Accuracy of steps measured by smartphones-based WeRun compared with ActiGraph-GT3X accelerometer in free-living conditions.

Objectives: The purpose of this study was to evaluate the accuracy and reliability of steps tracked by smartphone-based WeChat app compared with Actigraph-GT3X accelerometer in free-living conditions. Design: A cross-sectional study and repeated measures. Methods: A total of 103 employees in the Pudong New Area of Shanghai, China, participated in this study. The participants wore an ActiGraph-GT3X accelerometer during the period of August to September 2019 (Time 1), December 2019 (Time 2) and September 2020 (Time 3). Each time, they wore the ActiGraph-GT3X accelerometer continuously for 7 days to assess their 7-day step counts. The smartphone-based WeRun step counts were collected in the corresponding period when subjects wore accelerometers. The subjects were invited to complete basic demographic characteristics questionnaires and to perform physical examination to obtain health-related results such as height, body weight, body fat percentage, waist circumference, hip circumference, and blood pressure. Results: Based on 103 participants' 21 days of data, we found that the Spearman correlation coefficient between them was 0.733 (P < 0.01). The average number of WeRun steps measured by smartphones was 8,975 (4,059) per day, which was higher than those measured by accelerometers (8,462 ± 3,486 per day, P < 0.01). Demographic characteristics and different conditions can affect the consistency of measurements. The consistency was higher in those who were male, older, master's degree and above educated, and traveled by walking. Steps measured by smartphone and accelerometer in working days and August showed stronger correlation than other working conditions and time. Mean absolute percent error (MAPE) for step counts ranged from 0.5 to 15.9%. The test-retest reliability coefficients of WeRun steps ranged from 0.392 to 0.646. A multiple regression analysis adjusted for age, gender, and MVPA/step counts measured during Time 1 showed that body composition (body weight, BMI, body fat percentage, waist circumference, and hip circumference) was correlated with moderate-to-vigorous intensity physical activity, but it was not correlated with WeRun step counts. Conclusions: The smartphone-based WeChat app can be used to assess physical activity step counts and is a reliable tool for measuring steps in free-living conditions. However, WeRun step counts' utilization is potentially limited in predicting body composition.

The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS) is a progressive inflammatory and neurodegenerative disease of the central nervous system affecting over 2.5 million people globally. In-clinic six-minute walk test (6MWT) is a widely used objective measure to evaluate the progression of MS. Yet, it has limitations such as the need for a clinical visit and a proper walkway. The widespread use of wearable devices capable of depicting patients' activity profiles has the potential to assess the level of MS-induced disability in free-living conditions. METHODS: In this work, we extracted 96 features in different temporal granularities (from minute-level to day-level) from wearable data and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10 months' duration. We combined these features with participants' demographics using three regression models including elastic net, gradient boosted trees and random forest. In addition, we quantified the individual feature's contribution using feature importance in these regression models, linear mixed-effects models, generalized estimating equations, and correlation-based feature selection (CFS). RESULTS: The results showed promising estimation performance with R2 of 0.30, which was derived using random forest after CFS. This model was able to distinguish the participants with low disability from those with high disability. Furthermore, we observed that the minute-level (≤ 8 minutes) step count, particularly those capturing the upper end of the step count distribution, had a stronger association with 6MWT. The use of a walking aid was indicative of ambulatory function measured through 6MWT. CONCLUSIONS: This study demonstrates the utility of wearables devices in assessing ambulatory impairments in people with MS in free-living conditions and provides a basis for future investigation into the clinical relevance.

Automated Insulin Delivery with SGLT2i Combination Therapy in Type 1 Diabetes.

Background: Use of sodium-glucose cotransporter 2 inhibitors (SGLT2i) as adjunct therapy to insulin in type 1 diabetes (T1D) has been previously studied. In this study, we present data from the first free-living trial combining low-dose SGLT2i with commercial automated insulin delivery (AID) or predictive low glucose suspend (PLGS) systems. Methods: In an 8-week, randomized, controlled crossover trial, adults with T1D received 5 mg/day empagliflozin (EMPA) or no drug (NOEMPA) as adjunct to insulin therapy. Participants were also randomized to sequential orders of AID (Control-IQ) and PLGS (Basal-IQ) systems for 4 and 2 weeks, respectively. The primary endpoint was percent time-in-range (TIR) 70-180 mg/dL during daytime (7:00-23:00 h) while on AID (NCT04201496). Findings: A total of 39 subjects were enrolled, 35 were randomized, 34 (EMPA; n = 18 and NOEMPA n = 16) were analyzed according to the intention-to-treat principle, and 32 (EMPA; n = 16 and NOEMPA n = 16) completed the trial. On AID, EMPA versus NOEMPA had higher daytime TIR 81% versus 71% with a mean estimated difference of +9.9% (confidence interval [95% CI] 0.6-19.1); p = 0.04. On PLGS, the EMPA versus NOEMPA daytime TIR was 80% versus 63%, mean estimated difference of +16.5% (95% CI 7.3-25.7); p < 0.001. One subject on SGLT2i and AID had one episode of diabetic ketoacidosis with nonfunctioning insulin pump infusion site occlusion contributory. Interpretation: In an 8-week outpatient study, addition of 5 mg daily empagliflozin to commercially available AID or PLGS systems significantly improved daytime glucose control in individuals with T1D, without increased hypoglycemia risk. However, the risk of ketosis and ketoacidosis remains. Therefore, future studies with SGLT2i will need modifications to closed-loop control algorithms to enhance safety.

