Wrist-worn Activity Research Articles

Accuracy Of Wrist-worn Activity Monitors During Walking And Swimming

Accuracy Of Wrist-worn Activity Monitors During Walking And Swimming

Accuracy of Wrist-Worn Activity Monitors during Treadmill and Elliptical Ergometry

Accuracy of Wrist-Worn Activity Monitors during Treadmill and Elliptical Ergometry

The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers.

The Accuracy of Heart Rate Monitoring by Some Wrist-Worn Activity Trackers.

Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices

BackgroundPhysical activity tracking wearable devices have emerged as an increasingly popular method for consumers to assess their daily activity and calories expended. However, whether these wearable devices are valid at different levels of exercise intensity is unknown.ObjectiveThe objective of this study was to examine heart rate (HR) and energy expenditure (EE) validity of 3 popular wrist-worn activity monitors at different exercise intensities.MethodsA total of 62 participants (females: 58%, 36/62; nonwhite: 47% [13/62 Hispanic, 8/62 Asian, 7/62 black/ African American, 1/62 other]) wore the Apple Watch, Fitbit Charge HR, and Garmin Forerunner 225. Validity was assessed using 2 criterion devices: HR chest strap and a metabolic cart. Participants completed a 10-minute seated baseline assessment; separate 4-minute stages of light-, moderate-, and vigorous-intensity treadmill exercises; and a 10-minute seated recovery period. Data from devices were compared with each criterion via two-way repeated-measures analysis of variance and Bland-Altman analysis. Differences are expressed in mean absolute percentage error (MAPE).ResultsFor the Apple Watch, HR MAPE was between 1.14% and 6.70%. HR was not significantly different at the start (P=.78), during baseline (P=.76), or vigorous intensity (P=.84); lower HR readings were measured during light intensity (P=.03), moderate intensity (P=.001), and recovery (P=.004). EE MAPE was between 14.07% and 210.84%. The device measured higher EE at all stages (P<.01). For the Fitbit device, the HR MAPE was between 2.38% and 16.99%. HR was not significantly different at the start (P=.67) or during moderate intensity (P=.34); lower HR readings were measured during baseline, vigorous intensity, and recovery (P<.001) and higher HR during light intensity (P<.001). EE MAPE was between 16.85% and 84.98%. The device measured higher EE at baseline (P=.003), light intensity (P<.001), and moderate intensity (P=.001). EE was not significantly different at vigorous (P=.70) or recovery (P=.10). For Garmin Forerunner 225, HR MAPE was between 7.87% and 24.38%. HR was not significantly different at vigorous intensity (P=.35). The device measured higher HR readings at start, baseline, light intensity, moderate intensity (P<.001), and recovery (P=.04). EE MAPE was between 30.77% and 155.05%. The device measured higher EE at all stages (P<.001).ConclusionsThis study provides one of the first validation assessments for the Fitbit Charge HR, Apple Watch, and Garmin Forerunner 225. An advantage and novel approach of the study is the examination of HR and EE at specific physical activity intensities. Establishing validity of wearable devices is of particular interest as these devices are being used in weight loss interventions and could impact findings. Future research should investigate why differences between exercise intensities and the devices exist.

Clinical Evaluation of the Measurement Performance of the Philips Health Watch: A Within-Person Comparative Study.

