Application of wearables for remote monitoring of oncology patients: A scoping review.

Objective: This review aims to systematically map and categorize the current state of wearable applications among oncology patients and to identify determinants impeding clinical implementation. Methods: A Medline, Embase and clinicaltrials.gov search identified journal articles, conference abstracts, letters, reports, dissertations and registered studies on the use of wearables in patients with malignancies published up to 10 November 2021. Results: Of 2509 records identified, 112 met the eligibility criteria. Of these, 9.8% (11/112) were RCTs and 47.3% (53/112) of publications were observational. Wearables were investigated pre-treatment (2.7%; 3/112), during treatment (34.8%; 39/112), post-treatment (17.9%; 20/112), in survivors (27.7%; 31/112) and in non-specified or multiple treatment phases (17.0%; 19/112). Medical-grade wearables were applied in 22.3% (25/112) of publications. Primary objectives ranged from technical feasibility (8.0%; 9/112), user feasibility (42.9%; 48/112) and correlational analysis (40.2%; 45/112) to outcome change analysis (8.9%; 10/112). Outcome change was mostly investigated regarding physical activity improvement (80.0%; 8/10). Most publications (42.9%; 48/112) and registered studies (39.3%; 24/61) featured multiple cancer types, with breast cancer as the most prevalent specific type (22.3% in publications, 16.4% in registered studies). Conclusions: Most studies among oncology patients using wearables are focused on assessing the user feasibility of consumer-grade wearables, whereas rates of RCTs assessing clinical efficacy are low. Substantial improvements in clinically relevant endpoints by the use of wearables, such as morbidity and mortality are yet to be demonstrated.

Photoinduced bidirectional switching in lipid membranes containing azobenzene glycolipids.

Following the reaction of biological membranes to external stimuli reveals fundamental insights into cellular function. Here, self-assembled lipid monolayers act as model membranes containing photoswitchable azobenzene glycolipids for investigating structural response during isomerization by combining Langmuir isotherms with X-ray scattering. Controlled in-situ trans/cis photoswitching of the azobenzene N = N double bond alters the DPPC monolayer structure, causing reproducible changes in surface pressure and layer thickness, indicating monolayer reorientation. Interestingly, for monolayers containing azobenzene glycolipids, along with the expected DPPC phase transitions an additional discontinuity is observed. The associated reorintation represents a crossover point, with the surface pressure and layer thickness changing in opposite directions above and below. This is evidence that the azobenzene glycolipids themselves change orientation within the monolayer. Such behaviour suggests that azobenzene glycolipids can act as a bidirectional switch in DPPC monolayers providing a tool to investigate membrane structure-function relationships in depth.

Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies.

Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at -24 h and 0.88 at -48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management.

Influenza vaccination in patients affected by diabetes.

Type 2 diabetes, obesity-related metabolic syndrome, and insulin resistance are the most common metabolic disorders associated with increased cardiovascular risk. In addition, patients with Type 2 diabetes have an increased risk for a more severe course of influenza virus infection, a common pandemic. There is increasing evidence that influenza vaccination in patients with diabetes can safely and effectively reduce all-cause mortality and cardiovascular death. The effects of vaccination appear to be more effective when using higher-dose and quadrivalent vaccines, although subgroup-specific separate analyses in patients with diabetes are lacking. Clinical recommendations address influenza vaccination in all adults with diabetes. From our point of view, it should be an integral part of treatment strategies in patients with diabetes.

The German Fast Track Toward Reimbursement of Digital Health Applications (DiGA): Opportunities and Challenges for Manufacturers, Healthcare Providers, and People With Diabetes.

BACKGROUND: Digital health applications (DiGA) supporting the management of diabetes are among the most commonly available digital health technologies. However, transparent quality assurance of DiGA and clinical proof of a positive healthcare effect is often missing, which creates skepticism of some stakeholders regarding the usage and reimbursement of these applications. METHODS: This article reviews the recently established fast-track integration of DiGA in the German reimbursement market, with emphasis on the current impact for manufacturers, healthcare providers, and people with diabetes. The German DiGA fast track is contextualised with corresponding initiatives in Europe. RESULTS: The option of a provisional prescription and reimbursement of DiGA while proving a positive healthcare effect in parallel may expedite the adoption of DiGA in Germany and beyond. However, hurdles for a permanent prescription and reimbursement of DiGA are high and only one of 12 that have achieved this status specifically addresses people with diabetes. CONCLUSION: The DiGA fast track needs to be further enhanced to cope with remaining skepticism and contribute even more to a value-based diabetes care.