Your Physical Activity Is in Your Hand-Objective Activity Tracking Among University Students in Hungary, One of the Most Obese Countries in Europe.

Inadequate physical activity is currently one of the leading risk factors for mortality worldwide. University students are a high-risk group in terms of rates of obesity and lack of physical activity. In recent years, activity trackers have become increasingly popular for measuring physical activity. The aim of the present study is to examine whether university students in Hungary meet the health recommendations (10,000 steps/day) for physical activity and investigate the impact of different variables (semester-exam period, days-weekdays, days, months, sex) on the level of physical activity in free-living conditions for 3 months period. In free-living conditions, 57 healthy university students (male: 25 female: 32 mean age: 19.50 SD = 1.58) wore MiBand 1S activity tracker for 3 months. Independent sample t-tests were used to explore differences between sexes. A One-way analysis of variance (ANOVA) was used to explore differences in measures among different grouping variables and step count. A Two-way ANOVA was conducted to test for differences in the number of steps by days of the week, months, seasons and for sex differences. Tukey HSD post-hoc tests were used to examine significant differences. Students in the study achieved 10,000 steps per day on 17% of days (minimum: 0%; maximum: 76.5%; median: 11.1%). Unfortunately, 70% of the participants did not comply the 10,000 steps at least 80% of the days studied. No statistical difference were found between sexes. However, significant differences were found between BMI categories (underweight <18.50 kg/m2; normal range 18.50-24.99 kg/m2; overweight: 25.00-29.99 kg/m2 obese > 30 kg/m2, the number of steps in the overweight category was significantly lower (F = 72.073, p < 0.001). The average daily steps were significantly higher in autumn (t = 11.457, p < 0.001) than in winter. During exam period average steps/day were significantly lower than during fall semester (t = 13.696, p < 0.001). On weekdays, steps were significantly higher than on weekends (F = 14.017, p < 0.001), and even within this, the greatest physical activity can be done by the middle of the week. Our data suggest that university students may be priority groups for future physical activity interventions. Commercial activity trackers provide huge amount of data for relatively low cost therefore it has the potential to objectively analyze physical activity and plan interventions.

Activity detection and classification from wristband accelerometer data collected on people with type 1 diabetes in free-living conditions.

This paper introduces methods to estimate aspects of physical activity and sedentary behavior from three-axis accelerometer data collected with a wrist-worn device at a sampling rate of 32 [Hz] on adults with type 1 diabetes (T1D) in free-living conditions. In particular, we present two methods able to detect and grade activity based on its intensity and individual fitness as sedentary, mild, moderate or vigorous, and a method that performs activity classification in a supervised learning framework to predict specific user behaviors. Population results for activity level grading show multi-class average accuracy of 99.99%, precision of 98.0 ± 2.2%, recall of 97.9 ± 3.5% and F1 score of 0.9 ± 0.0. As for the specific behavior prediction, our best performing classifier, gave population multi-class average accuracy of 92.43 ± 10.32%, precision of 92.94 ± 9.80%, recall of 92.20 ± 10.16% and F1 score of 92.56 ± 9.94%. Our investigation showed that physical activity and sedentary behavior can be detected, graded and classified with good accuracy and precision from three-axial accelerometer data collected in free-living conditions on people with T1D. This is particularly significant in the context of automated glucose control systems for diabetes, in that the methods we propose have the potential to inform changes in treatment parameters in response to the intensity of physical activity, allowing patients to meet their glycemic targets.

Comparison and validation of accelerometer wear time and non-wear time algorithms for assessing physical activity levels in children and adolescents.

BACKGROUND: Accelerometers are widely used to measure sedentary time and daily physical activity (PA). However, data collection and processing criteria, such as non-wear time rules might affect the assessment of total PA and sedentary time and the associations with health variables. The study aimed to investigate whether the choice of different non-wear time definitions would affect the outcomes of PA levels in youth. METHODS: Seventy-seven healthy youngsters (44 boys), aged 10-17 years, wore an accelerometer and kept a non-wear log diary during 4 consecutives days. We compared 7 published algorithms (10, 15, 20, 30, 60 min of continuous zeros, Choi, and Troiano algorithms). Agreements of each algorithm with the log diary method were assessed using Bland-Altmans plots and by calculating the concordance correlation coefficient for repeated measures. RESULTS: Variations in time spent in sedentary and moderate to vigorous PA (MVPA) were 30 and 3.7%. Compared with the log diary method, greater discrepancies were found for the algorithm 10 min (p < 0.001). For the time assessed in sedentary, the agreement with diary was excellent for the 4 algorithms (Choi, r = 0.79; Troiano, r = 0.81; 30 min, r = 0.79; 60 min, r = 0.81). Concordance for each method was excellent for the assessment of time spent in MVPA (> 0.86). The agreement for the wear time assessment was excellent for 5 algorithms (Choi r = 0.79; Troiano r = 0.79; 20 min r = 0.77; 30 min r = 0.80; 60 min r = 0.80). CONCLUSIONS: The choice of non-wear time rules may considerably affect the sedentary time assessment in youth. Using of appropriate data reduction decision in youth is needed to limit differences in associations between health outcomes and sedentary behaviors and may improve comparability for future studies. Based on our results, we recommend the use of the algorithm of 30 min of continuous zeros for defining non-wear time to improve the accuracy in assessing PA levels in youth. TRIAL REGISTRATION: NCT02844101 (retrospectively registered at July 13th 2016).

