Impacts of an Acute Care Telenursing Program on Discharge, Patient Experience, and Nursing Experience: Retrospective Cohort Comparison Study

BACKGROUND

Despite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing.

OBJECTIVE

This study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients.

METHODS

A retrospective, observational cohort comparison was performed in a large academic hospital system (approximately 2100 beds) in Houston, Texas, comparing patients in our pilot units for the ACTN program (telenursing cohort) between June 15, 2022, and December 31, 2022, with patients who did not participate (nontelenursing cohort) in the same units and timeframe. We used a case mix index analysis to confirm comparable patient cases between groups. The outcomes investigated were patient experience, measured using the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHCPS) survey; nursing experience, measured by a web-based questionnaire with quantitative multiple-choice and qualitative open-ended questions; time of discharge during the day (from electronic health record data); and duration of discharge education processes.

RESULTS

Case mix index analysis found no significant case differences between cohorts (P=.75). For the first 4 units that rolled out in phase 1, all units experienced improvement in at least 4 and up to 7 HCAHCPS domains. Scores for "communication with doctors" and "would recommend hospital" were improved significantly (P=.03 and P=.04, respectively) in 1 unit in phase 1. The impact of telenursing in phases 2 and 3 was mixed. However, "communication with doctors" was significantly improved in 2 units (P=.049 and P=.002), and the overall rating of the hospital and the "would recommend hospital" scores were significantly improved in 1 unit (P=.02 and P=04, respectively). Of 289 nurses who were invited to participate in the survey, 106 completed the nursing experience survey (response rate 106/289, 36.7%). Of the 106 nurses, 101 (95.3%) indicated that the ACTN program was very helpful or somewhat helpful to them as bedside nurses. The only noticeable difference between the telenursing and nontelenursing cohorts for the time of day discharge was a shift in the volume of patients discharged before 2 PM compared to those discharged after 2 PM at a hospital-wide level. The ACTN admissions averaged 12 minutes and 6 seconds (SD 7 min and 29 s), and the discharges averaged 14 minutes and 51 seconds (SD 8 min and 10 s). The average duration for ACTN calls was 13 minutes and 17 seconds (SD 7 min and 52 s). Traditional cohort standard practice (nontelenursing cohort) of a bedside nurse engaging in discharge and admission processes was 45 minutes, consistent with our preimplementation time study.

CONCLUSIONS

This study shows that ACTN programs are feasible and associated with improved outcomes for patient and nursing experience and reducing time allocated to admission and discharge education.

Investigating patient engagement associations between a postdischarge texting programme and patient experience, readmission and revisit rates outcomes

OBJECTIVES

This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme.

SETTING

The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190-400, averaging 300 beds per hospital) in Houston, Texas.

METHODS

Retrospective, observational cohort study between non-engaged patients (responded with 0-2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and χ2 test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates.

RESULTS

From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals.

CONCLUSIONS

This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.

Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams

Interaction has been recognised as an essential lens to understand how cognition is formed in a complex adaptive team such as a multidisciplinary crisis management team (CMT). However, little is known about how interactions within and across CMTs give rise to the multi-team system's overall cognitive functioning, which is essential to avoid breakdowns in coordination. To address this gap, we characterise and compare the component CMTs' role-as-intended (RAI) and role-as-observed (RAO) in adapting to the complexity of managing informational needs. To characterise RAI, we conducted semi-structured interviews with subject matter experts and then made a qualitative synthesis using a thematic analysis method. To characterise RAO, we observed multiteam interaction networks in real-time at a simulated training environment and then analysed the component CMTs' relative importance using node centrality measures. The resulting inconsistencies between RAI and RAO imply the need to investigate cognition in multiple CMTs through the lens of interaction.Practitioner summary: When a disaster occurs, multidisciplinary CMTs are expected to serve their roles as described in written or verbal guidelines. However, according to our naturalistic observations of multiteam interaction networks, such descriptions may be (necessary but) insufficient for designing, training, and evaluating CMTs in the complexity of managing informational needs together.

Improving Healthcare Practice Through the Implementation of Human Factors and Ergonomics Principles

OBJECTIVE

Present a collection of papers focusing on improving healthcare practice through the implementation of human factors and ergonomics principles that were presented at the International Ergonomics Association (IEA) 2021 international conference.

BACKGROUND

The mission of the IEA is to elaborate and advance ergonomics science and practice and to expand its scope of application.

METHOD

We reviewed papers that were submitted for presentation at the IEA 2021 international conference and focused on improving healthcare practice through the implementation of human factors and ergonomics principles.

RESULTS

The eight papers that are included in this special issue cover varied aspects of human factors application and implementation.

CONCLUSION

This special issue provides clear evidence that the science of human factors is relevant and is continuing to grow and so is its implementation in healthcare.

APPLICATION

This special issue offers a selection of applied works, providing a wide scope of human factors guidelines, methods, and theories in healthcare work environments.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

OBJECTIVE

(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.

BACKGROUND

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.

METHODS

Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.

RESULTS

While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.

CONCLUSION

Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Field-based longitudinal evaluation of multimodal worker fatigue assessments in offshore shiftwork

Fatigue in offshore environments is a critical safety hazard, yet the utility of daily fatigue assessments has not been longitudinally examined in these environments. The aim of this exploratory longitudinal field study across two drillships in the Gulf of Mexico was to determine the changes in subjective, performance-based, and physiological fatigue measures over time across different shift types (day, night, and swing) and to identify correlations between these multimodal fatigue assessments. Repeated measures correlation analyses of daily fatigue data from seventy offshore workers revealed that while total sleep time remained unaffected by time on rig, workers' performances on the psychomotor vigilance test (PVT) deteriorated over time across all shift types. Several correlations between the various multimodal measures were consistent with the extant literature on worker fatigue symptoms and perceptual and physiological manifestations. These findings emphasize the utility of PVT and single item self-reports to capture worker fatigue in offshore shiftwork.

Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

User-Centered Design of a Diabetes Self-Management Tool for Underserved Populations

BACKGROUND

User-centered design (UCD) or user experience design (UXD) methods have gained recent popularity for the design of mobile health (mHealth) interventions. However, there is a gap in application of these methods for diabetes self-management. This study aims to document the UCD process for a self-management mobile application aimed for patients with diabetes in underserved communities.

METHODS

A UCD mixed-methods approach including interviews with patients and providers, a review of literature, and a technology landscape analysis were used to define the app functional information requirements that informed the user experience/user interface design process. Usability studies with the app designers and developers, intended users, and a focus group of nurse educators and dieticians were used to test and improve the design.

RESULTS

An mHealth app was developed with health-tracking features for stress, blood sugar, food, exercise, medications, weight, and blood pressure. We tackled a range of usability and user experience challenges, which encompassed addressing issues like low health literacy by employing a combination of user interface design principles, intuitive visualizations, customizable icons, seamless database integration, and automated data input features. Special attention was given to the design of educational content accounting for the intended users' cultural background and literacy levels.

CONCLUSIONS

User-centered design approach contributed to a better understanding of the intended users' needs, limitations, mental models, and expectations, facilitating the design of a comprehensive mobile app for patients with diabetes in underserved communities that includes essential features for self-management while providing a strong educational component, addressing an important gap in the literature.

Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Integrating a Virtual ICU with Cardiac and Cardiovascular ICUs: Managing the Needs of a Complex and High-Acuity Specialty ICU Cohort

A long-standing shortage of critical care intensivists and nurses, exacerbated by the coronavirus disease (COVID-19) pandemic, has led to an accelerated adoption of tele-critical care in the United States (US). Due to their complex and high-acuity nature, cardiac, cardiovascular, and cardiothoracic intensive care units (ICUs) have generally been limited in their ability to leverage tele-critical care resources. In early 2020, Houston Methodist Hospital (HMH) launched its tele-critical care program called Virtual ICU, or vICU, to improve its ICU staffing efficiency while providing high-quality, continuous access to in-person and virtual intensivists and critical care nurses. This article provides a roadmap with prescriptive specifications for planning, launching, and integrating vICU services within cardiac and cardiovascular ICUs-one of the first such integrations among the leading academic US hospitals. The success of integrating vICU depends upon the (1) recruitment of intensivists and RNs with expertise in managing cardiac and cardiovascular patients on the vICU staff as well as concerted efforts to promote mutual trust and confidence between in-person and virtual providers, (2) consultations with the bedside clinicians to secure their buy-in on the merits of vICU resources, and (3) collaborative approaches to improve workflow protocols and communications. Integration of vICU has resulted in the reduction of monthly night-call requirements for the in-person intensivists and an increase in work satisfaction. Data also show that support of the vICU is associated with a significant reduction in the rate of Code Blue events (denoting a situation where a patient requires immediate resuscitation, typically due to a cardiac or respiratory arrest). As the providers become more comfortable with the advances in artificial intelligence and big data-driven technology, the Cardiac ICU Cohort continues to improve methods to predict and track patient trends in the ICUs.

Occupational stress and burnout among intensive care unit nurses during the pandemic: A prospective longitudinal study of nurses in COVID and non-COVID units

Background

Intensive care unit (ICU) nurses are highly prone to occupational stress and burnout, affecting their physical and mental health. The occurrence of the pandemic and related events increased nurses' workload and further exacerbated their stress and burnout. This work investigates occupational stress and burnout experienced by ICU nurses working with COVID and non-COVID patients.

Method

A prospective longitudinal mixed-methods study was conducted with a cohort of ICU nurses working in medical ICU (COVID unit; n = 14) and cardiovascular ICU (non-COVID unit; n = 5). Each participant was followed for six 12-h shifts. Data on occupational stress and burnout prevalence were collected using validated questionnaires. Physiological indices of stress were collected using wrist-worn wearable technologies. Participants elaborated on the causes of stress experienced each shift by completing open-ended questions. Data were analyzed using statistical and qualitative methods.

Results

Participants caring for COVID patients at the COVID unit were 3.71 times more likely to experience stress (p < 0.001) in comparison to non-COVID unit participants. No differences in stress levels were found when the same participants worked with COVID and non-COVID patients at different shifts (p = 0.58) at the COVID unit. The cohorts expressed similar contributors to stress, based in communication tasks, patient acuity, clinical procedures, admission processes, proning, labs, and assisting coworkers.

Conclusion

Nurses in COVID units, irrespective of whether they care for a COVID patient, experience occupational stress and burnout.

Use of a Mobile Biofeedback Application to Provide Health Coaching for Stress Self-Management: Findings from a Pilot Quasi-Experiment

BACKGROUND

Mental health is an increasing concern among vulnerable populations, including college students and veterans.

OBJECTIVE

The purpose of this study was to determine if mobile Health (mHealth) technology combined with health coaching can better enable user to self-manage their mental health.

METHODS

This study evaluated a mobile Biofeedback app that provided health coaching on stress self-management for college student veterans' mental health concerns. Twenty-four college student veterans were recruited from a large public university in Texas during the spring 2020 semester, impacted by COVID-19. Ten participants were assigned to the intervention group where they used the mobile Biofeedback app on their smartphones and smartwatches, and 14 were assigned to the control group without the app; assignment was based on mobile phone compatibility. Both groups participated in one initial lab session where they learned a deep breathing exercise technique. The intervention group was then asked to use the mobile Biofeedback app during their daily lives using the Biofeedback app and smartwatch, and the control group was asked to perform the breathing exercises on their own. Both groups filled out Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) self-assessments at two-week intervals. At the end of the semester, both groups were given an exit interview to provide user experience and perceived benefits of health coaching via the mobile Biofeedback app.

RESULTS

Deep breathing exercise in the initial lab session reduced stress in both groups. Over the course of the study, the app recorded 565 coached breathing exercises with a significant decrease (approximately 3 beats per minute) in participants' heart rate during the 6-minute time period immediately after conducting the breathing exercises [Spearman's rank correlation coefficient of -0.61 (P<.001 and S=9,816,176)]. There was no significant difference between the two groups for PHQ-9 and GAD-7 scores over the course of the semester. Exit interview responses indicate that participants perceived that the mobile Biofeedback app improved their health and helped them address stress challenges. All participants reported that the intervention helped them manage their stress better and expressed that health coaching via a mobile device would improve their overall health.

CONCLUSIONS

Participants reported a positive perception of the app for their mental health self-management during a stressful semester. Future work should examine long-term effects of the app with a larger sample size balanced between male and female participants, randomized participant allocation, real-time detection of mental health symptoms, and additional features of the app.

CLINICALTRIAL

Investigating Popular Mental Health Mobile Application Downloads and Activity During the COVID-19 Pandemic

OBJECTIVE

This article analyzes the changes in downloads and activity of users of select popular mental health mobile applications (mHealth apps) during coronavirus disease 2019 (COVID-19).

BACKGROUND

The outbreak of the COVID-19 crisis has shown a negative impact on public mental health. Mobile health has the potential to help address the psychological needs of existing and new patients during the pandemic and beyond.

METHOD

Downloads data of 16 widely used apps were analyzed. The quality of apps was reviewed using the Mobile Application Rating Scale (MARS) framework. Correlation analysis was conducted to investigate the relationship between app quality and app popularity.

RESULTS

Among the 16 apps, 10 were meditational in nature, 13 showed increased downloads, with 11 apps showing above 10% increase in the downloads after the pandemic started. The popular apps were satisfactory in terms of functionality and esthetics but lacked clinical grounding and evidence base. There exists a gap between app quality and app popularity.

CONCLUSION

This study provided evidence for increased downloads of mental mHealth apps (primarily meditation apps) during the COVID-19 pandemic but revealed several gaps and opportunities to address deficiencies in evidence-based design, usability and effective assessment, and integration into current workflows.

APPLICATION

The COVID-19 pandemic is a potential turning point for mHealth applications for mental health care. Whereas the evidence suggests a need for alternative delivery of care, human factors and ergonomics methods should be utilized to ensure these tools are user-centered, easy to use, evidence-based, well-integrated with professional care, and used sustainably.

Workers' Acceptance of Digital Procedures: An Application of the Technology Acceptance Model

OCCUPATIONAL APPLICATIONSThere are increasing numbers of organizations that are implementing digital procedures (e.g., standard operating procedures). These efforts are often assumed to be a positive development but can be quite costly-both in terms of money and training for a digital rollout. As a result, organizations and practitioners may find themselves at risk for failure when implementing digital procedures. The results of the current study suggest that if workers perceive digital procedures as useful and easy to use, this perception translates into positive attitudes, which subsequently result in fewer deviations. Since acceptance is relatively easy to assess, practitioners can benefit from using these assessments prior to a digital transition/roll-out to both compare competing hardware and software applications, and to initiate and continuously monitor the development of digital procedures. We consider this approach as advantageous to having management develop a system and fully deploying digital procedures without any consideration of worker acceptance.

