Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors

Metabolic Costs of Walking with Weighted Vests

INTRODUCTION

The US Army Load Carriage Decision Aid (LCDA) metabolic model is used by militaries across the globe and is intended to predict physiological responses, specifically metabolic costs, in a wide range of dismounted warfighter operations. However, the LCDA has yet to be adapted for vest-borne load carriage, which is commonplace in tactical populations, and differs in energetic costs to backpacking and other forms of load carriage.

PURPOSE

The purpose of this study is to develop and validate a metabolic model term that accurately estimates the effect of weighted vest loads on standing and walking metabolic rate for military mission-planning and general applications.

METHODS

Twenty healthy, physically active military-age adults (4 women, 16 men; age, 26 ± 8 yr old; height, 1.74 ± 0.09 m; body mass, 81 ± 16 kg) walked for 6 to 21 min with four levels of weighted vest loading (0 to 66% body mass) at up to 11 treadmill speeds (0.45 to 1.97 m·s -1 ). Using indirect calorimetry measurements, we derived a new model term for estimating metabolic rate when carrying vest-borne loads. Model estimates were evaluated internally by k -fold cross-validation and externally against 12 reference datasets (264 total participants). We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured walking metabolic rate. Estimation accuracy, precision, and level of agreement were also evaluated by the bias, standard deviation of paired differences, and concordance correlation coefficient (CCC), respectively.

RESULTS

Metabolic rate estimates using the new weighted vest term were statistically equivalent ( P < 0.01) to measured values in the current study (bias, -0.01 ± 0.54 W·kg -1 ; CCC, 0.973) as well as from the 12 reference datasets (bias, -0.16 ± 0.59 W·kg -1 ; CCC, 0.963).

CONCLUSIONS

The updated LCDA metabolic model calculates accurate predictions of metabolic rate when carrying heavy backpack and vest-borne loads. Tactical populations and recreational athletes that train with weighted vests can confidently use the simplified LCDA metabolic calculator provided as Supplemental Digital Content to estimate metabolic rates for work/rest guidance, training periodization, and nutritional interventions.

Accuracy of the Apple Watch Series 4 and Fitbit Versa for Assessing Energy Expenditure and Heart Rate of Wheelchair Users During Treadmill Wheelchair Propulsion: Cross-sectional Study

BACKGROUND

The Apple Watch (AW) Series 1 provides energy expenditure (EE) for wheelchair users but was found to be inaccurate with an error of approximately 30%, and the corresponding error for heart rate (HR) provided by the Fitbit Charge 2 was approximately 10% to 20%. Improved accuracy of estimated EE and HR is expected with newer editions of these smart watches (SWs).

OBJECTIVE

This study aims to assess the accuracy of the AW Series 4 (wheelchair-specific setting) and the Fitbit Versa (treadmill running mode) for estimating EE and HR during wheelchair propulsion at different intensities.

METHODS

Data from 20 manual wheelchair users (male: n=11, female: n=9; body mass: mean 75, SD 19 kg) and 20 people without a disability (male: n=11, female: n=9; body mass: mean 75, SD 11 kg) were included. Three 4-minute wheelchair propulsion stages at increasing speed were performed on 3 separate test days (0.5%, 2.5%, or 5% incline), while EE and HR were collected by criterion devices and the AW or Fitbit. The mean absolute percentage error (MAPE) was used to indicate the absolute agreement between the criterion device and SWs for EE and HR. Additionally, linear mixed model analyses assessed the effect of exercise intensity, sex, and group on the SW error. Interclass correlation coefficients were used to assess relative agreement between criterion devices and SWs.

RESULTS

The AW underestimated EE with MAPEs of 29.2% (SD 22%) in wheelchair users and 30% (SD 12%) in people without a disability. The Fitbit overestimated EE with MAPEs of 73.9% (SD 7%) in wheelchair users and 44.7% (SD 38%) in people without a disability. Both SWs underestimated HR. The device error for EE and HR increased with intensity for both SWs (all comparisons: P<.001), and the only significant difference between groups was found for HR in the AW (-5.27 beats/min for wheelchair users; P=.02). There was a significant effect of sex on the estimation error in EE, with worse accuracy for the AW (-0.69 kcal/min; P<.001) and better accuracy for the Fitbit (-2.08 kcal/min; P<.001) in female participants. For HR, sex differences were found only for the AW, with a smaller error in female participants (5.23 beats/min; P=.02). Interclass correlation coefficients showed poor to moderate relative agreement for both SWs apart from 2 stage-incline combinations (AW: 0.12-0.57 for EE and 0.11-0.86 for HR; Fitbit: 0.06-0.85 for EE and 0.03-0.29 for HR).

CONCLUSIONS

Neither the AW nor Fitbit were sufficiently accurate for estimating EE or HR during wheelchair propulsion. The AW underestimated EE and the Fitbit overestimated EE, and both SWs underestimated HR. Caution is hence required when using SWs as a tool for training intensity regulation and energy balance or imbalance in wheelchair users.

Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

Accuracy of Apple Watch to Measure Cardiovascular Indices in Patients with Chronic Diseases: A Cross Sectional Study

Background

The validity of the Apple Watch to measure the heart rate (HR) and oxygen saturation (Spo2) for patients with chronic diseases such as diabetes mellitus (DM), dyslipidemia, and hypertension is still unclear. Therefore, this study aims to investigate the accuracy of the Apple Watch in measuring the Spo2 and HR in patients with chronic diseases.

Methods

Forty-one patients with chronic diseases, including 20 with hypertension, 10 with diabetes, and 11 with dyslipidemia, completed a cross-sectional study. All participants used the Apple Watch against the Polar chest strap and the pulse oximeter at rest and during moderate intensity exercise sessions to measure HR and the SpO2 at rest for 5 minutes, during exercise for 16 minutes, and followed by 3 minutes of rest. The HR was measured during all previous periods, but evaluation of the Spo2 included 5 measures, done only before and after exercise, with a minute interval between each measure.

Results

Overall, a strong correlation exists between measuring the SpO2 using the Apple Watch against the pulse oximeter (Contec) at rest (r = 0.92, p < 0.001) and after exercise (r = 0.86, p < 0.001) in all patients. The HR had a very strong correlation between the Apple Watch and the Polar chest strap (r = 0.99, p < 0.001) in all patients. There was no significant difference (p = 0.76) between the twenty-seven white and fourteen brown-skinned patients.

Conclusion

The Apple Watch is valid to measure the HR and SpO2 in patients with chronic diseases.

Clinical Trial Registration No

NCT05271864.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study

INTRODUCTION

This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry.

METHODS

Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph.

RESULTS

Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent.

CONCLUSIONS

Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Evaluation of Wrist-Worn Photoplethysmography Trackers with an Electrocardiogram in Patients with Ischemic Heart Disease: A Validation Study

PURPOSE

Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.

METHODS

Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.

RESULTS

At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.

CONCLUSIONS

Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions

GOAL AND AIMS

Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography.

FOCUS METHOD/TECHNOLOGY

Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4.

REFERENCE METHOD/TECHNOLOGY

Standard manual PSG sleep scoring.

SAMPLE

Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male).

DESIGN

Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices.

CORE ANALYTICS

Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography).

ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES

Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices).

CORE OUTCOMES

Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices.

IMPORTANT ADDITIONAL OUTCOMES

Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent.

