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Abstract The impact of multimodal prehabilitation on postoperative complications in upper abdominal surgeries is understudied. This review analyzes randomized trials on multimodal prehabilitation with patient and hospital outcomes. MEDLINE, Embase, CINAHL, and Cochrane CENTRAL were searched for trials on prehabilitation before elective (non-emergency) abdominal surgery. Two reviewers independently screened studies, extracted data, and assessed study quality. Primary outcomes of interest were postoperative pulmonary complications (PPCs) and all-cause complications; secondary outcomes included hospital and intensive care length of stay. A meta-analysis with random-effect models was performed, and heterogeneity was evaluated with I-square and Cochran’s Q test. Dichotomous variables were reported in log-odds ratio and continuous variables were presented as mean difference. Ten studies (total 1503 patients) were included. Odds of developing complications after prehabilitation were significantly lower compared to various control groups (− 0.38 [− 0.75– − 0.004], P = 0.048). Five studies described PPCs, and participants with prehabilitation had decreased odds of PPC (− 0.96 [− 1.38– − 0.54], P

BackgroundMental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs. ObjectiveThis review aims to investigate the scope of ML methodologies used in the detection, prediction, and analysis of mental stress and its consequent MDs. MethodsUsing a rigorous scoping review process with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, this investigation delves into the latest ML algorithms, preprocessing techniques, and data types used in the context of stress and stress-related MDs. ResultsA total of 98 peer-reviewed publications were examined for this review. The findings highlight that support vector machine, neural network, and random forest models consistently exhibited superior accuracy and robustness among all ML algorithms examined. Physiological parameters such as heart rate measurements and skin response are prevalently used as stress predictors due to their rich explanatory information concerning stress and stress-related MDs, as well as the relative ease of data acquisition. The application of dimensionality reduction techniques, including mappings, feature selection, filtering, and noise reduction, is frequently observed as a crucial step preceding the training of ML algorithms. ConclusionsThe synthesis of this review identified significant research gaps and outlines future directions for the field. These encompass areas such as model interpretability, model personalization, the incorporation of naturalistic settings, and real-time processing capabilities for the detection and prediction of stress and stress-related MDs.

Abstract 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.

BackgroundDespite widespread growth of televisits and telemedicine, it is unclear how telenursing could be applied to augment nurse labor and support nursing. ObjectiveThis study evaluated a large-scale acute care telenurse (ACTN) program to support web-based admission and discharge processes for hospitalized patients. MethodsA 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. ResultsCase 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. ConclusionsThis 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.

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.

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

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; F12,199=27.19; P

BackgroundDiabetes 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. ObjectiveIn this paper, we propose a noninvasive method to detect hypoglycemic events based on hand tremors using accelerometer data. MethodsWe 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. ResultsThe 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. ConclusionsOur 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.

BackgroundMental health is an increasing concern among vulnerable populations, including college students and veterans. ObjectiveThe purpose of this study was to determine if mobile health technology combined with health coaching can better enable a user to self-manage their mental health. MethodsThis study evaluated the mobile app “Biofeedback” 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 and a 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 2-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. ResultsThe 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 rank correlation coefficient –0.61, P

BackgroundIntensive 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.MethodA 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.ResultsParticipants caring for COVID patients at the COVID unit were 3.71 times more likely to experience stress (p

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.

Abstract Background A common challenge with all opioid use disorder treatment paths is withdrawal management. When withdrawal symptoms are not effectively monitored and managed, they lead to relapse which often leads to deadly overdose. A prerequisite for effective opioid withdrawal management is early identification and assessment of withdrawal symptoms. Objective The objective of this research was to describe the type and content of opioid withdrawal monitoring methods, including surveys, scales and technology, to identify gaps in research and practice that could inform the design and development of novel withdrawal management technologies. Methods A scoping review of literature was conducted. PubMed, EMBASE and Google Scholar were searched using a combination of search terms. Results Withdrawal scales are the main method of assessing and quantifying opioid withdrawal intensity. The search yielded 18 different opioid withdrawal scales used within the last 80 years. While traditional opioid withdrawal scales for patient monitoring are commonly used, most scales rely heavily on patients’ self-report and frequent observations, and generally suffer from lack of consensus on the criteria used for evaluation, mode of administration, type of reporting (e.g., scales used), frequency of administration, and assessment window. Conclusions It is timely to investigate how opioid withdrawal scales can be complemented or replaced with reliable monitoring technologies. Use of noninvasive wearable sensors to continuously monitor physiologic changes associated with opioid withdrawal represents a potential to extend monitoring outside clinical setting.

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.

Communities face unforeseen threats from natural and human-made disasters. As disasters grow more intense and critical infrastructure increases in complexity, resilience has emerged as an essential attribute of incident management systems. Despite concerted efforts to examine government organizations and their associated policies, understanding resilience traits exhibited by hospitals and healthcare systems during disasters is limited. We employ two fundamental viewpoints of safety to assess what went wrong (Safety I) and right (Safety II) during Hurricane Harvey in a large regional hospital. Through qualitative analysis of semi-structured interviews with hospital emergency management and operators, we examine both opportunities and challenges in six aspects of hospital incident management: organizational structure and functions; situational awareness; operating plans; human and physical resources; lessons learned from previous incidents; and leadership

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.

Following publication of the original article [1], the authors reported an error in one of the authors’ names. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.