Investigating the effect of an mHealth coaching intervention on health beliefs, adherence and blood pressure of patients with hypertension: A longitudinal single group pilot study

Background

Mobile health technologies have shown promise as delivery platforms for digital health coaching for chronic conditions. However, the impacts of such strategies on users' health beliefs, intentions and ultimately clinical outcomes are understudied.

Objective

This study sought (1) to evaluate the effects of a digital health coaching intervention on participants' belief constructs; and (2) to assess relationships between these belief constructs and intentions to utilize the technological intervention, actual adherence metrics and clinical outcomes related to hypertension.

Methods

Thirty-four participants with hypertension were recruited from a university community from January to May 2021. They self-measured weight and blood pressure (BP) for 30 days followed by digital coaching delivered via a mobile application for 30 days. Surveys assessed constructs from the Health Belief Model and Technology Acceptance Model, compared to intention, health belief, BP self-monitoring adherence and BP outcomes. A path analysis model was used to assess the relationships between constructs and intention, adherence metrics and clinical outcomes. A Kruskal-Wallis test was used to identify changes in beliefs.

Results

Participant health beliefs significantly improved after coaching, including self-efficacy (H(1) = 15.12, p < 0.001), cues to action (H(1) = 5.33, p = 0.02), attitude (H(1) = 10.35, p = 0.002), perceived usefulness (H(1) = 15.02, p < 0.001) and decreased resistance to change (H(1) = 4.05, p = 0.04). Adherence to BP measurements positively correlated with perceived health threat (β = .033, p = 0.007) and perceived ease of use (β = .0277, p < 0.001). Self-efficacy (β = -2.92, p = 0.02) and perceived usefulness (β = -3.75, p = 0.01) were linked with a decrease in diastolic BP.

Conclusions

A mobile health coaching intervention may help participants improve beliefs regarding hypertension self-management.

Investigating burn-out contributors and mitigators among intensive care unit nurses during COVID-19: a focus group interview study

OBJECTIVE

Past literature establishes high prevalence of burn-out among intensive care unit (ICU) nurses, and the influence of the COVID-19 pandemic in intensifying burn-out. However, the specific pandemic-related contributors and practical approaches to address burn-out have not been thoroughly explored. To address this gap, this work focuses on investigating the effect of the COVID-19 pandemic on the burn-out experiences of ICU nurses and identifying practical approaches for burn-out mitigation.

DESIGN

Semistructured focus group interviews were conducted via convenience sampling and qualitatively analysed to identify burn-out contributors and mitigators. Maslach Burnout Inventory for Medical Personnel (MBI-MP) and Post-traumatic Stress Disorder Checklist (PCL-5) were employed to quantify the prevalence of burn-out of the participants at the time of study.

SETTING

Two ICUs designated as COVID-19 ICUs in a large metropolitan tertiary care hospital in the Greater Houston area (Texas, USA).

PARTICIPANTS

Twenty registered ICU nurses (10 from each unit).

RESULTS

Participants experienced high emotional exhaustion (MBI-MP mean score 32.35, SD 10.66), moderate depersonalisation (M 9.75, SD 7.10) and moderate personal achievement (M 32.05, SD 7.59) during the pandemic. Ten out of the 20 participants exhibited post-traumatic stress disorder symptoms (PCL-5 score >33). Regarding contributors to burn-out in nurses during the pandemic, five thematic levels emerged-personal, patient related, coworker related, organisational and societal-with each factor comprising several subthemes (eg, emotional detachment from patients, constant need to justify motives to patients' family, lack of staffing and resources, and politicisation of COVID-19 and vaccination). Participants revealed several practical interventions to help overcome burn-out, ranging from mental health coverage to educating public on the severity of the pandemic and importance of vaccination.

CONCLUSIONS

By identifying the contributors to burn-out in ICU nurses at a systems level, the study findings inform the design and implementation of effective interventions to prevent or mitigate pandemic-related burn-out among nurses.

Understanding Patient Beliefs in Using Technology to Manage Diabetes: A Path Analysis Model from a National Online Sample (Preprint)

BACKGROUND

 With 425 million individuals globally living with diabetes, it is critical to support the self-management of this life-threatening condition. However, adherence and engagement with existing technologies are inadequate and need further research.

OBJECTIVE

 The objective of our study was to develop an integrated belief model that helps identify the significant constructs in predicting intention to use a diabetes self-management device for the detection of hypoglycemia.

METHODS

 Adults with type 1 diabetes living in the United States were recruited through Qualtrics to take a web-based questionnaire that assessed their preferences for a device that monitors their tremors and alerts them of the onset of hypoglycemia. As part of this questionnaire, a section focused on eliciting their response to behavioral constructs from the Health Belief Model, Technology Acceptance Model, and others.

RESULTS

 A total of 212 eligible participants responded to the Qualtrics survey. Intention to use a device for the self-management of diabetes was well predicted (R²=0.65; F_12,199=27.19; P<.001) by 4 main constructs. The most significant constructs were perceived usefulness (β=.33; P<.001) and perceived health threat (β=.55; P<.001) followed by cues to action (β=.17; P<.001) and a negative effect from resistance to change (β=-.19; P<.001). Older age (β=.025; P<.001) led to an increase in their perceived health threat.

CONCLUSIONS

For individuals to use such a device, they need to perceive it as useful, perceive diabetes as life-threatening, regularly remember to perform actions to manage their condition, and exhibit less resistance to change. The model predicted the intention to use a diabetes self-management device as well, with several constructs found to be significant. This mental modeling approach can be complemented in future work by field-testing with physical prototype devices and assessing their interaction with the device longitudinally.

Hypoglycemia Detection Using Hand Tremors: A Home Study in Patients with Type 1 Diabetes (Preprint)

BACKGROUND

Diabetes affects millions of people worldwide and is steadily increasing. A serious condition associated with diabetes is low glucose levels (hypoglycemia). Monitoring blood glucose is usually performed by invasive methods or intrusive devices, and these devices are currently not available to all patients with diabetes. Hand tremor is a significant symptom of hypoglycemia, as nerves and muscles are powered by blood sugar. However, to our knowledge, no validated tools or algorithms exist to monitor and detect hypoglycemic events via hand tremors.

OBJECTIVE

In this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data.

METHODS

We analyzed triaxial accelerometer data from a smart watch recorded from 33 patients with type 1 diabetes for 1 month. Time and frequency domain features were extracted from acceleration signals to explore different machine learning models to classify and differentiate between hypoglycemic and nonhypoglycemic states.

RESULTS

The mean duration of the hypoglycemic state was 27.31 (SD 5.15) minutes per day for each patient. On average, patients had 1.06 (SD 0.77) hypoglycemic events per day. The ensemble learning model based on random forest, support vector machines, and k-nearest neighbors had the best performance, with a precision of 81.5% and a recall of 78.6%. The results were validated using continuous glucose monitor readings as ground truth.

CONCLUSIONS

Our results indicate that the proposed approach can be a potential tool to detect hypoglycemia and can serve as a proactive, nonintrusive alert mechanism for hypoglycemic events.

Systematic review of interruptions in the emergency department work environment

OBJECTIVE

The purpose of this systematic review is to describe the operationalization of interruptions measurement and to synthesize the evidence on the causes and consequences of interruptions in the emergency department (ED) work environment.

METHODS

This systematic review of studies explores the causes and consequences of interruptions in the ED. Of 2836 abstract/titles screened, 137 full-text articles were reviewed, and 44 articles met inclusion criteria of measuring ED interruptions.

