A Cyber-Physical System for Near Real-Time Monitoring of At-Home Orthopedic Rehabilitation and Mobile-Based Provider-Patient Communications to Improve Adherence: Development and Formative Evaluation.

BACKGROUND: Knee extensor muscle performance is reduced after lower extremity trauma and orthopedic surgical interventions. At-home use of neuromuscular electrical stimulation (NMES) may improve functional recovery, but adherence to at-home interventions is low. Greater benefits from NMES may be realized with closer monitoring of adherence to at-home prescriptions and more frequent patient-provider interactions. OBJECTIVE: This study aimed to develop a cyber-physical system to monitor at-home adherence to NMES prescription and facilitate patient-provider communications to improve adherence in near real time. METHODS: The RehabTracker cyber-physical system was developed to accomplish this goal and comprises four components: (1) hardware modifications to a commercially available NMES therapy device to monitor device use and provide Bluetooth functionality; (2) an iPhone Operating System-based mobile health (mHealth) app that enables patient-provider communications in near real time; (3) a clinician portal to allow oversight of patient adherence with device use; and (4) a back-end server to store data, enable adherence analysis, and send automated push notifications to the patient. These four elements were designed to be fully compliant with the Health Insurance Portability and Accountability Act. The system underwent formative testing in a cohort of patients following anterior cruciate ligament rupture (n=7) to begin to assess face validity. RESULTS: Compared with the NMES device software-tracked device use, the RehabTracker system recorded 83% (40/48) of the rehabilitation sessions, with 100% (32/32) of all sessions logged by the system in 4 out of 7 patients. In patients for whom tracking of automated push notifications was enabled, 100% (29/29) of the push notifications sent by the back-end server were received by the patient. Process, hardware, and software issues contributing to these inaccuracies are detailed. CONCLUSIONS: RehabTracker represents a promising mHealth app for tracking and improving adherence with at-home NMES rehabilitation programs and warrants further refinement and testing.

The short-term dynamics of posttraumatic stress disorder symptoms during the acute posttrauma period.

BACKGROUND: The manner in which posttraumatic stress disorder (PTSD) develops remains largely unknown. PTSD is comprised of 20 symptoms across 4 clusters. These clusters were hypothesized to reflect a failure of recovery model in which intrusive symptoms appear first. Intrusive symptoms led to avoidance of trauma-related stimuli, which resulted in sustained arousal. The sustained arousal ultimately led to dysphoria. METHODS: This hypothesized symptom progression was evaluated during the acute posttrauma period (the first 30 days postevent). Participants (N = 80) reported their PTSD symptoms for 30 days via mobile devices. Using a short-term dynamic modeling framework, a temporal and contemporaneous model of PTSD symptoms was obtained. RESULTS: In the temporal network, a fear-conditioning component was identified that supported the hypothesized set of relations among symptom clusters. The contemporaneous network was classified by two subnetworks. The first corresponded to a fear-conditioning model that included symptoms of intrusions and avoidance. The second included symptoms of dysphoria and arousal. CONCLUSIONS: These findings suggest that, after a trauma, there may be a fear-conditioning process that involves intrusions, avoidance, and arousal symptoms. Dysphoric symptoms were also present but developed as a partially distinct component.

Predicting Posttraumatic Stress Disorder Risk: A Machine Learning Approach.

BACKGROUND: A majority of adults in the United States are exposed to a potentially traumatic event but only a handful go on to develop impairing mental health conditions such as posttraumatic stress disorder (PTSD). OBJECTIVE: Identifying those at elevated risk shortly after trauma exposure is a clinical challenge. The aim of this study was to develop computational methods to more effectively identify at-risk patients and, thereby, support better early interventions. METHODS: We proposed machine learning (ML) induction of models to automatically predict elevated PTSD symptoms in patients 1 month after a trauma, using self-reported symptoms from data collected via smartphones. RESULTS: We show that an ensemble model accurately predicts elevated PTSD symptoms, with an area under the curve (AUC) of .85, using a bag of support vector machines, naive Bayes, logistic regression, and random forest algorithms. Furthermore, we show that only 7 self-reported items (features) are needed to obtain this AUC. Most importantly, we show that accurate predictions can be made 10 to 20 days posttrauma. CONCLUSIONS: These results suggest that simple smartphone-based patient surveys, coupled with automated analysis using ML-trained models, can identify those at risk for developing elevated PTSD symptoms and thus target them for early intervention.

Implementing assessments via mobile during the acute posttrauma period: feasibility, acceptability and strategies to improve response rates.

