Feasibility and usefulness of postoperative mobilization goals in the enhanced recovery after surgery (ERAS®) clinical pathway for elective colorectal surgery.

PURPOSE: Despite mobilization is highly recommended in the ERAS® colorectal guideline, studies suggest that more than half of patients don't reach the daily goal of 360 min out of bed. However, data used to quantify mobilization are predominantly based on self-assessments, for which the accuracy is uncertain. This study aims to accurately measure postoperative mobilization in ERAS®-patients by validated motion data from body sensors. METHODS: ERAS®-patients with elective bowel resections were eligible. Self-assessments and motion sensors (movisens: ECG-Move 4 and Move 4; Garmin: Vivosmart4) were used to record mobilization parameter from surgery to postoperative day 3 (POD3): Time out of bed, time on feet and step count. RESULTS: 97 patients were screened and 60 included for study participation. Self-assessment showed a median out of bed duration of 215 min/day (POD1: 135 min, POD2: 225 min, POD3: 225 min). The goal of 360 min was achieved by 16.67% at POD1, 21.28% at POD2 and 20.45% at POD3. Median time on feet objectively measured by Move 4 was 109 min/day. During self-assessment, patients significantly underestimated their "time on feet"-duration with 85 min/day (p = 0.008). Median number of steps was 933/day (Move 4). CONCLUSION: This study confirmed with objectively supported data, that most patients don't reach the daily mobilization goal of 360 min despite being treated by an ERAS®-pathway with ERAS®-nurse. Even considering an empirically approximated underestimation, the ERAS®-target isn't achieved by more than 75% of patients. Therefore, we propose an adjustment of the general ERAS®-goals into more patient-centered, individualized and achievable goals. REGISTRATION: This study is part of the MINT-ERAS-project and was registered prospectively in the German Clinical Trials Register on 25.02.2022. Trial registration number is "DRKS00027863".

Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid.

Background: Accelerometers were traditionally worn on the hip to estimate energy expenditure (EE) during physical activity but are increasingly replaced by products worn on the wrist to enhance wear compliance, despite potential compromises in EE estimation accuracy. In the older population, where the prevalence of hearing loss is higher, a new, integrated option may arise. Thus, this study aimed to investigate the accuracy and precision of EE estimates using an accelerometer integrated into a hearing aid and compare its performance with sensors simultaneously worn on the wrist and hip. Methods: Sixty middle-aged to older adults (average age 64.0 ± 8.0 years, 48% female) participated. They performed a 20-min resting energy expenditure measurement (after overnight fast) followed by a standardized breakfast and 13 different activities of daily living, 12 of them were individually selected from a set of 35 activities, ranging from sedentary and low intensity to more dynamic and physically demanding activities. Using indirect calorimetry as a reference for the metabolic equivalent of task (MET), we compared the EE estimations made using a hearing aid integrated device (Audéo) against those of a research device worn on the hip (ZurichMove) and consumer devices positioned on the wrist (Garmin and Fitbit). Class-estimated and class-known models were used to evaluate the accuracy and precision of EE estimates via Bland-Altman analyses. Results: The findings reveal a mean bias and 95% limit of agreement for Audéo (class-estimated model) of -0.23 ± 3.33 METs, indicating a slight advantage over wrist-worn consumer devices (Garmin: -0.64 ± 3.53 METs and Fitbit: -0.67 ± 3.40 METs). Class-know models reveal a comparable performance between Audéo (-0.21 ± 2.51 METs) and ZurichMove (-0.13 ± 2.49 METs). Sub-analyses show substantial variability in accuracy for different activities and good accuracy when activities are averaged over a typical day's usage of 10 h (+61 ± 302 kcal). Discussion: This study shows the potential of hearing aid-integrated accelerometers in accurately estimating EE across a wide range of activities in the target demographic, while also highlighting the necessity for ongoing optimization efforts considering precision limitations observed across both consumer and research devices.

Reliability of continuous vital sign monitoring in post-operative patients employing consumer-grade fitness trackers: A randomised pilot trial.

