliable Tool For On-Site Physical Reaction Time (Rt) Measurement, Brent Harper, Michael Shiraishi, Rahul Soangra

Physical Therapy Faculty Articles and Research

The drop-stick test system (DTS) invention has the capability to measure reaction accurately for sideline mild traumatic brain injury (mTBI) assessment. The reaction time (RT) measurements showed moderate to good inter-instrument reliability with an overall ICC of 0.82 (95 % CI 0.78–0.85). RT is a useful biomarker of mTBI or concussion, but existing technologies in controlled laboratory environments are not feasible for assessments in the field. With wearable technologies and wireless connection with smartphones, it is now easier to conduct RT assessments on the field. The purpose was to develop a portable DTS involving wearable inertial sensors translatable from the …

Age And Sex Effects On Superg Performance Are Consistent Across Internet Devices, Andrew Hooyman, Sidney Y. Schaefer

Physical Therapy Faculty Articles and Research

There have been recent advances in the application of online games that assess motor skill acquisition/learning and its relationship to age and biological sex, both of which are associated with dementia risk. While this online motor learning assessment (called Super G), along with other computer-based cognitive tests, was originally developed to be completed on a computer, many people (including older adults) have been shown to access the internet through a mobile device. Thus, to improve the generalizability of our online motor skill learning game, it must not only be compatible with mobile devices but also yield replicable effects of various …

Investigating Interactive Methods In Remote Chestfeeding Support For Lactation Consulting Professionals In Brazil, Jessica De Souza, Cinthia Calsinski, Kristina Chamberlain, Franceli L. Cibrian, Edward Jay Wang

Engineering Faculty Articles and Research

Objective: Lactation consultants (LCs) positively impact chestfeeding rates by providing in-person support to struggling parents. In Brazil, LCs are a scarce resource and in high demand, risking chestfeeding rates across many communities nationwide. The transition to remote consultations during the COVID-19 pandemic made LCs face several challenges to solve chestfeeding problems due to limited technical resources for management, communication, and diagnosis. This study investigates the main technological issues LCs have in remote consultations and what technology features are helpful for chestfeeding problem-solving in remote settings.

Methods: This paper implements qualitative investigation through a contextual study (n = 10) and …

The U.S. Travel Health Pharmacists’ Role In A Post-Covid-19 Pandemic Era, Keri Hurley-Kim, Karina Babish, Eva Chen, Alexis Diaz, Nathan Hahn, Derek Evans, Sheila M. Seed, Karl Hess

Pharmacy Faculty Articles and Research

Background: Many countries have enforced strict regulations on travel since the emergence of the SARS-CoV-2 (COVID-19) pandemic in December 2019. However, with the development of several vaccines and tests to help identify it, international travel has mostly resumed in the United States (US). Community pharmacists have long been highly accessible to the public and are capable of providing travel health services and are in an optimal position to provide COVID-19 patient care services to those who are now starting to travel again. Objectives: (1) To discuss how the COVID-19 pandemic has changed the practice of travel health and …

Identifying App-Based Meditation Habits And The Associated Mental Health Benefits: Longitudinal Observational Study, Chad Stecher, Vincent Berardi, Ryan Fowers, Jaclyn Christ, Yunro Chung, Jennifer Huberty

Psychology Faculty Articles and Research

Background: Behavioral habits are often initiated by contextual cues that occur at approximately the same time each day; so, it may be possible to identify a reflexive habit based on the temporal similarity of repeated daily behavior. Mobile health tools provide the detailed, longitudinal data necessary for constructing such an indicator of reflexive habits, which can improve our understanding of habit formation and help design more effective mobile health interventions for promoting healthier habits.

