Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors
Open accessAbstract
Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.
AI snapshot
Written from the paper’s full text.
TL;DR
Consumer multi-sensor activity monitors varied widely in energy expenditure accuracy; Apple Watch and Fitbit Charge HR gave closer free-living estimates than Microsoft Band and Jawbone UP24, but all consumer devices underestimated daily energy expenditure and none were equivalent to the reference. The authors conclude independent quality standards or accuracy ratings are needed.
Key findings
- In free-living conditions, Apple Watch and Fitbit Charge HR underestimated daily energy expenditure by -394 ± 970 kcal/d and -405 ± 944 kcal/d, respectively, while Microsoft Band and Jawbone UP24 substantially underestimated it (-1128 kcal/d and -998 kcal/d, respectively, in the abstract; results report -1123 ± 1235 and -998 ± 1153 kcal/d; P<0.01).
- None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure; only the research-grade Sensewear/BodyMedia device fell within the proposed ±10% equivalence zone.
- In laboratory assessments, Apple Watch had the lowest mean absolute percent error among consumer devices (27% across activities; Actiheart 20%), while Fitbit Charge HR was less consistent for discrete activities but produced similar free-living estimates to Apple Watch.
- For all devices, there was a tendency for negative bias with greater daily energy expenditure, and more sensors did not guarantee better accuracy (e.g., Microsoft Band underpredicted despite multiple sensors).
Methods
- Thirty adults (15 men, 15 women; mean ± SD age 27 ± 6 y, BMI 23.4 ± 2.5 kg/m²) wore Apple Watch, Microsoft Band, Fitbit Charge HR, Jawbone UP24, BodyMedia Core, and individually calibrated Actiheart.
- Laboratory validation used two blocks—24-min activities of daily living (typing, dishwasher, sweeping, stairs) and 64-min exercise (walk, loaded walk, cycle, jog)—compared with COSMED K4b2 indirect calorimetry; free-living validation used a minimum 36 h wear to capture one full calendar day, with individually calibrated Actiheart as criterion.
- Analysis included Bland-Altman bias and 95% limits of agreement, mean signed/absolute error, repeated measures ANOVA with Holm-Bonferroni adjustment, and equivalence testing against ±10% of the criterion mean.
Limitations
- Stated: the sample was relatively young, healthy, and active; devices were assessed “out of box,” so additional calibration with prolonged wear may improve accuracy; the importance of specific exercise modes is unknown; possible lag in device detection could not be conclusively established; longer activity durations, other target groups, longer free-living periods, and doubly labelled water as a criterion are needed.
- Stated: manufacturers do not release algorithms, limiting explanation of results, and commercial devices did not readily allow raw energy expenditure data export.
- Evident: the small sample (n=30) and contrived laboratory activities may not represent real-life activity distribution; the free-living criterion was Actiheart rather than doubly labelled water, which the paper acknowledges as ideal.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 36 cited sources checked · 0 supported · 0 flagged
- Findingown data
The Jawbone UP24 substantially underestimated free-living energy expenditure by 998 kcal/d (P<0.01).
“and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01).”
Quote in the paperSubstantial claim - Findingown data
For all devices, there was a tendency for negative bias with greater daily energy expenditure.
“For all devices, there was a tendency for negative bias with greater daily energy expenditure.”
Quote in the paperSubstantial claim - Findingown data
The Microsoft Band underestimated most laboratory activities.
“The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128”
Quote in the paperSubstantial claim - Findingown data
The Microsoft Band substantially underestimated free-living energy expenditure by 1128 kcal/d (P<0.01).
“The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128”
Quote in the paperSubstantial claim - Findingown data
The Apple Watch underestimated free-living energy expenditure by 394 kcal/d (P<0.01).
“Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01).”
Quote in the paperSubstantial claim
- Findingown data
The Fitbit Charge HR was less consistent at measurement of discrete laboratory activities.
“The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch.”
Quote in the paperSubstantial claim - Findingown data
The Fitbit Charge HR produced similar free-living energy expenditure estimates to the Apple Watch.
“The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch.”
- Also stated in: § Free living energy expenditure · plain
“Each individual device is significantly different p < 0.01 from every other device, except for the comparisons between Microsoft vs. Jawbone, Fitbit vs. Apple and Bodymedia vs. Actiheart.”
Quote in the paperSubstantial claim - Also stated in: § Free living energy expenditure · plain
- Findingown data
During discrete laboratory activities, the Apple Watch performed similarly to criterion indirect calorimetry measures.
“During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures.”
- Also stated in: § Laboratory validation · plain
“Absolute errors of estimation for the activities considered separately (the mean of the absolute error of the individual activities) was lowest amongst the consumer monitoring devices for the Apple Watch (27 ± 10%) and this was not enormously different to that recorded for the Actiheart research device (20 ± 7%).”
Quote in the paperSubstantial claim - Also stated in: § Laboratory validation · plain
- Findingown data
The Jawbone UP24 underestimated most laboratory activities.
“and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01).”
Quote in the paperSubstantial claim - Findingown data
The Fitbit Charge HR underestimated free-living energy expenditure by 405 kcal/d (P<0.01).
“Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01).”
Quote in the paperSubstantial claim - Findingown data
None of the consumer devices were equivalent to the reference method for daily energy expenditure.
“None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure.”
- Also stated in: § Free living energy expenditure · plain
“Pairwise comparisons indicated that all of the consumer devices produced significantly different estimates from the research grade devices (which were not different from each other).”
Quote in the paperSubstantial claim - Also stated in: § Free living energy expenditure · plain
- Findingown data
In some but not all cases, consumer monitor estimates were close to criterion measurements.
“No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements.”
Quote in the paperSubstantial claim - Findingown data
No consumer monitors performed as well as the research-grade devices.
“No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements.”
Quote in the paperSubstantial claim - Findingown data
The Fitbit Charge HR showed less consistent prediction of the energy expenditure of discrete specific activities in the laboratory assessments.
“Of the other devices, less consistent prediction of the energy expenditure of discrete specific activities was observed from the Fitbit Charge HR in the laboratory assessments, although the free-living daily expenditure estimates from this device were similar to the Apple Watch.”
Quote in the paperSubstantial claim - Findingown data
The mean absolute error of estimation for activities considered separately was lowest among consumer devices for the Apple Watch (27 ± 10%).
“Absolute errors of estimation for the activities considered separately (the mean of the absolute error of the individual activities) was lowest amongst the consumer monitoring devices for the Apple Watch (27 ± 10%) and this was not enormously different to that recorded for the Actiheart research device (20 ± 7%).”
Quote in the paperSubstantial claim - Findingown data
The Apple Watch was the closest of the consumer devices in free-living conditions to the Actiheart for assessing daily energy expenditure.
“The Apple Watch was also the closest of the consumer devices in free-living conditions to the Actiheart™ for assessing daily energy expenditure.”
Quote in the paperSubstantial claim - Findingown data
The Sensewear device was the only monitor deemed equivalent to the Actiheart device for daily energy expenditure estimates.
“Fig 5 demonstrates that the Sensewear device was the only monitor deemed equivalent to the Actiheart™ device for daily energy expenditure estimates, with none of the consumer devices lying within the proposed equivalence zone of ±10% of the criterion mean.”
Quote in the paperSubstantial claim - Findingown data
The Apple Watch had a mean absolute percent error relative to the Actiheart device of 15 ± 10%.
“Both the Apple and Fitbit devices produced considerably lower error (15 ± 10% and 16 ± 8%, respectively).”
Quote in the paperSubstantial claim - Findingown data
Across all assessments, the Apple Watch displayed the tightest limits of agreement and mean absolute error of the consumer devices in the laboratory assessments.
“Across all assessments, strong overall performance was observed for the Apple Watch, which displayed the tightest limits of agreement and mean absolute error of the consumer devices in the laboratory assessments.”
Quote in the paperSubstantial claim - Findingown data
For laboratory activities, the Jawbone UP24 had an absolute bias of -0.9 kcal/min with 95% limits of agreement of ±5.4 kcal/min.
“For the commercially available monitors, the absolute bias ± 95% LoA values were -1.8 ± 3.9, -0.2 ± 3.4, 0.3 ± 4.6 and -0.9 ± 5.4 kcal/min for the Microsoft Band, Apple Watch, Fitbit Charge HR and Jawbone UP24, respectively.”
Quote in the paperNot a substantial claim - Findingown data
For laboratory activities, the Apple Watch had an absolute bias of -0.2 kcal/min with 95% limits of agreement of ±3.4 kcal/min.
“For the commercially available monitors, the absolute bias ± 95% LoA values were -1.8 ± 3.9, -0.2 ± 3.4, 0.3 ± 4.6 and -0.9 ± 5.4 kcal/min for the Microsoft Band, Apple Watch, Fitbit Charge HR and Jawbone UP24, respectively.”
