scholarly journals Research on physical activity variability and changes of metabolic profile in patients with prediabetes using Fitbit activity trackers data

2021 ◽  
pp. 1-12
Author(s):  
Antanas Bliudzius ◽  
Roma Puronaite ◽  
Justas Trinkunas ◽  
Audrone Jakaitiene ◽  
Vytautas Kasiulevicius
Keyword(s):  
Physical Activity ◽  
Wearable Devices ◽  
Fasting Glycemia ◽  
Physical Activity Monitoring ◽  
Activity Trackers ◽  
Patient Health ◽  
Using Data ◽  
Composition Changes ◽  
Lipid Parameters ◽  
Impaired Fasting Glycemia

BACKGROUND: Monitoring physical activity with consumers wearables is one of the possibilities to control a patient’s self-care and adherence to recommendations. However, clinically approved methods, software, and data analysis technologies to collect data and make it suitable for practical use for patient care are still lacking. OBJECTIVE: This study aimed to analyze the potential of patient physical activity monitoring using Fitbit physical activity trackers and find solutions for possible implementation in the health care routine. METHODS: Thirty patients with impaired fasting glycemia were randomly selected and participated for 6 months. Physical activity variability was evaluated and parameters were calculated using data from Fitbit Inspire devices. RESULTS: Changes in parameters were found and correlation between clinical data (HbA1c, lipids) and physical activity variability were assessed. Better correlation with variability than with body composition changes shows the potential to include nonlinear variability parameters analysing physical activity using mobile devices. Less expressed variability shows better relationship with control of prediabetic and lipid parameters. CONCLUSIONS: Evaluation of physical activity variability is essential for patient health, and these methods used to calculate it is an effective way to analyze big data from wearable devices in future trials.

scholarly journals Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study (Preprint)

2018 ◽  
Author(s):  
Jin-Ming Wu ◽  
Te-Wei Ho ◽  
Yao-Ting Chang ◽  
ChungChieh Hsu ◽  
Chia Jui Tsai ◽  
...  
Keyword(s):  
Physical Activity ◽  
Gastric Cancer ◽  
Cancer Patients ◽  
Mobile Health ◽  
Early Discharge ◽  
Wearable Devices ◽  
Wearable Device ◽  
Clinical Value ◽  
Focus Group Study ◽  
Physical Activity Monitoring

BACKGROUND Surgical cancer patients often have deteriorated physical activity (PA), which in turn, contributes to poor outcomes and early recurrence of cancer. Mobile health (mHealth) platforms are progressively used for monitoring clinical conditions in medical subjects. Despite prevalent enthusiasm for the use of mHealth, limited studies have applied these platforms to surgical patients who are in much need of care because of acutely significant loss of physical function during the postoperative period. OBJECTIVE The aim of our study was to determine the feasibility and clinical value of using 1 wearable device connected with the mHealth platform to record PA among patients with gastric cancer (GC) who had undergone gastrectomy. METHODS We enrolled surgical GC patients during their inpatient stay and trained them to use the app and wearable device, enabling them to automatically monitor their walking steps. The patients continued to transmit data until postoperative day 28. The primary aim of this study was to validate the feasibility of this system, which was defined as the proportion of participants using each element of the system (wearing the device and uploading step counts) for at least 70% of the 28-day study. “Definitely feasible,” “possibly feasible,” and “not feasible” were defined as ≥70%, 50%-69%, and <50% of participants meeting the criteria, respectively. Moreover, the secondary aim was to evaluate the clinical value of measuring walking steps by examining whether they were associated with early discharge (length of hospital stay <9 days). RESULTS We enrolled 43 GC inpatients for the analysis. The weekly submission rate at the first, second, third, and fourth week was 100%, 93%, 91%, and 86%, respectively. The overall daily submission rate was 95.5% (1150 days, with 43 subjects submitting data for 28 days). These data showed that this system met the definition of “definitely feasible.” Of the 54 missed transmission days, 6 occurred in week 2, 12 occurred in week 3, and 36 occurred in week 4. The primary reason for not sending data was that patients or caregivers forgot to charge the wearable devices (>90%). Furthermore, we used a multivariable-adjusted model to predict early discharge, which demonstrated that every 1000-step increment of walking on postoperative day 5 was associated with early discharge (odds ratio 2.72, 95% CI 1.17-6.32; P=.02). CONCLUSIONS Incorporating the use of mobile phone apps with wearable devices to record PA in patients of postoperative GC was feasible in patients undergoing gastrectomy in this study. With the support of the mHealth platform, this app offers seamless tracing of patients’ recovery with a little extra burden and turns subjective PA into an objective, measurable parameter.