CORE CONCLUSION

This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

Characterization of Female US Marine Recruits: Workload, Caloric Expenditure, Fitness, Injury Rates, and Menstrual Cycle Disruption during Bootcamp

Basic training is centered on developing the physical and tactical skills essential to train a recruit into a Marine. The abrupt increase in activity and energy expenditure in young recruits may contribute to high rates of musculoskeletal injuries, to which females are more susceptible. To date, the total workload of United State Marine Corps (USMC) bootcamp is unknown and should include movement around the military base (e.g., to and from dining facilities, training locations, and classrooms). Thus, the purpose of this effort was to quantify workload and caloric expenditure, as well as qualitatively assess the impact of female reproductive health and injury rates in female recruits. Female recruits (n = 79; age: 19.1 ± 0.2 years, weight: 59.6 ± 0.8 kg, height: 161.6 ± 0.7 cm) wore physiological monitors daily throughout 10 weeks of USMC bootcamp. Physical fitness test scores, physiological metrics from wearables, injury data, and menstrual cycle information were obtained. Female recruits on average expended 3096 ± 9 kcal per day, walked 11.0 ± 0.1 miles per day, and slept 5:43 ± 1:06 h:min per night throughout the 10 weeks of bootcamp. About one-third (35%) of female recruits sustained an injury. In a subset of females that were not taking birth control and had previously been menstruating, 85% experienced cycle dysfunction during boot camp. High levels of physical activity and caloric expenditure, coupled with the stress of a new environment and insufficient sleep, may lead to alterations in female reproductive cycles and musculoskeletal injuries in young USMC recruits.

Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Effects of vestibular rehabilitation on body composition and daily-living physical activity in chronic unilateral vestibular hypofunction

BACKGROUND

Unilateral vestibular hypofunction (UVH) may lead to modifications on metabolism and body composition. Vestibular rehabilitation (VR) demonstrated its effectiveness in ameliorating balance function and several other daily-living aspects.

OBJECTIVES

The aim of this study was to evaluate metabolic composition, by means of bioelectrical impedance analysis (BIA) and daily activity, with the use of a wrist-worn movement tracker, in UVH participants before and after VR, and to compare data with a healthy control group (CG) of adults.

METHODS

46 UVH and 60 CG participants underwent otoneurological testing, self-report and performance questionnaires, BIA, and wore a device tracking daily movement and energy expenditure for one full day; this was performed before and after VR.

RESULTS

UVH participants demonstrated a significant (p = 0.008) increase in muscle mass after VR, and, when compared to CG, no differences were present with respect to visceral fat and muscle mass. UVH adults reported a significant increase in energy expenditure spent in movement (p = 0.008) and during the day (p = 0.009), daily number of strides (p = 0.009) and calories spent in sweeping (p = 0.009) and stairing (p = 0.008).

CONCLUSIONS

Results from this study show that VR provided an improvement of metabolic function and body composition of people with UVH, possibly by contrasting structural modifications in neural pathways stemming from the vestibular nuclei and connected to autonomous function.

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model

PURPOSE

Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overcome this problem. It is essential to verify the accuracy of these devices. This study aims to validate the accuracy of energy expenditure estimation by a respiratory magnetometer plethysmography system in children, adolescents and adults using a deep learning model.

METHODS

Twenty-three healthy subjects in three groups (nine adults (A), eight post-pubertal (PP) males and six pubertal (P) females) first sat or stood for six minutes and then performed a maximal graded test on a bicycle ergometer until exhaustion. We measured energy expenditure, oxygen uptake, ventilatory thresholds 1 and 2 and maximal oxygen uptake. The respiratory magnetometer plethysmography system measured four chest and abdomen distances using magnetometers sensors. We trained the models to predict energy expenditure based on the temporal convolutional networks model.

RESULTS

The respiratory magnetometer plethysmography system provided accurate energy expenditure estimation in groups A (R² = 0.98), PP (R² = 0.98) and P (R² = 0.97). The temporal convolutional networks model efficiently estimates energy expenditure under sitting, standing and high levels of exercise intensities.

CONCLUSION

Our results proved the respiratory magnetometer plethysmography system's effectiveness in estimating energy expenditure for different age populations across various intensities of physical activity.

Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Background

Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

Objective

This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

Methods

Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

Results

Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

Conclusions

Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

A Smart System for the Contactless Measurement of Energy Expenditure

Energy Expenditure (EE) (kcal/day), a key element to guide obesity treatment, is measured from CO₂ production, VCO₂ (mL/min), and/or O₂ consumption, VO₂ (mL/min). Current technologies are limited due to the requirement of wearable facial accessories. A novel system, the Smart Pad, which measures EE via VCO₂ from a room’s ambient CO₂ concentration transients was evaluated. Resting EE (REE) and exercise VCO₂ measurements were recorded using Smart Pad and a reference instrument to study measurement duration’s influence on accuracy. The Smart Pad displayed 90% accuracy (±1 SD) for 14–19 min of REE measurement and for 4.8–7.0 min of exercise, using known room’s air exchange rate. Additionally, the Smart Pad was validated measuring subjects with a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m²), successfully validating the system accuracy across REE’s measures of ~1200 to ~3000 kcal/day. Furthermore, high correlation between subjects’ VCO₂ and λ for CO₂ accumulation was observed (p < 0.00001, R = 0.785) in a 14.0 m³ sized room. This finding led to development of a new model for REE measurement from ambient CO₂ without λ calibration using a reference instrument. The model correlated in nearly 100% agreement with reference instrument measures (y = 1.06x, R = 0.937) using an independent dataset (N = 56).

Accuracy and Acceptability of Wrist-Wearable Activity-Tracking Devices: Systematic Review of the Literature

Background

Numerous wrist-wearable devices to measure physical activity are currently available, but there is a need to unify the evidence on how they compare in terms of acceptability and accuracy.

Objective

The aim of this study is to perform a systematic review of the literature to assess the accuracy and acceptability (willingness to use the device for the task it is designed to support) of wrist-wearable activity trackers.

Methods

We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, and SPORTDiscus for studies measuring physical activity in the general population using wrist-wearable activity trackers. We screened articles for inclusion and, for the included studies, reported data on the studies’ setting and population, outcome measured, and risk of bias.

Results

A total of 65 articles were included in our review. Accuracy was assessed for 14 different outcomes, which can be classified in the following categories: count of specific activities (including step counts), time spent being active, intensity of physical activity (including energy expenditure), heart rate, distance, and speed. Substantial clinical heterogeneity did not allow us to perform a meta-analysis of the results. The outcomes assessed most frequently were step counts, heart rate, and energy expenditure. For step counts, the Fitbit Charge (or the Fitbit Charge HR) had a mean absolute percentage error (MAPE) <25% across 20 studies. For heart rate, the Apple Watch had a MAPE <10% in 2 studies. For energy expenditure, the MAPE was >30% for all the brands, showing poor accuracy across devices. Acceptability was most frequently measured through data availability and wearing time. Data availability was ≥75% for the Fitbit Charge HR, Fitbit Flex 2, and Garmin Vivofit. The wearing time was 89% for both the GENEActiv and Nike FuelBand.

Conclusions

The Fitbit Charge and Fitbit Charge HR were consistently shown to have a good accuracy for step counts and the Apple Watch for measuring heart rate. None of the tested devices proved to be accurate in measuring energy expenditure. Efforts should be made to reduce the heterogeneity among studies.

Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network

BACKGROUND

Consumer wearables and smartphone devices commonly offer an estimate of energy expenditure (EE) to assist in the objective monitoring of physical activity to the general population. Alongside consumers, healthcare professionals and researchers are seeking to utilise these devices for the monitoring of training and improving human health. However, the methods of validation and reporting of EE estimation in these devices lacks rigour, negatively impacting on the ability to make comparisons between devices and provide transparent accuracy.

OBJECTIVES

The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The network was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables and smartphones in the estimation of EE.

