Advance Directives Change Frequently in Nursing Home Residents.

OBJECTIVES: To describe the rate, timing, and pattern of changes in advance directives (ADs) of do not resuscitate (DNR) and do not hospitalize (DNH) orders among new admissions to nursing homes (NHs). DESIGN: A retrospective cohort study. SETTING AND PARTICIPANTS: Admissions to all publicly funded NHs in Ontario, Canada, between January 1, 2013, and December 31, 2017. METHODS: Residents were followed until discharged from incident NH stay, death, or were still present at the end of study (December 31, 2019). They were categorized into 3 mutually exclusive baseline composite AD groups: Full Code, DNR Only, and DNR+DNH. We used Poisson regression models to estimate the incidence rate ratios of AD change between different AD groups and different decision makers for personal care, adjusted for baseline clinical and sociodemographic variables. RESULTS: A total of 102,541 NH residents were eligible for inclusion. Residents with at least 1 AD change accounted for 46% of Full Code, 30% of DNR Only, and 25% of DNR+DNH group. Median time to first AD change ranged between 26 and 55 weeks. For Full Code and DNR Only residents, the most frequent change was to an AD 1 level lower in aggressiveness or intervention, whereas for DNR+DNH residents the most frequent change was to DNR Only. About 16% of residents had 2 or more AD changes during their stay. After controlling for covariates, residents with a DNR-only order or DNR+DNH orders at admission and those with a surrogate decision maker were associated with lower AD change rates. CONCLUSIONS AND IMPLICATIONS: Measuring AD adherence rates that are documented only at a particular time often underestimates the dynamics of AD changes during a resident's stay and results in an inaccurate measure of the effectiveness of AD on resident care. There should be more frequent reviews of ADs as they are quite dynamic. Mandatory review after an acute change in a resident's health would ensure that ADs are current.

eXplainable Artificial Intelligence (XAI) for improving organisational regility.

Since the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.

Feasibility of a wearable self-management application for patients with COPD at home: a pilot study.

BACKGROUND: Among people with COPD, smartphone and wearable technology may provide an effective method to improve care at home by supporting, encouraging, and sustaining self-management. The current study was conducted to determine if patients with COPD will use a dedicated smartphone and smartwatch app to help manage their COPD and to determine the effects on their self-management. METHODS: We developed a COPD self-management application for smartphones and smartwatches. Participants were provided with the app on a smartphone and a smartwatch, as well as a cellular data plan and followed for 6 months. We measured usage of the different smartphone app functions. For the primary outcome, we examined the change in self-management from baseline to the end of follow up. Secondary outcomes include changes in self-efficacy, quality of life, and COPD disease control. RESULTS: Thirty-four patients were enrolled and followed. Mean age was 69.8 years, and half of the participants were women. The most used functions were recording steps through the smartwatch, entering a daily symptom questionnaire, checking oxygen saturation, and performing breathing exercises. There was no significant difference in the primary outcome of change in self-management after use of the app or in overall total scores of health-related quality of life, disease control or self-efficacy. CONCLUSION: We found older patients with COPD would engage with a COPD smartphone and smartwatch application, but this did not result in improved self-management. More research is needed to determine if a smartphone and smartwatch application can improve self-management in people with COPD. TRIAL REGISTRATION: ClinicalTrials.Gov NCT03857061, First Posted February 27, 2019.

Impressions and Perceptions of a Smartphone and Smartwatch Self-Management Tool for Patients With COPD: A Qualitative Study.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) often do not seek care until they experience an exacerbation. Improving self-management for these patients may increase health-related quality of life and reduce hospitalizations. Patients are willing to use wearable technology for real-time data reporting and perceive mobile technology as potentially helpful in COPD management, but there are many barriers to the uptake of these technologies. OBJECTIVE: We aimed to understand patients' experiences using a wearable and mobile app and identify areas for improvement. METHODS: We conducted semi-structured interviews as part of a larger prospective cohort study wherein patients used a wearable and app for 6 months. We asked which features patients found accessible, acceptable and useful. RESULTS: We completed 26 interviews. We summarized our research findings into four main themes: (1) information, support and reassurance, (2) barriers to adoption, (3) impact on communication with health care providers, and (4) opportunities for improvement. Most patients found the feedback received through the app to be reassuring and useful. Some patients experienced technical difficulties with the app and found the wearable to be uncomfortable. CONCLUSIONS: Patients found a wearable device and mobile application to be acceptable and useful for the management of COPD. We identified barriers to adoption and opportunities for improvement to the design of our app. Further research is needed to understand what people with COPD and their healthcare providers want and will use in a mobile app and wearable for COPD management.

