Dataset Information

Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands.

ABSTRACT: (1) Background: Sleep disorders are a common problem for public health since they are considered potential triggers and predictors of some mental and physical diseases. Evaluating the sleep quality of a person may be a first step to prevent further health issues that diminish their independence and quality of life. Polysomnography (PSG) is the "gold standard" for sleep studies, but this technique presents some drawbacks. Thus, this study intends to assess the capability of the new Xiaomi Mi Smart Band 5 to be used as a tool for sleep self-assessment. (2) Methods: This study will be an observational and prospective study set at the sleep unit of a hospital in A Coruña, Spain. Forty-three participants who meet the inclusion criteria will be asked to participate. Specific statistical methods will be used to analyze the data collected using the Xiaomi Mi Smart Band 5 and PSG. (3) Discussion: This study offers a promising approach to assess whether the Xiaomi Mi Smart Band 5 correctly records our sleep. Even though these devices are not expected to replace PSG, they may be used as an initial evaluation tool for users to manage their own sleep quality and, if necessary, consult a health professional. Further, the device may help users make simple changes to their habits to improve other health issues as well. Trial registration: NCT04568408 (Registered 23 September 2020).

SUBMITTER: Concheiro-Moscoso P

PROVIDER: S-EPMC7908130 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands.

Concheiro-Moscoso Patricia P Martínez-Martínez Francisco José FJ Miranda-Duro María Del Carmen MDC Pousada Thais T Nieto-Riveiro Laura L Groba Betania B Mejuto-Muiño Francisco Javier FJ Pereira Javier J

International journal of environmental research and public health 20210127 3

(1) <i>Background</i>: Sleep disorders are a common problem for public health since they are considered potential triggers and predictors of some mental and physical diseases. Evaluating the sleep quality of a person may be a first step to prevent further health issues that diminish their independence and quality of life. Polysomnography (PSG) is the "gold standard" for sleep studies, but this technique presents some drawbacks. Thus, this study intends to assess the capability of the new Xiaomi ...[more]

PMID: 33513712

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Project description:BackgroundThe prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity.MethodsSixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a 'Posture' protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an 'Activity' protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a 'Proof of Concept' model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations.ResultsThe WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application.ConclusionsThe study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores.

Project description:IntroductionSepsis is a major cause of preventable deaths in hospitals. This study aims to investigate if sepsis incidence and quality of care can be assessed using inpatient administrative health data (IAHD).Methods and analysisDesign: Retrospective observational validation study using routine data to assess the diagnostic accuracy of sepsis coding in IAHD regarding sepsis diagnosis based on medical record review.ProcedureA stratified sample of 10 000 patients with an age ≥15 years treated in between 2015 and 2017 in 10 German hospitals is investigated. All available information of medical records is screened by trained physicians to identify true sepsis cases ('gold standard') both according to current ('sepsis-1') definitions and new ('sepsis-3') definitions. Data from medical records are linked to IAHD on patient level using a pseudonym.AnalysesProportions of cases with sepsis according to sepsis-1 and sepsis-3 definitions are calculated and compared with estimates from coding of sepsis in IAHD. Predictive accuracy (sensitivity, specificity) of different coding abstraction strategies regarding the gold standard is estimated. Predictive accuracy of mortality risk factors obtained from IAHD regarding the respective risk factors obtained from medical records is calculated. An IAHD-based risk model for hospital mortality is compared with a record-based risk model regarding model-fit and predicted risk of death. Analyses adjust for sampling weights. The obtained estimates of sensitivity and specificity for sepsis coding in IAHD are used to estimate adjusted incidence proportions of sepsis based on German national IAHD.Ethics and disseminationThe study has been approved by the ethics commission of the Jena University Hospital (No. 2018-1065-Daten). The results of the study will be discussed in an expert panel to write a memorandum on improving the utility of IAHD for epidemiological surveillance and quality management of sepsis care.Trial registration numberDRKS00017775; Pre-results.

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Dataset Information

Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands.

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Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands.

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