Quote in the paperNot a substantial claim - Findingown data
There was consistent under prediction from the Microsoft Band both inside and outside the laboratory.
“In spite of its many sensors, there was consistent under prediction from the Microsoft Band both inside and outside the laboratory.”
Quote in the paperNot a substantial claim - Findingown data
Both the Fitbit Charge HR and Apple Watch displayed similar 95% limits of agreement for free-living energy expenditure.
“Both the Fitbit Charge HR (-405 ± 944 kcal/d) and Apple Watch (-394 ± 970 kcal/d) displayed similar 95% limits of agreement.”
Quote in the paperSubstantial claim - Findingown data
The Fitbit device had a mean absolute percent error relative to the Actiheart device of 16 ± 8%.
“Both the Apple and Fitbit devices produced considerably lower error (15 ± 10% and 16 ± 8%, respectively).”
Quote in the paperSubstantial claim - Findingown data
None of the consumer devices lay within the proposed equivalence zone of ±10% of the criterion mean for daily energy expenditure estimates.
“Fig 5 demonstrates that the Sensewear device was the only monitor deemed equivalent to the Actiheart™ device for daily energy expenditure estimates, with none of the consumer devices lying within the proposed equivalence zone of ±10% of the criterion mean.”
Quote in the paperSubstantial claim - Findingown data
For consumer devices, there was a tendency for over prediction of higher intensity activity relative to more frequent under prediction of lower intensity activities in the laboratory.
“Visual inspection of the plots for the consumer devices highlights some tendency for over prediction of higher intensity activity relative to more frequent under prediction of lower intensity activities.”
Quote in the paperNot a substantial claim - Findingown data
For research-grade monitors, the Bodymedia Armband had an absolute bias of 0.6 kcal/min with 95% limits of agreement of ±3.8 kcal/min.
“Of the research grade wearable monitors, absolute bias ± 95% LoA values were 0.6 ± 3.8 kcal/min for the Bodymedia Armband with the smallest limits of agreement of any device observed for the Actiheart (-0.6 ± 2.5 kcal/min).”
Quote in the paperNot a substantial claim - Findingown data
The Jawbone device had a mean absolute percent error relative to the Actiheart device of 30 ± 11%.
“The mean absolute percent error relative to the Actiheart device was greatest in the Microsoft Band (34 ± 10%), followed by a similar magnitude of error for the Jawbone device (30 ± 11%).”
Quote in the paperNot a substantial claim - Findingown data
The Apple Watch mean absolute error (27 ± 10%) was not enormously different from the Actiheart research device (20 ± 7%).
“Absolute errors of estimation for the activities considered separately (the mean of the absolute error of the individual activities) was lowest amongst the consumer monitoring devices for the Apple Watch (27 ± 10%) and this was not enormously different to that recorded for the Actiheart research device (20 ± 7%).”
Quote in the paperSubstantial claim - Findingown data
For laboratory activities, the Microsoft Band had an absolute bias of -1.8 kcal/min with 95% limits of agreement of ±3.9 kcal/min.
“For the commercially available monitors, the absolute bias ± 95% LoA values were -1.8 ± 3.9, -0.2 ± 3.4, 0.3 ± 4.6 and -0.9 ± 5.4 kcal/min for the Microsoft Band, Apple Watch, Fitbit Charge HR and Jawbone UP24, respectively.”
Quote in the paperSubstantial claim - Findingown data
Visual inspection of Bland-Altman plots shows increasing negative bias with increasing daily energy expenditure for all consumer devices.
“Visual inspection of the Bland-Altman plots shows increasing negative bias with increasing daily energy expenditure for all of the consumer devices, with this effect particularly pronounced for the Microsoft Band and Jawbone UP24.”
Quote in the paperNot a substantial claim - Findingown data
The Microsoft Band displayed much greater bias and wider limits of agreement (-1123 ± 1235 kcal/d) than the criterion for free-living energy expenditure.
“The Microsoft Band and Jawbone UP24 devices displayed much greater bias and wider limits of agreement of -1123 ± 1235 kcal/d and -998 ± 1153 kcal/d, respectively.”
Quote in the paperSubstantial claim - Findingown data
The Fitbit Charge HR's free-living daily expenditure estimates were similar to the Apple Watch.
“Of the other devices, less consistent prediction of the energy expenditure of discrete specific activities was observed from the Fitbit Charge HR in the laboratory assessments, although the free-living daily expenditure estimates from this device were similar to the Apple Watch.”
Quote in the paperSubstantial claim - Findingown data
The Jawbone UP24 displayed much greater bias and wider limits of agreement (-998 ± 1153 kcal/d) than the criterion for free-living energy expenditure.
“The Microsoft Band and Jawbone UP24 devices displayed much greater bias and wider limits of agreement of -1123 ± 1235 kcal/d and -998 ± 1153 kcal/d, respectively.”
Quote in the paperSubstantial claim - Findingreasoning
The pattern of error for the commercial devices indicated a greater degree of underestimation relative to the Actiheart device with greater daily energy expenditure.
“The pattern of error for the commercial devices indicated a greater degree of underestimation relative to the Actiheart™ device with greater daily energy expenditure.”
Quote in the paperSubstantial claim - Findingown data
The mean absolute percent error relative to the Actiheart device was greatest in the Microsoft Band (34 ± 10%).
“The mean absolute percent error relative to the Actiheart device was greatest in the Microsoft Band (34 ± 10%), followed by a similar magnitude of error for the Jawbone device (30 ± 11%).”
Quote in the paperNot a substantial claim - Findingown data
For laboratory activities, the Fitbit Charge HR had an absolute bias of 0.3 kcal/min with 95% limits of agreement of ±4.6 kcal/min.
“For the commercially available monitors, the absolute bias ± 95% LoA values were -1.8 ± 3.9, -0.2 ± 3.4, 0.3 ± 4.6 and -0.9 ± 5.4 kcal/min for the Microsoft Band, Apple Watch, Fitbit Charge HR and Jawbone UP24, respectively.”
Quote in the paperNot a substantial claim - Findingown data
The difference between the group mean from the device predicting the lowest and highest energy expenditure (Microsoft Band versus Apple Watch) was >750 kcal/d.
“The difference between the group mean from the device predicting the lowest and highest energy expenditure (Microsoft Band versus Apple Watch) was >750 kcal/d which is clearly a meaningful difference.”
Quote in the paperSubstantial claim - Findingown data
The two devices that provided a metric for resting energy expenditure reported different values (Apple: 2181 ± 414 kcal/d and Jawbone 1494 ± 230 kcal/d).
“This discrepancy for similar outcomes was also apparent for the two devices that provided a metric for resting energy expenditure (Apple: 2181 ± 414 kcal/d and Jawbone 1494 ± 230 kcal/d).”
Quote in the paperNot a substantial claim - Findingown data
Our data illustrate substantial individual variability of estimates.
“This highlights that medical and other practitioners should be wary of attempting to use devices of this nature to provide guidance on energy intake or balance without prior knowledge of their accuracy, particularly as our data illustrate substantial individual variability of estimates.”
Quote in the paperSubstantial claim - Findingown data
Estimates of active time ranged from 818 ± 88 min (Apple, Total Active Time) to 70 ± 59 min (Fitbit, Active Minutes).
“Estimates ranged from 818 ± 88 min (Apple, “Total Active Time”) to 70 ± 59 min (Fitbit, “Active Minutes”) illustrating that individuals already attempting to establish physical activity status against a plethora of physical activity benchmarks [49] may easily be confused by such variable estimates for ostensibly similar outcomes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] D Thompson, 2009, Confusion and conflict in assessing the physical activity status of middle-aged men, PLoS One source
Not checked in the free run.
- Interpretationown data
Consumer devices are not equivalent for daily energy expenditure estimates.
“Clinicians and the public should not assume that all devices provide accurate daily energy expenditure estimates and certainly they are not equivalent.”
- Also stated in: § Conclusions · boosted
“However, this was not the case across all devices and certainly these consumer monitors do not all produce similar results (i.e., they are not equivalent).”
Quote in the paperSubstantial claim - Also stated in: § Conclusions · boosted
- Interpretationown data
The gap between consumer and research-grade devices was only modest in some cases.
“This study demonstrates that the gap between consumer and research-grade devices was only modest in some cases and industry-led technological innovation is clearly improving the accuracy of physical activity monitors that are available to consumers.”
Quote in the paperSubstantial claim - Interpretationown data
Not all consumer devices provide accurate daily energy expenditure estimates.
“Clinicians and the public should not assume that all devices provide accurate daily energy expenditure estimates and certainly they are not equivalent.”
Quote in the paperSubstantial claim - Interpretationown data
These sophisticated devices are yet to match the consistency of the best performing research device in measuring discrete activities.
“Our results demonstrate that these sophisticated devices are yet to match the consistency of the best performing research device in measuring discrete activities.”