RESULTS

All articles reported primary data collection, and most were cohort studies (n = 30, 68%). Conceptual or operational definitions of interruptions were included in 27 articles. Direct observation was the most common approach. In half of the studies, quantitative measures of interruptions in the ED were descriptive only, without measurements of interruptions' consequences. Twenty-two studies evaluated consequences, including workload, delays, satisfaction, and errors. Overall, relationships between ED interruptions and their causes and consequences are primarily derived from direct observation within large academic hospitals using heterogeneous definitions. Collective strengths of interruptions research in the ED include structured methods of naturalistic observation and definitions of interruptions derived from concept analysis. Limitations are conflicting and complex evaluations of consequences attributed to interruptions, including the predominance of descriptive reports characterizing interruptions without direct measurements of consequences.

CONCLUSIONS

The use of standardized definitions and measurements in interruptions research could contribute to measuring the impact and influence of interruptions on clinicians' productivity and efficiency as well as patients' outcomes, and thus provide a basis for intervention research.

The Effects of Drowsiness Detection Technology and Education on Nurses' Beliefs and Attitudes Towards Drowsy Driving

Background: Drowsy driving is prevalent among night-shift nurses, yet there is a gap in understanding nurses' beliefs and attitudes that may affect their intention to avoid drowsy driving.Objectives: The objectives of the study were twofold: 1) investigate how behavioral constructs such as beliefs and attitudes may affect nurses' intention to avoid drowsy driving; and 2) assess changes in such beliefs and attitudes during a study that evaluated the effectiveness of educational and technological interventions.Methods: Three-hundred night-shift nurses were recruited from a large hospital in Texas to participate in a randomized control trial. Participants were randomly assigned to three groups: 1) control; 2) educational intervention; and 3) combined educational and technological intervention. The study utilized an integrated model drawing from the constructs of the Theory of Planned Behavior and the Health Belief Model to elicit attitudes, beliefs, and intentions to use in-vehicle drowsiness detection technologies. Each group was surveyed pre- intervention and at post-intervention around 3 months later to assess changes in beliefs and attitudes. Structural equation models and path analysis were used to analyze changes in beliefs.Results: Seventy-nine participants completed the pre-intervention questionnaire, and 44 nurses completed the pre- and post-intervention surveys. Intention was predicted primarily by attitude and perceived health threat. Perceived health threat also mediated the relationship between behavioral intention and the influence of subjective norms as well as perceived behavioral control. Participants who received education about drowsy driving had positive changes in beliefs.Conclusions: Nurses' perceived health threat from driving drowsy and their attitude towards our intervention were important motivators to avoid drowsy driving. Interventions aiming at raising awareness of the risks associated with drowsy driving may be effective at motivating nurses to avoid drowsy driving.

Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data

Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.

Understanding Heart Rate Reactions to Post-Traumatic Stress Disorder (PTSD) Among Veterans: A Naturalistic Study

OBJECTIVE

We collected naturalistic heart rate data from veterans diagnosed with post-traumatic stress disorder (PTSD) to investigate the effects of various factors on heart rate.

BACKGROUND

PTSD is prevalent among combat veterans in the United States. While a positive correlation between PTSD and heart rate has been documented, specific heart rate profiles during the onset of PTSD symptoms remain unknown.

METHOD

Veterans were recruited during five cycling events in 2017 and 2018 to record resting and activity-related heart rate data using a wrist-worn device. The device also logged self-reported PTSD hyperarousal events. Regression analyses were performed on demographic and behavioral covariates including gender, exercise, antidepressants, smoking habits, sleep habits, average heart rate during reported hyperarousal events, age, glucocorticoids consumption, and alcohol consumption. Heart rate patterns during self-reported PTSD hyperarousal events were analyzed using Auto Regressive Integrated Moving Average (ARIMA). Heart rate data were also compared to an open-access non-PTSD representative case.

RESULTS

Of 99 veterans with PTSD, 91 participants reported at least one hyperarousal event, with a total of 1023 events; demographic information was complete for 38 participants who formed the subset for regression analyses. The results show that factors including smoking, sleeping, gender, and medication significantly affect resting heart rate. Moreover, unique heart rate patterns associated with PTSD symptoms in terms of stationarity, autocorrelation, and fluctuation characteristics were identified.

CONCLUSION

Our findings show distinguishable heart rate patterns and characteristics during PTSD hyperarousal events.

APPLICATION

These findings show promise for future work to detect the onset of PTSD symptoms.

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

OBJECTIVE

To identify physiological correlates to stress in intensive care unit nurses.

BACKGROUND

Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap.

METHOD

Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4).

RESULTS

Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008).

DISCUSSION

The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses.

APPLICATION

Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

Eliciting Requirements for a Diabetes Self-Management Application for Underserved Populations: A Multi-Stakeholder Analysis

Medically underserved communities have limited access to effective disease management resources in the U.S. Mobile health applications (mHealth apps) offer patients a cost-effective way to monitor and self-manage their condition and to communicate with providers; however, current diabetes self-management apps have rarely included end-users from underserved communities in the design process. This research documents key stakeholder-driven design requirements for a diabetes self-management app for medically underserved patients. Semi-structured survey interviews were carried out on 97 patients with diabetes and 11 healthcare providers from medically underserved counties in South Texas, to elicit perspectives and preferences regarding a diabetes self-management app, and their beliefs regarding such an app's usage and utility. Patients emphasized the need for accessible educational content and for quick access to guidance on regulating blood sugar, diet, and exercise and physical activity using multimedia rather than textual forms. Healthcare providers indicated that glucose monitoring, educational content, and the graphical visualization of diabetes data were among the top-rated app features. These findings suggest that specific design requirements for the underserved can improve the adoption, usability, and sustainability of such interventions. Designers should consider health literacy and numeracy, linguistic barriers, data visualization, data entry complexity, and information exchange capabilities.

Ethical Considerations When Using a Mobile Eye Tracker in a Patient-Facing Area: Lessons from an Intensive Care Unit Observational Protocol

This article describes the process of designing, approving, and conducting an investigator-initiated protocol to use an eye-tracking device in a health care setting. Participants wore the device, which resembles eyeglasses, in a front-facing manner in an intensive care unit for the study of personnel gaze patterns, producing a visual record of workflow. While the data of interest for our study was not specifically the health information protected by the privacy rule of the Health Insurance Portability and Accountability Act (HIPAA), a wide variety of such data was captured by the eye-tracking device, and the prospective consent of all people who might have been incidentally videotaped was not feasible. The protocol therefore required attention to unique ethical considerations-including consent, privacy and confidentiality, HIPAA compliance, institutional liability, and the use of secondary data. The richness of eye-tracker data suggests various beneficial applications in health care occupational research and quality improvement. Therefore, sharing our study's successful design and execution, including proactive researcher-institutional review board communication, can inform and encourage similarly valuable, ethical, and innovative audiovisual research techniques.

The Impact of Commutes, Work Schedules, and Sleep on Near-Crashes during Nurses' Post Shift-Work Commutes: A Naturalistic Driving Study

OCCUPATIONAL APPLICATIONSDriving and survey data were collected from nurses following the night-shift and analyzed with logistic regression and frequency analysis. The analyses showed that prior near-crashes and drive length contributed significantly to near-crashes. The frequency analysis showed that most near-crashes occurred on major roadways, including principal arterials, major collectors, and interstates, within the first 15 minutes of the drive. These results highlight the urgent need for countermeasures to prevent drowsy driving incidents among night-shift nurses. Specifically, nurses and hospital systems should focus on countermeasures that encourage taking a break on the post work commute and those that can intervene during the drive. This may include the use of educational programs to teach nurses the importance of adequate rest or taking a break to sleep during their drive home, or technology that can recognize drowsiness and alert nurses of their drowsiness levels, prompting them to take a break.