Objective: This study aims to use behavioral data from a commercial mindfulness meditation mobile phone app to construct an indicator of reflexive meditation habits …

Mobile Phone Sensors Can Discern Medication-Related Gait Quality Changes In Parkinson's Patients In The Home Environment, Albert Pierce, Niklas König Ignasiak, Wilford K. Eiteman-Pang, Cyril Rakovski, Vincent Berardi

Psychology Faculty Articles and Research

Patients with Parkinson's Disease (PD) experience daytime symptom fluctuations, which result in small amplitude, slow and unstable walking during times when medication attenuates. The ability to identify dysfunctional gait patterns throughout the day from raw mobile phone acceleration and gyroscope signals would allow the development of applications to provide real-time interventions to facilitate walking performance by, for example, providing external rhythmic cues. Patients (n = 20, mean Hoehn and Yahr: 2.25) had their ambulatory data recorded and were directly observed twice during one day: once after medication abstention, (OFF) and once approximately 30 min after intake of their medication …

Scoping Review: The Empowerment Of Alzheimer’S Disease Caregivers With Mhealth Applications, Eunhee Kim, Andrius Baskys, Anandi V. Law, Moom R. Roosan, Yan Li, Don Roosan

Pharmacy Faculty Articles and Research

Alzheimer’s Disease (AD) is one of the most prevalent neurodegenerative chronic diseases. As it progresses, patients become increasingly dependent, and their caregivers are burdened with the increasing demand for managing their care. Mobile health (mHealth) technology, such as smartphone applications, can support the need of these caregivers. This paper examines the published academic literature of mHealth applications that support the caregivers of AD patients. Following the PRISMA for scoping reviews, we searched published literature in five electronic databases between January 2014 and January 2021. Twelve articles were included in the final review. Six themes emerged based on the functionalities provided …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

A Feasibility Study Of Expanded Home-Based Telerehabilitation After Stroke, Steven C. Cramer, Lucy Dodakian, Vu Le, Alison Mckenzie, Jill See, Renee Augsburger, Robert J. Zhou, Sophia M. Raefsky, Thalia Nguyen, Benjamin Vanderschelden, Gene Wong, Daniel Bandak, Laila Nazarzai, Amar Dhand, Walt Scacchi, Jutta Heckhausen

Physical Therapy Faculty Articles and Research

Introduction: High doses of activity-based rehabilitation therapy improve outcomes after stroke, but many patients do not receive this for various reasons such as poor access, transportation difficulties, and low compliance. Home-based telerehabilitation (TR) can address these issues. The current study evaluated the feasibility of an expanded TR program.

Methods: Under the supervision of a licensed therapist, adults with stroke and limb weakness received home-based TR (1 h/day, 6 days/week) delivered using games and exercises. New features examined include extending therapy to 12 weeks duration, treating both arm and leg motor deficits, patient assessments performed with no therapist supervision, adding sensors …

Social Network Structure Is Related To Functional Improvement From Home-Based Telerehabilitation After Stroke, Archana Podury, Sophia M. Raefsky, Lucy Dodakian, Liam Mccafferty, Vu Le, Alison Mckenzie, Jill See, Robert J. Zhou, Thalia Nguyen, Benjamin Vanderschelden, Gene Wong, Laila Nazarzai, Jutta Heckhausen, Steven C. Cramer, Amar Dhand

Physical Therapy Faculty Articles and Research

Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms.

Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Children’S Cancer Pain In A World Of The Opioid Epidemic: Challenges And Opportunities, Michelle Fortier, Sun Yang, Michael T. Phan, Daniel Tomaszewski, Brooke N. Jenkins, Zeev N. Kain

Psychology Faculty Articles and Research

The opioid crisis in the United States has grown at an alarming rate. Children with cancer are at high risk for pain, and opioids are a first‐line treatment in this population. Accordingly, there is an urgent need to optimize pain management in children with cancer without contributing to the opioid crisis. This report details opportunities for this optimization, including clinical practice guidelines, comprehensive approaches to pain management, mobile health, and telemedicine. It is vital to balance appropriate use of analgesics with efforts to prevent misuse in order to reduce unnecessary suffering and minimize unintended harms.

Improving Medication Information Presentation Through Interactive Visualization In Mobile Apps: Human Factors Design, Don Roosan, Yan Li, Anandi Law, Huy Truong, Mazharul Karim, Jay Chok, Moom Roosan

Pharmacy Faculty Articles and Research

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective: The …

A Qualitative Study On The User Acceptance Of A Home-Based Stroke Telerehabilitation System, Yu Chen, Yunan Chen, Kai Zheng, Lucy Dodakian, Jill See, Robert Zhou, Renee Augsburger, Alison Mckenzie, Steven C. Cramer

Physical Therapy Faculty Articles and Research

Objective: This paper reports a qualitative study of a home-based stroke telerehabilitation system. The telerehabilitation system delivers treatment sessions in the form of daily guided rehabilitation games, exercises, and stroke education in the patient’s home. The aims of the current report are to investigate patient perceived benefits of and barriers to using the telerehabilitation system at home.

Methods: We used a qualitative study design that involved in-depth semi-structured interviews with 13 participants who were patients in the subacute phase after stroke and had completed a six-week intervention using the home-based telerehabilitation system. Thematic analysis was conducted to analyze …

Wavelet-Based Analysis Of Physical Activity And Sleep Movement Data From Wearable Sensors Among Obese Adults, Rahul Soangra, Vennila Krishnan

Physical Therapy Faculty Articles and Research

Decreased physical activity in obese individuals is associated with a prevalence of cardiovascular and metabolic disorders. Physicians usually recommend that obese individuals change their lifestyle, specifically changes in diet, exercise, and other physical activities for obesity management. Therefore, understanding physical activity and sleep behavior is an essential aspect of obesity management. With innovations in mobile and electronic health care technologies, wearable inertial sensors have been used extensively over the past decade for monitoring human activities. Despite significant progress with the wearable inertial sensing technology, there is a knowledge gap among researchers regarding how to analyze longitudinal multi-day inertial sensor data …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Exploring The Relationship Of Digital Information Sources And Medication Adherence, Cody Arbuckle, Daniel Tomaszewski, Lawrence Brown, Jon C. Schommer, Donald Morisky, Chelsea Parlett-Pelleriti, Erik J. Linstead

Pharmacy Faculty Articles and Research

We present a retrospective analysis of data collected in the United States from the 2015 National Consumer Survey on the Medication Experience and Pharmacists’ Role in order to model the relationship between health information sources and medication adherence and perception. Our results indicate that while the digital age has presented prescription users with many non-traditional alternatives for health information, the use of digital content has a significant negative correlation with pharmaceutical adherence and attitudes toward medication. These findings along with previous research suggest that in order to fully realize the potential benefits of the digital age in regards to patient …

The Chapman Bone Algorithm: A Diagnostic Alternative For The Evaluation Of Osteoporosis, Elise Levesque, Anton Ketterer, Wajiha Memon, Cameron James, Noah Barrett, Cyril Rakovski, Frank Frisch

Mathematics, Physics, and Computer Science Faculty Articles and Research

Osteoporosis is the most common metabolic bone disease and goes largely undiagnosed throughout the world, due to the inaccessibility of DXA machines. Multivariate analyses of serum bone turnover markers were evaluated in 226 Orange County, California, residents with the intent to determine if serum osteocalcin and serum pyridinoline cross-links could be used to detect the onset of osteoporosis as effectively as a DXA scan. Descriptive analyses of the demographic and lab characteristics of the participants were performed through frequency, means and standard deviation estimations. We implemented logistic regression modeling to find the best classification algorithm for osteoporosis. All calculations and …

A Markov Approach For Increasing Precision In The Assessment Of Data-Intensive Behavioral Interventions, Vincent Berardi, Ricardo Carretero-González, John Belletierre, Marc A. Adams, Suzanne C. Hughes, Melbourne Hovell

Psychology Faculty Articles and Research

Health interventions using real-time sensing technology are characterized by intensive longitudinal data, which has the potential to enable nuanced evaluations of individuals’ responses to treatment. Existing analytic tools were not developed to capitalize on this opportunity as they typically focus on first-order findings such as changes in the level and/or slope of outcome variables over different intervention phases. This paper introduces an exploratory, Markov-based empirical transition method that offers a more comprehensive assessment of behavioral responses when intensive longitudinal data are available. The procedure projects a univariate time-series into discrete states and empirically determines the probability of transitioning from one …