Quote in the paperSubstantial claim - Interpretationreasoning
The contrived nature of laboratory-based assessments probably explains the discrepancy between laboratory and free-living results.
“As discussed below, this probably reflects the contrived nature of laboratory-based assessments, and a strength of this work is that we also obtained energy expenditure estimates during free-living.”
Quote in the paperSubstantial claim - Interpretationown data
Industry-led technological innovation is clearly improving the accuracy of physical activity monitors available to consumers.
“This study demonstrates that the gap between consumer and research-grade devices was only modest in some cases and industry-led technological innovation is clearly improving the accuracy of physical activity monitors that are available to consumers.”
Quote in the paperSubstantial claim - Interpretationown data
Performance was not consistently good across all devices.
“However, performance was not consistently good across all devices; sometimes in spite of the inclusion of a plethora of sensors.”
Quote in the paperSubstantial claim - Interpretationreasoning
Greater accuracy for individuals with lower energy expenditure is unsurprising.
“As a substantial contributor to total energy expenditure (particularly with lower levels of activity) will be RMR, which is a broadly predictable component of energy expenditure [42], the greater accuracy for individuals with lower energy expenditure is unsurprising.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Despite more marked error in estimating the expenditure of some specific activities, this does not have a detrimental effect upon the total expenditure estimates from the Fitbit Charge HR outside the laboratory.
“Therefore, in comparison with the Apple device, it appears that despite more marked error in estimating the expenditure of some specific activities, this does not have a detrimental effect upon the total expenditure estimates from this device outside the laboratory.”
Quote in the paperSubstantial claim - Interpretationreasoning
The measurement of physical activity per se is more variable than the estimation of total energy expenditure would suggest.
“Assuming relative similarity of resting energy expenditure estimates between devices, this variance in total energy expenditure estimates between devices suggests that the measurement of physical activity per se is more variable than the estimation of total energy expenditure would suggest.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Resting energy expenditure estimates are relatively similar between devices.
- Interpretationnone given
Devices with more sensors do not necessarily produce better energy expenditure estimates than simple previous-generation accelerometry-only devices.
“It is also clear that devices with more sensors do not necessarily produce better energy expenditure estimates than simple previous-generation accelerometry-only devices.”
Quote in the paperSubstantial claim - Interpretationown data
A greater number of sensors does not automatically guarantee more accurate energy expenditure estimation.
“Our data shows that when it comes to activity tracking, a greater number of sensors does not automatically guarantee more accurate energy expenditure estimation.”
Quote in the paperSubstantial claim - Interpretationown data
Some consumer multi-sensor devices such as the Apple Watch and Fitbit Charge HR provided reasonably good estimates of energy expenditure both in the laboratory and during free-living conditions.
“Some consumer multi-sensor devices such as the Apple Watch and Fitbit Charge HR provided reasonably good estimates of energy expenditure both in the laboratory and during free-living conditions.”
Quote in the paperSubstantial claim - Premisecitation
Laboratory validations commonly employ protocols comprising a variety of activities treated as a single block to represent overall monitor performance.
“Commonly, laboratory validations employ protocols comprising a variety of activities treated as a single block to represent “overall” monitor performance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [43] GJ Welk, 2012, Protocols for evaluating equivalency of accelerometry-based activity monitors, Med Sci Sports Exerc source
Not checked in the free run.
- Premisecitation
Murakami and colleagues (2016) have shown that estimates for the same devices assessed during a standardized day and free-living wear do not necessarily produce similar directions or magnitudes of error in the different settings.
“Indeed, Murakami and colleagues (2016), have shown that estimates for the same devices assessed during a standardized day and free-living wear do not necessarily produce similar directions or magnitudes of error in the different settings [18].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] H Murakami, 2016, Accuracy of Wearable Devices for Estimating Total Energy Expenditure: Comparison With Metabolic Chamber and Doubly Labeled Water Method, JAMA Intern Med
Not checked in the free run.
- Premisecitation
Laboratory protocols tend to overemphasise active elements of daily living, relative to the reported 52% sedentary time from 24 hour measured activity in young men.
“However, laboratory protocols tend to overemphasise active elements of daily living, relative to the reported 52% sedentary time from 24 hour measured activity in young men [44].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [44] D Thompson, 2013, Towards integrated physical activity profiling, PLoS One source
Not checked in the free run.
- Interpretationreasoning
The marked underestimation with increasing energy expenditure for certain monitors highlights ongoing discrepancies in the ability to translate inputs from sensors into estimates of physical activity expenditure.
“Indeed, the marked underestimation with increasing energy expenditure for certain monitors highlights there are ongoing discrepancies in the ability to translate inputs from sensors into estimates of physical activity expenditure.”
Quote in the paperSubstantial claim - Premisecitation
McCaig and colleagues (2016) demonstrated that the framing of a set bout of exercise (50 kcal vs 265 kcal) affects subsequent intake, with higher energy expenditure information providing greater licence to eat.
“These authors have demonstrated that the framing of a set bout of exercise (50 kcal vs 265 kcal) affects subsequent intake, with higher energy expenditure information providing greater “licence to eat” [45].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [45] DC McCaig, 2016, Licence to eat: Information on energy expended during exercise affects subsequent energy intake, Appetite source
Not checked in the free run.
- May combine two claims. 10
- Classified as probably not a substantial claim. 6
May combine two claims.
Findingown dataAll consumer devices produced significantly different free-living energy expenditure estimates from the research-grade devices, which were not different from each other.
“Pairwise comparisons indicated that all of the consumer devices produced significantly different estimates from the research grade devices (which were not different from each other).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe Actiheart had the smallest limits of agreement of any device observed, with an absolute bias of -0.6 kcal/min and 95% limits of agreement of ±2.5 kcal/min.
“Of the research grade wearable monitors, absolute bias ± 95% LoA values were 0.6 ± 3.8 kcal/min for the Bodymedia Armband with the smallest limits of agreement of any device observed for the Actiheart (-0.6 ± 2.5 kcal/min).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe research-grade Bodymedia device had similar limits of agreement to the Apple and Fitbit devices but lower mean bias (-66 ± 965 kcal/d) compared to individually-calibrated Actiheart.
“For reference, the research grade Bodymedia device had similar limits of agreement to the Apple and Fitbit devices but with lower mean bias when compared to individually-calibrated Actiheart™ (-66 ± 965 kcal/d).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataEach individual device was significantly different (p < 0.01) from every other device, except for the comparisons between Microsoft vs. Jawbone, Fitbit vs. Apple and Bodymedia vs. Actiheart.
“Each individual device is significantly different p < 0.01 from every other device, except for the comparisons between Microsoft vs. Jawbone, Fitbit vs. Apple and Bodymedia vs. Actiheart.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenNone of the commercial devices tested readily allowed exporting of the raw energy expenditure data for detailed interrogation.
“Unfortunately, none of the commercial devices tested readily allowed exporting of the raw energy expenditure data for detailed interrogation that might be useful for research/health applications.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenThe COSMED K4b2 unit was calibrated prior to each session according to manufacturer's instructions.
“The unit was calibrated prior to each session according to manufacturer’s instructions.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe Actiheart has previously been validated against doubly labelled water and used as a criterion measure of free-living energy expenditure in epidemiology studies.
“The Actiheart™ has previously been validated against doubly labelled water [24,26,27] and has been used as a criterion measure of free-living energy expenditure in epidemiology studies [35].”
Quote in the paperNot a substantial claim0 of 4 sources checked- cites [24] S Brage, 2015, Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study, PLoS One source
Not checked in the free run.
- cites [26] FK Assah, 2011, Accuracy and validity of a combined heart rate and motion sensor for the measurement of free-living physical activity energy expenditure in adults in Cameroon, Int J Epidemiol source
Not checked in the free run.
- cites [27] C Villars, 2012, Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men, J Appl Physiol (1985) source
Not checked in the free run.
- cites [35] T Peters, 2012, Validity of a short questionnaire to assess physical activity in 10 European countries, Eur J Epidemiol source
Not checked in the free run.
May combine two claims.
Study design and conductcitationThe COSMED K4b2 portable metabolic system has been shown to be reliable and valid for measuring energy expenditure.
“This device has been shown to be both reliable and valid [30,31], and has also been used previously to determine criterion energy expenditure [28,32–34].”
Quote in the paperSubstantial claim0 of 6 sources checked- cites [30] JE McLaughlin, 2001, Validation of the COSMED K4 b2 portable metabolic system, Int J Sports Med source
Not checked in the free run.
- cites [31] R Duffield, 2004, Accuracy and reliability of a Cosmed K4b2 portable gas analysis system, J Sci Med Sport source
Not checked in the free run.
- cites [28] SE Crouter, 2008, Accuracy of the Actiheart for the assessment of energy expenditure in adults, Eur J Clin Nutr source
Not checked in the free run.
