scholarly journals Acceptability of Continuous Glucose Monitoring in Free-Living Healthy Individuals: Implications for the Use of Wearable Biosensors in Diet and Physical Activity Research (Preprint)

2018 ◽  
Author(s):  
Yue Liao ◽  
Susan Schembre
Keyword(s):  
Physical Activity ◽  
Wearable Sensors ◽  
Glucose Monitoring ◽  
Relevant Information ◽  
Sensor Technology ◽  
Physiological Data ◽  
Clinical Settings ◽  
Continuous Glucose Monitor ◽  
Diet And Physical Activity ◽  
Study Participants

BACKGROUND Wearable sensors have been increasingly used in behavioral research for real-time assessment and intervention purposes. The rapid advancement of biomedical technology typically used in clinical settings has made wearable sensors more accessible to a wider population. Yet the acceptability of this technology for nonclinical purposes has not been examined. OBJECTIVE The aim was to assess the acceptability of wearing a continuous glucose monitor (CGM) device among a sample of nondiabetic individuals, and to compare the acceptability of a CGM between a mobile diet tracking app (MyFitnessPal) and an accelerometer. METHODS A total of 30 nondiabetic adults went through a 7-day observational study. They wore a CGM sensor, tracked their diet and physical activity using the CGM receiver and MyFitnessPal, and wore an accelerometer on their waist. After the monitoring period, they completed a 10-item survey regarding acceptability of each of the study tools. Two-tailed paired-sample t tests were conducted to examine whether the summary acceptability scores were comparable between the CGM sensor/receiver and MyFitnessPal/accelerometer. RESULTS More than 90% of the study participants agreed that the CGM sensor and receiver were easy to use (28/30 and 27/30, respectively), useful (28/30 and 29/30, respectively), and provided relevant information that was of interest to them (27/30 and 28/30, respectively). The summary acceptability scores (out of a 5-point Likert scale) were mean 4.06 (SD 0.55) for the CGM sensor, mean 4.05 (SD 0.58) for the CGM receiver, mean 4.10 (SD 0.68) for MyFitnessPal, and mean 3.73 (SD 0.76) for the accelerometer. CONCLUSIONS The high acceptability of using a CGM from this study suggests a great potential for using CGMs in nondiabetic adults in research settings. Although potential selection bias might contribute to the high acceptability in this study, the continued advancements in wearable sensor technology will make the barriers to tracking and collecting personal physiological data more and more minimal.

scholarly journals Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 855 ◽  
Author(s):  
Sean Pham ◽  
Danny Yeap ◽  
Gisela Escalera ◽  
Rupa Basu ◽  
Xiangmei Wu ◽  
...  
Keyword(s):  
Physical Activity ◽  
Environmental Conditions ◽  
Wearable Sensors ◽  
Heat Exposure ◽  
Extreme Heat ◽  
Mobile Systems ◽  
Physiological Data ◽  
Activity Levels ◽  
Integrated Sensor ◽  
Healthcare Facilities

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

scholarly journals It’s not about the capture, it’s about what we can learn”: a qualitative study of experts’ opinions and experiences regarding the use of wearable sensors to measure gait and physical activity

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Alison Keogh ◽  
Kristin Taraldsen ◽  
Brian Caulfield ◽  
Beatrix Vereijken
Keyword(s):  
Physical Activity ◽  
Clinical Practice ◽  
Paradigm Shift ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Healthcare Research ◽  
Quality Data ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Structured Interviews

Abstract Background The use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants’ opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity. Methods Twenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis. Results Five themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress. Conclusions Multiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.

