Use of consumer monitors for estimating energy expenditure in youth

Accuracy of the Apple Watch Oxygen Saturation Measurement in Adults: A Systematic Review

The purpose of this review is to summarize the research on the accuracy of oxygen saturation (spO2) measurements using the Apple Watch (Apple Inc., Cupertino, California). The Medline and Google Scholar databases were searched for papers evaluating the spO2 measurements of the Apple Watch vs. any kind of ground truth and records were analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The five publications with 973 total patients that met the inclusion criteria all used the Apple Watch Series 6 and described 95% limits of agreement of +/- 2.7 to 5.9% spO2. However, outliers of up to 15% spO2 were reported. Only one study had patient-level data uploaded to a public repository. The Apple Watch Series 6 does not show a strong systematic bias compared to conventional, medical-grade pulse oximeters. However, outliers do occur and should not cause concern in otherwise healthy individuals. The impact of race on measurement accuracy should be investigated.

"suMus," a novel digital system for arm movement metrics and muscle energy expenditure

Objective: In the field of non-treatable muscular dystrophies, promising new gene and cell therapies are being developed and are entering clinical trials. Objective assessment of therapeutic effects on motor function is mandatory for economical and ethical reasons. Main shortcomings of existing measurements are discontinuous data collection in artificial settings as well as a major focus on walking, neglecting the importance of hand and arm movements for patients' independence. We aimed to create a digital tool to measure muscle function with an emphasis on upper limb motility. Methods: suMus provides a custom-made App running on smartwatches. Movement data are sent to the backend of a suMus web-based platform, from which they can be extracted as CSV data. Fifty patients with neuromuscular diseases assessed the pool of suMus activities in a first orientation phase. suMus performance was hence validated in four upper extremity exercises based on the feedback of the orientation phase. We monitored the arm metrics in a cohort of healthy volunteers using the suMus application, while completing each exercise at low frequency in a metabolic chamber. Collected movement data encompassed average acceleration, rotation rate as well as activity counts. Spearman rank tests correlated movement data with energy expenditure from the metabolic chamber. Results: Our novel application "suMus," sum of muscle activity, collects muscle movement data plus Patient-Related-Outcome-Measures, sends real-time feedback to patients and caregivers and provides, while ensuring data protection, a long-term follow-up of disease course. The application was well received from the patients during the orientation phase. In our pilot study, energy expenditure did not differ between overnight fasted and non-fasted participants. Acceleration ranged from 1.7 ± 0.7 to 3.2 ± 0.5 m/sec² with rotation rates between 0.9 ± 0.5 and 2.0 ± 3.4 rad/sec. Acceleration and rotation rate as well as derived activity counts correlated with energy expenditure values measured in the metabolic chamber for one exercise (r = 0.58, p < 0.03). Conclusion: In the analysis of slow frequency movements of upper extremities, the integration of the suMus application with smartwatch sensors characterized motion parameters, thus supporting a use in clinical trial outcome measures. Alternative methodologies need to complement indirect calorimetry in validating accelerometer-derived energy expenditure data.

Are Wrist-Worn Activity Trackers and Mobile Applications Valid for Assessing Physical Activity in High School Students? Wearfit Study

The purpose was to examine the validity of three wrist-worn commercial activity trackers (Samsung Galaxy Watch Active 2, Apple Watch Series 5, and Xiaomi Mi Band 5) and six mobile apps (Pedometer and Pacer for android and iPhone mobiles, Google Fit for android, and Apple Health for iPhone mobiles) for estimating high school students' steps and physical activity (PA) under free-living conditions. A sample of 56 (27 females; mean age = 14.7 years) and 51 (25 females; mean age = 14.0 years) high school students participated in Study 1 and 2, respectively. Study 1: Students performed a 200-meter course in four different conditions while wearing the wearables. Step counting through a video record was used as the golden standard. Study 2: Students wore the three wrist-worn commercial activity trackers during the waking time of one day, considering ActiGraph model wGT3X-BT accelerometers as a standard of reference. Afterward, the agreement between the PA scores measured by the commercial activity trackers and the video (study 1) or accelerometers (study 2) were calculated as follows: Equivalence test, Limits of Agreement (LOA); Mean Absolute Error (MAE); Mean Absolute Percentage Error (MAPE); and Intraclass Correlation Coefficient (ICC). Results showed that all the wearables presented excellent validity for assessing steps in structured free-living conditions (study 1; MAPE &lt; 5%), although their validity was between poor-excellent based on ICC (95% confidence interval) values (ICC = 0.56-1.00). Regarding Study 2, the Xiaomi wristband and the Samsung Watch presented acceptable-excellent (MAPE = 9.4-11.4%; ICC = 0.91-0.97) validity for assessing steps under unstructured free-living conditions (study 2). However, the Apple Watch presented questionable-excellent validity (MAPE = 18.0%; ICC = 0.69-0.95). Regarding moderate-to-vigorous PA (MVPA) and total PA, only the Apple Watch showed low-acceptable validity for MAPE value and questionable-excellent validity for the ICC values for MVPA assessment (MAPE = 22.6; ICC = 0.67-0.93). All wearables checked in this study have shown adequate validity results in order to assess steps in both structured and unstructured free-living conditions for both continuous and dichotomous variables. Moreover, for assessing MVPA, only the Apple Watch reported valid results for compliance or non-compliance with the daily PA recommendations. However, the results showed low validity for total PA and MVPA as continuous variables. In conclusion, depending on the user's/researcher's aim and context, one or another wearable activity tracker could be more adequate, mainly because of its valid measurements and its costs.

Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children’s free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.

Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems

Algorithms that can determine the type of physical activity (PA) and quantify the intensity can allow precision medicine approaches, such as automated insulin delivery systems that modulate insulin administration in response to PA. In this work, data from a multi-sensor wristband is used to design classifiers to distinguish among five different physical states (PS) (resting, activities of daily living, running, biking, and resistance training), and to develop models to estimate the energy expenditure (EE) of the PA for diabetes therapy. The data collected are filtered, features are extracted from the reconciled signals, and the extracted features are used by machine learning algorithms, including deep-learning techniques, to obtain accurate PS classification and EE estimation. The various machine learning techniques have different success rates ranging from 75.7% to 94.8% in classifying the five different PS. The deep neural network model with long short-term memory has 94.8% classification accuracy. We achieved 0.5 MET (Metabolic Equivalent of Task) root-mean-square error for EE estimation accuracy, relative to indirect calorimetry with randomly selected testing data (10% of collected data). We also demonstrate a 5% improvement in PS classification accuracy and a 0.34 MET decrease in the mean absolute error when using multi-sensor approach relative to using only accelerometer data.

Evaluating the Performance of Sensor-based Bout Detection Algorithms: The Transition Pairing Method

Bout detection algorithms are used to segment data from wearable sensors, but it is challenging to assess segmentation correctness.

Purpose

To present and demonstrate the Transition Pairing Method (TPM), a new method for evaluating the performance of bout detection algorithms.

Methods

The TPM compares predicted transitions to a criterion measure in terms of number and timing. A true positive is defined as a predicted transition that corresponds with one criterion transition in a mutually exclusive pair. The pairs are established using an extended Gale-Shapley algorithm, and the user specifies a maximum allowable within-pair time lag, above which pairs cannot be formed. Unpaired predictions and criteria are false positives and false negatives, respectively. The demonstration used raw acceleration data from 88 youth who wore ActiGraph GT9X monitors (right hip and non-dominant wrist) during simulated free-living. Youth Sojourn bout detection algorithms were applied (one for each attachment site), and the TPM was used to compare predicted bout transitions to the criterion measure (direct observation). Performance metrics were calculated for each participant, and hip-versus-wrist means were compared using paired T-tests (α = 0.05).

Results

When the maximum allowable lag was 1-s, both algorithms had recall <20% (2.4% difference from one another, p<0.01) and precision <10% (1.4% difference from one another, p<0.001). That is, >80% of criterion transitions were undetected, and >90% of predicted transitions were false positives.

Conclusion

The TPM improves on conventional analyses by providing specific information about bout detection in a standardized way that applies to any bout detection algorithm.

Youth Metabolic Equivalents Differ Depending on Operational Definitions

Youth metabolic equivalents (METy) are sometimes operationally defined as multiples of predicted basal metabolic rate (METyBMR) and other times as multiples of measured resting metabolic rate (METyRMR).

PURPOSE

This study aimed to examine the comparability of METyBMR and METyRMR.

METHODS

Indirect calorimetry data (Cosmed K4b) were analyzed from two studies, with a total sample of 245 youth (125 male participants, 6-18 yr old, 37.4% overweight or obese). The Schofield equations were used to predict BMR, and K4b data from 30 min of supine rest were used to assess RMR. Participants performed structured physical activities (PA) of various intensities, and steady-state oxygen consumption was divided by predicted BMR and measured RMR to calculate METyBMR and METyRMR, respectively. Two-way (activity-METy calculation) analysis of variance was used to compare METyBMR and METyRMR (α = 0.05), with Bonferroni-corrected post hoc tests. Intensity classifications were also compared after encoding METyBMR and METyRMR as sedentary behavior (≤1.50 METy), light PA (1.51-2.99 METy), moderate PA (3.00-5.99 METy), or vigorous PA (≥6.00 METy).

RESULTS

There was a significant interaction (F(30) = 3.6, P < 0.001), and METyBMR was significantly higher than METyRMR for 28 of 31 activities (P < 0.04), by 15.6% (watching television) to 23.1% (basketball). Intensity classifications were the same for both METy calculations in 69.0% of cases.

CONCLUSIONS

METyBMR and METyRMR differ considerably. Greater consensus is needed regarding how metabolic equivalents should be operationally defined in youth, and in the meantime, careful distinction is necessary between METyBMR and METyRMR.