METHODS

The recommendations were developed through (1) a systematic literature review; (2) an unstructured review of the wider literature discussing the potential factors that may introduce bias during validation studies; and (3) evidence-informed expert opinions from members of the INTERLIVE network.

RESULTS

The systematic literature review process identified 1645 potential articles, of which 62 were deemed eligible for the final dataset. Based on these studies and the wider literature search, a validation framework is proposed encompassing six key domains for validation: the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis.

CONCLUSIONS

The INTERLIVE network recommends that the proposed protocol, and checklists provided, are used to standardise the testing and reporting of the validation of any consumer wearable or smartphone device to estimate EE. This in turn will maximise the potential utility of these technologies for clinicians, researchers, consumers, and manufacturers/developers, while ensuring transparency, comparability, and replicability in validation.

TRIAL REGISTRATION

PROSPERO ID: CRD42021223508.

A meta-analysis of Fitbit devices: same company, different models, different validity evidence

Fitbit devices are among the most commonly used physical activity devices used by the general public. Multiple studies have examined the validity evidence of Fitbit devices of estimating energy expenditure during physical activity compared to criterion references. However, the literature lacks objective, summary validity evidence that supports the use of various models of Fitbit devices. Therefore, this study aims (a) to examine the validity evidence among the various models of Fitbit devices and (b) to investigate the influence of several device factors on the validity evidence of Fitbit models using meta-analysis. A total of 402 articles were identified through five databases. Upon review of the articles, 29 studies were included in the meta-analysis. Seven different moderator variables, including Fitbit model, device placement, type of device, heart rate capability, release year of devices, activity types and sedentary activity, were identified and included in the meta-analysis to examine their impact on the validity evidence of Fitbit devices. The summarised validity coefficient of energy expenditure during physical activity estimated by Fitbit devices and measured by criterion references was r=.64 (k = 29, 95% CI [.59, .69], p<.001). Fitbit model was not found to be a significant factor impacting validity evidence of Fitbit devices, but heart rate capability, activity types and sedentary activity were found to be significant factors impacting validity evidence. This study found that not all Fitbit models have a similar ability in estimating energy expenditure during physical activity. Continued research is needed in examining the validity evidence of Fitbit devices, especially considering some factors may affect the validity evidence in measuring energy expenditure during physical activity.

Modeling the Metabolic Costs of Heavy Military Backpacking

Introduction

Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter.

Purpose

This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool.

Methods

Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6–21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s⁻¹. A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively.

Results

Estimates from the LCDA backpacking equation were statistically equivalent (P < 0.01) to metabolic rates measured in the current study (bias, −0.01 ± 0.62 W·kg⁻¹; CCC, 0.965) and from the seven independent data sets (bias, −0.08 ± 0.59 W·kg⁻¹; CCC, 0.926).

Conclusions

The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.

Changes in daily energy expenditure and movement behavior in unilateral vestibular hypofunction: Relationships with neuro-otological parameters

The vestibular system has been found to affect energy homeostasis and body composition, due to its extensive connections to the brainstem and melanocortin nuclei involved in regulating the metabolism and feeding behavior. The aim of this study was to evaluate - by means of a wrist-worn physical activity tracker and bioelectrical impedance analysis (BIA) - the energy expenditure (EE) in resting (REE) and free-living conditions and movement behavior in a group of chronic unilateral vestibular hypofunction (UVH) patients when compared with a control group (CG) of healthy participants. Forty-six chronic UVH and 60 CG participants underwent otoneurological (including video-Head Impulse Test [vHIT] for studying vestibulo-ocular reflex [VOR] and static posturography testing [SPT]), and EE and movement measurements and self-report (SRM) andperformance measures (PM). As well as significant (p < 0.001) changes in SPT variables (area and path length) and SRM/PM, UVH participants also demonstrated significantly (p < 0.001) lower values in REE, movement EE, hours/day spent upright, number of strides and distance covered and total daily EE (p = 0.007) compared to the CG. UVH patients consumed significantly lower Kcal/min in sweeping (p = 0.001) and walking upstairs and downstairs (p < 0.001) compared to the CG. Multiple correlations were found between free-living and resting EE and neuro-otological parameters in UVH participants. Since the melanocortin system could be affected along the central vestibular pathways as a consequence of chronic vestibular deafferentation, data collected by reliable wearables could reflect the phenomena that constitute an increased risk of falls and sedentary lifestyle for patients affected by UVH, and could improve rehabilitation stages.

The understanding, acceptability, and relevance of personalised multidimensional physical activity feedback among urban adults: evidence from a qualitative feasibility study in Sri Lanka

Background

Wearable technologies are being used to provide personalised feedback across multiple physical activity dimensions in countries such as the UK, but their feasibility has not been tested in South Asia, where physical inactivity is increasing. This study assessed the understanding, acceptability, and relevance of personalised multidimensional physical activity feedback in urban dwellers in Colombo, Sri Lanka.

Methods

A qualitative feasibility study was conducted among 35 adults to assess a community-based approach to provide multidimensional physical activity feedback. Healthy adults, adults at risk of non-communicable diseases and community-based primary healthcare professionals wore a physical activity monitor for 7 days and were then guided through their personalised multidimensional physical activity feedback. One-to-one interviews were conducted, transcribed verbatim and analysed using framework analysis.

Results

Four themes were generated: understanding of personalised physical activity feedback, perceived novelty of the feedback, motivation, and consideration of the multidimensional nature of physical activity. A majority of participants required guidance initially to understand the feedback, following which most were quickly able to interpret the data shown, and were willing to use the feedback as a basis for identifying goals to improve physical activity. Participants perceived the feedback and its delivery as novel because it provided new knowledge about physical activity guidelines and awareness on their own behaviour through graphics. Comparisons of personal performance against recommended physical activity levels and information on sedentary time were the most commonly motivating aspects of the feedback, prompting talk about behaviour change. All three groups showed poor planning on goal achievement, with some noticeable differences between those with and without health risk of non-communicable diseases. Following the feedback, most participants understood that physical activity is composed of several dimensions, while around half could recognise more suitable options to change behaviour. Of the physical activity dimensions, calorie burn received more attention than others.

Conclusions

Multidimensional physical activity feedback was considered understandable and acceptable and has the potential to support behaviour change among urban Sri Lankans with or without identified health risk. These findings highlight the feasibility of this technology-enabled approach as a personalised intervention to improve knowledge and motivation for physical activity behaviour.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10774-0.

Comparison of two carbohydrate intake strategies to improve glucose control during exercise in adolescents and adults with type 1 diabetes

BACKGROUND AND AIMS

During aerobic physical activity (PA), hypoglycemia is common in people with type 1 diabetes (T1D). Few studies have compared the effectiveness of different carbohydrate (CHO) intake strategies to prevent PA-induced hypoglycemia. Our objective was to compare the efficacy of two CHO intake strategies, same total amount but different CHO intake timing, to maintain glucose levels in the target range (4.0-10.0 mmol/L) during PA in people with T1D.

METHODS AND RESULTS

An open-label, randomized, crossover study in 33 participants (21 adults; 12 adolescents). Participants practiced 60 min PA sessions (ergocyle) at 60% VO_2peak 3.5 h after lunch comparing an intake of 0.5 g of CHO per kg of body weight applied in a pre-PA single CHO intake (SCI) or in a distributed CHO intake (DCI) before and during PA. The percentage of time spent in glucose level target range during PA was not different between the two strategies (SCI: 75 ± 35%; DCI: 87 ± 26%; P = 0.12). Hypoglycemia (<4.0 mmol/L) occurred in 4 participants (12%) with SCI compared to 6 participants (18%) with DCI (P = 0.42). The SCI strategy led to a higher increase (P = 0.01) and variability of glucose levels (P = 0.04) compared with DCI.