Atherosclerotic Cardiovascular Disease, Cancer, and Financial Toxicity Among Adults in the United States

Background

Financial toxicity (FT) is a well-established side-effect of the high costs associated with cancer care. In recent years, studies have suggested that a significant proportion of those with atherosclerotic cardiovascular disease (ASCVD) experience FT and its consequences.

Objectives

This study aimed to compare FT for individuals with neither ASCVD nor cancer, ASCVD only, cancer only, and both ASCVD and cancer.

Methods

From the National Health Interview Survey, we identified adults with self-reported ASCVD and/or cancer between 2013 and 2018, stratifying results by nonelderly (age <65 years) and elderly (age ≥65 years). We defined FT if any of the following were present: any difficulty paying medical bills, high financial distress, cost-related medication nonadherence, food insecurity, and/or foregone/delayed care due to cost.

Results

The prevalence of FT was higher among those with ASCVD when compared with cancer (54% vs. 41%; p < 0.001). When studying the individual components of FT, in adjusted analyses, those with ASCVD had higher odds of any difficulty paying medical bills (odds ratio [OR]: 1.22; 95% confidence interval [CI]: 1.09 to 1.36), inability to pay bills (OR: 1.25; 95% CI: 1.04 to 1.50), cost-related medication nonadherence (OR: 1.28; 95% CI: 1.08 to 1.51), food insecurity (OR: 1.39; 95% CI: 1.17 to 1.64), and foregone/delayed care due to cost (OR: 1.17; 95% CI: 1.01 to 1.36). The presence of ≥3 of these factors was significantly higher among those with ASCVD and those with both ASCVD and cancer when compared with those with cancer (23% vs. 30% vs. 13%, respectively; p < 0.001). These results remained similar in the elderly population.

Conclusions

Our study highlights that FT is greater among patients with ASCVD compared with those with cancer, with the highest burden among those with both conditions.

Improving Psychiatric Care Through Integrated Digital Technologies

This manuscript provides an overview of our efforts to implement an integrated electronic monitoring and feedback platform to increase patient engagement, improve care delivery and outcome of treatment, and alert care teams to deterioration in functioning. Patients First utilizes CareSense, a digital care navigation and data collection system, to integrate traditional patient-reported outcomes monitoring with novel biological monitoring between visits to provide patients and caregivers with real-time feedback on changes in symptoms such as stress, anxiety, and depression. The next stage of project development incorporates digital therapeutics (computerized therapeutic interventions) for patients, and video resources for primary care physicians and nurse practitioners who serve as the de facto front line for psychiatric care. Integration of the patient-reported outcomes monitoring with continuous biological monitoring, and digital supports is a novel application of existing technologies. Video resources pushed to care providers whose patients trigger a symptom severity alert is, to our knowledge, an industry first.

Investigating the Food and Drug Administration Biotherapeutics Review and Approval Process: Narrative Review

Background

The development, review, and approval process of therapeutic biological products in the United States presents two primary challenges: time and cost. Advancing a biotherapeutic from concept to market may take an average of 12 years, with costs exceeding US $1 billion, and the product may still fail the US Food and Drug Administration (FDA) approval process. Despite the FDA’s practices to expedite the approval of new therapies, seeking FDA approval remains a long, costly, and risky process.

Objective

The objective of this paper is to explore the factors and gaps related to the FDA review and approval process that contribute to process inefficiencies and complexities as well as proposed methods and solutions to address such gaps. This paper also aims to investigate the available modeling efforts for the FDA approval process of therapeutic biological products.

Methods

A narrative review of literature was conducted to understand the scope of published knowledge about challenges, opportunities, and specific methods to address the factors and gaps related to the review and approval of new drugs, including therapeutic biological products. Relevant peer-reviewed journal articles, conference proceedings, book chapters, official reports from public policy professional centers, and official reports and guidelines from the FDA were reviewed.

Results

Of the 23 articles identified in this narrative literature review, none modeled the current FDA review and approval process structure to address issues related to the robustness, reliability, and efficiency of its operations from an external point of view. Although several studies summarize the FDA approval process with clarity, in addition to bringing to light the problems and challenges faced by the regulatory agency, only a few attempts have been made to provide solutions for the problems and challenges identified. In addition, although several reform models have been discussed, these models lack the application of scientific methodologies and modeling techniques in understanding FDA as a complex sociotechnical system. Furthermore, tools and methods to assess the efficacy of the models before implementation are largely absent.

Conclusions

The findings suggest the efficacy of model-based systems engineering approaches for identifying opportunities for significant improvements to the FDA review and approval process. Using this holistic approach will serve several investigative purposes: identify influential sources of variability that cause major delays, including individual, team, and organizational decision making; identify the human-system bottlenecks; identify areas of opportunity for design-driven improvements; study the effect of induced changes in the system; and assess the robustness of the structure of the FDA approval process in terms of enforcement and information symmetry.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR)

BACKGROUND

The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic.

OBJECTIVE

We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization.

METHODS

Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context.

RESULTS

The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources.

CONCLUSIONS

A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

The Use of Telehealth Technology to Support Health Coaching for Older Adults: Literature Review

BACKGROUND

Health coaching is an intervention process for driving behavior change through goal-setting, education, encouragement, and feedback on health-related behaviors. Telehealth systems that include health coaching and remote monitoring are making inroads in managing chronic conditions and may be especially suited for older populations.

OBJECTIVE

This literature review aimed to investigate the current status of health coaching interventions incorporating telehealth technology and the associated effectiveness of this intervention to deliver health care with an emphasis on older adults (aged 65 and older).

METHODS

A literature review was conducted to identify the research conducted on health coaching combined with remote monitoring for delivering health care to older adults. The Ovid MEDLINE and CINAHL databases were queried using a combination of relevant search terms (including middle aged, aged, older adult, elderly, health coaching, and wellness coaching). The search retrieved 196 papers published from January 2010 to September 2019 in English. Following a systematic review process, the titles and abstracts of the papers retrieved were screened for applicability to health coaching for older adults to define a subset for further review. Papers were excluded if the studied population did not include older adults. The full text of the 42 papers in this subset was then reviewed, and 13 papers related to health coaching combined with remote monitoring for older adults were included in this review.

RESULTS

Of the 13 studies reviewed, 10 found coaching supported by telehealth technology to provide effective outcomes. Effectiveness outcomes assessed in the studies included hospital admissions/re-admissions, mortality, hemoglobin A_1c (HbA_1c) level, body weight, blood pressure, physical activity level, fatigue, quality of life, and user acceptance of the coaching program and technology.

CONCLUSIONS

Telehealth systems that include health coaching have been implemented in older populations as a viable intervention method for managing chronic conditions with mixed results. Health coaching combined with telehealth may be an effective solution for providing health care to older adults. However, health coaching is predominantly performed by human coaches with limited use of technology to augment or replace the human coach. The opportunity exists to expand health coaching to include automated coaching.