Radially adjustable stent retriever for mechanical thrombectomy in acute ischemic stroke: Improved first-pass effect with rapid-inflation deflation technique.

INTRODUCTION: Evidence for improved first-pass effect with the novel radially adjustable radio-opaque stent retriever Tigertriever is lacking. OBJECTIVE: To compare improvement in first pass success with Tigertriever using two different techniques-rapid inflation deflation (RID) and suction thrombectomy (ST). METHODS: Retrospective analysis of patients with acute ischemic stroke who underwent mechanical thrombectomy with Tigertriever at a single comprehensive stroke center. RESULTS: Thirty patients were included. Mean age was 72.8 years. Twelve patients (48%) experienced successful first passes with Tigertriever. Successful revascularization (modified thrombolysis in cerebral infarction (mTICI) 2b/3) was achieved in all (100%) patients who received RID or ST technique for thrombectomy. Good clinical outcome (modified Rankin score = 0-2) was noted in 40% (n = 10). Total mortality in the cohort was 8% (n = 2). RID and ST groups comprised of 10 and 15 patients, respectively. Five patients underwent MT with Tigertriever as a rescue device. RID VS ST: No difference was noted in mean age (p = 0.27), gender (p = 0.29), location of occlusion (p = 0.46), and device used for first pass (p = 0.57). A 70% first-pass success rate in RID group and 37.5% in ST group was noticed (p = 0.06). Mean time from groin puncture to reperfusion (TICI 2b//3) was statistically similar (p = 0.29, RID: 19.9 min vs ST: 25 min). Both groups noted a 100% complete recanalization rate. The rate of mortality between the two groups were not statistically different (p = 0.46). CONCLUSION: The preliminary first-pass success rates of RID technique with Tigertriever compared to ST technique, are encouraging. Longitudinal studies with longer follow up are needed to elucidate the smaller learning curve with this device.

Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) - A Systematic Review.

Introduction: Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death globally and is responsible for over 3 million deaths annually. One of the factors contributing to the significant healthcare burden for these patients is readmission. The aim of this review is to describe significant predictors and prediction scores for all-cause and COPD-related readmission among patients with COPD. Methods: A search was conducted in Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, from database inception to June 7, 2022. Studies were included if they reported on patients at least 40 years old with COPD, readmission data within 1 year, and predictors of readmission. Study quality was assessed. Significant predictors of readmission and the degree of significance, as noted by the p-value, were extracted for each study. This review was registered on PROSPERO (CRD42022337035). Results: In total, 242 articles reporting on 16,471,096 patients were included. There was a low risk of bias across the literature. Of these, 153 studies were observational, reporting on predictors; 57 studies were observational studies reporting on interventions; and 32 were randomized controlled trials of interventions. Sixty-four significant predictors for all-cause readmission and 23 for COPD-related readmission were reported across the literature. Significant predictors included 1) pre-admission patient characteristics, such as male sex, prior hospitalization, poor performance status, number and type of comorbidities, and use of long-term oxygen; 2) hospitalization details, such as length of stay, use of corticosteroids, and use of ventilatory support; 3) results of investigations, including anemia, lower FEV1, and higher eosinophil count; and 4) discharge characteristics, including use of home oxygen and discharge to long-term care or a skilled nursing facility. Conclusion: The findings from this review may enable better predictive modeling and can be used by clinicians to better inform their clinical gestalt of readmission risk.

Development of Multivariable Prediction Models for the Identification of Patients Admitted to Hospital with an Exacerbation of COPD and the Prediction of Risk of Readmission: A Retrospective Cohort Study using Electronic Medical Record Data.

BACKGROUND: Approximately 20% of patients who are discharged from hospital for an acute exacerbation of COPD (AECOPD) are readmitted within 30 days. To reduce this, it is important both to identify all individuals admitted with AECOPD and to predict those who are at higher risk for readmission. OBJECTIVES: To develop two clinical prediction models using data available in electronic medical records: 1) identifying patients admitted with AECOPD and 2) predicting 30-day readmission in patients discharged after AECOPD. METHODS: Two datasets were created using all admissions to General Internal Medicine from 2012 to 2018 at two hospitals: one cohort to identify AECOPD and a second cohort to predict 30-day readmissions. We fit and internally validated models with four algorithms. RESULTS: Of the 64,609 admissions, 3,620 (5.6%) were diagnosed with an AECOPD. Of those discharged, 518 (15.4%) had a readmission to hospital within 30 days. For identification of patients with a diagnosis of an AECOPD, the top-performing models were LASSO and a four-variable regression model that consisted of specific medications ordered within the first 72 hours of admission. For 30-day readmission prediction, a two-variable regression model was the top performing model consisting of number of COPD admissions in the previous year and the number of non-COPD admissions in the previous year. CONCLUSION: We generated clinical prediction models to identify AECOPDs during hospitalization and to predict 30-day readmissions after an acute exacerbation from a dataset derived from available EMR data. Further work is needed to improve and externally validate these models.