CONCLUSIONS

In people living with T1D, for a 60 min moderate aerobic PA in the post-absorptive condition, a 0.5 g/kg CHO intake helped most participants maintain acceptable glycemic control with both strategies. No clinically significant difference was observed between the SCI and DCI strategies. ClinicalTrials.gov Identifier: NCT03214107 (July 11, 2017).

Factors associated with validity of consumer-oriented wearable physical activity trackers: a meta-analysis

The purposes of this study were to examine (1) the strength of the criterion validity evidence of various consumer-oriented wearable physical activity trackers, (2) the influence of brands of consumer-oriented wearable physical activity on validity evidence and (3) factors that may contribute to differences in the strength of the criterion validity evidence. A total of 589 articles were identified through four databases. Pairs of researchers reviewed the articles to determine eligibility. A total of 29 studies with 96 validity coefficients were included in the meta-analysis. Five different moderators, including the brands of physical activity trackers, placement of devices, type of activities (ambulatory vs. lifestyle activities), population, and release year, were analysed to examine which factors impact the validity evidence. The summarised validity coefficient between activity trackers and energy expenditure ranged from r = .41 to r = .91. Moderator analyses revealed that the brand, placement of the device, and population significantly impact the magnitude of the validity evidence, while the type of activity and release year of the devices do not. Device brand, population, andplacement are each factor that significantly affects the validity coefficientsbetween consumer-oriented wearable physical activity trackers. Efforts should be made to improve the accuracy of these devices to maintain the credibility of the research and the trust of consumers.

White Matter Integrity Is Associated With the Amount of Physical Activity in Older Adults With Super-aging

Previous studies have introduced the concept of “SuperAgers,” defined as older adults with youthful memory performance associated with the increased cortical thickness of the anterior cingulate cortex. Given that age-related structural brain changes are observed earlier in the white matter (WM) than in the cortical areas, we investigated whether WM integrity is different between the SuperAgers (SA) and typical agers (TA) and whether it is associated with superior memory performance as well as a healthy lifestyle. A total of 35 SA and 55 TA were recruited for this study. Further, 3.0-T magnetic resonance imaging (MRI), neuropsychological tests, and lifestyle factors related to cognitive function, such as physical activity and duration of sleep, were evaluated in all participants. SA was defined as individuals demonstrating the youthful performance of verbal and visual memory, as measured by the Seoul Verbal Learning Test (SVLT) and the Rey-Osterrieth Complex Figure Test (RCFT), respectively. Tract-based spatial statistics (TBSS) analysis was used to compare the diffusion values such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD) between the SA and TA. SA exhibited better performance in memory, attention, visuospatial, and frontal executive functions than the TA did. SA also exhibited greater amounts of physical activity than the TA did. As compared to TA, SA demonstrated higher FA with lower MD, RD, and AD in the corpus callosum and higher FA and lower RD in the right superior longitudinal fasciculus (SLF), which is significantly associated with memory function. Interestingly, FA values of the body of corpus callosum were correlated with the amount of physical activity. Our findings suggest that WM integrity of the corpus callosum is associated with superior memory function and a higher level of physical activities in SA compared to TA.

Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review

Background

Consumer-wearable activity trackers are small electronic devices that record fitness and health-related measures.

Objective

The purpose of this systematic review was to examine the validity and reliability of commercial wearables in measuring step count, heart rate, and energy expenditure.

Methods

We identified devices to be included in the review. Database searches were conducted in PubMed, Embase, and SPORTDiscus, and only articles published in the English language up to May 2019 were considered. Studies were excluded if they did not identify the device used and if they did not examine the validity or reliability of the device. Studies involving the general population and all special populations were included. We operationalized validity as criterion validity (as compared with other measures) and construct validity (degree to which the device is measuring what it claims). Reliability measures focused on intradevice and interdevice reliability.

Results

We included 158 publications examining nine different commercial wearable device brands. Fitbit was by far the most studied brand. In laboratory-based settings, Fitbit, Apple Watch, and Samsung appeared to measure steps accurately. Heart rate measurement was more variable, with Apple Watch and Garmin being the most accurate and Fitbit tending toward underestimation. For energy expenditure, no brand was accurate. We also examined validity between devices within a specific brand.

Conclusions

Commercial wearable devices are accurate for measuring steps and heart rate in laboratory-based settings, but this varies by the manufacturer and device type. Devices are constantly being upgraded and redesigned to new models, suggesting the need for more current reviews and research.

Comparative validity of energy expenditure prediction algorithms using wearable devices for people with spinal cord injury

STUDY DESIGN

Cross-sectional validation study.

OBJECTIVES

To conduct a literature search for existing energy expenditure (EE) predictive algorithms using ActiGraph activity monitors for manual wheelchairs users (MWUs) with spinal cord injury (SCI), and evaluate their validity using an out-of-sample dataset.

SETTING

Research institution in Pittsburgh, USA.

METHODS

A literature search resulted in five articles containing five sets of predictive equations using an ActiGraph activity monitor for MWUs with SCI. Out-of-sample data were collected from 29 MWUs with chronic SCI who were asked to follow an activity protocol while wearing an ActiGraph GT9X Link on the dominant wrist. They also wore a portable metabolic cart which provided the criterion measure for EE. The out-of-sample dataset was used to evaluate the validity of the five sets of EE predictive equations.

RESULTS

None of the five sets of predictive equations demonstrated equivalence within 20% of the criterion measure based on an equivalence test. The mean absolute error for the five sets of predictive equations ranged from 0.87 to 6.41 kilocalories per minute (kcal min-1) when compared with the criterion measure, and the intraclass correlation estimates ranged from 0.06 to 0.59. The range between the Bland-Altman upper and lower limits of agreement was from 4.70 kcal min-1 to 25.09 kcal min-1.

CONCLUSIONS

The existing EE predictive equations based on ActiGraph monitors for MWUs with SCI showed varied performance when compared with the criterion measure. Their accuracies may not be sufficient to support future clinical and research use. More work is needed to develop more accurate EE predictive equations for this population.

Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis

BACKGROUND

Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications.

RESEARCH QUESTION

In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls?

METHODS

We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group). Spatiotemporal gait characteristics were extracted and pairwise compared (Omega partial squared - ω_p²) between patients and controls.

RESULTS

We found that normal paced walking tests (6MWT, SPWT) are better suited for distinguishing gait characteristics between patients and controls. Among the sensor-based gait parameters, stance and double support phase timing were identified as the best gait characteristics for the OA population discrimination, whereas foot flat ratio, gait speed, stride length and cadence were identified as the best gait characteristics for the LSS population discrimination.

SIGNIFICANCE

These findings provide guidance on the selection of sensor-derived gait parameters and clinical walking tests to detect alterations in mobility for people with LSS and knee OA.

Estimating physical activity and sedentary behaviour in a free-living environment: A comparative study between Fitbit Charge 2 and Actigraph GT3X

BACKGROUND

Activity trackers such as the Fitbit Charge 2 enable users and researchers to monitor physical activity in daily life, which could be beneficial for changing behaviour. However, the accuracy of the Fitbit Charge 2 in a free-living environment is largely unknown.

OBJECTIVE

To investigate the agreement between Fitbit Charge 2 and ActiGraph GT3X for the estimation of steps, energy expenditure, time in sedentary behaviour, and light and moderate-to-vigorous physical activity under free-living conditions, and further examine to what extent placing the ActiGraph on the wrist as opposed to the hip would affect the findings.