BackgroundPhysical inactivity is an important modifiable risk factor for chronic diseases. A new wrist-worn heart rate and activity monitor has been developed for unobtrusive data collection to aid prevention and management of lifestyle-related chronic diseases by means of behavioral change programs.ObjectiveThe objective of the study was to evaluate the performance of total energy expenditure and resting heart rate measures of the Philips health watch. Secondary objectives included the assessment of accuracy of other output parameters of the monitor: heart rate, respiration rate at rest, step count, and activity type recognition.MethodsA within-person comparative study was performed to assess the performance of the health watch against (medical) reference measures. Participants executed a protocol including 15 minutes of rest and various activities of daily life. A two one-sided tests approach was adopted for testing equivalence. In addition, error metrics such as mean error and mean absolute percentage error (MAPE) were calculated.ResultsA total of 29 participants (14 males; mean age 41.2, SD 14.4, years; mean weight 77.2, SD 10.2, kg; mean height 1.8, SD 0.1, m; mean body mass index 25.1, SD 3.1, kg/m2) completed the 81-minute protocol. Their mean resting heart rate in beats per minute (bpm) was 64 (SD 7.3). With a mean error of −10 (SD 38.9) kcal and a MAPE of 10% (SD 8.7%), total energy expenditure estimation of the health watch was found to be within the 15% predefined equivalence margin in reference to a portable indirect calorimeter. Resting heart rate determined during a 15-minute rest protocol was found to be within a 10% equivalence margin in reference to a wearable electrocardiogram (ECG) monitor, with a mean deviation of 0 bpm and a maximum deviation of 3 bpm. Heart rate was within 10 bpm and 10% of the ECG monitor reference for 93% of the duration of the protocol. Step count estimates were on average 21 counts lower than a waist-mounted step counter over all walking activities combined, with a MAPE of 3.5% (SD 2.4%). Resting respiration rate was on average 0.7 (SD 1.1) breaths per minute lower than the reference measurement by the spirometer embedded in the indirect calorimeter during the 15-minute rest, resulting in a MAPE of 8.3% (SD 7.0%). Activity type recognition of walking, running, cycling, or other was overall 90% accurate in reference to the activities performed.ConclusionsThe health watch can serve its medical purpose of measuring resting heart rate and total energy expenditure over time in an unobtrusive manner, thereby providing valuable data for the prevention and management of lifestyle-related chronic diseases.Trial RegistrationNetherlands trial register NTR5552; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5552 (Archived by WebCite at http://www.webcitation.org/6neYJgysl)

Comparison of wrist-worn and hip-worn activity monitors under free living conditions

Purpose: Current recommendations state that individuals engage in 150 min of moderate or 75 min of vigorous intensity physical activity (PA) each week. Commercial PA monitors are becoming popular for everyday use. The accuracy of these devices, however, is not well understood. We sought to examine the accuracy of two commercial devices, one wrist and one hip-worn, under free-living conditions.Methods: Twenty-two subjects wore two commercially available devices and one ActiGraph (AG) for seven consecutive days under normal activity.Results: Mean steps per day between all three devices differed significantly. No differences were found in moderate-to-vigorous intensity physical activity (MPVA). Daily energy expenditure (EE) also differed significantly between the AG and the commercial devices. Bland–Altman analysis found poor agreement between the AG and the commercial devices with regards to steps and EE, but good agreement in MVPA.Conclusion: Results suggest that the commercial devices are less accurate in estimating steps and EE. These devices did show good agreement with regards to MVPA, suggesting that they may provide useful feedback for individuals seeking to achieve the current PA guidelines for MVPA. Improvements are needed with regards to steps and EE estimation.

The effect of ongoing feedback on physical activity levels following an exercise intervention in older adults: a randomised controlled trial protocol

BackgroundPhysical inactivity ranks as a major contributing factor in the development and progression of chronic disease. Lifestyle interventions reduce the progression of chronic disease, however, compliance decreases over time and health effects only persist as long as the new lifestyle is maintained. Telephone counselling (TC) is an effective way to provide individuals with ongoing support to maintain lifestyle changes. Remote physical activity monitoring and feedback (RAMF) via interactive technologies such as activity trackers and smartphones may be a cost-effective alternative to TC, however, this comparison has not been made. This study, therefore, aims to determine the effect of ongoing feedback (TC vs. RAMF) on the maintenance of physical activity following a 12-week individualised lifestyle program, and the effect of this on health risk factors and health services usage.Methods and designA randomised controlled trial with a parallel groups design. A total of 150 adults (≥60 years) who participate in a 12-week face-to-face individualised lifestyle program will be randomised to twelve months of RAMF (n = 50), TC (n = 50), or usual care (n = 50). Participants randomised to RAMF will use a smartphone activity tracker app, synced to a wrist worn activity tracker, to provide them with automated feedback regarding compliance to prescribed activity targets. Telephone counselling involves a follow-up phone call every fortnight for the first three months and a monthly call for the remaining nine months of the follow-up period.The primary outcome measures are physical activity compliance (accelerometry and Active Australia survey). Secondary outcome measures include cardiorespiratory fitness, muscle strength, dynamic balance, quality of life, blood pressure, body composition, and health services usage. Measures will be made before and after the individualised lifestyle program, and at three, six and twelve months during the intervention.DiscussionThe results of this study will help to determine the efficacy of RAMF devices on compliance to prescribed physical activity compared to the current gold standard of TC. If the remote monitoring proves effective, it may provide a cost efficient alternative method of assisting maintenance of behaviour change from lifestyle interventions.Trial registrationACTRN12615001104549. Retrospectively Registered 20/10/2015.