An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework.

Among all the gas disasters, gas concentration exceeding the threshold limit value (TLV) has been the leading cause of accidents. However, most systems still focus on exploring the methods and framework for avoiding reaching or exceeding TLV of the gas concentration from viewpoints of impacts on geological conditions and coal mining working-face elements. The previous study developed a Trip-Correlation Analysis Theoretical Framework and found strong correlations between gas and gas, gas and temperature, and gas and wind in the gas monitoring system. However, this framework's effectiveness must be examined to determine whether it might be adopted in other coal mine cases. This research aims to explore a proposed verification analysis approach-First-round-Second-round-Verification round (FSV) analysis approach to verify the robustness of the Trip-Correlation Analysis Theoretical Framework for developing a gas warning system. A mixed qualitative and quantitative research methodology is adopted, including a case study and correlational research. The results verify the robustness of the Triple-Correlation Analysis Theoretical Framework. The outcomes imply that this framework is potentially valuable for developing other warning systems. The proposed FSV approach can also be used to explore data patterns insightfully and offer new perspectives to develop warning systems for different industry applications.

Using Artificial Intelligence (AI) to predict organizational agility.

Since the pandemic organizations have been required to build agility to manage risks, stakeholder engagement, improve capabilities and maturity levels to deliver on strategy. Not only is there a requirement to improve performance, a focus on employee engagement and increased use of technology have surfaced as important factors to remain competitive in the new world. Consideration of the strategic horizon, strategic foresight and support structures is required to manage critical factors for the formulation, execution and transformation of strategy. Strategic foresight and Artificial Intelligence modelling are ways to predict an organizations future agility and potential through modelling of attributes, characteristics, practices, support structures, maturity levels and other aspects of future change. The application of this can support the development of required new competencies, skills and capabilities, use of tools and develop a culture of adaptation to improve engagement and performance to successfully deliver on strategy. In this paper we apply an Artificial Intelligence model to predict an organizations level of future agility that can be used to proactively make changes to support improving the level of agility. We also explore the barriers and benefits of improved organizational agility. The research data was collected from 44 respondents in public and private Australian industry sectors. These research findings together with findings from previous studies identify practices and characteristics that contribute to organizational agility for success. This paper contributes to the ongoing discourse of these principles, practices, attributes and characteristics that will help overcome some of the barriers for organizations with limited resources to build a framework and culture of agility to deliver on strategy in a changing world.

Using multi-focus group method as an effective tool for eliciting business system requirements: Verified by a case study.

This research aims to explore the multi-focus group method as an effective tool for systematically eliciting business requirements for business information system (BIS) projects. During the COVID-19 crisis, many businesses plan to transform their businesses into digital businesses. Business managers face a critical challenge: they do not know much about detailed system requirements and what they want for digital transformation requirements. Among many approaches used for understanding business requirements, the focus group method has been used to help elicit BIS needs over the past 30 years. However, most focus group studies about research practices mainly focus on a particular disciplinary field, such as social, biomedical, and health research. Limited research reported using the multi-focus group method to elicit business system requirements. There is a need to fill this research gap. A case study is conducted to verify that the multi-focus group method might effectively explore detailed system requirements to cover the Case Study business's needs from transforming the existing systems into a visual warning system. The research outcomes verify that the multi-focus group method might effectively explore the detailed system requirements to cover the business's needs. This research identifies that the multi-focus group method is especially suitable for investigating less well-studied, no previous evidence, or unstudied research topics. As a result, an innovative visual warning system was successfully deployed based on the multi-focus studies for user acceptance testing in the Case Study mine in Feb 2022. The main contribution is that this research verifies the multi-focus group method might be an effective tool for systematically eliciting business requirements. Another contribution is to develop a flowchart for adding to Systems Analysis & Design course in information system education, which may guide BIS students step by step on using the multi-focus group method to explore business system requirements in practice.

Guillain-Barré syndrome as an early manifestation of angioimmunoblastic T-cell lymphoma.

