Accuracy of Heart Rate and Energy Expenditure Estimations of Wrist-Worn and Arm-Worn Apple Watches

Background: The purpose of this study was to examine the accuracy of heart rate (HR) and energy expenditure (EE) estimated by the Apple Watch Series 1 worn both on the wrist and the upper arm. Methods: Thirty healthy, young adults (15 females) wore the two monitors while participating in a maximal exercise test. Criterion measures were obtained from the Parvo Medics TrueOne 2400 Metabolic Cart and an electrocardiograph. Results: The HR estimations of the arm-worn (AW) Apple Watch had the highest agreement with the electrocardiogram, with mean absolute percent error (MAPE) of <2.5% for the entire sample, for males, and for females, at all exercise intensities. The HR estimations of the wrist-worn Apple Watch had MAPEs ranging from 3.61% (females at very light intensity) to 14.97% (males at very vigorous intensity). When estimating EE for total exercise bout in the entire sample, the arm-worn Apple Watch overestimated EE, with a MAPE of 39.63%, whereas the wrist-worn underestimated EE, with a MAPE of 32.28%. Both the arm- and wrist-worn overestimated EE for females and underestimated EE for males. Conclusion: Wearing the Apple Watch Series 1 on the upper arm versus the wrist improves the MAPE for HR estimations, but does not improve MAPE for the EE calculations when compared to a criterion measure.

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Accelerometry combined with heart rate telemetry in the assessment of total energy expenditure

British Journal Of Nutrition ◽

10.1079/bjn20051527 ◽

2006 ◽

Vol 95 (3) ◽

pp. 631-639 ◽

Cited By ~ 22

Author(s):

H. Patrik Johansson ◽

Lena Rossander-Hulthén ◽

Frode Slinde ◽

Björn Ekblom

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Total Energy ◽

Total Energy Expenditure ◽

New Method ◽

Criterion Measure ◽

Analysis Model ◽

Free Living ◽

Advantages And Disadvantages ◽

Method Analysis

The aim of the present study was: (1) to develop a new method for total energy expenditure (TEE) assessment, using accelerometry (ACC) and heart rate (HR) telemetry in combination; (2) to validate the new method against the criterion measure (DLW) and to compare with two of the most common methods, FLEX-HR and ACC alone. In the first part of the study VO2, HR and ACC counts were measured in twenty-seven subjects during walking and running on a treadmill. Considering the advantages and disadvantages of the HR and ACC methods an analysis model was developed, using ACC at intensities of low and medium levels and HR at higher intensities. During periods of inactivity, RMR is used. A formula for determining TEE from ACC, HR and RMR was developed: TEE =1·1×(EQHR×TTHR+EQACC1×TTACC1+EQACC2×TTACC2+RMR×TTRMR). In the validation part of the study a sub-sample of eight subjects wore an accelerometer, HR was logged and TEE was measured for 14d with the DLW method. Analysis of the Bland–Altman plots with 95% CI indicates that there are no significant differences in TEE estimated with HR–ACC and ACC alone compared with TEE measured with DLW. It is concluded that the HR–ACC combination as well as ACC alone has potential as a method for assessment of TEE during free-living activities as compared with DLW

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Validity of wrist-worn consumer products to measure heart rate and energy expenditure

Digital Health ◽

10.1177/2055207618770322 ◽

2018 ◽

Vol 4 ◽

pp. 205520761877032 ◽

Cited By ~ 20

Author(s):

Robert S. Thiebaud ◽

Merrill D. Funk ◽

Jacelyn C. Patton ◽

Brook L. Massey ◽

Terri E. Shay ◽

...

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Energy Expenditure ◽

Consumer Products ◽

Mean Absolute Percentage Error ◽

Percentage Error ◽

Absolute Percentage Error ◽

Metabolic Cart ◽

General Exercise ◽

Measure Heart Rate

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.

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A New Equation to Estimate Energy Expenditure Using Heart Rate in Children

Sustainability ◽

10.3390/su13095092 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5092

Author(s):