Classification of Fatigue Phases in Healthy and Diabetic Adults Using Wearable Sensor

Fatigue is defined as "a loss of force-generating capacity" in a muscle that can intensify tremor. Tremor quantification can facilitate early detection of fatigue onset so that preventative or corrective controls can be taken to minimize work-related injuries and improve the performance of tasks that require high-levels of accuracy. We focused on developing a system that recognizes and classifies voluntary effort and detects phases of fatigue. The experiment was designed to extract and evaluate hand-tremor data during the performance of both rest and effort tasks. The data were collected from the wrist and finger of the participant's dominant hand. To investigate tremor, time, frequency domain features were extracted from the accelerometer signal for segments of 45 and 90 samples/window. Analysis using advanced signal processing and machine-learning techniques such as decision tree, k-nearest neighbor, support vector machine, and ensemble classifiers were applied to discover models to classify rest and effort tasks and the phases of fatigue. Evaluation of the classifier's performance was assessed based on various metrics using 5-fold cross-validation. The recognition of rest and effort tasks using an ensemble classifier based on the random subspace and window length of 45 samples was deemed to be the most accurate (96.1%). The highest accuracy (~98%) that distinguished between early and late fatigue phases was achieved using the same classifier and window length.

Neural Signatures of Handgrip Fatigue in Type 1 Diabetic Men and Women

Type 1 diabetes (T1D) is associated with reduced muscular strength and greater muscle fatigability. Along with changes in muscular mechanisms, T1D is also linked to structural changes in the brain. How the neurophysiological mechanisms underlying muscle fatigue is altered with T1D and sex related differences of these mechanisms are still not well investigated. The aim of this study was to determine the impact of T1D on the neural correlates of handgrip fatigue and examine sex and T1D related differences in neuromuscular performance parameters, neural activation and functional connectivity patterns between the motor regions of the brain. Forty-two adults, balanced by condition (healthy vs T1D) and sex (male vs female), and performed submaximal isometric handgrip contractions until voluntary exhaustion. Initial strength, endurance time, strength loss, force variability, and complexity measures were collected. Additionally, hemodynamic responses from motor-function related cortical regions, using functional near-infrared spectroscopy (fNIRS), were obtained. Overall, females exhibited lower initial strength (p < 0.0001), and greater strength loss (p = 0.023) than males. While initial strength was significantly lower in the T1D group (p = 0.012) compared to the healthy group, endurance times and strength loss were comparable between the two groups. Force complexity, measured as approximate entropy, was found to be lower throughout the experiment for the T1D group (p = 0.0378), indicating lower online motor adaptability. Although, T1D and healthy groups fatigued similarly, only the T1D group exhibited increased neural activation in the left (p = 0.095) and right (p = 0.072) supplementary motor areas (SMA) over time. A sex × condition × fatigue interaction effect (p = 0.044) showed that while increased activation was observed in both T1D females and healthy males from the Early to Middle phase, this was not observed in healthy females or T1D males. These findings demonstrate that T1D adults had lower adaptability to fatigue which they compensated for by increasing neural effort. This study highlights the importance of examining both neural and motor performance signatures when investigating the impact of chronic conditions on neuromuscular fatigue. Additionally, the findings have implications for developing intervention strategies for training, rehabilitation, and ergonomics considerations for individuals with chronic conditions.

Disaster Ergonomics: Human Factors in COVID-19 Pandemic Emergency Management

OBJECTIVE

We aimed to identify opportunities for application of human factors knowledge base to mitigate disaster management (DM) challenges associated with the unique characteristics of the COVID-19 pandemic.

BACKGROUND

The role of DM is to minimize and prevent further spread of the contagion over an extended period of time. This requires addressing large-scale logistics, coordination, and specialized training needs. However, DM-related challenges during the pandemic response and recovery are significantly different than with other kinds of disasters.

METHOD

An expert review was conducted to document issues relevant to human factors and ergonomics (HFE) in DM.

RESULTS

The response to the COVID-19 crisis has presented complex and unique challenges to DM and public health practitioners. Compared to other disasters and previous pandemics, the COVID-19 outbreak has had an unprecedented scale, magnitude, and propagation rate. The high technical complexity of response and DM coupled with lack of mental model and expertise to respond to such a unique disaster has seriously challenged the response work systems. Recent research has investigated the role of HFE in modeling DM systems' characteristics to improve resilience, accelerating emergency management expertise, developing agile training methods to facilitate dynamically changing response, improving communication and coordination among system elements, mitigating occupational hazards including guidelines for the design of personal protective equipment, and improving procedures to enhance efficiency and effectiveness of response efforts.

CONCLUSION

This short review highlights the potential for the field's contribution to proactive and resilient DM for the ongoing and future pandemics.

Use of telecritical care for family visitation to ICU during the COVID-19 pandemic: an interview study and sentiment analysis

BACKGROUND

When the COVID-19 pandemic restricted visitation between intensive care unit patients and their families, the virtual intensive care unit (vICU) in our large tertiary hospital was adapted to facilitate virtual family visitation. The objective of this paper is to document findings from interviews conducted with family members on three categories: (1) feelings experienced during the visit, (2) barriers, challenges or concerns faced using this service, and (3) opportunities for improvements.

METHODS

Family members were interviewed postvisit via phone. For category 1 (feelings), automated analysis in Python using the Valence Aware Dictionary for sentiment Reasoner package produced weighted valence (extent of positive, negative or neutral emotive connotations) of the interviewees' word choices. Outputs were compared with a manual coder's valence ratings to assess reliability. Two raters conducted inductive thematic analysis on the notes from these interviews to analyse categories 2 (barriers) and 3 (opportunities).

RESULTS

Valence-based and manual sentiment analysis of 230 comments received on feelings showed over 86% positive sentiments (88.2% and 86.8%, respectively) with some neutral (7.3% and 6.8%) and negative (4.5% and 6.4%) sentiments. The qualitative analysis of data from 57 participants who commented on barriers showed four primary concerns: inability to communicate due to patient status (44% of respondents); technical difficulties (35%); lack of touch and physical presence (11%); and frequency and clarity of communications with the care team (11%). Suggested improvements from 59 participants included: on demand access (51%); improved communication with the care team (17%); improved scheduling processes (10%); and improved system feedback and technical capabilities (17%).

CONCLUSIONS

Use of vICU for remote family visitations evoked happiness, joy, gratitude and relief and a sense of closure for those who lost loved ones. Identified areas for concern and improvement should be addressed in future implementations of telecritical care for this purpose.

Adapting an Outpatient Psychiatric Clinic to Telehealth During the COVID-19 Pandemic: A Practice Perspective

As the demand for telepsychiatry increases during the COVID-19 pandemic, the strengths and challenges of telepsychiatry implementation must be articulated to improve clinical practices in the long term. Currently, observations within US contexts are lacking; therefore, we report on the rapid implementation of telepsychiatry and workflow experiences in a psychiatric practice based within a large health care system in southeast Texas with a national catchment area. We discuss the logistics of the implementation, including modes of communication, scheduling, coordination, and capacity; the psychological effects of web-based services, including both the loss of the physical therapeutic environment and the unique interpersonal dynamics experienced in the virtual environment; and postadoption patterns of engagement with our services and with other clinical functions affected by the rapid adaptation to telemedicine. Our art therapy group programming serves as an applied case study, demonstrating the value of a well-managed web-based program (eg, patients were receptive and well-engaged, and they appreciated the continuity of accessible service) as well as the challenges (eg, the need for backup plans and technological fallbacks, managing interruptions and telecommunication learning curves, and working around the difference in resources for art and music therapy between a well-stocked clinical setting versus clients’ home spaces). We conclude from our experience that the overall strengths of telepsychiatry include receptive and well-engaged responses from patients as well as the expansion of boundaries, which provides a directly contextualized view into patients’ home lives. Challenges and corresponding recommendations include the need for more careful safety planning for high-risk patients; maintaining professional boundaries in the newly informal virtual setting; designing the physical space to both frame the patient encounter and maintain work-life balance for the therapist; allowing for delays and interruptions (including an initial acclimation session); and preserving interprofessional care team collaboration when the physical locations that normally facilitate such encounters are not accessible. We believe that careful observations of the strengths and challenges of telepsychiatry during this pandemic will better inform practices that are considering telepsychiatry adoption both within pandemic contexts and more broadly thereafter.