Activity/rest rhythm of depressed adolescents undergoing therapy: case studies.

Disorders of circadian rhythms have been reported in studies of both depressed children and of depressed adolescents. The aim of this study was to evaluate whether there is a relationship between the 24-hour spectral power (24h SP) of the activity/rest rhythm and the clinical course of depression in adolescents. Six 14 to 17-year-old adolescents were recruited for the study. They were all suffering from major depressive disorder, according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria, as identified by the Schedule for Affective Disorders and Schizophrenia for School Aged Children: Present and Lifetime Version (K-SADS-PL). Depressive symptoms were assessed using the Children's Depression Rating Scale - Revised (CDRS-R) and clinical evaluations. Locomotor activity was monitored over a period of 13 consecutive weeks. Activity was measured for 10-minute periods using wrist-worn activity monitors. All patients were prescribed sertraline from after the first week up until the end of the study. We found a relationship between high CDRS values and low 24-hour spectral power. The 24h SP of the activity/rest rhythm correlated significantly (negatively) with the clinical ratings of depression.

Feasibility of an online intervention (STAK-D) to promote physical activity in children with type 1 diabetes: protocol for a randomised controlled trial.

BackgroundRegular physical activity has important health benefits for children with type 1 diabetes mellitus (T1DM), yet children and their parents face barriers to participation such as lack of self-efficacy or concerns around hypoglycaemia. Multimedia interventions are useful for educating children about their health and demonstrate potential to improve children’s health-related self-efficacy, but few paediatric clinics offer web-based resources as part of routine care. The Steps to Active Kids with Diabetes (STAK-D) programme is an online intervention grounded in psychological theory (social cognitive theory) and informed by extensive preliminary research. The aim of the programme is to encourage and support safe engagement with physical activity for children with T1DM. The aim of this research is to explore the feasibility of delivering the STAK-D programme to children aged 9–12 years with T1DM, and to assess the feasibility of further research to demonstrate its clinical and cost-effectiveness.MethodsUp to 50 children aged 9–12 years with T1DM and their parents will be recruited from two paediatric diabetes clinics in the UK. Child-parent dyads randomised to the intervention group will have access to the intervention website (STAK-D) and a wrist-worn activity monitor for 6 months. The feasibility of intervention and further research will be assessed by rate of recruitment, adherence, retention, data completion and adverse events. Qualitative interviews will be undertaken with a subsample of children and parents (up to 25 dyads) and health care professionals (up to 10). Health outcomes and the feasibility of outcome measurement tools will be assessed. These include self-efficacy (CSAPPA), objective physical activity, self-reported physical activity (PAQ), fear of hypoglycaemia (CHFS; PHFS), glycaemic control (HbA1c), insulin dose, Body Mass Index (BMI), health-related quality of life (CHU9D; CHQ-PF28), health service use and patient-clinician communication. Assessments will be taken at baseline (T0), 8 weeks (T1) and at 6-month follow-up (T2).DiscussionThe goal of this feasibility trial is to assess the delivery of STAK-D to promote physical activity among children with T1DM, and to assess the potential for further, definitive research to demonstrate its effectiveness. Results will provide the information necessary to design a larger randomised controlled trial and maximise the recruitment rate, intervention delivery and trial retention.Trial registrationISRCTN, ISRCTN48994721. Registered on 28 October 2016.Electronic supplementary materialThe online version of this article (doi:10.1186/s13063-016-1719-0) contains supplementary material, which is available to authorized users.