Mi-Hyun Lee ◽

Jeong-Hui Park ◽

Myong-Won Seo ◽

Seoung-Ki Kang ◽

Jung-Min Lee

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Cross Validation ◽

Explanatory Power ◽

Potential Method ◽

Validation Group ◽

Heart Rate Monitors ◽

Vigorous Intensity ◽

New Equation ◽

The Right

The first aim of this study was to develop equations to predict physical activity energy expenditure (PAEE) for children utilizing heart rate monitors (HRM) and vector magnitudes (VM) from accelerometers. The second aim was to cross-validate the developed PAEE prediction equations and compare the equations to the pre-existing accelerometer-based PAEE equation (i.e., Trost). Seventy-five students in elementary school (from 10 to 13 years old) were classified into an equation calibration group (N = 50, 33 boys and 17 girls) and a cross-validation group (N = 25, 20 boys and 5 girls). Participants simultaneously wore a portable indirect calorimeter (Cosmed’s K4b2), a heart rate monitor on the chest, and an accelerometer on the right side of the waist. Then, the participants performed a series of various intensity activities. The energy expenditure (EE) measured by K4b2 was set as the dependent variable. Multiple regression analysis was performed to derive the heart rate and accelerometer-based equations. The heart-rate-based EE equation had an explanatory power of adj. R2 = 0.814 and the accelerometer-based EE equation had an explanatory power of adj. R2 = 0.802. The VM-based EE indicated high mean absolute percent errors (MAPE) at light, moderate, and vigorous intensity. The heart-rate-based EE was included in the range of equivalence limit in all activities, but the VM and pre-existing equation showed some overestimation beyond the equivalence range. The agreement errors between the criterion EE and the estimated EE were lower in the heart-rate-based equation than the accelerometer-based equations (i.e., VM and Trost). The approach with the heart-rate-based EE equation demonstrated higher accuracy than the accelerometer-based EE equations. The results of the current study indicate that the heart-rate-based PAEE equation can be a potential method for estimating children’s PAEE.

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Correction of estimation bias of predictive equations of energy expenditure based on wrist/waist-mounted accelerometers

PeerJ ◽

10.7717/peerj.7973 ◽

2019 ◽

Vol 7 ◽

pp. e7973 ◽

Cited By ~ 1

Author(s):

Chin-Shan Ho ◽

Chun-Hao Chang ◽

Kuo-Chuan Lin ◽

Chi-Chang Huang ◽

Yi-Ju Hsu

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Athletic Training ◽

Empirical Equation ◽

Inertial Sensors ◽

Estimation Bias ◽

Criterion Measure ◽

Training Process ◽

Compensation Factor ◽

Wearable Inertial Sensors

Background Using wearable inertial sensors to accurately estimate energy expenditure (EE) during an athletic training process is important. Due to the characteristics of inertial sensors, however, the positions in which they are worn can produce signals of different natures. To understand and solve this issue, this study used the heart rate reserve (HRR) as a compensation factor to modify the traditional empirical equation of the accelerometer EE sensor and examine the possibility of improving the estimation of energy expenditure for sensors worn in different positions. Methods Indirect calorimetry was used as the criterion measure (CM) to measure the EE of 90 healthy adults on a treadmill (five speeds: 4.8, 6.4, 8.0, 9.7, and 11.3 km/h). The measurement was simultaneously performed with the ActiGraph GT9X-Link (placed on the wrist and waist) with the Polar H10 Heart Rate Monitor. Results At the same exercise intensity, the EE measurements of the GT9X on the wrist and waist had significant differences from those of the CM (p < 0.05). By using multiple regression analysis—utilizing values from vector magnitudes (VM), body weight (BW) and HRR parameters—accuracy of EE estimation was greatly improved compared to traditional equation. Modified models explained a greater proportion of variance (R2) (wrist: 0.802; waist: 0.805) and demonstrated a good ICC (wrist: 0.863, waist: 0.889) compared to Freedson’s VM3 Combination equation (R2: wrist: 0.384, waist: 0.783; ICC: wrist: 0.073, waist: 0.868). Conclusions The EE estimation equation combining the VM of accelerometer measurements, BW and HRR greatly enhanced the accuracy of EE estimation based on data from accelerometers worn in different positions, particularly from those on the wrist.

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Accuracy and precision of energy expenditure, heart rate, and steps measured by combined-sensing Fitbits against reference measures: Systematic Review and Meta-Analyses (Preprint)

10.2196/preprints.35626 ◽

2021 ◽

Author(s):

Guillaume Chevance ◽

Natalie M. Golaszewski ◽

Elizabeth Tipton ◽

Eric B. Hekler ◽

Matthew Buman ◽

...

