Добірка наукової літератури з теми "Wrist-activity monitors"

Purpose: To examine the effect of activity monitor placement on daily step counts when monitors are worn at different positions on the wrist/forearm and the hip. Methods: Participants (N = 18) wore eight different models (four wrist and four hip models) across four days. Each day, one hip and one wrist model were selected, and four identical monitors of each model were worn on the right hip and the non-dominant wrist/forearm, respectively, during all waking hours. Step counts of each monitor were compared to the same model worn in the referent position (wrist: proximal to ulnar styloid process; hip: midline of thigh). Percent of referent steps and mean difference between observed and referent positions were computed. Significant differences in steps between positions for each method were determined using one-way repeated measures ANOVAs. For significant main effects, pairwise comparisons with Bonferroni corrections were used to determine which positions were significantly different. Results: All wrist methods showed a significant main effect for placement (p < .05) and alternate positions were 1–16% lower than the referent position. For hip methods, only the Omron HJ-325 differed across positions (p < .05), but differences were among non-referent positions and all were within ±2% of steps recorded by the referent position. Conclusions: Researchers should be aware that positions that deviate from the manufacturer’s recommended position at the wrist could influence step counts. Of all hip methods examined, the Omron had a significant placement effect which did not constitute a practical difference.

Background: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. Methods: Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. Results: Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3–38.2% during overground walking, 48.2–861.2% during ADLs, and 11.2–47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. Conclusion: This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

Introduction The ability to monitor physical activity throughout the day and during various activities continues to improve with the development of wrist-worn monitors. However, the accuracy of wrist-worn monitors to measure both heart rate and energy expenditure during physical activity is still unclear. The purpose of this study was to determine the accuracy of several popular wrist-worn monitors at measuring heart rate and energy expenditure. Methods Participants wore the TomTom Cardio, Microsoft Band and Fitbit Surge on randomly assigned locations on each wrist. The maximum number of monitors per wrist was two. The criteria used for heart rate and energy expenditure were a three-lead electrocardiogram and indirect calorimetry using a metabolic cart. Participants exercised on a treadmill at 3.2, 4.8, 6.4, 8 and 9.7 km/h for 3 minutes at each speed, with no rest between speeds. Heart rate and energy expenditure were manually recorded every minute throughout the protocol. Results Mean absolute percentage error for heart rate varied from 2.17 to 8.06% for the Fitbit Surge, from 1.01 to 7.49% for the TomTom Cardio and from 1.31 to 7.37% for the Microsoft Band. The mean absolute percentage error for energy expenditure varied from 25.4 to 61.8% for the Fitbit Surge, from 0.4 to 26.6% for the TomTom Cardio and from 1.8 to 9.4% for the Microsoft Band. Conclusion Data from these devices may be useful in obtaining an estimate of heart rate for everyday activities and general exercise, but energy expenditure from these devices may be significantly over- or underestimated.

We assessed the agreement of two ActiGraph activity monitors (wGT3X vs. GT9X) placed at the hip and the wrist and determined an appropriate epoch length for physical activity levels in an exergaming setting. Forty-seven young adults played a 30-min exergame while wearing wGT3X and GT9X on both hip and wrist placement sites and a heart rate sensor below the chest. Intraclass correlation coefficient indicated that intermonitor agreement in steps and activity counts was excellent on the hip and good on the wrist. Bland-Altman plots indicated good intermonitor agreement in the steps and activity counts on both placement sites but a significant intermonitor difference was detected in steps on the wrist. Time spent in sedentary and physical activity intensity levels varied across six epoch lengths and depended on the placement sites, whereas time spent from a 1-s epoch of the hip-worn monitors most accurately matched the relative exercise intensity by heart rate. Hip placement site was associated with better step-counting accuracy for both activity monitors and more valid estimation of physical activity levels. A 1-s epoch was the most appropriate epoch length to detect short bursts of intense physical activity and may be the best choice for data processing and analysis in exergaming studies examining intermittent physical activities.

This study aimed to: (1) compare acceleration output between ActiGraph (AG) hip and wrist monitors and GENEActiv (GA) wrist monitors; (2) identify raw acceleration sedentary and stationary thresholds for the two brands and placements; and (3) validate the thresholds during a free-living period. Twenty-seven from 9- to 10-year-old children wore AG accelerometers on the right hip, dominant- and non-dominant wrists, GA accelerometers on both wrists, and an activPAL on the thigh, while completing seven sedentary and light-intensity physical activities, followed by 10 minutes of school recess. In a subsequent study, 21 children wore AG and GA wrist monitors and activPAL for two days of free-living. The main effects of activity and brand and a significant activity × brand × placement interaction were observed (all p < 0.0001). Output from the AG hip was lower than the AG wrist monitors (both p < 0.0001). Receiver operating characteristic (ROC) curves established AG sedentary thresholds of 32.6 mg for the hip, 55.6 mg and 48.1 mg for dominant and non-dominant wrists respectively. GA wrist thresholds were 56.5 mg (dominant) and 51.6 mg (non-dominant). Similar thresholds were observed for stationary behaviours. The AG non-dominant threshold came closest to achieving equivalency with activPAL during free-living.