Guillain-Barré syndrome (GBS) is a rare condition caused by autoimmune damage of peripheral nerves. We describe a case where a man in his 80s presented with subacute, progressive fatigue and weakness. He had received an outpatient work-up for possible haematological malignancy, but eventually presented to the emergency department for worsening weakness. A physical exam and cerebrospinal fluid analysis suggested a diagnosis of GBS. Subsequently, a pathological diagnosis of angioimmunoblastic T-cell lymphoma was made. The patient underwent intravenous immunoglobulin treatment for GBS and was started on cyclophosphamide, doxorubicin, vincristine and prednisone therapy. Prior research has suggested that incident malignancy may be associated with GBS, which may be caused by a paraneoplastic-type phenomenon, malignancy-associated immune dysregulation or an autoimmune reaction triggered by a common exposure. Clinicians should be aware of the possible association between these two conditions and should remain open minded to the possibility of non-infectious triggers for GBS.

Perceptions of patients and nurses regarding the use of wearables in inpatient settings: a mixed methods study.

Wearable devices for hospitalized patients could help improve care. The purpose of this study was to highlight key barriers and facilitators involved in adopting wearable technology in acute care settings using patient and clinician feedback. Hospitalized patients, 18 years or older, were recruited at the General Medicine inpatient units in Toronto, Ontario to wear the Fitbit® Charge 2 or Charge 3. Fifty General Medicine adult inpatients were recruited. Patients and nurses provided feedback on structured questionnaires. Key themes from open-ended questions were analyzed. Primary outcomes of interest included the exploring patient and nurse perceptions of their experiences with wearable devices as well as their feasibility in clinical settings. Overall, both patients (n = 39) and nurses (n = 28) valued the information provided by Fitbits and shared concerns about device functionality and wearable design. Specifically, patients were interested in using wearables to enhance their self-monitoring, while nurses questioned data validity, as well as ease of incorporating wearables into their workflow. We found that patients wanted improved device design and functionality and valued the opportunity to improve their self-efficacy and to work in partnership with the medical team using wearable technology. Nurses wanted more device functionality and validation and easier ways to incorporate them into their workflow. To achieve the potential benefits of using wearable devices for enhanced monitoring, this study identifies challenges that must first be addressed in order for this technology to be widely adopted in clinical settings.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement.

BACKGROUND: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. METHODS: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. RESULTS: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. CONCLUSIONS: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Small-for-gestational age placentas associate with an increased risk of adverse outcomes in pregnancies complicated by either type I or type II pre-gestational diabetes mellitus.

INTRODUCTION: One-fifth of pregnancies with pre-gestational diabetes mellitus (pre-DM) yield placentas <10th percentile small for gestational age (SGA), compared to a non-diabetic population. We hypothesized that SGA placentas of women with pre-DM, whether type I (T1DM) or type II (T2DM), exhibit distinct histopathological changes and pregnancy outcomes compared to pre-DM pregnancies with an AGA placenta. METHODS: We conducted a retrospective, cohort study of placentas from pregnant women enrolled in the Diabetes in Pregnancy Program at Brown University between 2003 and 2011, by comparing pre-DM patients with SGA placentas to pre-DM patients with AGA placental weights. RESULTS: The SGA placenta groups were associated with an increased risk for adverse clinical outcomes, compared to AGA placentas in pregnancies complicated by either T1DM or T2DM. Compared to their AGA pre-DM counterparts, T1DM, SGA placentas show increased peri-villous fibrin/fibrinoid deposition, thrombosis in fetal blood vessels, and meconium staining. Moreover, the histopathology of SGA placentas from T2DM is characterized by decidual vasculopathy, accelerated villous maturity, and erythroblastosis, compared to T2DM AGA placentas. The contrasting placental pathologies between the two pre-DM SGA phenotypes evolved independent of patient demographics and were unrelated to indicators of the glycemic control present at early gestational ages. DISCUSSION: A sub-population of pre-DM women with either T1DM or T2DM diabetes that have an SGA placenta are at increased risk for adverse clinical outcomes in pregnancy, compared to pre-DM women with AGA placental weights.

Soldier neural architecture is temporarily modality specialized but poorly predicted by repertoire size in the stingless bee Tetragonisca angustula.

Individual heterogeneity within societies provides opportunities to test hypotheses about adaptive neural investment in the context of group cooperation. Here, we explore neural investment in defense specialist soldiers of the eusocial stingless bee (Tetragonisca angustula) which are age subspecialized on distinct defense tasks and have an overall higher lifetime task repertoire than other sterile workers within the colony. Consistent with predicted behavioral demands, soldiers had higher relative visual (optic lobe) investment than nonsoldiers but only during the period when they were performing the most visually demanding defense task (hovering guarding). As soldiers aged into the less visually demanding task of standing guarding this difference disappeared. Neural investment was otherwise similar across all colony members. Despite having larger task repertoires, soldiers had similar absolute brain size and the smaller relative brain size compared to other workers, meaning that lifetime task repertoire size was a poor predictor of brain size. Both high behavioral specialization in stable environmental conditions and reassignment across task groups during a crisis occur in T. angustula. The differences in neurobiology we report here are consistent with these specialized but flexible defense strategies. This work broadens our understanding of how neurobiology mediates age and morphological task specialization in highly cooperative societies.