- cites [32] D Thompson, 2006, Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling, J Nutr source
Not checked in the free run.
- cites [33] TE Nightingale, 2014, Predicting physical activity energy expenditure in manual wheelchair users, Med Sci Sports Exerc source
Not checked in the free run.
- cites [34] DK Spierer, 2011, A comparison of energy expenditure estimates from the Actiheart and Actical physical activity monitors during low intensity activities, walking, and jogging, Eur J Appl Physiol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBodyMedia SenseWear accurately measures energy expenditure relative to criterion measures.
“Previous research has shown that BodyMedia SenseWear accurately measures energy expenditure relative to criterion measures [11,20,21] and has been used to quantify physical activity and energy expenditure in experimental trials [37,38].”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [11] JM Lee, 2014, Validity of consumer-based physical activity monitors, Med Sci Sports Exerc source
Not checked in the free run.
- cites [20] DL Johannsen, 2010, Accuracy of armband monitors for measuring daily energy expenditure in healthy adults, Med Sci Sports Exerc source
Not checked in the free run.
- cites [21] MA Calabro, 2014, Validity of physical activity monitors for assessing lower intensity activity in adults, Int J Behav Nutr Phys Act source
Not checked in the free run.
- cites [37] OJ Peacock, 2015, Multidimensional individualised Physical ACTivity (Mi-PACT)—a technology-enabled intervention to promote physical activity in primary care: study protocol for a randomised controlled trial, Trials source
Not checked in the free run.
- cites [38] DS Bond, 2014, B-MOBILE—a smartphone-based intervention to reduce sedentary time in overweight/obese individuals: a within-subjects experimental trial, PLoS One source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationIndividual calibration improves Actiheart energy expenditure prediction in free-living and laboratory settings.
“Individual calibration has been shown to improve energy expenditure prediction in free-living [24,26,27] and laboratory settings [23,25].”
Quote in the paperNot a substantial claim0 of 5 sources checked- cites [24] S Brage, 2015, Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study, PLoS One source
Not checked in the free run.
- cites [26] FK Assah, 2011, Accuracy and validity of a combined heart rate and motion sensor for the measurement of free-living physical activity energy expenditure in adults in Cameroon, Int J Epidemiol source
Not checked in the free run.
- cites [27] C Villars, 2012, Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men, J Appl Physiol (1985) source
Not checked in the free run.
- cites [23] S Brage, 2007, Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity, J Appl Physiol source
Not checked in the free run.
- cites [25] TE Nightingale, 2015, Predicting physical activity energy expenditure in wheelchair users with a multisensor device, BMJ Open Sport & Exercise Medicine source
Not checked in the free run.
May combine two claims.
PremisecitationThe research devices BodyMedia Core and Actiheart have been extensively validated and shown to accurately predict energy expenditure.
“These research devices (BodyMedia Core and Actiheart™) have been extensively validated and shown to accurately predict energy expenditure [11,20–28].”
Quote in the paperSubstantial claim0 of 10 sources checked- cites [11] JM Lee, 2014, Validity of consumer-based physical activity monitors, Med Sci Sports Exerc source
Not checked in the free run.
- cites [20] DL Johannsen, 2010, Accuracy of armband monitors for measuring daily energy expenditure in healthy adults, Med Sci Sports Exerc source
Not checked in the free run.
- cites [21] MA Calabro, 2014, Validity of physical activity monitors for assessing lower intensity activity in adults, Int J Behav Nutr Phys Act source
Not checked in the free run.
- cites [22] S Brage, 2005, Reliability and validity of the combined heart rate and movement sensor Actiheart, Eur J Clin Nutr source
Not checked in the free run.
- cites [23] S Brage, 2007, Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity, J Appl Physiol source
Not checked in the free run.
- cites [24] S Brage, 2015, Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study, PLoS One source
Not checked in the free run.
- cites [25] TE Nightingale, 2015, Predicting physical activity energy expenditure in wheelchair users with a multisensor device, BMJ Open Sport & Exercise Medicine source
Not checked in the free run.
- cites [26] FK Assah, 2011, Accuracy and validity of a combined heart rate and motion sensor for the measurement of free-living physical activity energy expenditure in adults in Cameroon, Int J Epidemiol source
Not checked in the free run.
- cites [27] C Villars, 2012, Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men, J Appl Physiol (1985) source
Not checked in the free run.
- cites [28] SE Crouter, 2008, Accuracy of the Actiheart for the assessment of energy expenditure in adults, Eur J Clin Nutr source
Not checked in the free run.
May combine two claims.
Interpretationown dataConsumer multi-sensor devices are not yet equivalent to the best research-grade devices or to each other.
“Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationResting metabolic rate is a substantial contributor to total energy expenditure, particularly with lower levels of activity, and is a broadly predictable component of energy expenditure.
“As a substantial contributor to total energy expenditure (particularly with lower levels of activity) will be RMR, which is a broadly predictable component of energy expenditure [42], the greater accuracy for individuals with lower energy expenditure is unsurprising.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [42] D Frankenfield, 2005, Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review, J Am Diet Assoc source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningLaboratory assessments can produce data about specific activities which is useful for individuals interested in measuring a particular activity.
“Laboratory assessments can produce data about specific activities which is useful for individuals interested in measuring a particular activity (e.g., someone taking up jogging wanting to assess energy expenditure during running).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationcitationIt is beneficial to assess prolonged wear in free-living conditions in addition to laboratory assessments of monitor performance for specific activities.
“Therefore, it is beneficial to assess prolonged wear in free-living conditions in addition to laboratory assessments of monitor performance for specific activities.”
Quote in the paperNot a substantial claim- Rests on an assumption: The specifics of any given activity protocol, including the present study's own protocol, is unlikely to represent the distribution and importance of various activities in normal life.
May combine two claims.
InterpretationreasoningLaboratory and free-living assessments both give valuable but unique information with which to assess monitor performance in different contexts.
“Laboratory and free-living assessments both give valuable but unique information with which to assess monitor performance in different contexts.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
There has been no examination of other activities of daily living or daily energy expenditure estimates of the next generation of sophisticated multi-sensor devices including those from large technology companies such as Apple.
“However, there has been no examination of other activities of daily living or daily energy expenditure estimates of the next generation of sophisticated multi-sensor devices including those large technology companies such as Apple.”
The paper relies on this without support.
The latest generation of consumer devices share similar technology as established multi-sensor devices used in research studies.
“This step-change in consumer monitor sophistication has resulted in the latest generation of consumer devices sharing similar technology as established multi-sensor devices used in research studies.”
The paper relies on this without support.
Little is known about the accuracy of energy expenditure estimates from consumer multi-sensor physical activity monitors.
“However, little is known about the accuracy of their energy expenditure estimates.”
The authors state this as an assumption.
Resting energy expenditure estimates are relatively similar between devices.
“Assuming relative similarity of resting energy expenditure estimates between devices, this variance in total energy expenditure estimates between devices suggests that the measurement of physical activity per se is more variable than the estimation of total energy expenditure would suggest.”
The paper relies on this without support.
This is the first examination of these consumer multi-sensor devices in both laboratory and free-living settings.
“To our knowledge, this is the first examination of these consumer multi-sensor devices in both laboratory and free-living settings.”
The paper relies on this without support.
Manufacturers do not release the algorithms used in estimation of energy expenditure.
“Perhaps understandably, manufacturers do not release the algorithms used in estimation of energy expenditure.”
The paper relies on this without support.
The specifics of any given activity protocol, including the present study's own protocol, is unlikely to represent the distribution and importance of various activities in normal life.
“Furthermore, the specifics of any given activity protocol (including our own) is unlikely to represent the distribution and importance of various activities in normal life.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The two devices showed reasonable accuracy in HR and EE estimations under certain exercise conditions.
“Similar to previous studies [13,28,30-32], we found reasonable accuracy in HR and EE estimations for these 2 devices under certain exercise conditions.”
From Accuracy of Wrist-Worn Activity Monitors During Common Daily Physical Activities and Types of Structured Exercise: Evaluation Study (2018) · cites this paper as [13]
The Results (Laboratory validation; page unavailable) report activity-specific energy-expenditure errors for the Apple Watch and Fitbit Charge HR, supporting reasonable EE accuracy under some exercise conditions. The Methods (Statistical analysis; page unavailable) compare device EE estimates with criterion EE measurements, but our copy does not report a validation of the devices’ HR-estimation accuracy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredNine studies (22 comparisons) examined energy expenditure in free-living settings compared with doubly labeled water and accelerometry.
“Nine studies (22 comparisons) examined energy expenditure in free-living settings compared with the criterion measures of doubly labeled water [104] and accelerometry [29,49,79,101,131,172,174,175].”
From Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review (2020) · cites this paper as [29]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Microsoft Band daily bias differs between abstract and results
MinorThe abstract reports a free-living bias of −1128 kcal/d for the Microsoft Band, while the results report −1123 kcal/d for the same device.
“substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01)”
“The Microsoft Band and Jawbone UP24 devices displayed much greater bias and wider limits of agreement of -1123 ± 1235 kcal/d and -998 ± 1153 kcal/d, respectively.”
Cites
Cook, R. J., Dickens, B. M., & Fathalla, M. F. (2003) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199241323.003.0025 · Cited by 10,813 · see citing works
The Rise of Consumer Health Wearables: Promises and Barriers
Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016) · PLoS Medicine · doi:10.1371/journal.pmed.1001953 · Cited by 1,301 · see citing works
Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.02.009 · Cited by 923 · cited 1× · see citing works
“Resting metabolic rate (RMR) was measured following fifteen minutes of rest in a seated positon in accordance with minimal criteria for best practice recommendations [36], and taken as the average of fifteen minutes rest.”
MethodWearable Devices as Facilitators, Not Drivers, of Health Behavior Change
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015) · JAMA · doi:10.1001/jama.2014.14781 · Cited by 808 · see citing works
Frankenfield, D. C., Roth‐Yousey, L., & Compher, C. W. (2005) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2005.02.005 · Cited by 762 · see citing works
Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015) · JAMA · doi:10.1001/jama.2014.17841 · Cited by 694 · see citing works
Reliability and validity of the combined heart rate and movement sensor Actiheart
Brage, S., Brage, N., Franks, P. W., Ekelund, U., & Wareham, N. J. (2005) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602118 · Cited by 663 · see citing works
MULTIPLE COMPARISON PROCEDURES UPDATED
Ludbrook, J. (1998) · Clinical and Experimental Pharmacology and Physiology · doi:10.1111/j.1440-1681.1998.tb02179.x · Cited by 613 · see citing works
Validation of the COSMED K4 b2 Portable Metabolic System
McLaughlin, J. E., King, G. A., Howley, E. T., Bassett, D. R., & Ainsworth, B. (2001) · International Journal of Sports Medicine · doi:10.1055/s-2001-13816 · Cited by 500 · see citing works
Brage, S., Brage, N., Franks, P. W., Ekelund, U., Wong, M.-Y., Andersen, L. B., Froberg, K., & Wareham, N. J. (2004) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00703.2003 · Cited by 473 · cited 1× · see citing works
“, accelerometers), more recent models integrate other physiological outputs in recognition of how this improves estimates of energy expenditure [9,10].”
BackgroundRandomized Trial of a Fitbit-Based Physical Activity Intervention for Women
Cadmus-Bertram, L. A., Marcus, B. H., Patterson, R. E., Parker, B. A., & Morey, B. L. (2015) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2015.01.020 · Cited by 469 · see citing works
Ferguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0201-9 · Cited by 468 · see citing works
Accuracy of Armband Monitors for Measuring Daily Energy Expenditure in Healthy Adults
Johannsen, D. L., Calabro, M. A., Stewart, J. W., Franke, W. D., Rood, J. C., & Welk, G. J. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181e0b3ff · Cited by 424 · see citing works
Fitbit®: An accurate and reliable device for wireless physical activity tracking
Diaz, K. M., Krupka, D. J., Chang, M. J., Peacock, J., Ma, Y., Goldsmith, J., Schwartz, J. E., & Davidson, K. W. (2015) · International Journal of Cardiology · doi:10.1016/j.ijcard.2015.03.038 · Cited by 405 · see citing works
Validity of Consumer-Based Physical Activity Monitors
LEE, J.-M., KIM, Y., & WELK, G. J. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000287 · Cited by 401 · cited 5× · see citing works
“Previous research has shown that BodyMedia SenseWear accurately measures energy expenditure relative to criterion measures [11,20,21] and has been used to quantify physical activity and energy expenditure in experimental trials [37,38].”
BackgroundMethodInfluentialPatient-centered activity monitoring in the self-management of chronic health conditions
Chiauzzi, E. J., Rodarte, C., & DasMahapatra, P. (2015) · BMC Medicine · doi:10.1186/s12916-015-0319-2 · Cited by 342 · see citing works
Accuracy of Heart Rate Watches: Implications for Weight Management
Wallen, M. P., Gomersall, S. R., Keating, S. E., Wisløff, U., & Coombes, J. S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154420 · Cited by 336 · see citing works
Accuracy and reliability of a Cosmed K4b2 portable gas analysis system
Duffield, R., Dawson, B., Pinnington, H., & Wong, P. (2004) · Journal of science and medicine in sport · doi:10.1016/s1440-2440(04)80039-2 · Cited by 327 · see citing works
Brage, S., Ekelund, U., Brage, N., Hennings, M. A., Froberg, K., Franks, P. W., & Wareham, N. J. (2007) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00092.2006 · Cited by 303 · see citing works
Twenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices
Rosenberger, M. E., Buman, M. P., Haskell, W. L., McConnell, M. V., & Carstensen, L. L. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000778 · Cited by 298 · see citing works
Powell, A. C., Landman, A., & Bates, D. W. (2014) · JAMA · doi:10.1001/jama.2014.2564 · Cited by 236 · see citing works
Accuracy of the Actiheart for the assessment of energy expenditure in adults
Crouter, S. E., Churilla, J. R., & Bassett, D. R. (2008) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602766 · Cited by 236 · see citing works
Validity of a short questionnaire to assess physical activity in 10 European countries
The InterAct Consortium (614230) (2011) · European Journal of Epidemiology · doi:10.1007/s10654-011-9625-y · Cited by 230 · see citing works
Murakami, H., Kawakami, R., Nakae, S., Nakata, Y., Ishikawa-Takata, K., Tanaka, S., & Miyachi, M. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0152 · Cited by 204 · see citing works
Bond, D. S., Thomas, J. G., Raynor, H. A., Moon, J., Sieling, J., Trautvetter, J., Leblond, T. L., & Wing, R. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100821 · Cited by 202 · see citing works
Brage, S., Westgate, K., Franks, P. W., Stegle, O., Wright, A., Ekelund, U., & Wareham, N. J. (2015) · PLOS ONE · doi:10.1371/journal.pone.0137206 · Cited by 151 · see citing works
Comparison of Consumer and Research Monitors under Semistructured Settings
BAI, Y., WELK, G. J., NAM, Y. H., LEE, J. A., LEE, J.-M., KIM, Y., MEIER, N. F., & DIXON, P. M. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000727 · Cited by 149 · see citing works
Assah, F. K., Ekelund, U., Brage, S., Wright, A., Mbanya, J. C., & Wareham, N. J. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyq098 · Cited by 132 · see citing works
Thompson, D., Batterham, A. M., Bock, S., Robson, C., & Stokes, K. (2006) · The Journal of Nutrition · doi:10.1093/jn/136.4.1037 · Cited by 129 · see citing works
Self-citationIntegration of physiological and accelerometer data to improve physical activity assessment
STRATH, S. J., BRAGE, S., & EKELUND, U. (2005) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000185650.68232.3f · Cited by 126 · cited 1× · see citing works
“, accelerometers), more recent models integrate other physiological outputs in recognition of how this improves estimates of energy expenditure [9,10].”
BackgroundA Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors
Dannecker, K. L., Sazonova, N. A., Melanson, E. L., Sazonov, E. E., & Browning, R. C. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318299d2eb · Cited by 124 · see citing works
Protocols for evaluating equivalency of accelerometry-based activity monitors
WELK, G. J., MCCLAIN, J., & AINSWORTH, B. E. (2012) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3182399d8f · Cited by 124 · see citing works
Multidimensional Physical Activity
Thompson, D., Peacock, O. J., Western, M. J., & Batterham, A. M. (2015) · Exercise and Sport Sciences Reviews · doi:10.1249/jes.0000000000000039 · Cited by 120 · see citing works
Self-citationStatistical Considerations in the Analysis of Accelerometry-Based Activity Monitor Data
STAUDENMAYER, J. W., Zhu, W., & Catellier, D. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3182399e0f · Cited by 110 · see citing works
Villars, C., Bergouignan, A., Dugas, J., Antoun, E., Schoeller, D. A., Roth, H., Maingon, A. C., Lefai, É., Blanc, S., & Simon, C. (2012) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01413.2011 · Cited by 107 · see citing works
Validity of physical activity monitors for assessing lower intensity activity in adults
Calabr, M. A. S., Lee, J. M., Saint‐Maurice, P. F., Yoo, H., & Welk, G. J. (2014) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-014-0119-7 · Cited by 93 · see citing works
Wearable, yes, but able…?: it is time for evidence-based marketing claims!