Introduction: Fitness trackers can provide continuous monitoring of vital signs and thus have the potential to become a complementary, mobile and effective tool for early detection of patient deterioration and post-operative complications. Methods: To evaluate potential implementations in acute care setting, we included 36 patients after moderate to major surgery in a recent randomised pilot trial to compare the performance of vital sign monitoring by three different fitness trackers (Apple Watch 7, Garmin Fenix 6pro and Withings ScanWatch) with established standard clinical monitors in post-anaesthesia care units and monitoring wards. Results: During a cumulative period of 56 days, a total of 53,197 heart rate (HR) measurements, as well as 12,219 measurements of the peripheral blood oxygen saturation (SpO2) and 28,954 respiratory rate (RR) measurements were collected by fitness trackers. Under real-world conditions, HR monitoring was accurate and reliable across all benchmarked devices (r = [0.95;0.98], p < 0.001; Bias = [-0.74 bpm;-0.01 bpm]; MAPE∼2%). However, the performance of SpO2 (r = [0.21;0.68]; p < 0.001; Bias = [-0.46%;-2.29%]; root-mean-square error = [2.82%;4.1%]) monitoring was substantially inferior. RR measurements could not be obtained for two of the devices, therefore exclusively the accuracy of the Garmin tracker could be evaluated (r = 0.28, p < 0.001; Bias = -1.46/min). Moreover, the time resolution of the vital sign measurements highly depends on the tracking device, ranging from 0.7 to 117.94 data points per hour. Conclusion: According to the results of the present study, tracker devices are generally reliable and accurate for HR monitoring, whereas SpO2 and RR measurements should be interpreted carefully, considering the clinical context of the respective patients.

Assessing the current utilization status of wearable devices in clinical research.

BACKGROUND/AIMS: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography.

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.

Comparing Self-Reported Running Distance and Pace With a Commercial Fitness Watch Data: Reliability Study.

BACKGROUND: There is substantial evidence exploring the reliability of running distance self-reporting and GPS wearable technology, but there are currently no studies investigating the reliability of participant self-reporting in comparison to GPS wearable technology. There is also a critical sports science and medical research gap due to a paucity of reliability studies assessing self-reported running pace. OBJECTIVE: The purpose of this study was to assess the reliability of weekly self-reported running distance and pace compared to a commercial GPS fitness watch, stratified by sex and age. These data will give clinicians and sports researchers insights into the reliability of runners' self-reported pace, which may improve training designs and rehabilitation prescriptions. METHODS: A prospective study of recreational runners was performed. Weekly running distance and average running pace were captured through self-report and a fitness watch. Baseline characteristics collected included age and sex. Intraclass correlational coefficients were calculated for weekly running distance and running pace for self-report and watch data. Bland-Altman plots assessed any systemic measurement error. Analyses were then stratified by sex and age. RESULTS: Younger runners reported improved weekly distance reliability (median 0.93, IQR 0.92-0.94). All ages demonstrated similar running pace reliability. Results exhibited no discernable systematic bias. CONCLUSIONS: Weekly self-report demonstrated good reliability for running distance and moderate reliability for running pace in comparison to the watch data. Similar reliability was observed for male and female participants. Younger runners demonstrated improved running distance reliability, but all age groups exhibited similar pace reliability. Running pace potentially should be monitored through technological means to increase precision.

Statistical analysis plan for a randomized controlled trial examining pedometer-based walking intervention in patients with heart failure with reduced ejection fraction: the WATCHFUL trial.

BACKGROUND: Physical activity is an effective management strategy for heart failure with reduced ejection fraction, but patients' compliance is challenging. Walking is a suitable form of physical activity due to its convenience and sustainability, and it can potentially improve functional capacity in heart failure patients. OBJECTIVES: The WATCHFUL trial aims to determine whether a pedometer-based walking intervention combined with face-to-face sessions and regular telephone contact improves functional capacity in heart failure patients. METHODS: The WATCHFUL trial is a 6-month multicenter, parallel-group, randomized, controlled, superiority trial with a 6-month follow-up. A total of 202 patients were recruited for the trial. The primary analysis will evaluate the change in distance walked during the 6-min walk test from baseline to 6 months based on the intention-to-treat population; the analysis will be performed using a linear mixed-effect model adjusted for baseline values. Missing data will be imputed using multiple imputations, and the impact of missing data will be assessed using a sensitivity analysis. Adverse events are monitored and recorded throughout the trial period. DISCUSSION: The trial has been designed as a pragmatic trial with a scalable intervention that could be easily translated into routine clinical care. The trial has been affected by the COVID-19 pandemic, which slowed patients' recruitment and impacted their physical activity patterns. CONCLUSIONS: The present publication provides details of the planned statistical analyses for the WATCHFUL trial to reduce the risks of reporting bias and erroneous data-driven results. TRIAL REGISTRATION: ClinicalTrials.gov (identifier: NCT03041610, registered: 3/2/2017).