Keyword(s):

Physical Activity ◽

Systematic Review ◽

Heart Rate ◽

Energy Expenditure ◽

Low Cost ◽

Objective Assessment ◽

Criterion Measure ◽

Battery Capacity ◽

Adults And Children ◽

Meta Analyses

BACKGROUND Although it is widely recognized that physical activity is an important determinant of health there is considerable challenge in assessing this complex behavior. Tools for the objective assessment of the frequency, intensity, and duration of physical activity in adults and children have largely been developed for short-term use within research or public health surveillance environments. However, recent advances in microtechnology, data processing, wireless communication, and battery capacity have resulted in the proliferation of low-cost, non-invasive, wrist-worn devices with attractive designs that can easily be used by consumers to track their physical activity over long periods of time. OBJECTIVE The purpose of the present systematic-review and meta-analyses is to examine, quantify, and report on the current state of evidence for the analytical validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits. METHODS Systematic-review and Bland-Altman meta-analyses of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate and steps. RESULTS A total of 52 studies were included in the systematic review. Among them, 41 were included in the meta-analyses, representing 203 individual comparisons between Fitbit devices and a criterion measure (i.e., 117 for heart rate, 49 for energy expenditure, and 37 for steps). Overall, the majority of authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared to criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of, respectively, -2,99 bpm, -2,77 kcal/min and -3,11 steps/min of the Fitbit compared to criterion measure (results obtained after removing high risk of bias studies). CONCLUSIONS Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by quality of study, age of the participants, type of activities, and by model of Fitbit. The qualitative conclusions of the majority of studies aligned with the results of meta-analyses. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. Information about energy expenditure however are likely to be too unprecise.

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Heart Rate Responses and Exercise Intensity During A Prolonged 4-Hour Individual Cycling Race among Japanese Recreational Cyclists

Sports ◽

10.3390/sports7050109 ◽

2019 ◽

Vol 7 (5) ◽

pp. 109

Author(s):

Takashi Nakagata ◽

Shinichiro Murade ◽

Shizuo Katamoto ◽

Hisashi Naito

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Exercise Intensity ◽

Energy Cost ◽

Fixed Time ◽

Moderate Intensity ◽

Healthy Male ◽

Professional Cyclist ◽

Endurance Cycling ◽

Vigorous Intensity

Heart rate (HR) during different endurance cycling races and events are investigated for professional cyclist, however, enduro races to compete for total laps and distance covered within a fixed time using a circuit course has not yet been investigated. This study examined the heart rate (HR) and exercise intensity during an enduro cycling race. Ten male Japanese amateur cyclists performed cycling individually for at least 2 consecutive hours. HR was measured using an HR monitor during the race, and we estimated the energy expenditure (EE) during the race using the HR–VO2 relationship in advance. Exercise intensities were defined as percentages of HRmax based on ACSM exercise guideline as follows: moderate intensity, 64–76% HRmax; vigorous intensity, 77–95% HRmax. The HR during the race was 158.9 ± 10.6 bpm (86.4 ± 2.2% HRmax), and exercise intensity is categorized as vigorous intensity. The EE during the race using HR–VO2 relationship were 12.9 ± 1.2 kcal/kg/hr, which would require a large energy expenditure (EE) during the race. However, energy cost was 0.36 ± 0.04 kcal/kg/km regardless of total distance. The findings indicate that enduro cycling racing is categorized as vigorous intensity (>77% HRmax) for healthy male recreational cyclists though, cycling is an efficient form of transportation.

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Heart Rate Equivalency of the Fitbit Charge HR During Continuous Aerobic Exercise

Journal for the Measurement of Physical Behaviour ◽

10.1123/jmpb.2018-0009 ◽

2018 ◽

Vol 1 (3) ◽

pp. 122-129

Author(s):

Joseph M. Stock ◽

Ryan T. Pohlig ◽

Matthew J. Botieri ◽

David G. Edwards ◽

Gregory M. Dominick

Keyword(s):

Heart Rate ◽

Aerobic Exercise ◽

Exercise Bout ◽

Total Activity ◽

Equivalence Testing ◽

Warm Up ◽

Activity Monitors ◽

Continuous Exercise ◽

Exercise Onset ◽

Absolute Percent Error

Purpose: Consumer-grade wrist-worn activity monitors frequently include photoplethysmography (PPG) sensors for estimating heart rate (HR). The Fitbit Charge HR is marketed specifically for tracking fitness; therefore, HR accuracy is critical, especially during exercise. This study examined HR equivalency of the Fitbit Charge HR during continuous aerobic exercise. Method: Participants (N = 19) concurrently wore a Polar H1 and Fitbit Charge HR during a measurement visit that included seated rest (5 minutes), warm-up (5 minutes), continuous treadmill exercise (30 minutes), and cool-down (5–10 minutes). Mean HR differences were examined by protocol phase, total activity (i.e., warm-up, exercise, and cool-down combined), and the first, middle, and last 5 minutes of continuous exercise. Mean absolute percent error (MAPE), Bland-Altman plots, and 95% equivalence testing explained overall and individual HR agreement between devices. Results: The Fitbit Charge HR significantly underestimated HR for all measurement phases (all p ≤ .01) except cool-down (p > .33). HR agreement was notably weaker during warm-up (r = 0.66, d = 0.57) and differences were greatest for the first 5 minutes compared to the middle and end of exercise (6.94±2.16 beats per minute [bpm] vs. 1.76±0.59 bpm, and 1.74±0.58 bpm), F = 4.87, p = .04). Mean exercise HRs were equivalent between devices (±2.69 bpm, 95% CI: 1.41–3.97 bpm); MAPE was 1.96%. Conclusion: The Fitbit Charge HR is relatively accurate for measuring HR during continuous aerobic exercise. Whereas the accuracy of PPG-based HR appears limited at exercise onset, agreement improves throughout the exercise bout and HR differences are negligible.