Validity and inter-device reliability of dominant and non-dominant wrist worn activity trackers in suburban walking

Wearable activity trackers have become a popular way for general and athletic populations to measure daily physical activity and rest patterns. The validity and reliability of step count is often unknown for these devices. The aims of this study were to evaluate the validity of the step count of the Fitbit Charge HR and the inter-device reliability between devices worn on the dominant and non-dominant wrists in an ecologically valid walking setting. A secondary aim was to compare these findings with data from an inertial measurement unit (IMU). Six participants were fitted with one Fitbit Charge HR on each wrist and an IMU positioned on the left and right hip. Data from the Fitbit Charge HRs and IMUs were compared against the participant’s self-reported step count. Each participant walked the same suburban circuit whilst counting their steps. When assessed for validity, the Fitbit Charge HR was found to have a low correlation with the self-reported step count (dominant arm, ICC 2, 1 = .19; non-dominant arm, ICC 2, 1 =.21), underestimating the number of steps taken. In comparison, the inter-device reliability of the dominant and non-dominant wrist worn Fitbit Charge HRs was good ( ICC 2,1 = .81). Moderate validity was found between the self-reported step count and IMUs (dominant hip, ICC 2, 1 = .74; non-dominant hip, ICC 2, 1 = .72). The findings suggest that inter-device measurement from dominant and non-dominant hands is reasonably reliable, however less valid as compared to more robust research-grade devices.

A 30-month worksite-based lifestyle program to promote cardiovascular health in middle-aged bank employees: Design of the TANSNIP-PESA randomized controlled trial

Cardiovascular disease (CVD) is the leading cause of death worldwide. With atherosclerosis as the underlying cause for many CVD events, prevention or reduction of subclinical atherosclerotic plaque burden (SAPB) through a healthier lifestyle may have substantial public health benefits. The objective was to describe the protocol of a randomized controlled trial investigating the effectiveness of a 30-month worksite-based lifestyle program aimed to promote cardiovascular health in participants having a high or a low degree of SAPB compared with standard care. We will conduct a randomized controlled trial including middle-aged bank employees from the Progression of Early Subclinical Atherosclerosis cohort, stratified by SAPB (high SAPB n=260, low SAPB n=590). Within each stratum, participants will be randomized 1:1 to receive a lifestyle program or standard care. The program consists of 3 elements: (a) 12 personalized lifestyle counseling sessions using Motivational Interviewing over a 30-month period, (b) a wrist-worn physical activity tracker, and (c) a sit-stand workstation. Primary outcome measure is a composite score of blood pressure, physical activity, sedentary time, body weight, diet, and smoking (ie, adapted Fuster-BEWAT score) measured at baseline and at 1-, 2-, and 3-year follow-up. The study will provide insights into the effectiveness of a 30-month worksite-based lifestyle program to promote cardiovascular health compared with standard care in participants with a high or low degree of SAPB.

A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression.

Late-life depression (LLD) is associated with a decline in physical activity. Typically this is assessed by self-report questionnaires and, more recently, with actigraphy. We sought to explore the utility of a bespoke activity monitor to characterize activity profiles in LLD more precisely. The activity monitor was worn for 7 days by 29 adults with LLD and 30 healthy controls. Subjects underwent neuropsychological assessment and quality of life (QoL) (36-item Short-Form Health Survey) and activities of daily living (ADL) scales (Instrumental Activities of Daily Living Scale) were administered. Physical activity was significantly reduced in LLD compared with controls (t = 3.63, p < 0.001), primarily in the morning. LLD subjects showed slower fine motor movements (t = 3.49, p < 0.001). In LLD patients, activity reductions were related to reduced ADL (r = 0.61, p < 0.001), lower QoL (r = 0.65, p < 0.001), associative learning (r = 0.40, p = 0.036), and higher Montgomery-Åsberg Depression Rating Scale score (r = -0.37, p < 0.05). Patients with LLD had a significant reduction in general physical activity compared with healthy controls. Assessment of specific activity parameters further revealed the correlates of impairments associated with LLD. Our study suggests that novel wearable technology has the potential to provide an objective way of monitoring real-world function.

Sleep Experts Issue Recommendations for Children and Adolescent Sleep

Sleep Experts Issue Recommendations for Children and Adolescent Sleep

Reduced post‐illumination pupil response in patients with mild‐moderate cataracts is associated with impaired sleep quality

PurposeCataracts absorb light, especially in the blue‐range of the visible light spectrum, and reduce retinal illumination. Recent studies showed improved sleep and faster reaction times following cataract extraction. These non‐visual benefits are perhaps due to increased blue light transmission and melanopsin‐dependent photoreception. We compared visual function, pupil responses, sleep quality and sleep‐wake cycle in patients with cataracts and controls.Methods30 patients with cataracts and 22 age‐matched pseudophakic controls were tested during the winter season. Ophthalmologic examination with automated perimetry and optical coherence tomography was performed. Pupil responses were recorded to a 1s red or blue light having pre‐selected intensities. All participants completed a questionnaire for subjective sleep quality (Pittsburgh Sleep Quality Index; PSQI). They were asked to maintain a regular sleep‐wake rhythm with approximately 8 h of sleep. Rest‐activity cycles were recorded for one week by wrist worn activity watches.ResultsMean acuity was 1.0 ± 0.1 for both groups. The pupil contraction amplitude to dim blue light and the post‐illumination pupil response to bright blue light were significantly reduced in patients with cataracts (p &lt; 0.05). This was associated with a significantly higher PSQI score relative to controls (p = 0.02), indicating impaired sleep, and a lower post‐illumination response to bright blue light (R = 0.43; p &lt; 0.05). Sleep analysis from the activity watches revealed a significantly lower sleep efficiency in cataract patients than in controls (78.6 ± 8.3 vs. 83.2 ± 4.5; %; mean ± SD; p = 0.02).ConclusionsPatients with cataracts, not visually impaired, demonstrate reduced melanopsin activity, as assessed by the pupil response. This latter is correlated with impaired subjective sleep quality and greater sleep fragmentation.

Objective Assessment of Strength Training Exercises using a Wrist-Worn Accelerometer.

The 2008 Physical Activity Guidelines for Americans recommend that all adults perform muscle-strengthening exercises to work all of the major muscle groups of the body on at least 2 d·wk, in addition to aerobic activity. Studies using objective methods of monitoring physical activity have focused primarily on the assessment of aerobic activity. To date, a method for assessing resistance training (RT) exercises has not been developed using a wrist-worn activity monitor. The purpose of this study was to examine the use of a wrist-worn triaxial accelerometer-based activity monitor for classifying upper- and lower-body dumbbell RT exercises. Sixty participants performed 10 repetitions each of 12 different upper- and lower-body dynamic dumbbell exercises. Algorithms for classifying the exercises were developed using two different methods: support vector machine and cosine similarity. Confusion matrices were developed for each method, and intermethod reliabilities were assessed using Cohen's kappa. A repeated-measures ANOVA was used to compare the predicted repetitions, identified from the largest acceleration peaks, with the actual repetitions. The results indicated that support vector machine and cosine similarity accurately classified the 12 different RT exercises 78% and 85% of the time, respectively. Both methods struggled to correctly differentiate bench press versus shoulder press and squat versus walking lunges. Repetition estimates were not significantly different for 8 of the 12 exercises. For the four exercises that were significantly different, the differences amount to less than 10%. This study demonstrated that RT exercises can be accurately classified using a single activity monitor worn on the wrist.

Validation of Consumer-Based Hip and Wrist Activity Monitors in Older Adults With Varied Ambulatory Abilities.

