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BACKGROUND: People with multiple sclerosis (PwMS) fall frequently causing injury, social isolation, and decreased quality of life. Identifying locations and behaviors associated with high fall risk could help direct fall prevention interventions. Here we describe a smart-home system for assessing how mobility metrics relate to real-world fall risk in PwMS. METHODS: We performed a secondary analysis of a dataset of real-world falls collected from PwMS to identify patterns associated with increased fall risk. Thirty-four individuals were tracked over eight weeks with an inertial sensor comprising a triaxial accelerometer and time-of-flight radio transmitter, which communicated with beacons positioned throughout the home. We evaluated associations between locations in the home and movement behaviors prior to a fall compared with time periods when no falls occurred using metrics including gait initiation, time-spent-moving, movement length, and an entropy-based metric that quantifies movement complexity using transitions between rooms in the home. We also explored how fall risk may be related to the percent of times that a participant paused while walking (pauses-while-walking). RESULTS: Seventeen of the participants monitored sustained a total of 105 falls that were recorded. More falls occurred while walking (52%) than when stationary despite participants being largely sedentary, only walking 1.5±3.3% (median ± IQR) of the time that they were in their home. A total of 28% of falls occurred within one second of gait initiation. As the percentage of pauses-while-walking increased from 20 to 60%, the likelihood of a fall increased by nearly 3 times from 0.06 to 0.16%. Movement complexity, which was quantified using the entropy of room transitions, was significantly higher in the 10 min preceding falls compared with other 10-min time segments not preceding falls (1.15 ± 0.47 vs. 0.96 ± 0.24, P = 0.02). Path length was significantly longer (151.3 ± 156.1 m vs. 95.0 ± 157.2 m, P = 0.003) in the ten minutes preceding a fall compared with non-fall periods. Fall risk also varied among rooms but not consistently across participants. CONCLUSIONS: Movement metrics derived from wearable sensors and smart-home tracking systems are associated with fall risk in PwMS. More pauses-while-walking, and more complex, longer movement trajectories are associated with increased fall risk. FUNDING: Department of Veterans Affairs (RX001831-01A1). National Science Foundation (#2030859).
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Esclerosis Múltiple , Dispositivos Electrónicos Vestibles , Humanos , Calidad de Vida , Movimiento , Marcha , CaminataRESUMEN
Introduction: DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and Methods: Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use. Results: There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001). Conclusions: Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number: NCT04428645.
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Diabetes Mellitus Tipo 1 , Insulina , Adulto , Humanos , Insulina/uso terapéutico , Diabetes Mellitus Tipo 1/tratamiento farmacológico , Automonitorización de la Glucosa Sanguínea , Glucemia , Hipoglucemiantes/uso terapéutico , Hemoglobina Glucada/análisisRESUMEN
Background: Considering current insulin action profiles and the nature of glycemic responses to insulin, there is an acute need for longer term, accurate, blood glucose predictions to inform insulin dosing schedules and enable effective decision support for the treatment of type 1 diabetes (T1D). However, current methods achieve acceptable accuracy only for prediction horizons of up to 1 h, whereas typical postprandial excursions and insulin action profiles last 4-6 h. In this study, we present models for prediction horizons of 60-240 min developed by leveraging "shallow" neural networks, allowing for significantly lower complexity compared with related approaches. Methods: Patient-specific neural network-based predictive models are developed and tested on previously collected data from a cohort of 24 subjects with T1D. Models are designed to avoid serious pitfalls through incorporating essential physiological knowledge into model structure. Patient-specific models were generated to predict glucose 60, 90, 120, 180, and 240 min ahead, and a "transfer learning" approach to improve accuracy for patients where data are limited. Finally, we determined subgroup characteristics that result in higher model accuracy overall. Results: Root mean squared error was 28 ± 4, 33 ± 4, 38 ± 6, 40 ± 8, and 43 ± 12 mg/dL for 60, 90, 120, 180, and 240 min, respectively. For all prediction horizons, at least 93% of predictions were clinically acceptable by the Clarke error grid. Variance of historic continuous glucose monitor (CGM) values was a strong predictor for the need of transfer learning approaches. Conclusions: A shallow neural network, using features extracted from past CGM data and insulin logs, can achieve multi-hour glucose predictions with satisfactory accuracy. Models are patient specific, learnt on readily available data without the need for additional tests, and improve accuracy while lowering complexity compared with related approaches, paving the way for new advisory and closed loop algorithms able to encompass most of the insulin action timeframe.
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Glucemia/análisis , Diabetes Mellitus Tipo 1 , Redes Neurales de la Computación , Algoritmos , Automonitorización de la Glucosa Sanguínea , Diabetes Mellitus Tipo 1/sangre , Diabetes Mellitus Tipo 1/tratamiento farmacológico , Humanos , Hipoglucemiantes/uso terapéutico , Insulina/uso terapéuticoRESUMEN
Continuous glucose monitors (CGM) display real-time glucose values enabling greater glycemic awareness with reduced management burden. Factory-calibrated CGM systems allow for glycemic assessment without the pain and inconvenience of fingerstick glucose testing. Advances in sensor chemistry and CGM algorithms have enabled factory-calibrated systems to have greater accuracy than previous generations of CGM technology. Despite these advances many patients and providers are hesitant about the idea of removing fingerstick testing from their diabetes care. In this commentary, we aim to review the clinical trials on factory-calibrated CGM systems, present the algorithms which facilitate factory-calibrated CGMs to improve accuracy, discuss clinical use of factory-calibrated CGMs, and finally present two cases demonstrating the dangers of utilizing exploits in commercial systems to prolong sensor life.