Hyunhwa Lee

Background: Mild traumatic brain injury (mTBI) is frequently associated with persistent cognitive and psychosocial symptoms, yet biological correlates of recovery remain poorly understood, particularly among Asian and Pacific Islander (API) populations. Pre-injury psychosocial adversity may further shape post-injury recovery trajectories. This pilot study examined associations between participation in a 2-week, home-based, dual-task cognitive–walking intervention (Daily Brain Exercise; DBE) and changes in cognitive, psychological, and salivary microRNA (miRNAs) measures among APIs with and without a self-reported history of mTBI. Methods: API participants completed remote cognitive testing (CNS Vital Signs), psychosocial assessments (Neuro-QoL), and saliva collection before and after DBE participation. Salivary RNA was purified, and miRNA expression was profiled using nCounter^® Human v3 miRNA Expression Panels (NanoString). Differential expression analyses were conducted using ROSALIND^® platform (OnRamp Bioinformatics, San Diego, CA, USA), a cloud-based bioinformatics analysis system, to calculate fold changes and p-values. Pre-injury psychosocial adversity was assessed via the Trauma History Screen and examined descriptively as a contextual modifier of functional outcomes. Results: Twenty-one APIs (mean age 22.9 years; 76.7% female) were enrolled, including 14 individuals with a self-reported history of mTBI (mean 4.64 years post-injury; 50% with multiple injuries). Following DBE participation, increases in cognitive flexibility and executive function scores were observed in both mTBI and control groups. Additional increases in psychomotor speed, processing speed, sleep disturbance, and depressive symptoms were observed descriptively within the mTBI group. Subgroup analyses suggested variability in pre–post patterns across combinations of mTBI history and pre-injury psychosocial adversity. Exploratory miRNA analyses identified seven miRNAs that were differentially expressed in the mTBI group following DBE (unadjusted p < 0.005), including hsa-miR-7-5p, previously reported in association with neurodevelopmental and neurological pathways. Conclusions: In this pilot, feasibility-focused study, participation in a brief, home-based, dual-task intervention was associated with descriptive changes in selected cognitive and psychosocial measures among APIs, particularly those with a history of mTBI and pre-injury adversity. The observed subgroup patterns warrant confirmation in adequately powered, controlled studies. Exploratory changes in salivary miRNAs co-occurred with functional improvements, thus generating a hypothesis for a future investigation.

AIM: This scoping literature review was conducted to determine what is currently known about holistic wellness support systems (HWSSs) for nursing faculty. BACKGROUND: The nurse faculty shortage has contributed to the national nursing shortage. Providing nurse faculty an HWSS may help grow the faculty pipeline. METHOD: Five electronic databases and Google Scholar were searched using key words related to the research question. Literature published from 2003 to 2023 was included. Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews were used to conduct this review. RESULTS: The search strategy found 1,525 potential publications; five articles were included in the review. The five wellness support systems strived to create a more cohesive, positive, and healthy work environment for nurse faculty. CONCLUSION: This review provides a solid inventory of nursing faculty wellness support systems and identifies gaps in areas of wellness and research that could be supported.

This study identifies seven human subjects’ walking features by training a deep learning model with sensor data. Using the proposed Mobile Health Application developed for collecting sensor data from an Android device, we collected data from human subjects with a history of mild traumatic brain injury. The sensors measure acceleration in m/s² with respect to: the X, Y, and Z directions using an accelerometer, the rate of rotation around a spatial axis with a gyroscope, and nine parameters of a rotation vector with rotation vector components along the X, Y, Z axes using a rotation vector software-based sensor. We made a deep learning model using Tensorflow and Keras to identify the walking features of the seven subjects. The data are classified into the following categories: Accelerometer (X, Y, Z); Gyroscope (X, Y, Z); Rotation (X, Y, Z); Rotation vector (nine parameters); and a combination of the preceding categories. Each dataset was then used for training and testing the accuracy of the deep learning model. According to the Keras evaluation function, the deep learning model trained with Rotation vector data shows 99.5% accuracy for classifying walking characteristics of subjects. In addition, the ability of the model to accurately classify the characteristics of subjects’ walking with all datasets combined is 99.9%.

With the prevalence of obesity in adolescents, and its long-term influence on their overall health, there is a large body of research exploring better ways to reduce the rate of obesity. A traditional way of maintaining an adequate body mass index (BMI), calculated by measuring the weight and height of an individual, is no longer enough, and we are in need of a better health care tool. Therefore, the current research proposes an easier method that offers instant and real-time feedback to the users from the data collected from the motion sensors of a smartphone. The study utilized the mHealth application to identify participants presenting the walking movements of the high BMI group. Using the feedforward deep learning models and convolutional neural network models, the study was able to distinguish the walking movements between nonobese and obese groups, at a rate of 90.5%. The research highlights the potential use of smartphones and suggests the mHealth application as a way to monitor individual health.

Chronic mild traumatic brain injury (mTBI) has long-term consequences, such as neurological disability, but its pathophysiological mechanism is unknown. Exosomal microRNAs (exomiRNAs) may be important mediators of molecular and cellular changes involved in persistent symptoms after mTBI. We profiled exosomal microRNAs (exomiRNAs) in plasma from young adults with or without a chronic mTBI to decipher the underlying mechanisms of its long-lasting symptoms after mTBI. We identified 25 significantly dysregulated exomiRNAs in the chronic mTBI group (n = 29, with 4.48 mean years since the last injury) compared to controls (n = 11). These miRNAs are associated with pathways of neurological disease, organismal injury and abnormalities, and psychological disease. Dysregulation of these plasma exomiRNAs in chronic mTBI may indicate that neuronal inflammation can last long after the injury and result in enduring and persistent post-injury symptoms. These findings are useful for diagnosing and treating chronic mTBIs.

Background: This study presents a new approach to measure and analyze the walking balance of humans by collecting motion sensor data in a smartphone. Objective: We aimed to develop a mobile health (mHealth) app that can measure the walking movements of human individuals and analyze the differences in the walking movements of different individuals based on their health conditions. A smartphone’s motion sensors were used to measure the walking movements and analyze the rotation matrix data by calculating the variation of each xyz rotation, which shows the variables in walking-related movement data over time. Methods: Data were collected from 3 participants, that is, 2 healthy individuals (1 female and 1 male) and 1 male with back pain. The participant with back pain injured his back during strenuous exercise but he did not have any issues in walking. The participants wore the smartphone in the middle of their waistline (as the center of gravity) while walking. They were instructed to walk straight at their own pace in an indoor hallway of a building. The walked a distance of approximately 400 feet. They walked for 2-3 minutes in a straight line and then returned to the starting location. A rotation vector in the smartphone, calculated by the rotation matrix, was used to measure the pitch, roll, and yaw angles of the human body while walking. Each xyz-rotation vector datum was recalculated to find the variation in each participant’s walking movement. Results: The male participant with back pain showed a diminished level of walking balance with a wider range of xyz-axis variations in the rotations compared to those of the healthy participants. The standard deviation in the xyz-axis of the male participant with back pain was larger than that of the healthy male participant. Moreover, the participant with back pain had the widest combined range of right-to-left and forward-to-backward motions. The healthy male participant showed smaller standard deviation while walking than the male participant with back pain and the female healthy participant, indicating that the healthy male participant had a well-balanced walking movement. The walking movement of the female healthy participant showed symmetry in the left-to-right (x-axis) and up-to-down (y-axis) motions in the x-y variations of rotation vectors, indicating that she had lesser bias in gait than the others. Conclusions: This study shows that our mHealth app based on smartphone sensors and rotation vectors can measure the variations in the walking movements of different individuals. Further studies are needed to measure and compare walking movements by age, gender, as well as types of health problems or disease. This app can help in finding differences in gait in people with diseases that affect gait.