The accuracy of step detection in consumer-based wearable activity monitors in older adults with varied ambulatory abilities is not known. We assessed the validity of two hip-worn (Fitbit One and Omron HJ-112) and two wrist-worn (Fitbit Flex and Jawbone UP) activity monitors in 99 older adults of varying ambulatory abilities and also included the validity results from the ankle-worn StepWatch as a comparison device. Nonimpaired, impaired (Short Physical Performance Battery Score < 9), cane-using, or walker-using older adults (62 and older) ambulated at a self-selected pace for 100 m wearing all activity monitors simultaneously. The criterion measure was directly observed steps. Intraclass correlation coefficients (ICC), mean percent error and mean absolute percent error, equivalency, and Bland-Altman plots were used to assess accuracy. Nonimpaired adults steps were underestimated by 4.4% for StepWatch (ICC = 0.87), 2.6% for Fitbit One (ICC = 0.80), 4.5% for Omron HJ-112 (ICC = 0.72), 26.9% for Fitbit Flex (ICC = 0.15), and 2.9% for Jawbone UP (ICC = 0.55). Impaired adults steps were underestimated by 3.5% for StepWatch (ICC = 0.91), 1.7% for Fitbit One (ICC = 0.96), 3.2% for Omron HJ-112 (ICC = 0.89), 16.3% for Fitbit Flex (ICC = 0.25), and 8.4% for Jawbone UP (ICC = 0.50). Cane-user and walker-user steps were underestimated by StepWatch by 1.8% (ICC = 0.98) and 1.3% (ICC = 0.99), respectively, where all other monitors underestimated steps by >11.5% (ICCs < 0.05). StepWatch, Omron HJ-112, Fitbit One, and Jawbone UP appeared accurate at measuring steps in older adults with nonimpaired and impaired ambulation during a self-paced walking test. StepWatch also appeared accurate at measuring steps in cane-users.

Effect Of Hand Dominance On Accuracy Of Wrist-worn Physical Activity Trackers

The increasing public awareness regarding the importance of physical activity (PA) for health has contributed to the rising popularity of personal PA trackers that track energy expenditure, activity intensity, and steps. Many popular PA trackers are designed to be worn on the wrist, but it is unclear if their accuracy is impacted by whether the trackers are worn on the dominant (D) or non-dominant (ND) wrist. PURPOSE: The purpose of this study was to investigate the impact of D vs. ND wrist placement on estimates of energy expenditure (EE, kcals), active minutes (ActMin), and steps between two popular personal PA trackers during a semi-structured protocol. METHODS: Thirty adults (15 male; age 49±20 years) wore two types of PA trackers (FB and JB, both worn on each wrist) and a portable metabolic analyzer while completing an 80-min, semi-structured PA protocol. Participants performed at least 12 of 21 sedentary, household, and exercise/ambulatory activities, with at least half of the visit time spent in sedentary activities. PA tracker estimates for EE, ActMin (METS >3.0), and steps were compared to criterion values measured by the metabolic analyzer (EE and ActMin) and manually counted steps (steps). Repeated measures analysis of variance and dependent t-tests were used to analyze differences between D and ND wrist placement and the accuracy of the trackers compared to criterion measures. RESULTS: Estimates of EE, ActMin, and steps between the D and ND FB trackers were not significantly different (276+19 vs. 258+11 kcals; 12+1 vs. 11+1 ActMin; 2483+147 vs. 2502+141 steps, respectively). Similarly, EE, ActMin, and steps from the D and ND JB trackers were not significantly different (217.5+13 vs. 213+12 kcals, 19+1 vs. 19 +1 min, 2394+181 vs. 2341+167 steps, respectively). However, both the FB and JB on both wrists underestimated EE (13-28% p<0.001), ActMin (30-55%, p<0.001), and steps (24-29%, p<0.001) relative to criterion measures. CONCLUSIONS: Choice of wrist for PA tracker placement did not affect PA tracker accuracy during an 80-min, semi-structured protocol. Estimates from wrist-worn, commercial PA trackers should be interpreted with caution due to their underestimation of PA variables. Support from the Ball State University ASPiRE Student Research Grant, and CAST Internal Grant.