METHODS

41 adults (n = 10 males, n = 31 females) were asked to wear a Fitbit Charge 2 device and two ActiGraph GT3X devices (one on the hip and one on the wrist) for seven consecutive days and fill out a log of wear times. Agreement was assessed through Bland-Altman plots combined with multilevel analysis.

RESULTS

The Fitbit measured 1,492 steps/day more than the hip-worn ActiGraph (limits of agreement [LoA] = -2,250; 5,234), while for sedentary time, it measured 25 min/day less (LoA = -137; 87). Both Bland-Altman plots showed fixed bias. For time in light physical activity, the Fitbit measured 59 min/day more (LoA = -52;169). For time in moderate-to-vigorous physical activity, the Fitbit measured 31 min/day less (LoA = -132; 71) and for activity energy expenditure it measured 408 kcal/day more than the hip-worn ActiGraph (LoA = -385; 1,200). For the two latter outputs, the plots indicated proportional bias. Similar or more pronounced discrepancies, mostly in opposite direction, appeared when comparing to the wrist-worn ActiGraph.

CONCLUSION

Moderate to substantial differences between devices were found for most outputs, which could be due to differences in algorithms. Caution should be taken if replacing one device with another and when comparing results.

Body Fat Is Related to Sedentary Behavior and Light Physical Activity but Not to Moderate-Vigorous Physical Activity in Type 2 Diabetes Mellitus

BACKGROUND

Sedentary behavior (SB) has emerged as a new risk factor for cardiovascular accidents. We investigated whether physical activity levels or SB were related to percent body fat (%BF) in type 2 diabetes mellitus (T2DM).

METHODS

In this cross sectional study, we measured the duration of SB, light physical activity (LPA), moderate to vigorous physical activity (MVPA), total energy expenditure, and step counts using a wireless activity tracker (Fitbit HR; FB) for 7 days in free-living conditions, along with %BF using a bio impedance analyzer (Inbody; Biospace) in 120 smartphone users with T2DM. Subjects were divided into exercise (Exe, n=68) and non-exercise (nonExe, n=52) groups based on self-reports of whether the recommended exercises (30 min/day, 3 days/week for 3 months) were performed. SBt, LPAt, MVPAt were transformed from SB, LPA, MVPA for normally distributed variables.

RESULTS

Participants were: female, 59.2%; age, 59.3±8.4 years; body mass index, 25.5±3.4 kg/m²; glycosylated hemoglobin (HbA1c), 7.6%±1.2%; %BF, 30.4%±7.1%. They performed SB for 15.7±3.7 hr/day, LPA for 4.4±1.7 hr/day, and MVPA for 0.9±0.8 hr/day. The %BF was related to SBt and LPAt, but not to MVPA after adjustments for age, gender, and HbA1c. VPA was significantly higher in the Exe group than in the nonExe group, but SB, LPA, and moderate physical activity were not different. Predicted %BF was 89.494 to 0.105 (age), -13.047 (gender), -0.507 (HbA1c), -7.655 (LPAt) (F[4, 64]=62.929, P<0.001), with an R² of 0.785 in multiple linear regression analysis.

CONCLUSION

Reduced body fat in elderly diabetic patients might be associated with reduced inactivity and increased LPA.

Standardizing Analytic Methods and Reporting in Activity Monitor Validation Studies

INTRODUCTION

A lack of standardization with accelerometry-based monitors has made it hard to advance applications for both research and practice. Resolving these challenges is essential for developing methods for consistent, agnostic reporting of physical activity outcomes from wearable monitors in clinical applications.

METHODS

This article reviewed the literature on the methods used to evaluate the validity of contemporary consumer activity monitors. A rationale for focusing on energy expenditure as a key outcome measure in validation studies was provided followed by a summary of the strengths and limitations of different analytical methods. The primary review included 23 recent validation studies that collectively reported energy expenditure estimates from 58 monitors relative to values from appropriate criterion measures.

RESULTS

The majority of studies reported weak indicators such as correlation coefficients (87%), but only half (52%) reported the recommended summary statistic of mean absolute percent error needed to evaluate actual individual error. Fewer used appropriate tests of agreement such as equivalence testing (22%).

CONCLUSIONS

The use of inappropriate analytic methods and incomplete reporting of outcomes is a major limitation for systematically advancing research with both research grade and consumer-grade activity monitors. Guidelines are provided to standardize analytic methods and reporting in these types of studies to enhance the utility of the devices for clinical mHealth applications.

Usefulness of Modern Activity Trackers for Monitoring Exercise Behavior in Chronic Cardiac Patients: Validation Study

BACKGROUND

Improving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population.

OBJECTIVE

The aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients.

METHODS

EE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF).

RESULTS

A total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal; P=.09), in contrast to a significant difference between MS and OM (88 kcal, SD 108 kcal; P=.003). The HFrEF group showed significant differences in EE between FC2 and OM (38 kcal, SD 57 kcal; P=.01), as well as between MS and OM (106 kcal, SD 167 kcal; P=.02). Agreement of the activity trackers was low in both groups (CAD: intraclass correlation coefficient [ICC] FC2=0.10, ICC MS=0.12; HFrEF: ICC FC2=0.42, ICC MS=0.11). The responsiveness of FC2 was poor, whereas MS was able to detect changes in cycling loads only.

CONCLUSIONS

Both activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms.

The validity of two widely used commercial and research-grade activity monitors, during resting, household and activity behaviours

Wearable devices are increasingly prevalent in research environments for the estimation of energy expenditure (EE) and heart rate (HR). The aim of this study was to validate the HR and EE estimates of the Fitbit charge 2 (FC2), and the EE estimates of the Sensewear armband mini (SWA). We recruited 59 healthy adults to participate in walking, running, cycling, sedentary and household tasks. Estimates of HR from the FC2 were compared to a HR chest strap (Polar) and EE to a stationary metabolic cart (Vyntus CPX). The SWA overestimated overall EE by 0.03 kcal/min−1 and was statistically equivalent to the criterion measure, with a mean absolute percentage error (MAPE) of 29%. In contrast, the FC2 was not equivalent overall (MAPE = 44%). In household tasks, MAPE values of 93% and 83% were observed for the FC2 and SWA, respectively. The FC2 HR estimates were equivalent to the criterion measure overall. The SWA is more accurate than the commercial-grade FC2. Neither device is consistently accurate across the range of activities used in this study. The HR data obtained from the FC2 is more accurate than its EE estimates and future research may focus more on this variable.

Assessment of energy expenditure during high intensity cycling and running using a heart rate and activity monitor in young active adults

Objective

Although high intensity physical activities may represent a great proportion of the total energy expenditure in active people, only sparse studies have investigated the accuracy of wearable monitors to assess activity related energy expenditure (AEE) during high intensity exercises. Therefore, the purpose of the present study was to investigate the accuracy of the Actiheart, a light portable monitor estimating AEE based on heart rate (HR) and activity counts (ACT), during two popular activities (running and cycling) performed at high intensities. The benefit of an individual calibration of the HR-AEE relationship established during a preliminary maximal test was also evaluated.

Methods

AEE was estimated in eighteen active adults (4 women and 14 men; 25 ± 4 yr) with indirect calorimetry using a respiratory gas analysis system (reference method) and the Actiheart during 5-min running and cycling at 60, 75 and 85% of maximal oxygen uptake (VO₂max) previously determined during a maximal test performed on a treadmill or cycle ergometer. For the Actiheart, AEE was estimated either using the group or individual calibrated equations available in the dedicated software, and their respective HR, ACT or combined HR/ACT algorithms.