BACKGROUND This study presents a new approach to measure and analyze the walking balance of humans by collecting motion sensor data in a smartphone. OBJECTIVE We aimed to develop a mobile health (mHealth) app that can measure the walking movements of human individuals and analyze the differences in the walking movements of different individuals based on their health conditions. A smartphone’s motion sensors were used to measure the walking movements and analyze the rotation matrix data by calculating the variation of each xyz rotation, which shows the variables in walking-related movement data over time. METHODS Data were collected from 3 participants, that is, 2 healthy individuals (1 female and 1 male) and 1 male with back pain. The participant with back pain injured his back during strenuous exercise but he did not have any issues in walking. The participants wore the smartphone in the middle of their waistline (as the center of gravity) while walking. They were instructed to walk straight at their own pace in an indoor hallway of a building. The walked a distance of approximately 400 feet. They walked for 2-3 minutes in a straight line and then returned to the starting location. A rotation vector in the smartphone, calculated by the rotation matrix, was used to measure the pitch, roll, and yaw angles of the human body while walking. Each xyz-rotation vector datum was recalculated to find the variation in each participant’s walking movement. RESULTS The male participant with back pain showed a diminished level of walking balance with a wider range of xyz-axis variations in the rotations compared to those of the healthy participants. The standard deviation in the xyz-axis of the male participant with back pain was larger than that of the healthy male participant. Moreover, the participant with back pain had the widest combined range of right-to-left and forward-to-backward motions. The healthy male participant showed smaller standard deviation while walking than the male participant with back pain and the female healthy participant, indicating that the healthy male participant had a well-balanced walking movement. The walking movement of the female healthy participant showed symmetry in the left-to-right (x-axis) and up-to-down (y-axis) motions in the x-y variations of rotation vectors, indicating that she had lesser bias in gait than the others. CONCLUSIONS This study shows that our mHealth app based on smartphone sensors and rotation vectors can measure the variations in the walking movements of different individuals. Further studies are needed to measure and compare walking movements by age, gender, as well as types of health problems or disease. This app can help in finding differences in gait in people with diseases that affect gait.

People who suffer a mild traumatic brain injury (mTBI) have heterogeneous symptoms and disease trajectories, which make it difficult to precisely assess long-term complications. This pilot study assessed and compared deficits in cognitive, psychosocial, visual functions, and balance performance between college students with and without histories of mTBI. Global DNA methylation ratio (5-mC%) in blood was also compared as a peripheral epigenetic marker. Twenty-five volunteers participated, including 14 healthy controls (64.3% females; mean age of 22.0) and 11 mTBI cases (27.3% females; mean age of 28.7 years) who self-reported mTBI history (63.6% multiple; 2.5 ± 1.29 injuries) with 7.1 years on average elapsed following the last injury. Every participant was assessed for cognitive (executive function, memory, and processing speed), psychological (depression, anxiety, and sleep disturbances), and visual function (by King–Devick and binocular accommodative tests); force-plate postural balance performance; and blood 5-mC% levels. Students with mTBI showed poorer episodic memory, severe anxiety, and higher blood 5-mC% ratio, compared to controls (all p’s < 0.05), which were still significant after adjusting for age. No differences were detected in sleep problems (after adjusting for age), visual function, and postural balance. These findings identified changes in multiple functions and peripheral epigenetics long after mTBI.

People who suffer a mild traumatic brain injury (mTBI) have heterogeneous symptoms and disease trajectories, which make it difficult to precisely diagnose and assess complications long-term. Insufficient information is available regarding how to precisely diagnose and assess mTBI. This study identified and compared deficits in cognitive, psychosocial, visual functions, and balance performance between college students with and without histories of mTBI. Global DNA methylation ratio (5-mC%) in blood was also compared as a peripheral epigenetic marker. Twenty-five volunteers participated in this pilot study, including 11 mTBI cases (27.3% females; mean age of 28.7 years, SD=5.92) and 14 healthy controls (64.3% females; mean age of 22.0, SD=4.13). All the participants were assessed for cognitive (by NIH toolbox—executive function, memory, and processing speed), psychological (by PROMIS—depression, anxiety, and sleep disturbances), visual function (by King-Devick and binocular accommodative tests), postural balance performance (by a force plate), and blood 5-mC% (global methylation) levels. Students with mTBI reported significantly poorer episodic memory, severe anxiety, and more sleep disturbance problems. They also had higher blood 5-mC% level (all p’s<.05). No significant differences were found in visual function and postural balance. These findings validate changes in cognitive, psychosocial, and global DNA methylation long after mTBI.