Background: No accepted standard exists to evaluate nonsleep-related activity in nursing facility residents where monitors are variously placed at the ankle, waist, wrist, thigh, or embedded in sheeting and set to record activity frequency. Objectives: To determine optimal placement of activity monitors by site—at the ankle, waist, or wrist for nursing facility residents. Methods: Nursing facility residents (N = 16) wore accelerometers at three sites: the nondominant ankle, waist, and wrist, while recording activity in three modes: frequency, duration, and intensity. Results: The natural log activity mean for each mode by site and time revealed no significant differences between the three sites for activity intensity, F(2, 62.78) = .15, p = .86; activity duration, F(2, 69.84) = .50, p = .61; and activity frequency, F(2, 70.04) = 1.25, p = .29. There were no significant site—time interactions. The natural log activity by site and mode indicated no significant differences by site for the 24-hr mean, F(2, 107.64) = .20, p = .82; activity median, F(2, 100.42) = .47, p = .63; and activity standard deviation, F(2, 108.69) = 1.5, p = .23. A significant difference was seen by site for the acceleration index, F(2, 106.32) = 9.57, p < .001. No significant site—mode interactions were found. Conclusions: Similarity between ankle, waist, or wrist sites when measuring activity by various modes, frequency, duration, or intensity, suggests the monitors measure nonsleep-related activity equally well at any of the sites.

Introduction: To capitalize on the increasing availability of accelerometry data for epidemiological research it is desirable to compare and/or pool data from surveys worldwide. This study aimed to establish whether free-living physical activity outcomes can be considered equivalent between three research-grade accelerometer brands worn on the dominant and non-dominant wrist. Of prime interest were the average acceleration (ACC) and the intensity gradient (IG). These two metrics describe the volume and intensity of the complete activity profile; further, they are comparable across populations making them ideal for comparing and/or pooling activity data. Methods: Forty-eight adults wore a GENEActiv, Axivity, and ActiGraph on both wrists for up to 7-days. Data were processed using open-source software (GGIR) to generate physical activity outcomes, including ACC and IG. Agreement was assessed using pairwise 95% equivalence tests (±10% equivalence zone) and intra-class correlation coefficients (ICC). Results: ACC was equivalent between brands when measured at the non-dominant wrist (ICC ≥ 0.93), but approximately 10% higher when measured at the dominant wrist (GENEActiv and Axivity only, ICC ≥ 0.83). The IG was equivalent irrespective of monitor brand or wrist (ICC ≥ 0.88). After adjusting ACC measured at the dominant wrist by −10% (GENEActiv and Axivity only), ACC was also within (or marginally outside) the 10% equivalence zone for all monitor pairings. Conclusion: If average acceleration is decreased by 10% for studies deploying monitors on the dominant wrist (GENEActiv and Axivity only), ACC and IG may be suitable for comparing and/or collating physical activity outcomes across accelerometer datasets, regardless of monitor brand and wrist.

Background: Devices for monitoring physical activity have focused mainly on measuring aerobic activity; however, the 2018 Physical Activity Guidelines for Americans also recommend muscle-resistance training two or more days per week. Recently, a wrist-worn activity monitor, the Atlas Wristband2, was developed to recognize resistance training exercises. Purpose: To assess the ability of the Wristband2 to identify the type and number of repetitions of resistance training exercises, when worn on the left wrist as directed by the manufacturer, and when worn on the right wrist. Methods: While wearing monitors on both wrists, 159 participants completed a circuit-style workout consisting of two sets of 12 repetitions of 14 different resistance training exercises. Data from the monitors were used to determine classification accuracies for identifying exercise type verses direct observation. The average repetitions and mean absolute error (MAE) for repetitions were calculated for each exercise. Results: The Wristband2 classification accuracy for exercise type was 78.4 ± 2.5%, ranging from 54.7 ± 3.4% (dumbbell [DB] bench press) to 97.5 ± 1.0% (DB biceps curls), when worn on the left wrist. An average of 11.0 ± 0.2 repetitions, ranging from 9.0 ± 0.3 repetitions (DB lunges) to 11.9 ± 0.1 repetitions (push-ups), were identified. For all exercises, MAE ranged from 0.0–4.6 repetitions. When worn on the right wrist, exercise type classification accuracy dropped to 24.2 ± 5.1%, and repetitions decreased to 8.1 ± 0.8 out of 12. Conclusions: The Wristband2, worn on the left wrist, had acceptable exercise classification and repetition counting capabilities for many of the 14 exercises used in this study, and may be a useful tool to objectively track resistance training.