Results

When the HR algorithm was used for cycling and the HR or HR/ACT algorithms for running, AEE measured by the Actiheart increased proportionally to exercise intensity from 60 to 85% VO₂max (P<0.001). Compared to indirect calorimetry, the Actiheart group calibrated equations slightly to moderately underestimated (3 to 20%) AEE for the three exercise intensities (P<0.001). Accuracy of AEE estimation was greatly improved by individual calibration of the HR-AEE relationship (underestimation below 5% and intraclass correlation coefficient [ICC]: 0.79–0.93) compared to group calibration (ICC: 0.64–0.79).

Conclusion

The Actiheart enables to assess AEE during high intensity running and cycling when the appropriate algorithm is applied. Since an underestimation was present for group calibration, an individual and sport-specific calibration should be performed when a high accuracy is required.

Comparison of Step Count Assessed Using Wrist- and Hip-Worn Actigraph GT3X in Free-Living Conditions in Young and Older Adults

Background: Walking represents a major component of physical activity (PA), and its restriction could degrade autonomy and quality of life. An important objective for preventive and/or rehabilitative strategies to improve balance and gait in normal and pathological aging conditions is to focus on physical activity. Activity monitors have recently been getting increasingly popular and represent a modern solution to measure—and communicate—PA notably in terms of steps/day. These activity monitors are well-suited for various populations as they can be worn on a variety of locations on the body, including the wrist and the hip (i.e., the two most common locations), in an undifferentiated way according to the manufacturer's instruction. The aim of this study was hence to verify potential differences in step count (SC) by comparing this parameter assessed using wrist- and hip-worn activity trackers over a 24-h period in free-living conditions in young and older adults.

Methods: Young adults (n = 22) and older adults (n = 22) voluntarily participated in this study. They were required to wear two commercially-available Actigraph GT3X+ activity monitors simultaneously at two locations recommended by the manufacturer, i.e., one positioned around the wrist and one above the hip, over a 24-h period in free-living conditions. The manufacturer's software was used to obtain estimates of the SC.

Results: For both groups, the wrist-worn activity tracker provided significantly higher SC than the hip-worn activity tracker did. For both placements on the body, older adults exhibited significantly lower SC than young adults. Interestingly, for both young and older participants, the difference between both measurements tended to decrease for longer distances.

Conclusion: The different estimations of the step count provided by the comparison between two identical Actigraph GT3x on the wrist or the hip during the 24-h observation period in free-living conditions in young and older adults strongly suggests that caution is needed when using total step per day values as an outcome to quantify walking behavior. Probably we can suggest the same caution across implementation of different activity Tracker.

Wearable Sensors in Ambulatory Individuals With a Spinal Cord Injury: From Energy Expenditure Estimation to Activity Recommendations

Inappropriate physical inactivity is a global health problem increasing the risk of cardiometabolic diseases. Wearable sensors show great potential to promote physical activity and thus a healthier lifestyle. While commercial activity trackers are available to estimate energy expenditure (EE) in non-disabled individuals, they are not designed for reliable assessments in individuals with an incomplete spinal cord injury (iSCI). Furthermore, activity recommendations for this population are currently rather vague and not tailored to their individual needs, and activity guidelines provided for the non-disabled population may not be easily translated for this population. However, especially in iSCI individuals with impaired abilities to stand and walk, the assessment of physical activities and appropriate recommendations for a healthy lifestyle are challenging. Therefore, the study aimed at developing an EE estimation model for iSCI individuals able to walk based on wearable sensor data. Additionally, the data collected within this study was used to translate common activity recommendations for the non-disabled population to easily understandable activity goals for ambulatory individuals with an iSCI. In total, 30 ambulatory individuals with an iSCI were equipped with wearable sensors while performing 12 different physical activities. EE was measured continuously and demographic and anthropometric variables, clinical assessment scores as well as wearable-sensor-derived features were used to develop different EE estimation models. The best EE estimation model comprised the estimation of resting EE using the updated Harris-Benedict equation, classifying activities using a k-nearest neighbor algorithm, and applying a multiple linear regression-based EE estimation model for each activity class. The mean absolute estimation error of this model was 15.2 ± 6.3% and the corresponding mean signed error was −3.4 ± 8.9%. Translating activity recommendations of global health institutions, we suggest a minimum of 2,000–3,000 steps per day for ambulatory individuals with an iSCI. If ambulatory individuals with an iSCI targeted the popular 10,000 steps a day recommendation for the non-disabled population, their equivalent would be around 8,000 steps a day. The combination of the presented dedicated EE estimation model for ambulatory individuals with an iSCI and the translated activity recommendations is an important step toward promoting an active lifestyle in this population.

Agreement between the Apple Series 1, LifeTrak Core C200, and Fitbit Charge HR with Indirect Calorimetry for Assessing Treadmill Energy Expenditure

The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean age = 20.1 (1.7) years; 13 females, 17 males). Participants completed six structured 10-minute exercise sessions on a treadmill with speeds ranging from 53.6 m·min^-1 to 187.7 m·min^-1. Indirect calorimetry was used as the criterion. Participants wore the Apple Watch, LifeTrak, and Fitbit activity monitors on their wrists. Group-level agreement was examined using equivalence testing, relative agreement was examined using Spearman's rho, and individual-level agreement was examined using Mean Absolute Percent Error (MAPE) and Bland-Altman Plots. Activity monitor agreement with indirect calorimetry was supported using the Apple Watch at 160.9 m·min^-1 (Mean difference = -2.7 kcals, 90% C.I.: -8.3 kcals, 2.8 kcals; MAPE = 11.9%; r_s = 0.64) and 187.7 m·min^-1 (Mean difference = 3.7 kcals, 90% C.I.: -2.2 kcals, 9.7 kcals; MAPE = 10.7%; r_s = 0.72) and the Fitbit at 187.7 m·min^-1 (Mean difference = -0.2 kcals, 90% C.I.: -8.8 kcals, 8.5 kcals; MAPE = 20.1%; r_s = 0.44). No evidence for statistical equivalence was seen for the LifeTrak at any speed. Bland-Altman Plot Limits of Agreement were narrower for the Apple Series 1 Watch compared to other monitors, especially at slower treadmill speeds. The results support the utility of the Apple Series 1 Watch and Fitbit Charge HR for assessing energy expenditure during specific treadmill running speeds in young adults.

Accuracy of the Multisensory Wristwatch Polar Vantage's Estimation of Energy Expenditure in Various Activities: Instrument Validation Study

Background

Sport watches and fitness trackers provide a feasible way of obtaining energy expenditure (EE) estimations in daily life as well as during exercise. However, today’s popular wrist-worn technologies show only poor-to-moderate EE accuracy. Recently, the invention of optical heart rate measurement and the further development of accelerometers in wrist units have opened up the possibility of measuring EE.

Objective

This study aimed to validate the new multisensory wristwatch Polar Vantage and its EE estimation in healthy individuals during low-to-high-intensity activities against indirect calorimetry.

Methods

Overall, 30 volunteers (15 females; mean age 29.5 [SD 5.1] years; mean height 1.7 [SD 0.8] m; mean weight 67.5 [SD 8.7] kg; mean maximal oxygen uptake 53.4 [SD 6.8] mL/min·kg) performed 7 activities—ranging in intensity from sitting to playing floorball—in a semistructured indoor environment for 10 min each, with 2-min breaks in between. These activities were performed while wearing the Polar Vantage M wristwatch and the MetaMax 3B spirometer.