Selecting, evaluating, and translating psychophysiological measures into clinical settings: From lab to practice

2018 ◽  
Vol 7 (1) ◽  
pp. 232-236 ◽  
Author(s):  
Lauren Kennedy ◽  
Nathan Lau ◽  
Scott Pappada ◽  
Sarah Henrickson Parker
Keyword(s):  
Data Collection ◽  
Physiological State ◽  
Healthcare Setting ◽  
Sensor Technology ◽  
Physiological Data ◽  
Clinical Settings ◽  
Clinical Workflow ◽  
Healthcare Settings ◽  
Passive Data ◽  
Performance Estimates

Physiological data collection methods are unobtrusive, passive, continuous, and objective. The information afforded by sensors collecting physiological data can be transformed to represent operator performance estimates and stress state visualizations in real time. This technology is conducive to healthcare settings, creating the potential to inform healthcare operators of their current performance and physiological statuses. Despite the broad and pervasive utility of sensor technology, its applications in healthcare are underutilized and misunderstood. This is likely due to the combination of a lack of understanding of the full capabilities of sensor technology, a scarcity of demonstrated uses in healthcare, and an uncertainty surrounding translation and implementation into practice. Implementing findings from providers’ physiological data can be met with challenges, especially in the healthcare setting. Clinicians are most frequently concerned with patient care, and may not always recognize the importance of their own physiological state. While transitioning sensor technology from personal monitoring purposes to a data collection tool can be challenging, passive data collection via sensor technology may have significant value for learners and experienced practitioners. The goals of this paper are to: 1. raise awareness of sensor technology and its utility in clinical settings; 2. provide empirical examples of how to use sensor technology to answer basic and applied questions pertaining to clinical workflow; and 3. exemplify scalability and translatability of findings from sensor technology studies in clinical settings.

scholarly journals Deprivation Index and Lifestyle: Baseline Cross-Sectional Analysis of the PREDIMED-Plus Catalonia Study

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3408
Author(s):  
Josep Basora ◽  
Felipe Villalobos ◽  
Meritxell Pallejà-Millán ◽  
Nancy Babio ◽  
Albert Goday ◽  
...  
Keyword(s):  
Physical Activity ◽  
Cluster Analysis ◽  
Deprivation Index ◽  
P Value ◽  
Multinomial Regression ◽  
Cross Sectional ◽  
High Deprivation ◽  
Diet And Physical Activity ◽  
Study Participants ◽  
Sectional Analysis

This baseline cross-sectional analysis from data acquired in a sub-sample of the PREDIMED-Plus study participants aimed to evaluate the relation between the Composite Socioeconomic Index (CSI) and lifestyle (diet and physical activity). This study involved 1512 participants (759 (52.2%) women) between 55 and 80 years with overweight/obesity and metabolic syndrome assigned to 137 primary healthcare centers in Catalonia, Spain. CSI and lifestyle (diet and physical activity) were assessed. Multiple linear regression or multinomial regression were applied to the data. Cluster analysis was performed to identify dietary patterns. The multiple linear regression model showed that a high deprivation index was related to a higher consumption of refined cereals (11.98 g/d, p-value = 0.001) and potatoes (6.68 g/d, p-value = 0.001), and to a lower consumption of fruits (−17.52 g/d, p-value = 0.036), and coffee and tea (−8.03 g/d, p-value = 0.013). Two a posteriori dietary patterns were identified by cluster analysis and labeled as “healthy” and “unhealthy”. In addition, the multinomial regression model showed that a high deprivation index was related to an unhealthy dietary pattern and low physical activity (OR 1.42 [95% CI 1.06–1.89]; p-value < 0.05). In conclusion, a high deprivation index was related to an unhealthy lifestyle (diet and physical activity) in PREDIMED-Plus study participants.

scholarly journals Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity (Preprint)

2017 ◽  
Author(s):  
Peter Düking ◽  
Franz Konstantin Fuss ◽  
Hans-Christer Holmberg ◽  
Billy Sperlich
Keyword(s):  
Physical Activity ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Insurance Companies ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Scientific Evaluation ◽  
Reliability Sensitivity ◽  
The Individual ◽  
Recommendations For Assessment

UNSTRUCTURED Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.

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