Effect of intraperitoneal local anesthesia on enhanced recovery outcomes after bariatric surgery: a randomized controlled pilot study.

BACKGROUND: Patients with extreme obesity are at high risk for adverse perioperative events, especially when opioid-centric analgesic protocols are used, and perioperative pain management interventions in bariatric surgery could improve safety, outcomes and satisfaction. We aimed to evaluate the impact of intraperitoneal local anesthesia (IPLA) on enhanced recovery after bariatric surgery (ERABS) outcomes. METHODS: We conducted a prospective double-blind randomized controlled pilot study in adherence to an a priori peer-reviewed protocol. Patients undergoing laparoscopic Roux-en-Y gastric bypass surgery (LRYGB) with an established ERABS protocol between July 2014 and February 2015 were randomly allocated to receive either IPLA with 0.2% ropivacaine (intervention group) or normal saline (control group). We measured pain scores, analgesic consumption and adverse effects. Functional prehabilitation outcomes, including peak expiratory flow (PEF) and the Six Minute Walk Test (6MWT) and Quality of Recovery Survey-40 (QoR-40) scores, were assessed before surgery, and 1 day and 7 days postoperatively. RESULTS: One hundred patients were randomly allocated to the study groups, of whom 92 completed the study, 46 in each group. There were no statistically significant differences between the 2 groups in baseline characteristics or any primary or secondary outcomes. Pain scores and analgesic consumption were low in both groups. There were no adverse events. Significant declines in PEF and 6MWT and QoR-40 scores were noted on postoperative day 1 in both groups; the values returned to baseline on postoperative day 7 in both groups. CONCLUSION: Intraperitoneal local anesthesia with ropivacaine did not reduce postoperative pain or analgesic consumption when administered intraoperatively to patients undergoing LRYGB. Standardization of the ERABS protocol benefited patients, with functional prehabilitation outcomes returning to baseline postoperatively. Trial registration: ClinicalTrials.gov no. NCT02154763.

Evaluating the Validity and Utility of Wearable Technology for Continuously Monitoring Patients in a Hospital Setting: Systematic Review.

BACKGROUND: The term posthospital syndrome has been used to describe the condition in which older patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep during hospital stay may contribute to posthospital syndrome, the continuous monitoring of such parameters by using affordable wearables may help to reduce the prevalence of this syndrome. Although there have been systematic reviews of wearables for physical activity monitoring in hospital settings, there are limited data on the use of wearables for measuring other health variables in hospitalized patients. OBJECTIVE: This systematic review aimed to evaluate the validity and utility of wearable devices for monitoring hospitalized patients. METHODS: This review involved a comprehensive search of 7 databases and included articles that met the following criteria: inpatients must be aged >18 years, the wearable devices studied in the articles must be used to continuously monitor patients, and wearables should monitor biomarkers other than solely physical activity (ie, heart rate, respiratory rate, blood pressure, etc). Only English-language studies were included. From each study, we extracted basic demographic information along with the characteristics of the intervention. We assessed the risk of bias for studies that validated their wearable readings by using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. RESULTS: Of the 2012 articles that were screened, 14 studies met the selection criteria. All included articles were observational in design. In total, 9 different commercial wearables for various body locations were examined in this review. The devices collectively measured 7 different health parameters across all studies (heart rate, sleep duration, respiratory rate, oxygen saturation, skin temperature, blood pressure, and fall risk). Only 6 studies validated their results against a reference device or standard. There was a considerable risk of bias in these studies due to the low number of patients in most of the studies (4/6, 67%). Many studies that validated their results found that certain variables were inaccurate and had wide limits of agreement. Heart rate and sleep were the parameters with the most evidence for being valid for in-hospital monitoring. Overall, the mean patient completion rate across all 14 studies was >90%. CONCLUSIONS: The included studies suggested that wearable devices show promise for monitoring the heart rate and sleep of patients in hospitals. Many devices were not validated in inpatient settings, and the readings from most of the devices that were validated in such settings had wide limits of agreement when compared to gold standards. Even some medical-grade devices were found to perform poorly in inpatient settings. Further research is needed to determine the accuracy of hospitalized patients' digital biomarker readings and eventually determine whether these wearable devices improve health outcomes.