Results

After EE estimation, a mean (SD) of 69.1 (42.7) kcal and 71.4 (37.8) kcal per 10-min activity were reported for the MetaMax 3B and the Polar Vantage, respectively, with a strong correlation of r=0.892 (P<.001). The systematic bias was 2.3 kcal (3.3%), with 37.8 kcal limits of agreement. The lowest mean absolute percentage errors were reported during the sitting and reading activities (9.1%), and the highest error rates during household chores (31.4%). On average, 59.5% of the mean EE values obtained by the Polar Vantage were within ±20% of accuracy when compared with the MetaMax 3B. The activity intensity quantified by perceived exertion (odds ratio [OR] 2.028; P<.001) and wrist circumference (OR −1.533; P=.03) predicted 29% of the error rates within the Polar Vantage.

Conclusions

The Polar Vantage has a statistically moderate-to-good accuracy in EE estimation that is activity dependent. During sitting and reading activities, the EE estimation is very good, whereas during nonsteady activities that require wrist and arm movement, the EE accuracy is only moderate. However, compared with other available wrist-worn EE monitors, the Polar Vantage can be recommended, as it performs among the best.

Stretchable, Patch-Type Calorie-Expenditure Measurement Device Based on Pop-Up Shaped Nanoscale Crack-Based Sensor

Demands for precise health information tracking techniques are increasing, especially for daily dietry requirements to prevent obesity, diabetes, etc. Many commercially available sensors that detect dynamic motions of the body lack accuracy, while novel strain sensors at the research level mostly lack the capability to analyze measurements in real life conditions. Here, a stretchable, patch-type calorie expenditure measurement system is demonstrated that integrates an ultrasensitive crack-based strain sensor and Bluetooth-enabled wireless communication circuit to offer both accurate measurements and practical diagnosis of motion. The crack-based strain gauge transformed into a pop-up-shaped structure provides reliable measurements and broad range of strain (≈100%). Combined with the stretchable analysis circuit, the skin attachable tool translates variation of the knee flexion angle into calorie expenditure amount, using relative resistance change (R/R₀ ) data from the flexible sensor. As signals from the knee joint angular movement translates velocity and walking/running behavior, the total amount of calorie expenditure is accurately analyzed. Finally, theoretical, experimental, and simulation analysis of signal stability, dynamic noises, and calorie expenditure calculation obtained from the device during exercise are demonstrated. For further applications, the devices are expected to be used in broader range of dynamic motion of the body for diagnosis of abnormalities and for rehabilitation.

Wearable oxygen uptake and energy expenditure monitors

Obesity is a major health issue in both developed and developing countries. The balance between energy intake and exercise is important, and measurements of both energy intake and energy expenditure are required. Many studies have attempted to monitor energy intake via wearable technology, but no standard methods have yet been developed for this purpose. This is in marked contrast to the long history of measurement and estimation of energy expenditure. Indirect calorimetry is commonly used in the laboratory. Energy expenditure associated with daily activity is the most important measure, although a number of alternative measures have also been proposed. This mini-review discusses the current status of energy expenditure measurement.

Accuracy of 12 Wearable Devices for Estimating Physical Activity Energy Expenditure Using a Metabolic Chamber and the Doubly Labeled Water Method: Validation Study

Background

Self-monitoring using certain types of pedometers and accelerometers has been reported to be effective for promoting and maintaining physical activity (PA). However, the validity of estimating the level of PA or PA energy expenditure (PAEE) for general consumers using wearable devices has not been sufficiently established.

Objective

We examined the validity of 12 wearable devices for determining PAEE during 1 standardized day in a metabolic chamber and 15 free-living days using the doubly labeled water (DLW) method.

Methods

A total of 19 healthy adults aged 21 to 50 years (9 men and 10 women) participated in this study. They followed a standardized PA protocol in a metabolic chamber for an entire day while simultaneously wearing 12 wearable devices: 5 devices on the waist, 5 on the wrist, and 2 placed in the pocket. In addition, they spent their daily lives wearing 12 wearable devices under free-living conditions while being subjected to the DLW method for 15 days. The PAEE criterion was calculated by subtracting the basal metabolic rate measured by the metabolic chamber and 0.1×total energy expenditure (TEE) from TEE. The TEE was obtained by the metabolic chamber and DLW methods. The PAEE values of wearable devices were also extracted or calculated from each mobile phone app or website. The Dunnett test and Pearson and Spearman correlation coefficients were used to examine the variables estimated by wearable devices.

Results

On the standardized day, the PAEE estimated using the metabolic chamber (PAEEcha) was 528.8±149.4 kcal/day. The PAEEs of all devices except the TANITA AM-160 (513.8±135.0 kcal/day; P>.05), SUZUKEN Lifecorder EX (519.3±89.3 kcal/day; P>.05), and Panasonic Actimarker (545.9±141.7 kcal/day; P>.05) were significantly different from the PAEEcha. None of the devices was correlated with PAEEcha according to both Pearson (r=−.13 to .37) and Spearman (ρ=−.25 to .46) correlation tests. During the 15 free-living days, the PAEE estimated by DLW (PAEEdlw) was 728.0±162.7 kcal/day. PAEE values of all devices except the Omron Active style Pro (716.2±159.0 kcal/day; P>.05) and Omron CaloriScan (707.5±172.7 kcal/day; P>.05) were significantly underestimated. Only 2 devices, the Omron Active style Pro (r=.46; P=.045) and Panasonic Actimarker (r=.48; P=.04), had significant positive correlations with PAEEdlw according to Pearson tests. In addition, 3 devices, the TANITA AM-160 (ρ=.50; P=.03), Omron CaloriScan (ρ=.48; P=.04), and Omron Active style Pro (ρ=.48; P=.04), could be ranked in PAEEdlw.

Conclusions

Most wearable devices do not provide comparable PAEE estimates when using gold standard methods during 1 standardized day or 15 free-living days. Continuous development and evaluations of these wearable devices are needed for better estimations of PAEE.

Measurement of physical activity in clinical practice using accelerometers

Accelerometers are commonly used in clinical and epidemiological research for more detailed measures of physical activity and to target the limitations of self-report methods. Sensors are attached at the hip, wrist and thigh, and the acceleration data are processed and calibrated in different ways to determine activity intensity, body position and/or activity type. Simple linear modelling can be used to assess activity intensity from hip and thigh data, whilst more advanced machine-learning modelling is to prefer for the wrist. The thigh position is most optimal to assess body position and activity type using machine-learning modelling. Frequency filtering and measurement resolution needs to be considered for correct assessment of activity intensity. Simple physical activity measures and statistical methods are mostly used to investigate relationship with health, but do not take advantage of all information provided by accelerometers and do not consider all components of the physical activity behaviour and their interrelationships. More advanced statistical methods are suggested that analyse patterns of multiple measures of physical activity to demonstrate stronger and more specific relationships with health. However, evaluations of accelerometer methods show considerable measurement errors, especially at individual level, which interferes with their use in clinical research and practice. Therefore, better objective methods are needed with improved data processing and calibration techniques, exploring both simple linear and machine-learning alternatives. Development and implementation of accelerometer methods into clinical research and practice requires interdisciplinary collaboration to cover all aspects contributing to useful and accurate measures of physical activity behaviours related to health.

