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

Hendrikx, Jos; Ruijs, L.; Cox, Lieke Ge; Lemmens, Paul; Schuijers, Erik Gp; Goris, Annelies H. C.

doi:10.2196/mhealth.6893

Cited by 19 publications

(18 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because effective prevention of eGWG is based on being sufficiently active throughout the entire day, the overall estimation error is the most relevant error to consider; additionally, the OHRM does accurately monitor energy expenditure during exercise activities. The overall underestimation is similar to the − 3% (and 10% MAPE) error reported by Hendrikx and colleagues [ 34 ] when they validated a medical class-2a activity monitor in the general population in a 48 min protocol. The high similarity of these observed errors highlights that the OHRM can be readily used in pregnant women as well as in the general population.…”

Section: Discussionsupporting

confidence: 83%

“…This is a low speed for cycling that may have further reduced the already limited movement of the wrist and that may have resulted in an accelerometer contribution that is too low, thus resulting in underestimation of energy expenditure. An additional explanation is proposed by Hendrikx and colleagues [ 34 ] that also applies to our study due to (also) having been performed during winter time. Their explanation revolves around cold outside temperatures that result in (additional) vasoconstriction that, in turn, excessively reduces the amount of time in which the estimated heart rate is within a reasonable distance from the reference heart rate thus increasing the difference between reference and estimated energy expenditure.…”

Section: Discussionmentioning

confidence: 70%

“…The second device was a watch (referred to as Optical Heart Rate Monitor, OHRM) that was worn on the participants’ left wrist. The OHRM is based on the Philips Cardio and Motion Monitoring Module (CM3-Generation-1) that is an accelerometer and optical heart rate sensor module developed by Philips [ 21 , 34 ]. It estimates energy expenditure using contributions of measured heart rate and accelerometry.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of an activity monitor for use in pregnancy to help reduce excessive gestational weight gain

Lemmens¹,

Sartor²,

Cox³

et al. 2018

BMC Pregnancy Childbirth

Self Cite

View full text Add to dashboard Cite

BackgroundExcessive weight gain during pregnancy increases the risk for negative effects on mother and child during pregnancy, delivery, and also postnatally. Excessive weight gain can be partially compensated by being sufficiently physically active, which can be measured using activity trackers. Modern activity trackers often use accelerometer data as well as heart rate data to estimate energy expenditure. Because pregnancy affects the metabolism and cardiac output, it is not evident that activity trackers that are calibrated to the general population can be reliably used during pregnancy. We evaluated whether an activity monitor designed for the general population is sufficiently accurate for estimating energy expenditure in pregnant women.MethodsForty pregnant women (age: 30.8 ± 4.7 years, BMI: 25.0 ± 4.0) from all three trimesters performed a 1-h protocol including paced and self-paced exercise activities as well as household activities. We tracked reference energy expenditure using indirect calorimetry and used equivalence testing to determine whether the estimated energy expenditure from the activity monitor was within the limits of equivalence.ResultsOverall we found an averaged underestimation of 10 kcal (estimated energy expenditure was 97% of the reference measurement). The 90% CI for the cumulative total energy expenditure was 94–100%. The activities of self-paced cycling, household activities, stair-walking, and yoga had one of their equivalence boundaries outside a 80–125% range of equivalence; for exercise on a cross-trainer, for self-paced and fixed-pace walking, fixed-paced cycling, and resting, the estimations were within the limits of equivalence.ConclusionsWe conclude that the activity monitor is sufficiently accurate for every-day use during pregnancy. The observed deviations can be accounted for and are acceptable from a statistical and an applied perspective because the positive and negative deviations that we observed cancel out to an accurate average energy expenditure over a day, and estimations during exercise are sufficiently accurate to enable coaching on physical activity. The positive and negative deviations themselves were relatively small. Therefore, the activity monitor can be used to help in preventing excessive weight gain during pregnancy by accurately tracking physical activity.

show abstract

Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 70%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of an activity monitor for use in pregnancy to help reduce excessive gestational weight gain

Lemmens¹,

Sartor²,

Cox³

et al. 2018

BMC Pregnancy Childbirth

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the authors of [14] validate the use of a commercial HR monitor, which employs PPG to track HR waveforms during rest. A similar conclusion is found in [15] with a smartwatch. This validation of PPG-based HR tracking has led to developments that seek to strengthen HR monitoring on environments where sensor signals can be corrupted by body movements [16].…”

Section: Introductionsupporting

confidence: 85%

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Manjarrés

Narváez

Gasser

et al. 2019

Sensors

View full text Add to dashboard Cite

In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

show abstract

“…Activity in this study was identified using daily heart rate profiles obtained by a wrist-worn device. Previous studies assessing the accuracy of such devices have found an overall high accuracy for measuring heart rate 22 , 23 as well as steps 24 – 26 , whereas different intensity levels 24 and energy expenditure 25 , 26 could only be determined imprecisely. The combined time spent in light/moderate activity correlates with daily steps (Fig.…”

Section: Discussionmentioning

confidence: 99%

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

et al. 2020

View full text Add to dashboard Cite

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R 2 = 0.48). The uncertainty of these predictions is demonstrated.

show abstract

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

Cited by 19 publications

References 74 publications

Evaluation of an activity monitor for use in pregnancy to help reduce excessive gestational weight gain

Evaluation of an activity monitor for use in pregnancy to help reduce excessive gestational weight gain

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

Contact Info

Product

Resources

About