Accuracy of Wrist-Worn Activity Monitors during Wheelchair Use and Arm Ergometry

Recently, the use of wrist worn activity monitoring devices using tri-axial accelerometers to estimate step counts has become widespread in the general population. PURPOSE: To evaluate whether common, commercially available fitness monitoring devices can accurately estimate wheelchair stroke counts and arm ergometer revolutions. METHODS: 30 able-bodied wore three commercially available activity trackers using tri-axial accelerometers on the right wrist in random order. Participants propelled a wheelchair on a roller treadmill at three separate frequencies (30, 45 and 60 strokes per minute), 3 minutes each. They also propelled the wheelchair on the rollers at pre-determined varied frequencies, ranging from 30 bpm to 80 bpm for 2 minutes. Each frequency was performed 3 times in random order. The participants were shown a video recording of a metronome at each of the frequencies to maintain the prescribed frequencies for each task. Participants also freely wheeled through an obstacle course twice. Two experimenters used tally counters to count actual wheelchair strokes. A second group of subjects performed an arm ergometry task at three different frequencies (40, 60 and 80 cycles per minute) for three minutes each in randomized order. RESULTS: Mean (SD) percentage error for 30, 45 and random stroke frequencies were ≥20 (11)% for all monitors. At 60 rpm, mean percent error was 12 (13), 12 (11), 5 (5)% for monitors A, B and C, respectively. For obstacle course strokes, intraclass correlation coefficients (95% CI) were 0.888 (.767-.946), 0.853 (.693-.930), 0.917 (.828-.961) and Lin’s concordance coefficient (95% CI) were 0.792 (0.655-0.879), 0.740 (0.531-0.864), 0.846 (0.715-0.919) for monitors A, B and C, respectively. For arm ergometry, standard error of the measurement varied widely for each monitor across all frequencies from 20, 33 and 261 strokes for monitors A, B and C, respectively at 40 rpm; 49, 190 and 8 strokes at 60 rpm; to 268, 267 and 107 strokes at 80 rpm. CONCLUSION: At low wheel chair stroke frequencies, and at low and high ergometer cycling frequencies, commercially available wrist worn fitness monitors perform rather poorly in estimating counts. Activity monitors specific to activities commonly performed by persons with lower limb paralysis are necessary to accurately estimate their activity.

Objective Assessment Of Resistance Training Exercises Using A Wrist-worn Tri-axial Accelerometer

The 2008 Physical Activity Guidelines for Americans recommend that all adults perform muscle-strengthening exercises to work all of the major muscle groups of the body on at least two days per week, in addition to aerobic activity. Studies using objective methods of monitoring physical activity have focused primarily on the assessment of aerobic activity. To date, a method to objectively classify resistance training exercises has not been developed using a wrist-worn activity monitor. PURPOSE: The purpose of this study was to examine the use of a wrist-worn, tri-axial accelerometer-based activity monitor (ActiGraph GT3X+) for classifying upper- and lower-body dumbbell resistance training exercises. METHODS: Sixty participants performed 12 different dynamic dumbbell exercises. The exercises included: bench press, shoulder press, biceps curls, upright rows, lateral raises, triceps extensions, triceps kickbacks, bent-over row (standing), bent-over row (kneeling), squats, walking lunges, and calf raises. Algorithms for classifying the exercises were developed using two different METHODS: support vector machine (SVM) and cosine similarity (CS). Confusion matrices were developed for each method and inter-method reliability was assessed using Cohen’s kappa. RESULTS: The results indicated that SVM and CS accurately classified the 12 different resistance training exercises 78% and 85% of the time, respectively. Both methods struggled to correctly differentiate bench press vs shoulder press and squat vs walking lunges. If those exercises were excluded, the accuracy of SVM and CS increased to 85% and 93%, respectively. CONCLUSION: This study demonstrated that individual resistance training exercises can be classified using a single activity monitor worn on the wrist. Future studies using larger sample sizes and additional data channels may be able to improve on the precision of the classification of resistance training exercises.

Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill

Accuracy Of Wrist-worn Activity Monitors At Three Walking Speeds On The Treadmill