Sperlich, B., & Holmberg, H. (2016) · British Journal of Sports Medicine · doi:10.1136/bjsports-2016-097295 · Cited by 76 · see citing works
Spierer, D. K., Hagins, M., Rundle, A., & Pappas, E. (2010) · European Journal of Applied Physiology · doi:10.1007/s00421-010-1672-7 · Cited by 73 · see citing works
Confusion and Conflict in Assessing the Physical Activity Status of Middle-Aged Men
Thompson, D., Batterham, A. M., Markovitch, D., Dixon, N. C., Lund, A. J. S., & Walhin, J.-P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004337 · Cited by 61 · see citing works
Self-citationChallenges and Opportunities for Harmonizing Research Methodology: Raw Accelerometry
van Hees, V. T., Thaler-Kall, K., Wolf, K.-H., Brønd, J. C., Bonomi, A., Schulze, M., Vigl, M., Morseth, B., Hopstock, L. A., Gorzelniak, L., et al. (2016) · Methods of Information in Medicine · doi:10.3414/me15-05-0013 · Cited by 58 · see citing works
Nightingale, T. E., Walhin, J., Thompson, D., & Bilzon, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126086 · Cited by 57 · see citing works
Self-citationTowards Integrated Physical Activity Profiling
Thompson, D., & Batterham, A. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056427 · Cited by 53 · see citing works
Self-citationPredicting Physical Activity Energy Expenditure in Manual Wheelchair Users
Nightingale, T. E., Walhin, J., Thompson, D., & Bilzon, J. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000291 · Cited by 49 · see citing works
Self-citationPredicting physical activity energy expenditure in wheelchair users with a multisensor device
Nightingale, T. E., Walhin, J., Thompson, D., & Bilzon, J. (2015) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2015-000008 · Cited by 46 · see citing works
Self-citationLicence to eat: Information on energy expended during exercise affects subsequent energy intake
McCaig, D. C., Hawkins, L. A., & Rogers, P. J. (2016) · Appetite · doi:10.1016/j.appet.2016.08.107 · Cited by 31 · see citing works
Peacock, O. J., Western, M. J., Batterham, A. M., Stathi, A., Standage, M., Tapp, A., Bennett, P., & Thompson, D. (2015) · Trials · doi:10.1186/s13063-015-0892-x · Cited by 28 · see citing works
Self-citationWestern, M. J., Peacock, O. J., Stathi, A., & Thompson, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126156 · Cited by 26 · see citing works
Self-citationThe Challenges of Mobile Health Regulation
Schoenfeld, A. J., Sehgal, N. J., & Auerbach, A. D. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0326 · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Fuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715 · see citing works
Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data
Feehan, L. M., Geldman, J., Sayre, E. C., Park, C., Ezzat, A. M., Yoo, J. Y., Hamilton, C. B., & Li, L. C. (2018) · JMIR mHealth and uHealth · doi:10.2196/10527 · Cited by 616 · see citing works
Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9157 · Cited by 542 · see citing works
Measurement of physical activity in clinical practice using accelerometers
Arvidsson, D., Fridolfsson, J., & Börjesson, M. (2019) · Journal of Internal Medicine · doi:10.1111/joim.12908 · Cited by 243 · see citing works
Germini, F., Noronha, N., Debono, V. B., Philip, B. A., Pete, D., Navarro, T., Keepanasseril, A., Parpia, S., de Wit, K., & Iorio, A. (2021) · Journal of Medical Internet Research · doi:10.2196/30791 · Cited by 222 · see citing works
Current State of Commercial Wearable Technology in Physical Activity Monitoring 2015-2017
Bunn, J., Navalta, J. W., Fountaine, C. J., & Reece, J. (2018) · International Journal of Exercise Science · doi:10.70252/njqx2719 · Cited by 194 · see citing works
O’Driscoll, R., Turicchi, J., Beaulieu, K., Scott, S., Matu, J., Deighton, K., Finlayson, G., & Stubbs, R. J. (2018) · British Journal of Sports Medicine · doi:10.1136/bjsports-2018-099643 · Cited by 189 · see citing works
Reddy, R. K., Pooni, R., Zaharieva, D. P., Senf, B., El Youssef, J., Dassau, E., Doyle III, F. J., Clements, M. A., Rickels, M. R., Patton, S. R., et al. (2018) · JMIR mHealth and uHealth · doi:10.2196/10338 · Cited by 157 · see citing works
Falter, M., Budts, W., Goetschalckx, K., Cornelissen, V., & Buys, R. (2019) · JMIR mHealth and uHealth · doi:10.2196/11889 · Cited by 142 · see citing works
Standardizing Analytic Methods and Reporting in Activity Monitor Validation Studies
Welk, G. J., Bai, Y., Lee, J. M., Godino, J., Saint‐Maurice, P. F., & Carr, L. J. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001966 · Cited by 139 · see citing works
Mitchell, C. J., Milan, A. M., Mitchell, S. M., Zeng, N., Ramzan, F., Sharma, P., Knowles, S. O., Roy, N. C., Sjödin, A., Wagner, K.-H., et al. (2017) · The American Journal of Clinical Nutrition · doi:10.3945/ajcn.117.160325 · Cited by 132 · see citing works
Peart, D. J., Balsalobre‐Fernández, C., & Shaw, M. P. (2017) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002344 · Cited by 105 · see citing works
How Sensors Might Help Define the External Exposome
Loh, M. M., Sarigiannis, D. A., Gotti, A., Karakitsios, S., Pronk, A., Kuijpers, E., Annesi‐Maesano, I., Baïz, N., Madureira, J., Fernandes, E. O., et al. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14040434 · Cited by 95 · see citing works
Murakami, H., Kawakami, R., Nakae, S., Yamada, Y., Nakata, Y., Ohkawara, K., Sasai, H., Ishikawa‐Takata, K., Tanaka, S., & Miyachi, M. (2019) · JMIR mhealth and uhealth · doi:10.2196/13938 · Cited by 84 · see citing works
Estimating Energy Expenditure during Level, Uphill, and Downhill Walking
Looney, D. P., Santee, W. R., Hansen, E. O., Bonventre, P., Chalmers, C. R., & Potter, A. W. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002002 · Cited by 66 · see citing works
Validity of wrist-worn consumer products to measure heart rate and energy expenditure
Thiebaud, R. S., Funk, M. D., Patton, J. C., Massey, B. L., Shay, T. E., Schmidt, M. G., & Giovannitti, N. (2018) · Digital Health · doi:10.1177/2055207618770322 · Cited by 56 · see citing works
Improving energy expenditure estimates from wearable devices: A machine learning approach
O’Driscoll, R., Turicchi, J., Hopkins, M., Horgan, G. W., Finlayson, G., & Stubbs, R. J. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1746088 · Cited by 56 · see citing works
Argent, R., Hetherington‐Rauth, M., Stang, J. S., Tarp, J., Ortega, F. B., Molina‐García, P., Schumann, M., Bloch, W., Cheng, S., Grøntved, A., et al. (2022) · Sports Medicine · doi:10.1007/s40279-022-01665-4 · Cited by 49 · see citing works
Mikkelsen, M.-L. K., Berg‐Beckhoff, G., Frederiksen, P., Horgan, G. W., O’Driscoll, R., Palmeira, A. L., Scott, S., Stubbs, R. J., Heitmann, B. L., & Larsen, S. C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234426 · Cited by 51 · see citing works
O'Brien, M. W. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2021-0021 · Cited by 46 · see citing works
Giurgiu, M., Timm, I., Becker, M. L., Schmidt, S., Wunsch, K., Nissen, R., Davidovski, D., Bussmann, J. B. J., Nigg, C. R., Reichert, M., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/36377 · Cited by 46 · see citing works
Mandigout, S., Lacroix, J., Perrochon, A., Svoboda, Z., Aubourg, T., & Vuillerme, N. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00252 · Cited by 43 · see citing works
Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?
Martín-Escudero, P., Cabanas, A. M., Dotor-Castilla, M. L., Galindo-Canales, M., Miguel‐Tobal, F., Fernández, C., Fuentes, M. E., & Giannetti, R. (2023) · Bioengineering · doi:10.3390/bioengineering10020254 · Cited by 40 · see citing works
Validity of energy expenditure estimation methods during 10 days of military training
Siddall, A. G., Powell, S. D., Needham‐Beck, S. C., Edwards, V. C., Thompson, J. E. S., Kefyalew, S. S., Singh, P., Orford, E. R., Venables, M., Jackson, S., et al. (2019) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13488 · Cited by 40 · see citing works
Reliability of Trail Walking and Running Tasks Using the Stryd Power Meter
Navalta, J. W., Montes, J., Bodell, N. G., Aguilar, C. D., Radzak, K. N., Manning, J. W., & DeBeliso, M. (2019) · International Journal of Sports Medicine · doi:10.1055/a-0875-4068 · Cited by 41 · see citing works
Popp, W., Schneider, S., Bär, J., Bösch, P., Spengler, C. M., Gassert, R., & Curt, A. E. P. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.01092 · Cited by 35 · see citing works
Shirota, C., Balasubramanian, S., & Melendez-Calderon, A. (2019) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0519-7 · Cited by 32 · see citing works
Modeling the Metabolic Costs of Heavy Military Backpacking
LOONEY, D. P., Lavoie, E. M., VANGALA, S. V., HOLDEN, L. D., FIGUEIREDO, P. S., FRIEDL, K. E., Frykman, P., HANCOCK, J. W., MONTAIN, S. J., PRYOR, J. L., et al. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002833 · Cited by 32 · see citing works
Kim, B. R., Kwon, H., Chun, M. Y., Park, K. D., Lim, S. M., Jeong, J. H., & Kim, G. H. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.549983 · Cited by 32 · see citing works
Kwon, K. Y., Shin, Y. J., Shin, J. H., Jeong, C., Jung, Y. H., Park, B., & Kim, T. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801593 · Cited by 27 · see citing works
White, J. W., Finnegan, O., Tindall, N., Nelakuditi, S., Brown, D. E., Pate, R. R., Welk, G. J., de Zambotti, M., Ghosal, R., Wang, Y., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0286898 · Cited by 24 · see citing works
Gilgen-Ammann, R., Schweizer, T., & Wyss, T. (2019) · JMIR mhealth and uhealth · doi:10.2196/14534 · Cited by 27 · see citing works
O’Driscoll, R., Turicchi, J., Hopkins, M., Gibbons, C., Larsen, S. C., Palmeira, A. L., Heitmann, B. L., Horgan, G. W., Finlayson, G., & Stubbs, R. J. (2019) · Health and Technology · doi:10.1007/s12553-019-00392-7 · Cited by 24 · see citing works
Yavelberg, L., Zaharieva, D. P., Çınar, A., Riddell, M. C., & Jamnik, V. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817750401 · Cited by 23 · see citing works
Odonkor, C. A., Kuwabara, A. M., Tomkins‐Lane, C., Zhang, W., Muaremi, A., Leutheuser, H., Sun, R., & Smuck, M. W. (2020) · Gait & Posture · doi:10.1016/j.gaitpost.2020.05.019 · Cited by 23 · see citing works
A meta-analysis of Fitbit devices: same company, different models, different validity evidence
Leung, W. C. W., Case, L., Sung, M., & Jung, J. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.2006350 · Cited by 23 · see citing works
A Survey on Energy Expenditure Estimation Using Wearable Devices
Álvarez-García, J. A., Cvetković, B., & Luštrek, M. (2020) · ACM Computing Surveys · doi:10.1145/3404482 · Cited by 22 · see citing works
Weaver, R. G., de Zambotti, M., White, J., Finnegan, O., Nelakuditi, S., Zhu, X., Burkart, S. A., Beets, M. W., Brown, D. E., PATE, R. R., et al. (2023) · Sleep Health · doi:10.1016/j.sleh.2023.04.005 · Cited by 23 · see citing works
An, K. H., Han, K. A., Sohn, T. S., Park, I. B., Kim, H. J., Moon, S. D., & Min, K. W. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2019.0029 · Cited by 21 · see citing works
Use of consumer monitors for estimating energy expenditure in youth
LaMunion, S. R., Blythe, A. L., Hibbing, P. R., Kaplan, A. S., Clendenin, B. J., & Crouter, S. E. (2019) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2019-0129 · Cited by 20 · see citing works
Herkert, C., Kraal, J. J., van Loon, E. M. A., van Hooff, M., & Kemps, H. M. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/15045 · Cited by 19 · see citing works
Energy expenditure estimation from respiration variables
Gilgen-Ammann, R., Koller, M., Huber, C., Ahola, R., Korhonen, T., & Wyss, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-16135-5 · Cited by 19 · see citing works
Maijers, M. C., Verschuren, O. W., Stolwijk-Swüste, J. M., van Koppenhagen, C. F., de Groot, S., & Post, M. W. M. (2018) · Spinal Cord Series and Cases · doi:10.1038/s41394-018-0113-4 · Cited by 19 · see citing works
Ibrahim, N. S., Rampal, S., Lee, W. L., Pek, E. W., & Suhaimi, A. (2023) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-023-00693-z · Cited by 16 · see citing works
James, R., James, L. J., & Clayton, D. J. (2020) · Appetite · doi:10.1016/j.appet.2020.104719 · Cited by 17 · see citing works
Givens, A. C., Bernards, J. R., & Kelly, K. R. (2023) · Nutrients · doi:10.3390/nu15071639 · Cited by 16 · see citing works
Klass, M., Faoro, V., & Carpentier, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0224948 · Cited by 17 · see citing works
Are Wearable ECG Devices Ready for Hospital at Home Application?
Medina, J., Silva, R. D. S., & Holgado-Terriza, J. A. (2025) · Sensors · doi:10.3390/s25102982 · Cited by 15 · see citing works
Accuracy of swimming wearable watches for estimating energy expenditure
Lee, M., Lee, H., & Park, S. (2018) · IJASS(International Journal of Applied Sports Sciences) · doi:10.24985/ijass.2018.30.1.80 · Cited by 15 · see citing works
Predicting ambulatory energy expenditure in lower limb amputees using multi-sensor methods
Ladlow, P., Nightingale, T. E., McGuigan, M. P., Bennett, A. N., Phillip, R. D., & Bilzon, J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0209249 · Cited by 15 · see citing works
Self-citationKhushhal, A. A., Abdelaal, A. A. M., & Elsayed, M. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s449071 · Cited by 14 · see citing works
Danielsson, M. L., Vergeer, M., Plasqui, G., & Baumgart, J. K. (2024) · JMIR Formative Research · doi:10.2196/52312 · Cited by 13 · see citing works
Physical Activity Epidemiology
Weber, A., Schmid, D., & Leitzmann, M. F. (2023) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_67-1 · Cited by 15 · see citing works
Weaver, R. G., White, J., Finnegan, O., Nelakuditi, S., Zhu, X., Burkart, S. A., Beets, M. W., Brown, T., PATE, R. R., Welk, G. J., et al. (2023) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003294 · Cited by 13 · see citing works
Shwetar, Y. J., Veerubhotla, A. L., Huang, Z., & Ding, D. (2020) · Spinal Cord · doi:10.1038/s41393-020-0427-5 · Cited by 12 · see citing works
Freundlich, R. E., & Ehrenfeld, J. M. (2017) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2017.11.002 · Cited by 11 · see citing works
Energy balance dynamics during short-term high-intensity functional training
Schubert, M. M., & Palumbo, E. (2018) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2018-0311 · Cited by 11 · see citing works
Clayton, D. J., Mode, W. J. A., & Slater, T. (2020) · Nutrition Bulletin · doi:10.1111/nbu.12467 · Cited by 11 · see citing works
Alessandrini, M., Viziano, A., Pistillo, R., Granito, I., Basso, L., Preziosi, N., & Micarelli, A. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.07.012 · Cited by 12 · see citing works
Apple watch accuracy in monitoring health metrics: a systematic review and meta-analysis
Choe, J.-P., & Kang, M. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adca82 · Cited by 10 · see citing works
Quantification of energy expenditure during daily living activities after stroke by multi-sensor
Compagnat, M., Daviet, J., Batcho, C. S., David, R., Salle, J., & Mandigout, S. (2019) · Brain Injury · doi:10.1080/02699052.2019.1641840 · Cited by 10 · see citing works
Metabolic Costs of Walking with Weighted Vests
Looney, D. P., Lavoie, E. M., Notley, S. R., HOLDEN, L. D., Arcidiacono, D. M., Potter, A. W., Silder, A., Pasiakos, S. M., Arellano, C. J., Karis, A. J., et al. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003400 · Cited by 10 · see citing works
Schellewald, V., Kleinert, J., & Ellegast, R. P. (2018) · Applied Ergonomics · doi:10.1016/j.apergo.2018.04.002 · Cited by 10 · see citing works
Goulet-Gélinas, L., Saade, M., Suppère, C., Fortin, A., Messier, V., Taleb, N., Tagougui, S., Shohoudi, A., Legault, L., Henderson, M., et al. (2020) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2020.12.011 · Cited by 9 · see citing works