Quantification of energy expenditure during daily living activities after stroke by multi-sensor

Objective: To explore the validity of energy expenditure estimates using the SenseWear Armband during a sequence of four daily living activities in patients post-stroke. Method: Patients with stroke who were able to walk during 6 min without human assistance were asked to wear the SenseWear Armband on the non-paretic arm while performing transfers, a manual task, walking, and walking up and down stairs. The energy expenditure estimated using the SenseWear Armband was compared to the energy expenditure calculated from oxygen consumption, measured by a portable indirect calorimeter (Metamax 3B). The mean of energy expenditure was pooled for each task. Accuracy was explored by mean bias (MB) of Bland-Altman analysis and root mean square error (RMSE), agreement by 95% of limits of agreement (95%LoA) and coefficient of correlation (r). Results: Thirty-eight participants (65.7 ± 13.5 years) were included. The SenseWear Armband globally underestimated energy expenditure, MB = 9.77 kcal for the whole sequence. RMSE were large, accounting for 15% to 41% of the measured energy expenditure. Agreement was low with r < 0.70 and 95%LoA from 42% to 93% of the measured energy expenditure. Conclusions: This study reported a global underestimation and a low level of agreement of the energy expenditure estimated by SenseWear Armband in four daily living activities in patients after stroke. Abbreviations: EE: Energy Expenditure; NIHSS: National Institute of Health Stroke Score.

Use of consumer monitors for estimating energy expenditure in youth

The purpose of this study was to compare energy expenditure (EE) estimates from 5 consumer physical activity monitors (PAMs) to indirect calorimetry in a sample of youth. Eighty-nine youth (mean (SD); age, 12.3 (3.4) years; 50% female) performed 16 semi-structured activities. Activities were performed in duplicate across 2 visits. Participants wore a Cosmed K4b² (criterion for EE), an Apple Watch 2 (left wrist), Mymo Tracker (right hip), and Misfit Shine 2 devices (right hip; right shoe). Participants were randomized to wear a Samsung Gear Fit 2 or a Fitbit Charge 2 on the right wrist. Oxygen consumption was converted to EE by subtracting estimated basal EE (Schofield's equation) from the measured gross EE. EE from each visit was summed across the 2 visit days for comparison with the total EE recorded from the PAMs. All consumer PAMs estimated gross EE, except for the Apple Watch 2 (net Active EE). Paired t tests were used to assess differences between estimated (PAM) and measured (K4b²) EE. Mean absolute percent error (MAPE) was used to assess individual-level error. The Mymo Tracker was not significantly different from measured EE and was within 15.9 kcal of measured kilocalories (p = 0.764). Mean percent errors ranged from 3.5% (Mymo Tracker) to 48.2% (Apple Watch 2). MAPE ranged from 16.8% (Misfit Shine 2 - right hip) to 49.9% (Mymo Tracker). Novelty Only the Mymo Tracker was not significantly different from measured EE but had the greatest individual error. The Misfit Shine 2 - right hip had the lowest individual error. Caution is warranted when using consumer PAMs in youth for tracking EE.

Validity of energy expenditure estimation methods during 10 days of military training

Wearable physical activity (PA) monitors have improved the ability to estimate free-living total energy expenditure (TEE) but their application during arduous military training alongside more well-established research methods has not been widely documented. This study aimed to assess the validity of two wrist-worn activity monitors and a PA log against doubly labeled water (DLW) during British Army Officer Cadet (OC) training. For 10 days of training, twenty (10 male and 10 female) OCs (mean ± SD: age 23 ± 2 years, height 1.74 ± 0.09 m, body mass 77.0 ± 9.3 kg) wore one research-grade accelerometer (GENEActiv, Cambridge, UK) on the dominant wrist, wore one commercially available monitor (Fitbit SURGE, USA) on the non-dominant wrist, and completed a self-report PA log. Immediately prior to this 10-day period, participants consumed a bolus of DLW and provided daily urine samples, which were analyzed by mass spectrometry to determine TEE. Bivariate correlations and limits of agreement (LoA) were employed to compare TEE from each estimation method to DLW. Average daily TEE from DLW was 4112 ± 652 kcal·day^-1 against which the GENEActiv showed near identical average TEE (mean bias ± LoA: -15 ± 851 kcal^. day^-1 ) while Fitbit tended to underestimate (-656 ± 683 kcal·day^-1 ) and the PA log substantially overestimate (+1946 ± 1637 kcal·day^-1 ). Wearable physical activity monitors provide a cheaper and more practical method for estimating free-living TEE than DLW in military settings. The GENEActiv accelerometer demonstrated good validity for assessing daily TEE and would appear suitable for use in large-scale, longitudinal military studies.

Use of Mobile Applications to Collect Data in Sport, Health, and Exercise Science: A Narrative Review

Peart, DJ, Balsalobre-Fernández, C, and Shaw, MP. Use of mobile applications to collect data in sport, health, and exercise science: A narrative review. J Strength Cond Res 33(4): 1167-1177, 2019-Mobile devices are ubiquitous in the population, and most have the capacity to download applications (apps). Some apps have been developed to collect physiological, kinanthropometric, and performance data; however, the validity and reliability of such data is often unknown. An appraisal of such apps is warranted, as mobile apps may offer an alternative method of data collection for practitioners and athletes with money, time, and space constraints. This article identifies and critically reviews the commercially available apps that have been tested in the scientific literature, finding evidence to support the measurement of the resting heart through photoplethysmography, heart rate variability, range of motion, barbell velocity, vertical jump, mechanical variables during running, and distances covered during walking, jogging, and running. The specific apps with evidence, along with reported measurement errors are summarized in the review. Although mobile apps may have the potential to collect data in the field, athletes and practitioners should exercise caution when implementing them into practice as not all apps have support from the literature, and the performance of a number of apps have only been tested on 1 device.

Technology-aided assessments of sensorimotor function: current use, barriers and future directions in the view of different stakeholders

BACKGROUND

There is growing interest in the use of technology in neurorehabilitation, from robotic to sensor-based devices. These technologies are believed to be excellent tools for quantitative assessment of sensorimotor ability, addressing the shortcomings of traditional clinical assessments. However, clinical adoption of technology-based assessments is very limited. To understand this apparent contradiction, we sought to gather the points-of-view of different stakeholders in the development and use of technology-aided sensorimotor assessments.

METHODS

A questionnaire regarding motivators, barriers, and the future of technology-aided assessments was prepared and disseminated online. To promote discussion, we present an initial analysis of the dataset; raw responses are provided to the community as Supplementary Material. Average responses within stakeholder groups were compared across groups. Additional questions about respondent's demographics and professional practice were used to obtain a view of the current landscape of sensorimotor assessments and interactions between different stakeholders.

RESULTS

One hundred forty respondents from 23 countries completed the survey. Respondents were a mix of Clinicians (27%), Research Engineers (34%), Basic Scientists (15%), Medical Industry professionals (16%), Patients (2%) and Others (6%). Most respondents were experienced in rehabilitation within their professions (67% with > 5 years of experience), and had exposure to technology-aided assessments (97% of respondents). In general, stakeholders agreed on reasons for performing assessments, level of details required, current bottlenecks, and future directions. However, there were disagreements between and within stakeholders in aspects such as frequency of assessments, and important factors hindering adoption of technology-aided assessments, e.g., Clinicians' top factor was cost, while Research Engineers indicated device-dependent factors and lack of standardization. Overall, lack of time, cost, lack of standardization and poor understanding/lack of interpretability were the major factors hindering the adoption of technology-aided assessments in clinical practice. Reimbursement and standardization of technology-aided assessments were rated as the top two activities to pursue in the coming years to promote the field of technology-aided sensorimotor assessments.

CONCLUSIONS

There is an urgent need for standardization in technology-aided assessments. These efforts should be accompanied by quality cross-disciplinary activities, education and alignment of scientific language, to more effectively promote the clinical use of assessment technologies.

TRIAL REGISTRATION

NA; see Declarations section.