Cognition in crisis management teams: an integrative analysis of definitions

In large-scale extreme events, multidisciplinary crisis management teams (CMTs) are required to function together cognitively. Despite theoretical maturity in team cognition and recurrent emphasis on cognition in the crisis management practices, no synthesis of theoretical and practical discourses is currently available, limiting empirical investigations of cognition in CMTs. To address this gap, this paper aims to review the definitions of cognition in CMTs, with a particular focus on examining if and to what extent they are diversified. Through a systematic process to search peer-reviewed journal articles published in English from 1990 to 2019, 59 articles were selected with 62 coded definitions of 11 different constructs. The similarities and variabilities of the definitions were examined in terms of their theoretical and practical emphases and then synthesised into an integrative definition expected to serve as a general guide of reference for future researchers seeking an operational definition of cognition in CMTs. Practitioner summary: Understanding of cognition in CMTs is grounded in various theories and models with varying assumptions. An integrative conceptualisation of such cognition as interaction within and across CMTs to perceive, diagnose, and adapt to the crisis may facilitate the accumulation of knowledge and future operationalisations. Abbreviations: CMT(s): crisis management team(s); SMM: shared mental model; TMM: team mental model; COP: common operating picture; SSA: shared situation awareness; TSA: team situation awareness; DC: distributed cognition; ITC: interactive team cognition; TMS: transitive memory system(s); DSA: distributed situation awareness.

Investigating Mental Health of US College Students During the COVID-19 Pandemic: Cross-Sectional Survey Study

BACKGROUND

Evidence suggests that the COVID-19 pandemic has generally increased levels of stress and depression among the public. However, the impact on college students in the United States has not been well-documented.

OBJECTIVE

This paper surveys the mental health status and severity of depression and anxiety of college students in a large university system in the United States during the COVID-19 pandemic.

METHODS

An online survey was conducted among undergraduate and graduate students recruited from Texas A&M University via email. The survey consisted of two standardized scales-the Patient Health Questionnaire-9 and the General Anxiety Disorder-7-for depression and anxiety, and additional multiple-choice and open-ended questions regarding stressors and coping mechanisms specific to COVID-19.

RESULTS

Among the 2031 participants, 48.14% (n=960) showed a moderate-to-severe level of depression, 38.48% (n=775) showed a moderate-to-severe level of anxiety, and 18.04% (n=366) had suicidal thoughts. A majority of participants (n=1443, 71.26%) indicated that their stress/anxiety levels had increased during the pandemic. Less than half of the participants (n=882, 43.25%) indicated that they were able to cope adequately with the stress related to the current situation.

CONCLUSIONS

The proportion of respondents showing depression, anxiety, and/or suicidal thoughts is alarming. Respondents reported academic-, health-, and lifestyle-related concerns caused by the pandemic. Given the unexpected length and severity of the outbreak, these concerns need to be further understood and addressed.

Effects of COVID-19 on College Students' Mental Health in the United States: Interview Survey Study

BACKGROUND

Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus.

OBJECTIVE

Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students.

METHODS

We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods.

RESULTS

Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms.

CONCLUSIONS

Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

Rapid Implementation and Innovative Applications of a Virtual Intensive Care Unit During the COVID-19 Pandemic: Case Study

BACKGROUND

The COVID-19 pandemic has necessitated a rapid increase of space in highly infectious disease intensive care units (ICUs). At Houston Methodist Hospital (HMH), a virtual intensive care unit (vICU) was used amid the COVID-19 outbreak.

OBJECTIVE

The aim of this paper was to detail the novel adaptations and rapid expansion of the vICU that were applied to achieve patient-centric solutions while protecting staff and patients' families during the pandemic.

METHODS

The planned vICU implementation was redirected to meet the emerging needs of conversion of COVID-19 ICUs, including alterations to staged rollout timing, virtual and in-person staffing, and scope of application. With the majority of the hospital critical care physician workforce redirected to rapidly expanded COVID-19 ICUs, the non-COVID-19 ICUs were managed by cardiovascular surgeons, cardiologists, neurosurgeons, and acute care surgeons. HMH expanded the vICU program to fill the newly depleted critical care expertise in the non-COVID-19 units to provide urgent, emergent, and code blue support to all ICUs.

RESULTS

Virtual family visitation via the Consultant Bridge application, palliative care delivery, and specialist consultation for patients with COVID-19 exemplify the successful adaptation of the vICU implementation. Patients with COVID-19, who were isolated and separated from their families to prevent the spread of infection, were able to virtually see and hear their loved ones, which bolstered the mental and emotional status of those patients. Many families expressed gratitude for the ability to see and speak with their loved ones. The vICU also protected medical staff and specialists assigned to COVID-19 units, reducing exposure and conserving personal protective equipment.

CONCLUSIONS

Telecritical care has been established as an advantageous mechanism for the delivery of critical care expertise during the expedited rollout of the vICU at Houston Methodist Hospital. Overall responses from patients, families, and physicians are in favor of continued vICU care; however, further research is required to examine the impact of innovative applications of telecritical care in the treatment of critically ill patients.

Investigating resilience in emergency management: An integrative review of literature

There is a growing need for resilience in dealing with unexpected events during disasters. The purpose of this review was to summarize and synthesize the literature that examined resilience in the context of emergency management (EM). Four groups of findings were synthesized: definitions, key dimensions, technical tools, and research settings employed in the research. First, definitions of resilience, improvisation, and adaptation were summarized and critically evaluated. Second, four key dimensions of EM resilience were identified: collective sensemaking, team decision making, harmonizing work-as-imagined and work-as-done, and interaction and coordination. Third, this review identified five prevalent technical tools used to enhance resilience in EM: mapmaking, event history logging, mobile communication applications, integrated information management system, and decision support tools. Fourth, two major design features of emergency simulations, incident scenarios and participant roles, are evaluated. For each finding, directions for future research efforts to improve resilience in EM are proposed.

Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework

BACKGROUND

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric condition that is associated with symptoms such as hyperarousal and overreactions. Treatments for PTSD are limited to medications and in-session therapies. Assessing the way the heart responds to PTSD has shown promise in detecting and understanding the onset of symptoms.

OBJECTIVE

This study aimed to extract statistical and mathematical approaches that researchers can use to analyze heart rate (HR) data to understand PTSD.

METHODS

A scoping literature review was conducted to extract HR models. A total of 5 databases including Medical Literature Analysis and Retrieval System Online (Medline) OVID, Medline EBSCO, Cumulative Index to Nursing and Allied Health Literature (CINAHL) EBSCO, Excerpta Medica Database (Embase) Ovid, and Google Scholar were searched. Non-English language studies, as well as studies that did not analyze human data, were excluded. A total of 54 studies that met the inclusion criteria were included in this review.