How Well iPhones Measure Steps in Free-Living Conditions: Cross-Sectional Validation Study.

BACKGROUND: Smartphones have great potential for monitoring physical activity. Although a previous laboratory-based study reported that smartphone apps were accurate for tracking step counts, little evidence on their accuracy in free-living conditions currently exists. OBJECTIVE: We aimed to investigate the accuracy of step counts measured using iPhone in the real world. METHODS: We recruited a convenience sample of 54 adults (mean age 31 [SD 10] years) who owned an iPhone and analyzed data collected in 2016 and 2017. Step count was simultaneously measured using a validated pedometer (Kenz Lifecorder) and the iPhone. Participants were asked to carry and use their own iPhones as they typically would while wearing a pedometer on the waist for 7 consecutive days during waking hours. To assess the agreement between the two measurements, we calculated Spearman correlation coefficients and prepared a Bland-Altman plot. RESULTS: The mean step count measured using the iPhone was 9253 (3787) steps per day, significantly lower by 12% (1277/10,530) than that measured using the pedometer, 10,530 (3490) steps per day (P<.001). The Spearman correlation coefficient between devices was 0.78 (P<.001). The largest underestimation of steps by the iPhone was observed among those who reported to have seldom carried their iPhones (seldom carry: mean -3036, SD 2990, steps/day; sometimes carry: mean -1424, SD 2619, steps/day; and almost always carry: mean -929, SD 1443, steps/day; P for linear trend=.08). CONCLUSIONS: Smartphones may be of practical use to individuals, clinicians, and researchers for monitoring physical activity. However, their data on step counts should be interpreted cautiously because of the possibility of underestimation due to noncarrying time.

Awareness of wearing an accelerometer does not affect physical activity in youth.

BACKGROUND: This study aimed to investigate whether awareness of being monitored by an accelerometer has an effect on physical activity in young people. METHODS: Eighty healthy participants aged 10-18 years were randomized between blinded and nonblinded groups. The blinded participants were informed that we were testing the reliability of a new device for body posture assessment and these participants did not receive any information about physical activity. In contrast, the nonblinded participants were informed that the device was an accelerometer that assessed physical activity levels and patterns. The participants were instructed to wear the accelerometer for 4 consecutive days (2 school days and 2 school-free days). RESULTS: Missing data led to the exclusion of 2 participants assigned to the blinded group. When data from the blinded group were compared with these from the nonblinded group, no differences were found in the duration of any of the following items: (i) wearing the accelerometer, (ii) total physical activity, (iii) sedentary activity, and (iv) moderate-to-vigorous activity. CONCLUSIONS: Our study shows that the awareness of wearing an accelerometer has no influence on physical activity patterns in young people. This study improves the understanding of physical activity assessment and underlines the objectivity of this method. TRIAL REGISTRATION: NCT02844101 (retrospectively registered at July 13th 2016).

The eMouveRecherche application competes with research devices to evaluate energy expenditure, physical activity and still time in free-living conditions.

The proliferation of smartphones is creating new opportunities to monitor and interact with human subjects in free-living conditions since smartphones are familiar to large segments of the population and facilitate data collection, transmission and analysis. From accelerometry data collected by smartphones, the present work aims to estimate time spent in different activity categories and the energy expenditure in free-living conditions. Our research encompasses the definition of an energy-saving function (PredEE) considering four physical categories of activities (still, light, moderate and vigorous), their duration and metabolic cost (MET). To create an efficient discrimination function, the method consists of classifying accelerometry-transformed signals into categories and of associating each category with corresponding Metabolic Equivalent Tasks. The performance of the PredEE function was compared with two previously published functions (f(η,d)aedes,f(η,d)nrjsi), and with two dedicated sensors (Armband® and Actiheart®) in free-living conditions over a 12-h monitoring period using 30 volunteers. Compared to the two previous functions, PredEE was the only one able to provide estimations of time spent in each activity category. In relative value, all the activity categories were evaluated similarly to those given by Armband®. Compared to Actiheart®, the function underestimated still activities by 10.1% and overestimated light- and moderate-intensity activities by 7.9% and 4.2%, respectively. The total energy expenditure error produced by PredEE compared to Armband® was lower than those given by the two previous functions (5.7% vs. 14.1% and 17.0%). PredEE provides the user with an accurate physical activity feedback which should help self-monitoring in free-living conditions.