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Assessing Heart Rate Using Consumer Technology Association Standards

Technologies ◽

10.3390/technologies9030046 ◽

2021 ◽

Vol 9 (3) ◽

pp. 46

Author(s):

Joel D. Reece ◽

Jennifer A. Bunn ◽

Minsoo Choi ◽

James W. Navalta

Keyword(s):

Heart Rate ◽

Activities Of Daily Living ◽

Daily Living ◽

Sedentary Lifestyle ◽

Wearable Technology ◽

Wearable Devices ◽

Criterion Measure ◽

Mean Absolute Percent Error ◽

Absolute Percent Error ◽

Acceptable Agreement

It is difficult for developers, researchers, and consumers to compare results among emerging wearable technology without using a uniform set of standards. This study evaluated the accuracy of commercially available wearable technology heart rate (HR) monitors using the Consumer Technology Association (CTA) standards. Participants (N = 23) simultaneously wore a Polar chest strap (criterion measure), Jabra Elite earbuds, Scosche Rhythm 24 armband, Apple Watch 4, and Garmin Forerunner 735 XT during sitting, activities of daily living, walking, jogging, running, and cycling, totaling 57 min of monitored activity. The Apple Watch mean bias was within ±1 bpm, and mean absolute percent error (MAPE) was <3% in all six conditions. Garmin underestimated HR in all conditions, except cycling and MAPE was >10% during sedentary, lifestyle, walk-jog, and running. The Jabra mean bias was within ±5 bpm for each condition, and MAPE exceeded 10% for walk-jog. The Scosche mean bias was within ±1 bpm and MAPE was <5% for all conditions. In conclusion, only the Apple Watch Series 4 and the Scosche Rhythm 24 displayed acceptable agreement across all conditions. By employing CTA standards, future developers, researchers, and consumers will be able to make true comparisons of accuracy among wearable devices.

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Children’s Choice of Wii Sports Games and Energy Expenditure

Californian Journal of Health Promotion ◽

10.32398/cjhp.v8i1.2028 ◽

2010 ◽

Vol 8 (1) ◽

pp. 32-39 ◽

Cited By ~ 2

Author(s):

Bryan L. Haddock ◽

Shannon R. Siegel ◽

Linda D. Wilkin

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Media Use ◽

Free Access ◽

Middle School Children ◽

Kaiser Family Foundation ◽

Metabolic Cart ◽

Prevalence Of Obesity ◽

Playing Time ◽

Typical Day

According to a study from the Kaiser Family Foundation, total media use among all 8- to 18-year-olds was seven hours and thirty-eight minutes on a typical day (Rideout, Foehr, & Roberts, 2010). The large amount of time spent in media use has been implicated as one of the causes of the increased prevalence of obesity. The purposes of this study were to measure: 1) the total level of energy expenditure (EE) while middle school children played the Wii Sports games when given free access to all of the games, 2) the length of time they played each game, and 3) the differences in EE between games. Thirty-seven children (15 males and 22 females) with an average age of 12.4 ± 1.0 years participated in this study. Each had experience with Wii Sports. Participants were given 20 minutes to play any of the Wii Sports games they desired while their expired gases were captured by a calibrated portable metabolic cart. Heart rate was monitored with a Polar® heart rate monitor. Baseball and bowling were the most popular games. Energy expenditure was greater after playing the Wii for each game except golf. The average EE during the game playing time was 2.8 ± 0.9 kcal/min, compared to 1.4 ± 0.4 kcal/min while at rest prior to testing. Playing Wii Sports can moderately increase the EE of children over rest.

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The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

10.2196/preprints.23681 ◽

2020 ◽

Author(s):

Anis Davoudi ◽

Mamoun T. Mardini ◽

Dave Nelson ◽

Fahd Albinali ◽

Sanjay Ranka ◽

...

Keyword(s):

Physical Activity ◽

Older Adults ◽

Energy Expenditure ◽

Activity Recognition ◽

Recognition Accuracy ◽

Body Position ◽

Computer Work ◽

Accelerometer Data ◽

Upper Arm ◽

Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

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