Effectiveness of Wearable Device-based Intervention on Glycemic Control in Patients with Type 2 Diabetes: A System Review and Meta-Analysis

Luo, Jingsong; Zhang, Ke; Xu, Yaxin; Tao, Yanmin; Zhang, Qi

doi:10.1007/s10916-021-01797-6

Cited by 8 publications

(2 citation statements)

References 38 publications

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“…Wearable device refers to electronic health information recorder that can be worn during daily activities. It is featured with convenience, efficiency, and intelligence and shows good effects in chronic disease management [24]. Zhang et al [25] fitted the device to patients with senile dementia and followed them regularly for 1 year.…”

Section: Discussionmentioning

confidence: 99%

Computer Internet of Things-Based Intelligent Medical System to Be Applied in Home Care of Senile Dementia Patients

Wang¹

2022

Wireless Communications and Mobile Computing

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It was to explore the accuracy of intelligent home medical system based on Internet of Things (IoT) technology and its application value in home care of senile dementia patients. Based on IoT, 5th generation wireless systems (5G), and smart wearable technology, intelligent home medical system was designed from the perspectives of system environment and system design. Smart wearable technology-based wearable smart clothes could recognize and collect electrocardiosignal, breathing signal, body sway signal, and body temperature signal. The data were transmitted to the application layer through the information transmission and processing modules of middle layer, and the extraction accuracy of behavioral features and behavior correct recognition rate of the system were analyzed. 64 senile dementia patients treated in the hospital between January 2019 and June 2020 were selected as the research objects. They were divided into control group (routine family care) and observation group (intelligent medical care system) according to different nursing methods. Each group included 32 cases. Activity of daily living (ADL) scores, nursing satisfaction, and the accidents during care of the patients in two groups before and after care were summarized. The results showed that the behavioral feature extraction accuracy of intelligent home medical system was above 74.59% and its correct recognition rate of different behaviors reached over 98.5%. ADL score in the observation group was lower than that in the control group 3 months after care ( P < 0.05 ). ADL score in the observation group was significantly lower than that in the control group ( P < 0.01 ) 6 months after care. The satisfaction of the observation group was 78.13% (25 cases), which was remarkably higher than that of the control group (31.25%, 10 cases) ( P < 0.001 ). The total satisfaction of the observation group amounted to 93.75% (30 cases), which was higher than that of the control group (68.75%, 22 cases) ( P < 0.01 ). The total incidence of accidents in the control and observation groups was 28.13% and 3.13%, respectively. Obviously, the total incidence of accidents in the observation group was higher than that in the control group ( P < 0.001 ). The above results showed that the established intelligent medical care system demonstrated potential application values in home care, which provided a new idea for the nursing methods for senile dementia patients.

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Section: Discussionmentioning

confidence: 99%

Computer Internet of Things-Based Intelligent Medical System to Be Applied in Home Care of Senile Dementia Patients

Wang¹

2022

Wireless Communications and Mobile Computing

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“…A change in glucose levels, especially hypoglycemia, mediates autonomic activation and a change in the heart rate, sweating, and even consciousness level, which can be detected using wearables technologies [18,25,26]. An alteration in these variables may allow an indirect assessment of blood glucose levels [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Alhaddad

Aly

Gad

et al. 2023

Sensors

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Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

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Health 4.0, Prevention, and Health Promotion in Companies: A Systematic Literature Review

Domínguez-Miranda,

Rodríguez-Aguilar

2023

EAI/Springer Innovations in Communication and Computing

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Effectiveness of Wearable Device-based Intervention on Glycemic Control in Patients with Type 2 Diabetes: A System Review and Meta-Analysis

Cited by 8 publications

References 38 publications

Computer Internet of Things-Based Intelligent Medical System to Be Applied in Home Care of Senile Dementia Patients

Computer Internet of Things-Based Intelligent Medical System to Be Applied in Home Care of Senile Dementia Patients

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Health 4.0, Prevention, and Health Promotion in Companies: A Systematic Literature Review

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