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial.

BACKGROUND: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. OBJECTIVE: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. METHODS: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). RESULTS: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). CONCLUSIONS: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. TRIAL REGISTRATION: ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881.

Accuracy of wrist-worn wearable devices for determining exercise intensity.

Objective: As an indicator of exercise intensity, heart rate can be measured in a timely manner using wrist-worn devices. No study has attempted to estimate a target exercise intensity using wearable devices. The objective of the study was to evaluate the validity of prescribing exercise intensity using wrist-worn devices. Methods: Thirty healthy subjects completed a maximal cardiopulmonary exercise test. Their heart rates were recorded using an electrocardiogram and two devices-Apple Watch Series 6 and Garmin Forerunner 945. Exercise intensity with the target heart rate was defined as resting heart rate + (maximal heart rate - resting heart rate) *n% (n%: 40-60% for moderate-intensity exercise and 60-89% for vigorous-intensity exercise). Heart rate was analyzed at the lower and upper limits of each exercise intensity (HR40, HR60, and HR89). The mean absolute percentage error and concordance correlation coefficient were calculated, and Bland-Altman plots and scatterplots were constructed. Results: Both devices showed a low mean absolute error (1.16-1.48 bpm for Apple and 1.35-2.25 for Garmin) and mean absolute percentage error (<1% for Apple and 1.16-1.39% for Garmin) in all intensities. A substantial correlation with electrocardiogram-measured heart rate was observed for moderate to vigorous intensity with concordance correlation coefficient > 0.95 for both devices, except that Garmin showed moderate correlation at the upper limit of vigorous activity with concordance correlation coefficient = 0.936. Moreover, Bland-Altman plots and scatterplots demonstrated a strong correlation without systematic error when the values obtained via the two devices were compared with electrocardiogram measurements. Conclusions: Our findings indicate the high validity of exercise prescriptions based on the heart rate measured by the two devices. Additional research should explore other populations to confirm these findings.

A Clinician's Guide to Smartwatch "Interrogation".

PURPOSE OF REVIEW: Wearable technology is rapidly evolving and the data that it can provide regarding an individual's health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way. RECENT FINDINGS: This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of. Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.

Garmin GPSMAP 66sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning.

In 2020, Garmin released one of the first consumer devices with a dual-frequency GNSS chip and a quadrifilar helix antenna: GPSMAP 66sr. The device is intended to serve as a positioning and navigation device for outdoor recreation purposes with positioning accuracies on the few meter level. However, due to its highly accurate GNSS dual-frequency carrier-phase observations, the equipment can also be used for centimeter-accurate positioning. We performed extensive test measurements and analyzed the quality of its code and carrier-phase observations. We calibrated the Garmin GPSMAP 66sr antenna with respect to its phase-center offset and phase-center variations. We also performed dual-frequency GPS/Galileo precise point positioning (PPP) and precise relative positioning in baselines to virtual reference stations (VRS). We demonstrate and explain how centimeter-accurate positioning can be achieved with this novel kind of equipment.

Accuracy of Heart Rate Measurements of Three Smartwatch Models in Dogs.