Background: PTSD is posited to develop in the acute posttrauma period. Few studies have examined psychopathology symptoms within this period due to the demands on individuals in the first month after a trauma. Mobile devices can overcome these barriers. The feasibility of using mobile devices for this purpose, however, is unclear. Objective: The present study evaluated the acceptability of administering PTSD symptom assessments via a mobile application throughout the acute posttrauma period. Method: Participants (N = 90) were recruited from a Level 1 Trauma Center within M = 4.88 days of experiencing a traumatic event. A mobile application was placed on their smartphone that administered a daily self-report assessment of PTSD symptoms for 30 days. Participants were compensated US$1 for each assessment completed. Results: The overall response rate was 61.1% or M = 18.33, SD = 9.12 assessments. Assessments were accessed M = 65.2 minutes after participants were notified to complete them and took M = 2.52 minutes to complete. Participants reported that the daily assessments were not bothersome and were moderately helpful. Conclusion: The present study suggests that using mobile devices to monitor mental health symptoms during the acute posttrauma period is feasible and acceptable. Strategies are needed to determine how to best take advantage of these data once collected.

Antecedentes: Se ha propuesto que el Trastorno por Estrés Post-Traumático (TEPT) se desarrolla en el período post-trauma agudo. Pocos estudios han estudiado síntomas psicopatológicos durante este periodo, debido a las demandas de los individuos en el primer mes después de un trauma. Los dispositivos móviles pueden superar estas barreras. Sin embargo, la viabilidad de usar dispositivos móviles para este propósito no está clara. Objetivo: Este estudio evaluó la aceptabilidad de la administración de evaluaciones de síntomas de TEPT a través de una aplicación para dispositivos móviles durante el periodo agudo post-trauma. Método: Los participantes (N=90) fueron reclutados desde un Centro de Trauma de Nivel 1 con M=4,88 días de haber experimentado un evento traumático. Se instaló una aplicación en sus teléfonos móviles, que administró una evaluación diaria de autoreporte de síntomas de TEPT, por 30 días. Los participantes fueron compensados con US$1 por cada evaluación completada. Resultados: La tasa de respuesta general fue 61,1% o M=18,33, SD=9,12 evaluaciones. Se tuvo acceso a las evaluaciones M=65,2 minutos después que los participantes fueron notificados para completarlas y les tomó M=2,52 minutos completarlas. Los participantes reportaron que las evaluaciones diarias no fueron tediosas y fueron moderadamente útiles. Conclusión: El presente estudio sugiere que usar dispositivos móviles para monitorear síntomas de salud mental durante el periodo post-trauma agudo es viable y aceptable. Se necesitan estrategias para determinar cómo sacar el mayor provecho de estos datos una vez obtenidos.

Tracking post-trauma psychopathology using mobile applications: A usability study.

Trauma exposure markedly increases risk for psychopathology including posttraumatic stress disorder (PTSD). Understanding the course by which PTSD develops after a traumatic event is critical to enhancing early intervention. Although prior work has explored the course of PTSD symptoms in the subsequent months, relatively few studies have explored the course of symptoms in the acute post-trauma period, defined as the 30 days after a traumatic event. A key challenge to conducting such studies is the lack of efficient means to collect data that does not impose significant burden on the participant during this time. The present study evaluated the use of a mobile phone application to collect symptom data during the acute post trauma period. Data was obtained from 23 individuals who experienced a Criterion A traumatic event and were recruited from the Emergency Department of a Level 1 Trauma Center. Participants completed 44.93% of daily assessments across a 30-day period. Responses rates were uncorrelated with PTSD symptoms or depression symptoms at 1-month and 3-month posttrauma. Participants reported that the surveys were moderately helpful and posed minimal burden. These findings suggest that mobile applications can be used to learn about the course of post-trauma recovery.

Usability Evaluation of a Mobile Monitoring System to Assess Symptoms After a Traumatic Injury: A Mixed-Methods Study.

BACKGROUND: Victims of trauma are at high risk for mental health conditions such as posttraumatic stress disorder and depression. Regular assessment of mental health symptoms in the post-trauma period is necessary to identify those at greatest risk and provide treatment. The multiple demands of the acute post-trauma period present numerous barriers to such assessments. Mobile apps are a method by which to overcome these barriers in order to regularly assess symptoms, identify those at risk, and connect patients to needed services. OBJECTIVE: The current study conducted a usability evaluation of a system to monitor mental health symptoms after a trauma. The system was developed to promote ease of use and facilitate quick transmission of data. METHODS: A sample of 21 adults with a history of trauma completed a standardized usability test in a laboratory setting followed by a qualitative interview. RESULTS: Usability testing indicated that the app was easy to use and that patients were able to answer several questions in less than 1 minute (mean [SD] 29.37 [7.53]; range 15-57). Qualitative analyses suggested that feedback should be included in such an app and recommendations for the type of feedback were offered. CONCLUSIONS: The results of the current study indicate that a mobile app to monitor post-trauma mental health symptoms would be well received by victims. Personalized feedback to the user was identified as critical to promote the usability of the software.