scholarly journals Assessing Physical Activity in Older Adults: Required Days of Trunk Accelerometer Measurements for Reliable Estimation

2015 ◽  
Vol 23 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Kimberley S. van Schooten ◽  
Sietse M. Rispens ◽  
Petra J.M. Elders ◽  
Paul Lips ◽  
Jaap H. van Dieën ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Median Duration ◽  
Activity Monitoring ◽  
Physical Activity Monitoring ◽  
Lower Back ◽  
Maximum Duration ◽  
Intraclass Correlations ◽  
Movement Intensity ◽  
Using Data

We investigated the reliability of physical activity monitoring based on trunk accelerometry in older adults and assessed the number of measured days required to reliably assess physical activity. Seventy-nine older adults (mean age 79.1 ± 7.9) wore an accelerometer at the lower back during two nonconsecutive weeks. The duration of locomotion, lying, sitting, standing and shuffling, movement intensity, the number of locomotion bouts and transitions to standing, and the median and maximum duration of locomotion were determined per day. Using data of week 2 as reference, intraclass correlations and smallest detectable differences were calculated over an increasing number of consecutive days from week 1. Reliability was good to excellent when whole weeks were assessed. Our results indicate that a minimum of two days of observation are required to obtain an ICC ≥ 0.7 for most activities, except for lying and median duration of locomotion bouts, which required up to five days.

scholarly journals Evaluating the accuracy and utility of wearable technology to continuously monitor patients in a hospital setting: a systematic review (Preprint)

2019 ◽  
Author(s):  
Vikas Patel ◽  
Ani-Orchanian Cheff ◽  
Robert Wu
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Heart Rate ◽  
Hospital Setting ◽  
Wearable Technology ◽  
Wearable Devices ◽  
Hospitalized Patients ◽  
Poor Sleep ◽  
Inpatient Setting ◽  
Physical Activity Monitoring

BACKGROUND The term ‘post-hospital syndrome’ has been used to describe the condition in which elderly patients are transiently frail after hospitalization and have a high chance of readmission. Since low activity and poor sleep contribute to ‘post-hospital syndrome’, continuous inpatient monitoring of these important parameters using affordable wearables may help and reduce this syndrome. While there have been systematic reviews of wearables for physical activity monitoring in the hospital setting, there is limited data on use of wearables measuring other parameters in hospitalized patients. OBJECTIVE This systematic review aimed to evaluate the utility and accuracy of wearable devices in their ability to monitor inpatients. METHODS This review incorporated a comprehensive search of seven databases and included articles which met the following inclusion criteria: inpatients above age 18, device studied in the articles had to be wearable technology and have at least one sensor, articles had to describe an element of continuous monitoring (greater than 24 hours) and monitoring had to include more than just physical activity. There were no restrictions on publication period, but only English language studies were included. From each study we extracted basic demographic information along with characteristics of the intervention. RESULTS From 2,012 articles that were screened, 15 articles met the selection criteria. All articles included were observational in design. Nine different commercial wearables, with various body locations, were examined in this review. The devices collectively measured 7 different health parameters across all studies. Only 6 studies validated their results against a reference device or standard. Of those that did validate results, many found that certain variables were inaccurate with wide limits of agreement. Heart rate and sleep had the most evidence for being valid in the hospital. Overall, wearable devices were found to be a feasible alternative for inpatient monitoring as 13 of the 15 studies had a mean participation completion rate greater than 80%. CONCLUSIONS Overall, assessment of studies in this review suggested that wearable devices showed promise in monitoring the heart rate and sleep of patients in hospital. The results demonstrate that many devices have not been validated in the inpatient setting, and amongst those that do, some wearable measurements were not found to be valid. Further research is needed to validate the wearable health variables in hospitalized patients and eventually determine whether these devices improve health outcomes.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Physical activity monitoring devices: energy expenditure comparison in a setting of free-living activities