This study was aimed to evaluate the accuracy of heart rate (HR) measurements using 3 smartwatch models designed for human use when applied to dogs. A group of 15 mixed breed dogs, ages ranging from 2 to 3 years, weighing between 20 and 30 kg were used. The Garmin Fenix 5X plus (GF5Xp), Samsung Gear S3 (SGS3), and Polygold A-6 (PDA6), were randomly placed on the tibia of dogs to collect the HR recordings every 5 minutes for 1 hour. A veterinary patient monitor (VPM) was used as the reference method. A Bland-Altman plot was applied to determine the difference between smartwatches. The receiver operating characteristic curves were developed analysis for sensitivity and specificity. The HR measured by VPM ranged from 65 to 200 bpm (mean, 149.3 ± 28.1 bpm; median, 154 bpm). Among the smartwatches, the lowest (44 bpm) HR was measured by PDA6, while the highest (201 bpm) was measured by SGS3. Among the smartwatches; GF5Xp and SGS3 had slopes equal to 1 and residual standard deviation closer to 0, in agreement with VPM. At the cut-off HR (160 bpm), SGS3 had the highest sensitivity (97.4%), whereas PDA6 had the lowest sensitivity (68.0%). The specificity of GF5Xp, SGS3, and PDA6 was 97.1%, 98.0%, and 98.0%, respectively. The SGS3 and GF5Xp can be ideal tools for veterinarians to monitor the HR of dogs without requiring an additional device.

Smart Watch Versus Classic Receivers: Static Validity of Three GPS Devices in Different Types of Built Environments.

In order to study the relationship between human physical activity and the design of the built environment, it is important to measure the location of human movement accurately. In this study, we compared an inexpensive GPS receiver (Holux RCV-3000) and a frequently used Garmin Forerunner 35 smart watch, with a device that has been validated and recommended for physical activity research (Qstarz BT-Q1000XT). These instruments were placed on six geodetic points, which represented a range of different environments (e.g., residential, open space, park). The coordinates recorded by each device were compared with the known coordinates of the geodetic points. There were no differences in accuracy among the three devices when averaged across the six sites. However, the Garmin was more accurate in the city center and the Holux was more accurate in the park and housing estate areas compared to the other devices. We consider the location accuracy of the Holux and the Garmin to be comparable to that of the Qstarz. Therefore, we consider these devices to be suitable instruments for locating physical activity. Researchers must also consider other differences among these devices (such as battery life) when determining if they are suitable for their research studies.

How Precisely Can Easily Accessible Variables Predict Achilles and Patellar Tendon Forces during Running?

Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.

Examining Response to Negative Life Events Through Fitness Tracker Data.

Negative life events, such as the death of a loved one, are an unavoidable part of life. These events can be overwhelmingly stressful and may lead to the development of mental health disorders. To mitigate these adverse developments, prior literature has utilized measures of psychological responses to negative life events to better understand their effects on mental health. However, psychological changes represent only one aspect of an individual's potential response. We posit measuring additional dimensions of health, such as physical health, may also be beneficial, as physical health itself may be affected by negative life events and measuring its response could provide context to changes in mental health. Therefore, the primary aim of this work was to quantify how an individual's physical health changes in response to negative life events by testing for deviations in their physiological and behavioral state (PB-state). After capturing post-event, PB-state responses, our second aim sought to contextualize changes within known factors of psychological response to negative life events, namely coping strategies. To do so, we utilized a cohort of professionals across the United States monitored for 1 year and who experienced a negative life event while under observation. Garmin Vivosmart-3 devices provided a multidimensional representation of one's PB-state by collecting measures of resting heart rate, physical activity, and sleep. To test for deviations in PB-state following negative life events, One-Class Support Vector Machines were trained on a window of time prior to the event, which established a PB-state baseline. The model then evaluated participant's PB-state on the day of the life event and each day that followed, assigning each day a level of deviance relative to the participant's baseline. Resulting response curves were then examined in association with the use of various coping strategies using Bayesian gamma-hurdle regression models. The results from our objectives suggest that physical determinants of health also deviate in response to negative life events and that these deviations can be mitigated through different coping strategies. Taken together, these observations stress the need to examine physical determinants of health alongside psychological determinants when investigating the effects of negative life events.

Do Critical and Functional Threshold Powers Equate in Highly-Trained Athletes?