Mei, L., He, Z., & Hu, L. (2024) · JMIR Formative Research · doi:10.2196/59521 · Cited by 9 · see citing works
Heart Rate Alters, But Does Not Improve, Calorie Predictions in Fitbit Activity Monitors
Montoye, A. H. K., Vusich, J., Mitrzyk, J., & Wiersma, M. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0003 · Cited by 8 · see citing works
Impact of Placement of Fitbit HR under Laboratory and Free-Living Conditions
Jung, H. C., Kang, M., Lee, N. H., Jeon, S., & Lee, S. (2020) · Sustainability · doi:10.3390/su12166306 · Cited by 8 · see citing works
Zhou, F., Yin, X., Hu, R., Houssein, A., Gastinger, S., Martin, B., Li, S., & Prioux, J. (2022) · Nutrients · doi:10.3390/nu14194190 · Cited by 7 · see citing works
Zhang, P., Burns, R. D., Fu, Y., Godin, S., & Byun, W. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16203812 · Cited by 7 · see citing works
Wearable oxygen uptake and energy expenditure monitors
TAMURA, T. (2019) · Physiological Measurement · doi:10.1088/1361-6579/ab3827 · Cited by 7 · see citing works
Thompson, D., Angel, M. D., Nunes, M. A., Gonzalez, J. T., Betts, J. A., & Peacock, O. J. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70079 · Cited by 6 · see citing works
Self-citationAccuracy of Heart Rate and Energy Expenditure Estimations of Wrist-Worn and Arm-Worn Apple Watches
Nuss, K. J., Sanford, J. L., Archambault, L. J., Schlemer, E. J., Blake, S., Courtney, J. B., Hulett, N. A., & Li, K. (2019) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0061 · Cited by 5 · see citing works
Adamakis, M. (2020) · Sport Sciences for Health · doi:10.1007/s11332-020-00654-2 · Cited by 6 · see citing works
Storniolo, J. L., Pavei, G., & Minetti, A. E. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00868 · Cited by 6 · see citing works
Schellewald, V., Kleinert, J., & Ellegast, R. P. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15112501 · Cited by 6 · see citing works
Adhikary, R., Sadeh, M., Batra, N., & Goel, M. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631422 · Cited by 6 · see citing works
Determining the Reliability of Several Consumer-Based Physical Activity Monitors
Bock, J. M., Kaminsky, L. A., Harber, M. P., & Montoye, A. H. K. (2017) · Technologies · doi:10.3390/technologies5030047 · Cited by 5 · see citing works
Smartwatch/Smartphone Cooperative Indoor Lifelogging System
Nguyen-Huu, K., Song, C. G., & Lee, S. (2018) · International Journal of Engineering and Technology Innovation · Cited by 5 · see citing works
Analysing longitudinal wearable physical activity data using non-stationary time series models
Angel, M. D., Nunes, M. A., Peacock, O. J., Cranwell, E. J., & Thompson, D. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01779-8 · Cited by 4 · see citing works
Self-citationMicarelli, A., Viziano, A., Carbini, V., Misici, I., Guzzo, F., Micarelli, B., & Alessandrini, M. (2022) · Journal of Vestibular Research · doi:10.3233/ves-220019 · Cited by 4 · see citing works
Stutz, J., Eichenberger, P. A., Stumpf, N. F., Knobel, S. E. J., Herbert, N. C., Hirzel, I., Huber, S., Oetiker, C., Urry, E., Lambercy, O., et al. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1400535 · Cited by 4 · see citing works
Graded Walking Energetics under Cold Strain
Looney, D. P., Schafer, E. A., Chapman, C. L., Pryor, J. L., Roberts, B. M., Stein, J. A., Friedl, K. E., & Potter, A. W. (2025) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003664 · Cited by 4 · see citing works
Arambepola, C., Perera, M., Gillison, F., Peacock, O. J., & Thompson, D. (2021) · BMC Public Health · doi:10.1186/s12889-021-10774-0 · Cited by 5 · see citing works
Self-citationAccuracy of Wearable Devices for Estimating Energy Expenditure and Heart Rate During Golf
Lee, H., Lee, M., & Park, S. (2022) · Exercise Science · doi:10.15857/ksep.2022.00178 · Cited by 4 · see citing works
Estimation of energy expenditure in adults with accelerometry and heart rate
Bazuelo-Ruiz, B., Rosario, H. D., & Durá‐Gil, J. V. (2022) · Science & Sports · doi:10.1016/j.scispo.2021.08.007 · Cited by 4 · see citing works
Leung, W. C. W., Case, L., Jung, J., & Yun, J. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.1893395 · Cited by 4 · see citing works
Liu, Y., Liu, F., Yu, W., Xiao, Y., Liu, D., Li, Z., Chen, W., Gao, F., & Le, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331399 · Cited by 4 · see citing works
Choi, J.-H., & Kim, H.-J. (2019) · Journal of people, plants, and environment · doi:10.11628/ksppe.2019.22.2.219 · Cited by 2 · see citing works
A Smart System for the Contactless Measurement of Energy Expenditure
Sprowls, M., Victor, S., Mora, S. J., Osorio, O., Pyznar, G., Destaillats, H., Wheatley, C. M., Johnson, B. D., Kulick, D., & Forzani, E. (2022) · Sensors · doi:10.3390/s22041355 · Cited by 2 · see citing works
An, K.-H. (2021) · 한국여성체육학회지 · doi:10.16915/jkapesgw.2021.12.35.4.99 · Cited by 1 · see citing works
Accuracy of energy expenditure estimation by the Apple Watch in EMS-supported exercise
Heinz, V., Pilz, N., Lindner, T., Brandt, H. F., Opatz, O. S., Fesseler, L., Patzak, A. R., & Bothe, T. L. (2025) · Physiological Measurement · doi:10.1088/1361-6579/adfcaf · Cited by 1 · see citing works
Heart rate-based metabolic rate estimation for military-relevant exercises
Looney, D. P., Chapman, C. L., Notley, S. R., Friedl, K. E., Pryor, J. L., & Potter, A. W. (2026) · Applied Ergonomics · doi:10.1016/j.apergo.2026.104777 · Cited by 1 · see citing works
User preference-based human-in-the-loop tuning of exoskeleton assistance during walking
Schäfer, N., Zhao, G., Li, B., Kupnik, M., Andre Seyfarth, A., Beckerle, P., & Grimmer, M. (2026) · npj Biomedical Innovations. · doi:10.1038/s44385-026-00085-7 · Cited by 2 · see citing works
Lepley, A. S., Davis, F., Melvin, A. C., & Zhao, Z.-Y. (2026) · Sensors · doi:10.3390/s26144486 · Cited by 1 · see citing works
Nivel de actividad física y grado de discapacidad en pacientes con dolor lumbar crónico
Vera, I. L. D., & Illera, I. M. U. (2017) · Hacia la Promoción de la Salud · doi:10.17151/hpsal.2017.22.1.9 · Cited by 1 · see citing works
Quantifying free-living in adults physical activity and sedentary behaviours
Peacock, O. J., Chowdhury, E. A., & Thompson, D. (2022) · Routledge eBooks · doi:10.4324/9781003045267-25 · Cited by 1 · see citing works
Self-citationOh, I.-Y., Jeon, J.-J., Kim, J.-B., Hyeon, J.-Y., Yeon, J.-M., Ha, H.-J., Han, S.-M., Kang, D.-G., & So, W.-Y. (2024) · The Asian Journal of Kinesiology · doi:10.15758/ajk.2024.26.1.60 · Cited by 1 · see citing works
Lathauwer, I. L. J. D., van Es, V. A. A., van Leunen, M. M. C. J., Onkelinx, S., Brouwers, R. W. M., van de Sande, D. A., Funk, M., & Kemps, H. (2024) · doi:10.2196/preprints.69343 · Cited by 1 · see citing works
DIFFERENCES ABOUT CALORIE INTAKE MEASUREMENT ACROSS FITNESS DEVICE AND MOBILE APPS
Arnis, V., Buliņa, R., Veseta, U., Upeniece, I., & Vīnberga, I. (2020) · SOCIETY INTEGRATION EDUCATION Proceedings of the International Scientific Conference · doi:10.17770/sie2020vol6.5183
- Authors
- Chowdhury, E. A., Western, M. J., Nightingale, T. E., Peacock, O. J., & Thompson, D.
- Affiliations
- University of Bath (GB)
- Corresponding author
- Thompson, Dylan
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-02-24
- Volume
- 12
- Issue
- 2
- Pages
- e0171720
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5325221
- OpenAlex
- W2589673251
- Semantic Scholar
- 22b5f5790b6d771ec36ca30dd97502dce1ea9ea3
- Cited by
- 118
- References
- 49
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 10 · 2023: 7 · 2022: 11 · 2021: 10 · 2020: 16 · 2019: 23 · 2018: 17 · 2017: 8
- FWCI
- 12.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure (University of Bath) (publishedVersion, cc-by)
- The University of Bath Online Publications Store (The University of Bath) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Funders
- National Prevention Research Initiative (MR/J00040X/1)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- energy expenditure estimation, consumer wearable devices, device validation, Apple Watch, Actiheart, accelerometry, indirect calorimetry, free-living energy expenditure, activities of daily living, structured exercise
- MeSH terms
- Fitness Trackers, Adult, Calorimetry, Indirect, Energy Metabolism, Female, Humans, Male, Reference Values, Exercise, Accelerometry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