2019 ◽  
Vol 8 (4) ◽  
pp. 45-54
Author(s):  
Matteo Vandoni ◽  
Vittoria Carnevale Pellino ◽  
Stefano Dell'Anna ◽  
Elena Ricagno ◽  
Giulia Liberali ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Wearable Devices ◽  
Activity Monitoring ◽  
Percentage Error ◽  
Free Living ◽  
Sensewear Armband ◽  
Absolute Percentage Error ◽  
Physical Activity Monitoring ◽  
Monitoring Devices

The aim of this study was to evaluate the validity of Energy Expenditure (EE) estimation provided by 3 wearable devices [Fitbit-One (FO), Sensewear Armband (AR) and Actiheart (AC)] in a setting of free-living activities. 43 participants (24 females; 23.4±.4,5yrs) performed 9 activities: sedentary (watching video, reading), walking (on treadmill and outdoor), running (on treadmill and outdoor) and moderate-to-vigorous activities (Wii gaming, taking the stairs and playing football). Mean Absolute Percentage Error (MAPE) and Pearson’s correlation were calculated to assess the validity of each instrument in comparison to a portable metabolic analyser (PMA). In overall comparison MAPE’s were 7,7% for AR (r=.86; p<.0001), 8,6% for FO (r=.69; P<.001), and 11.6% for AC (r=.81; p<.0001). These findings support the accuracy of the wearables. The AR was the most accurate in the whole protocol. However, MAPE results suggest that devices algorithms should be improved for better measure of EE during moderate-to-vigorous activities.

scholarly journals Physical activity surveillance during the COVID-19 pandemic: Using consumer-based activity trackers as a tool for physical activity monitoring in epidemiological studies (Preprint)

2021 ◽  
Author(s):  
André Henriksen ◽  
Erlend Johannessen ◽  
Gunnar Hartvigsen ◽  
Sameline Grimsgaard ◽  
Laila Hopstock
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Vigorous Physical Activity ◽  
Activity Levels ◽  
Activity Data ◽  
Activity Tracker ◽  
Physical Activity Monitoring ◽  
Activity Trackers ◽  
Physical Activity Levels ◽  
Activity Energy

BACKGROUND Consumer-based physical activity trackers increase in popularity. The widespread use of these devices and the long-term nature of the recorded data provides a valuable source of physical activity data for epidemiological research. Major challenges include the large number of activity tracker providers and models, and the difference in how and what data are recorded and shared. OBJECTIVE The aim of this study was to develop a system to record data on physical activity from different providers of consumer-based activity trackers, and to examine its usability as a tool for physical activity monitoring in epidemiological research. The longitudinal nature of the data and the concurrent pandemic outbreak allowed us to show how the system can be used for surveillance of physical activity levels before, during, and after a COVID-19 lockdown. METHODS We developed a system (mSpider) for automatic recording of data on physical activity from participants wearing activity trackers from Apple, Fitbit, Garmin, Oura, Polar, Samsung, and Withings, as well as trackers storing data in Google Fit and Apple Health. To test the system throughout development, we recruited 35 volunteers to wear a provided activity tracker from primo 2019 and onwards. In addition, we recruited 113 participants with privately owned activity trackers worn before, during, and after the COVID-19 lockdown in Norway. We examined monthly change in number of steps, minutes of moderate-to-vigorous physical activity, and activity energy expenditure during 2019-2020 using bar plots and two-sided paired sample t-tests and Wilcoxon signed-rank test. RESULTS Compared to March 2019, there was a significant reduction in mean step count and mean activity energy expenditure during the March 2020 lockdown period. The reduction was temporary, and the year to year comparison show a small increase in moderate-to-vigorous physical activity and no change in steps and activity energy expenditure. CONCLUSIONS mSpider is a working prototype currently able to record physical activity data from providers of consumer-based activity trackers. The system was successfully used to examine change in physical activity levels during the COVID-19 period.

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