RESULTS

We identified 4 categories of models: descriptive time-independent output, descriptive and time-dependent output, predictive and time-independent output, and predictive and time-dependent output. Descriptive and time-independent output models include analysis of variance and first-order exponential; the descriptive time-dependent output model includes a classical time series analysis and mixed regression. Predictive time-independent output models include machine learning methods and analysis of the HR-based fluctuation-dissipation method. Finally, predictive time-dependent output models include the time-variant method and nonlinear dynamic modeling.

CONCLUSIONS

All of the identified modeling categories have relevance in PTSD, although the modeling selection is dependent on the specific goals of the study. Descriptive models are well-founded for the inference of PTSD. However, there is a need for additional studies in this area that explore a broader set of predictive models and other factors (eg, activity level) that have not been analyzed with descriptive models.

Diabetes Management Experience and the State of Hypoglycemia: National Online Survey Study

BACKGROUND

Hypoglycemia, or low blood sugar levels, in people with diabetes can be a serious life-threatening condition, and serious outcomes can be avoided if low levels of blood sugar are proactively detected. Although technologies exist to detect the onset of hypoglycemia, they are invasive or costly or exhibit a high incidence of false alarms. Tremors are commonly reported symptoms of hypoglycemia and may be used to detect hypoglycemic events, yet their onset is not well researched or understood.

OBJECTIVE

This study aimed to understand diabetic patients' perceptions of hypoglycemic tremors, as well as their user experiences with technology to manage diabetes, and expectations from a self-management tool to ultimately inform the design of a noninvasive and cost-effective technology that detects tremors associated with hypoglycemia.

METHODS

A cross-sectional internet panel survey was administered to adult patients with type 1 diabetes using the Qualtrics platform in May 2019. The questions focused on 3 main constructs: (1) perceived experiences of hypoglycemia, (2) experiences and expectations about a diabetes management device and mobile app, and (3) beliefs and attitudes regarding intention to use a diabetes management device. The analysis in this paper focuses on the first two constructs. Nonparametric tests were used to analyze the Likert scale data, with a Mann-Whitney U test, Kruskal-Wallis test, and Games-Howell post hoc test as applicable, for subgroup comparisons to highlight differences in perceived frequency, severity, and noticeability of hypoglycemic tremors across age, gender, years living with diabetes, and physical activity.

RESULTS

Data from 212 respondents (129 [60.8%] females) revealed statistically significant differences in perceived noticeability of tremors by gender, whereby males noticed their tremors more (P<.001), and age, with the older population reporting lower noticeability than the young and middle age groups (P<.001). Individuals living longer with diabetes noticed their tremors significantly less than those with diabetes for ≤1 year but not in terms of frequency or severity. Additionally, the majority of our participants (150/212, 70.7%) reported experience with diabetes-monitoring devices.

CONCLUSIONS

Our findings support the need for cost-efficient and noninvasive continuous monitoring technologies. Although hypoglycemic tremors were perceived to occur frequently, such tremors were not found to be severe compared with other symptoms such as sweating, which was the highest rated symptom in our study. Using a combination of tremor and galvanic skin response sensors may show promise in detecting the onset of hypoglycemic events.

Technologies for Opioid Use Disorder Management: Mobile App Search and Scoping Review

Background

Advances in technology engender the investigation of technological solutions to opioid use disorder (OUD). However, in comparison to chronic disease management, the application of mobile health (mHealth) to OUD has been limited.

Objective

The overarching aim of our research was to design OUD management technologies that utilize wearable sensors to provide continuous monitoring capabilities. The objectives of this study were to (1) document the currently available opioid-related mHealth apps, (2) review past and existing technology solutions that address OUD, and (3) discuss opportunities for technological withdrawal management solutions.

Methods

We used a two-phase parallel search approach: (1) an app search to determine the availability of opioid-related mHealth apps and (2) a scoping review of relevant literature to identify relevant technologies and mHealth apps used to address OUD.

Results

The app search revealed a steady rise in app development, with most apps being clinician-facing. Most of the apps were designed to aid in opioid dose conversion. Despite the availability of these apps, the scoping review found no study that investigated the efficacy of mHealth apps to address OUD.

Conclusions

Our findings highlight a general gap in technological solutions of OUD management and the potential for mHealth apps and wearable sensors to address OUD.

Muddling through troubled water: resilient performance of incident management teams during Hurricane Harvey

Modern communities face escalating threats from natural disasters. Thus, the resilience of incident management teams (IMTs) during adverse events becomes crucial to protect lives and physical systems. However, prior studies have only partially highlighted factors related to IMT resilience. To provide a holistic understanding of the resilience of the IMTs, this study conducted semi-structured interviews with 10 experienced IMT personnel during Hurricane Harvey. Thematic analysis revealed six characteristics of resilient IMTs during a hurricane event: (i) establishing a common operating picture, (ii) adopting and adapting plans and protocols, (iii) proactive, re-prioritizing, and unconventional decision-making, (iv) enhancing resourcefulness and redundancy, (v) learning for improved anticipation and response readiness, and (vi) inter-organisational relationship to promote IMT functions. As an empirical investigation of the resilience of the IMTs, the findings inform future endeavours for developing incident information technologies and strategies to harmonise pre-established plans with adaptive actions in the field and fostering capabilities to learn from incidents. Practitioner summary: Resilient incident management teams establish a common operating picture; effectively adopt and adapt plans and protocols; make decisions in an unconventional and anticipatory fashion; constantly re-prioritize goals and tasks; enhance resourcefulness and redundancy; continuously learn skills for improved anticipation and response readiness; and exhibit good inter-organisational coordination and planning skills.

Veteran-Centered Investigation of Architectural and Space Design Considerations for Post-Traumatic Stress Disorder (PTSD)

AIM

This study sought to investigate architectural and space design considerations for veterans with post-traumatic stress disorder (PTSD).

BACKGROUND

Anecdotal evidence suggests that urban design features could have a positive impact on the mental well-being of individuals suffering from PTSD. However, evidence-based architectural and space design guidelines for PTSD are largely absent.

METHODS

Semi-structured interviews were conducted with 17 veterans diagnosed with PTSD to gain insights into their personal experiences with physical indoor and outdoor spaces, and to inquire about their needs and expectations for future architectural design. Transcripts were analyzed thematically.

RESULTS

Architectural design features including windows, entrances and exits, walkways and hallways, open space, defensible space, and green space; interior design features including furnishings and color; and ambient features including light, air quality, and noise levels were identified as most influential design features.

CONCLUSIONS

Our results underscore the first important step to developing comprehensive architectural and space design guidelines for veterans with PTSD. Work is in progress to solicit more feedback from veterans.

Understanding the scope of downtime threats: A scoping review of downtime-focused literature and news media

Electronic health record downtimes are any period where the computer systems are unavailable, either for planned or unexpected events. During an unexpected downtime, healthcare workers are rapidly forced to use rarely-practiced, paper-based methods for healthcare delivery. In some instances, patient safety is compromised or data exposed to parties seeking profit. This review provides a foundational perspective of the current state of downtime readiness as organizations prepare to handle downtime events. A search of technical news media related to healthcare informatics and a scoping review of the research literature were conducted. Findings ranged from theoretical exploration of downtime to empirical direct comparison of downtime versus normal operation. Overall, 166 US hospitals experienced a total of 701 days of downtime in 43 events between 2012 and 2018. Almost half (48.8%) of the published downtime events involved some form of cyber-attacks. Downtime contingency planning is still predominantly considered through a top-down organizational focus. We propose that a bottom-up approach, involving the front-line clinical staff responsible for executing the downtime procedure, will be beneficial. Significant new research support for the development of contingency plans will be needed.