Shift work in the mining industry is a risk factor for sleep loss leading to impaired alertness, which may adversely impact health and safety risks. This risk is being increasingly recognised by leaders and shift workers in the mining industry, however, there is limited knowledge available on the extent of sleep loss and other potential contributing factors. Furthermore, knowledge of the efficacy of individual interventions to assist shift workers to improve their sleep, and the management of risk at an organisational level is scarce. This PhD thesis involved three studies. The first two studies involved the recruitment of 88 shift workers on a fly-in, fly-out (FIFO) mining operation in Western Australia (WA), undertaken within a business-as-usual model. The third study develops a diagnostic tool to support the systematic assessment of an organisation's Fatigue Risk Management System (FRMS). Study 1 (Chapter 4) investigated sleep behaviours, the prevalence of risk of sleep disorders and the predicted impact on alertness across the roster schedule. Sleep was objectively measured using wrist-activity monitors for the 21-day study period and biomathematical modelling was used to predict alertness across the roster schedule. The prevalence of risk for sleep problems and disorders was determined using scientifically validated sleep questionnaires. We found sleep loss was significantly greater following days shift and night shift compared to days off, which resulted in a 20% reduced alertness across the 14 consecutive shifts at the mining operation. Shift workers reported a high prevalence of risk for sleep disorders including shift work disorder (44%), obstructive sleep apnoea (OSA) (31%) and insomnia (8%); a high proportion of shift workers were obese with a body mass index (BMI) > 30kg/m2 (23%) and consumed hazardous levels of alcohol (36%). All of which may have contributed to sleep loss. In addition, the design of shifts and rosters, specifically, early morning shift start times ( < 06:00) and long shift durations ( > 12 hrs.) may have also adversely impacted sleep duration, as they did not allow for sufficient sleep opportunity. Study 2 (Chapter 5) was a randomised control trial (RCT) that investigated the efficacy of interventions to improve sleep, which included a two-hour sleep education program and biofeedback on sleep through a smartphone application. Sleep was objectively measured using wrist-activity monitors across two roster cycles (42 days) with an intervention received on day 21. Our results were inconclusive and suggest that further research is required to determine the efficacy of these commonly used interventions in the mining industry. In line with the results from Study 1, our interventions may not have been effective in improving sleep duration as the shift and roster design did not allow adequate time off between shifts for sleep ( ≥ 7 h) and daily routines. Study 3 (Chapter 6) used a modified Delphi process that involved 16 global experts, with experience and knowledge in sleep science, chronobiology, and applied fatigue risk management within occupational settings, to define and determine the elements considered essential as part of an FRMS. This study resulted in the development of an FRMS diagnostic tool to systematically assist an organisation in assessing its current level of implementation of an FRMS. The results of the studies within this PhD thesis present several potential benefits for the mining industry. These include an enhanced understanding of the extent of sleep loss and the potential impact on alertness, in addition to contributing factors, including shift and roster design elements and unmanaged sleep disorders. The development of the FRMS diagnostic tool may practically guide mining operations on the elements required to manage risk. These findings may also inform government, occupational health and safety regulatory authorities and shift work organisations more broadly, on the need to identify and manage fatigue, as a result of sleep loss, as a critical risk.

For monitoring the progress of athletes in various sports and disciplines, several different approaches are nowadays available. Recently, miniature wearables have gained popularity for this task due to being lightweight and typically cheaper than other approaches. They can be positioned on the athlete’s body, or in some cases, the devices are incorporated into sports requisites, like tennis racquet handles, balls, baseball bats, gloves, etc. Their purpose is to monitor the performance of an athlete by gathering essential information during match or training. In this chapter, the focus will be on the different possibilities of tennis game monitoring analysis. A miniature wearable device, which is worn on a player’s wrist during the activity, is going to be presented and described. The smart wearable device monitors athletes’ arm movements with sampling the output of the 6 DOF IMU. Parallel to that, it also gathers biometric information like pulse rate and skin temperature. All the collected information is stored locally on the device during the sports activity. Later, it can be downloaded to a PC and transferred to a cloud-based service, where visualization of the recorded data and more detailed game/training statistics can be performed.