The purpose of this investigation was to determine whether Critical Power (CP) and Functional Threshold Power (FTP) can be used interchangeably for a highly-trained group of cyclists and triathletes. CP was ascertained using multiple fixed load trials and FTP determined from a single cycling trial. Three different models for the determination of CP were initially addressed, one hyperbolic (Hmodel) and two linear (Jmodel and Imodel). The Jmodel was identified as most appropriate for a comparison with FTP. The Jmodel and FTP were not found to be interchangeable as ANOVA detected significant differences (282 ± 53 vs. 266 ± 55 W, p < 0.001) between these indices and the associated Bland-Altman 95% limits of agreement exceeded those set a priori. As the Jmodel was found to be consistently higher than FTP, a correction factor was posited to anticipate CP from FTP in this homogenous group of athletes using the mean bias (16 W). An alternate method for assessing CP trial intensities using Dmax as a proxy for ventilatory threshold is also proposed. The concept of both CP and FTP representing a maximal metabolic steady-state requires further investigation as the mechanical power at CP was significantly greater than at FTP.

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults.

Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit's SmartTrack, and Garmin's Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS' pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Assessing Moderate to Vigorous Physical Activity in Older Adults: Validity of a Commercial Activity Tracker.

Purpose: Despite the potential for commercial activity devices to promote moderate to vigorous physical activity (MVPA), limited information is available in older adults, a high-priority target population with unique gait dynamics and energy expenditure. The study purpose was to investigate the content validity of the Garmin Vivosmart HR device for step counts and MVPA in adults ≥65 years of age in free-living conditions. Methods: Thirty-five participants (M age= 73.7 (6.3) years) wore Garmin and ActiGraph GT3X+ devices for a minimum of 2 days. Accuracy and intra-person reliability were tested against a hip worn ActiGraph device. Separate analyses were conducted using different accelerometer cut-off values to define MVPA, a population-based threshold (≥2,020 counts/minute) and a recommended threshold for older adults (≥1,013 counts/minute). Results: Overall, the Garmin device overestimated MVPA compared with the hip-worn ActiGraph. However, the difference was small using the lower, age-specific, MVPA cut-off value [median (IQR) daily minutes; 50(85) vs. 32(49), p = 0.35] in contrast to the normative standard (50(85) vs. 7(24), p < 0.001). Regardless of the MVPA cut-off, intraclass correlation showed poor reliability [ICC (95% CI); 0.16(-0.40, 0.55) to 0.35(-0.32, 0.7)] which was supported by Bland-Altman plots. Garmin step count was both accurate (M step difference: 178.0, p = 0.22) and reliable [ICC (95% CI; 0.94) (0.88, 0.97)]. Conclusion: Results support the accuracy of a commercial activity device to measure MVPA in older adults but further research in diverse patient populations is needed to determine clinical utility and reliability over time.

Comparison between conventional human energy measurement and physical human energy measurement methods in wetland rice production.

Measurement of human energy expenditure during crop production helps in the optimization of production operations and costs by identifying steps which that can benefit from the use of appropriate mechanization technologies. This study measures human energy expenditure associated with all 6 major rice (Oryza sativa L.) cultivation operations using two measurement methods-i.e. conventional human energy expenditure method and direct measurement with a Garmin forerunner 35 body media. The aim of this study was to provide a detailed comparison of these two methods and document the human energy costs in a manner that will identify steps to be taken to help optimize agricultural practices. Results (mean + 95%CL) revealed that the total human energy expenditure obtained through the conventional method was 25.5% higher (33.3 ± 1 versus 26.6 ± 1.3) in transplanting and 26.1% higher (30.3 ± 1.9 versus 24.0 ± 2.1) than the human energy expenditure recorded using the Garmin method in broadcast seeding method. Similarly, during the harvesting operation, the conventional measurement and Garmin measurement methods differed significantly, with the conventional method the human energy expenditure was 89.9% higher (3.2 ± 0.4 versus 1.68 ± 0.2) in the fields using the transplanting and 88.7% higher (3.3 ± 0.5 versus 1.8 ± 0.3) in the fields using the broadcast seeding than the human energy expenditure recorded using the Garmin method. When using Garmin method, the human energy expenditure in the case of using the midsize combine harvester was 13.49% lesser (592.4 ± 67.2 versus 522.0 ± 75.1) than the case of using conventional one. Results based on heart rate also indicated that operations such as tillage were less intensive (72 ± 3.3 bpm) compared with operations such as chemicals spraying (135 ± 4 bpm). Although we did not have a criterion measure available to determine which method was the most accurate, the Garmin measurement gives an estimate of actual physical human energy expended in performing a specific task with consider all conditions and thus more information to aid in identifying critical operations that could be optimized and mechanized.

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.