Glycaemic variability is associated with all-cause mortality in COVID-19 patients with ARDS, a retrospective subcohort study.

There is high mortality among intensive care unit (ICU) patients with acute respiratory distress syndrome (ARDS) caused by coronavirus disease (COVID-19). Important factors for COVID-19 mortality are diabetes status and elevated fasting plasma glucose (FPG). However, the effect of glycaemic variability on survival has not been explored in patients with COVID-19 and ARDS. This single-centre cohort study compared several metrics of glycaemic variability for goodness-of-fit in patients requiring mechanical ventilation due to COVID-19 ARDS in the ICU at University Hospital Aachen, Germany. 106 patients had moderate to severe ARDS (P/F ratio median [IQR]: 112 [87-148] mmHg). Continuous HRs showed a proportional increase in mortality risk with daily glycaemic variability (DGV). Multivariable unadjusted and adjusted Cox-models showed a statistically significant difference in mortality for DGV (HR: 1.02, (P) < 0.001, LR(P) < 0.001; HR: 1.016, (P) = 0.001, LR(P) < 0.001, respectively). Kaplan-Meier estimators yielded a shorter median survival (25 vs. 87 days) and a higher likelihood of death (75% vs. 31%) in patients with DGV ≥ 25.5 mg/dl (P < 0.0001). High glycaemic variability during ICU admission is associated with significant increase in all-cause mortality for patients admitted with COVID-19 ARDS to the ICU. This effect persisted even after adjustment for clinically predetermined confounders, including diabetes, median procalcitonin and FPG.

Feasibility of Wearable-Based Remote Monitoring in Patients During Intensive Treatment for Aggressive Hematologic Malignancies.

PURPOSE: Intensive treatment protocols for aggressive hematologic malignancies harbor a high risk of serious clinical complications, such as infections. Current techniques of monitoring vital signs to detect such complications are cumbersome and often fail to diagnose them early. Continuous monitoring of vital signs and physical activity by means of an upper arm medical wearable allowing 24/7 streaming of such parameters may be a promising alternative. METHODS: This single-arm, single-center observational trial evaluated symptom-related patient-reported outcomes and feasibility of a wearable-based remote patient monitoring. All wearable data were reviewed retrospectively and were not available to the patient or clinical staff. A total of 79 patients (54 inpatients and 25 outpatients) participated and received standard-of-care treatment for a hematologic malignancy. In addition, the wearable was continuously worn and self-managed by the patient to record multiple parameters such as heart rate, oxygen saturation, and physical activity. RESULTS: Fifty-one patients (94.4%) in the inpatient cohort and 16 (64.0%) in the outpatient cohort reported gastrointestinal symptoms (diarrhea, nausea, and emesis), pain, dyspnea, or shivering in at least one visit. With the wearable, vital signs and physical activity were recorded for a total of 1,304.8 days. Recordings accounted for 78.0% (63.0-88.5; median [interquartile range]) of the potential recording time for the inpatient cohort and 84.6% (76.3-90.2) for the outpatient cohort. Adherence to the wearable was comparable in both cohorts, but decreased moderately over time during the trial. CONCLUSION: A high adherence to the wearable was observed in patients on intensive treatment protocols for a hematologic malignancy who experience high symptom burden. Remote patient monitoring of vital signs and physical activity was demonstrated to be feasible and of primarily sufficient quality.

Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?

BACKGROUND: Wearables (= wearable computer) enable continuous and noninvasive monitoring of a range of vital signs. Mobile and cost-effective devices, combined with powerful data analysis tools, open new dimensions in assessing body functions ("digital biomarkers"). METHODS: To answer the question whether wearables are ready for use in the medical context, a PubMed literature search and analysis for their clinical-scientific use using publications from the years 2008 to 2018 was performed. RESULTS: A total of 79 out of 314 search hits were publications on clinical trials with wearables, of which 16 were randomized controlled trials. Motion sensors were most frequently used to measure defined movements, movement disorders, or general physical activity. Approximately 20% of the studies used sensors to detect cardiovascular parameters. As for the sensor location, the wrist was chosen in most studies (22.8%). CONCLUSION: Wearables can be used in a precisely defined medical context, when taking into account complex influencing factors.

Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions.

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study sims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF.