Healthcare Applications of Smart Watches

Lu, Tsung-Chien; Fu, Chia-Ming; Huei‐Ming, Matthew; Fang, Cheng-Chung; Turner, Anne M.

doi:10.4338/aci-2016-03-r-0042

Cited by 104 publications

(60 citation statements)

References 35 publications

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“…Wu et al (2012) observe that the healthcare industry employs various mobile communication technologies, including mobile phones, smartphones and hands-free devices. Since 2013, smartwatches have been increasingly used in healthcare applications (Lu, Fu, Ma, Fang, & Turner, 2016). The primary use of these devices has been to continuously track and collect biometric data, including heart rate, number of steps, distance walked and calories consumed (Stegemann, 2015;Varga, Bokor, & Takács, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluating a smartwatch notification system in a simulated nursing home

Ali

2019

Int J Older People Nursing

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Aims This paper evaluates a high‐fidelity prototype of a smartwatch‐based communication system for nursing homes. The system could improve communication between residents and caregivers, thus reducing staff response time and improving residents' safety. Background Across the country, nursing homes are often cited as having serious quality and safety issues. Deficiencies in the communication systems that connect residents with caregivers significantly impact the quality of care. Most nursing homes still use traditional call light systems, that give visual and auditory cues to caregivers, who may or may not be in proximity to the system. This study evaluates a smartwatch communication system developed to quickly relay a resident's requests for assistance to staff anywhere in the nursing home. Method Certified Nursing Assistants (CNAs) employed by nursing homes were recruited to perform routine tasks using both a traditional system and a smart system. Response time to call lights, staff performance and outcomes were measured through direct and recorded observations and through the surveys of participants. Results The smartwatch system reduced staff response time to call lights from bedrooms by 40%, from bathrooms by 58% and from bed exit alarms by 29%. The smartwatch system also reduced perceived workload by about 50% and was highly accepted by the CNAs. Conclusion A smartwatch‐based notification system may increase staff performance and decrease workload, thus leading to greater staff and patient satisfaction, which could ultimately lead to better quality of care and patient safety. Relevance to clinical practice This study provides empirical evidence for the usefulness of smartwatches in facilitating the notification and communication processes in healthcare settings and is also among the first to actually examine a smartwatch system in a healthcare setting.

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Section: Introductionmentioning

confidence: 99%

Evaluating a smartwatch notification system in a simulated nursing home

Ali

2019

Int J Older People Nursing

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“…Therefore, our models can eventually be used within eHealth applications that do not require wearing a sensor during sleep. This is of special interest for the development of smart watch health apps [33], as they might require frequent battery charging.…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies have found that these consumer-grade devices can sometimes have similar precision to clinical-grade actigraphy sensors [31]. There are successful examples of the integration of physical activity wearable data into eHealth tailored applications [32], including smart-watch health applications [33] that can collect physical activity and sleep data directly from the watch.…”

Section: Introductionmentioning

confidence: 99%

Sleep Quality Prediction From Wearable Data Using Deep Learning

Sathyanarayana¹,

Joty²,

Fernández-Luque³

et al. 2016

JMIR Mhealth Uhealth

172

124

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BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

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“…Consumers are attracted toward such devices for example, alarming device, pill count device etc. 2,[20][21][22] Availability of automated devices have simplified the procedure with improved medication adherence devices which are more advanced. With the advent of smart devices, adherence practices have improved a lot.…”

Section: Use Of Colored Codes To Identify Pillsmentioning

confidence: 99%

Strengthening Medication Adherence Practices in Chronic Disease Patients-clinical Pharmacist Driven Focused Approach

Wadhwa¹,

Jalal²,

Merghani³

et al. 2019

IJOPP

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Medication adherence practices are becoming an important concern for healthcare professionals and patients. Globally, medication non-adherence is quite prevalent and affecting the healthcare of patients leading to inadequate response to treatment and augmented healthcare cost. With the advent of improvised practices in clinical care, patient-related problems mainly non-adherence have been conquered to a greater extent. Clinical practice is changing over a period contributing more towards patient care. Expansion of clinical pharmacy activities with a focused approach towards patient care especially optimized pharmaceutical care has seen a tremendous revolution in modern era. According to a study, 50% of patients with chronic conditions are not adherent to their medications with an estimated adherence range of 17% to 80%. Drugs used for asymptomatic chronic conditions have been shown to have low adherence rates. Risk of non-adherence has been shown to be rapidly increasing with the initiation of chronic medication especially in the first year of its commencement. Medication nonadherence is one of the most significant reason for inadequate response to drug therapy, initial hospitalization, frequent hospital visits and Adverse Drug Events (ADEs) leading to inappropriate therapeutic outcome and in turn, raised healthcare cost. Due to lack of sufficient support or appropriate channel, there is a growing concern for healthcare professionals to take initiative and strengthen the medication adherence practices. Clinical pharmacist's initiatives in this direction has been appreciated and documented with clinically proven evidence of better impact on patient's health-related quality of life. Patient tools such as pill reminders, alarming system, dose indicator, standard day-wise schedule, colorful pills etc. have brought pill revolution with the intention of improvising medication adherence practices worldwide. The current requirement is to monitor such patients in clinical practice and encourage them to comply with medication-related instructions and adhere to their medication schedule at the most.

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Healthcare Applications of Smart Watches

Abstract: Smart watches are unobtrusive and easy to wear. While smart watch technology supplied with biosensors has potential to be useful in a variety of healthcare applications, rigorous research with their use in clinical settings is needed.

Cited by 104 publications

References 35 publications

Evaluating a smartwatch notification system in a simulated nursing home

Evaluating a smartwatch notification system in a simulated nursing home

Sleep Quality Prediction From Wearable Data Using Deep Learning

Strengthening Medication Adherence Practices in Chronic Disease Patients-clinical Pharmacist Driven Focused Approach

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