A total of 26 healthy young adults from the Sport Sciences University of Calais, France, who were practicing physical activities on a weekly basis, volunteered and were included in this study, which was advertised on the university campus and via social networks. No inclusion or exclusion criteria were used.

A minimum HR sample size was calculated with G*Power (version 3.1.9.6) [25] by using a significance level (α) of 5%, a statistical power of 1 – β = 80%, and an effect size of 0.075 (computed from the expected HR mean and SD). The number of necessary HR samples was below 6000, representing 100 minutes of recording (sample rate=1 s⁻¹). Measurements were performed during participants’ regular training sessions for soccer, badminton, orienteering running, basketball, tennis, and road biking.

This study was approved by the National Commission for Data Protection and Liberties (CNIL-France; registration number: 2224247). All participants gave their written informed consent, with the possibility to opt out of the protocol at any point. Data collected throughout the protocol were deidentified for privacy and confidentiality reasons. Finally, although participants could not be financially compensated for their participation, which did not impact their usual routine, each of them personally received an individualized analysis of their HR data, so that they could receive information about cardiac demand during various phases of their training sessions (intensity levels, duration, and cardiac work zone) and adjust their sessions’ contents if needed.

PPG HR signals from the FC4 were compared to those from the Polar H10 thoracic belt (Polar Electro Oy), which was used as the criterion sensor [26]. The assessment of skin tone was performed with the Fitzpatrick Skin Scale, which ranges from 1 (lightest tone) to 6 (darkest tone) [27].

Participants were asked to wear both sensors simultaneously at each session. The FC4 was placed on the wrist of the nondominant arm (ie, around 2 cm away [proximal] from the ulnar styloid process), whereas the Polar H10 thoracic belt was placed under the thorax (ie, on the xyphoid process) and paired with Polar V800 wristwatches for HR recording. Each device was placed firmly against the skin, as recommended by the manufacturer’s instructions.

Data were extracted through both companies’ web services (ie, the Fitbit app [28] and Polar Flow website [29] for the FC4 and Polar H10, respectively). A MATLAB script (The MathWorks Inc) was then used to synchronize the two devices’ HR measurements. Record alignments were performed by using a least square method to minimize squared deviation between FC4 and Polar H10 records, and they were smoothed over a 10-second window to calibrate the sample rate from both devices, as previously described [18,30]. Data normality was verified by a Kolmogorov-Smirnov test.

FC4 artifact data were defined as values that deviated from the criterion data by 20 beats per minute (bpm). Bland-Altman tests were performed on smoothed data to assess the accuracy of FC4 HR data by participant and by activity [31]. Means (bias) and SDs of the differences between the FC4 and H10 values were used to evaluate upper and lower limits of agreement (LOAs), per the following formula: upper/lower LOA = bias ± 1.96 × SD. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were calculated to quantify mean differences between FC4 and Polar H10 HR data. Two tests were performed to evaluate the reliability of the FC4: (1) 2-way random intraclass correlation coefficients (ICCs) with an absolute consistency type were calculated and interpreted according to current guidelines (ICC<0.5: poor; 0.5<ICC<0.75: moderate; 0.75<ICC<0.90: good; ICC>0.90: excellent reliability) [32], and (2) a computation of Lin concordance correlation coefficients (CCCs) was performed, interpreted following McBride’s [33] recommendations (CCC<0.90: poor; 0.90<CCC<0.95: moderate; 0.95<CCC<0.99: very good; CCC>0.99: almost perfect strength of agreement).

All statistical analyses were stratified by activity type, and a Kruskal-Wallis test was performed to compare activity bias, with activity types as independent groups. Mann-Whitney U tests were implemented to compare bias from 0 among activity types (independent groups). Further, a linear univariate model was used to estimate the effect of skin tone on bias while controlling the impact of activity type. Statistics were performed using IBM SPSS statistics 25 software (IBM Corp).

A total of 26 young adults (11 women and 15 men) were included in this study. Their characteristics are compiled in Table 1. In total, 77.5 hours of practice were recorded, distributed across 55 sessions (Table 2).

Biases, LOAs, and artifact percentages are shown in Table 3.

Biases were different between each activity (P<.001), with all of them also being different from 0 (P<.001). The lowest bias values, MAEs, and MAPEs were found for running and cycling, and the highest ones were found for badminton and soccer. Furthermore, the narrowest LOA width was found for running, and the widest was found for badminton (Figure 1). We found similar results by grouping activities; the lowest bias, MAE, MAPE, and LOA width were found for running activities (running and orienteering running), and the largest ones were found for racket sports (badminton and tennis; Table 3).

ICCs and CCCs are presented in Table 4. ICCs and CCCs indicated poor reliability (<0.50 and <0.90, respectively) for racket sports and soccer but excellent reliability for running overall (the total ICC and CCC for all running activities overall were >0.90 and >0.99, respectively). The largest percentages of artifact HR data were found for badminton and soccer, whereas running exhibited the lowest (Table 3). The results were similar when pooling activities; the highest rate of artifact HR data was found for racket sports, and the lowest was found for running sports (Table 3).

Mean Fitzpatrick Skin Scale scores are shown in Table 1. No overall interaction and no interaction in each activity were found between bias and skin tone, while being standardized by activity type.

In this study, we evaluated the HR accuracy and reliability of the FC4 by comparing it to the Polar H10 chest belt (criterion sensor) in non–laboratory-controlled conditions. Our results showed negative biases for most activities (except running and cycling; Table 3), the presence of artifact data, and HR underevaluation by the FC4 (Figure 2). These results are similar to earlier findings that show the tendency of PPG wrist sensors to overestimate or underestimate HR [34-36]. ICCs and CCCs were fluctuant, mainly depending on the activity type. The FC4 shows good reliability for running activities, with almost perfect and moderate CCCs and excellent and good ICCs for running and orienteering running, respectively. Additionally, running was the activity with the lowest bias, the lowest MAPE, and the smallest LOA width. On the other hand, we found lower ICCs for badminton, soccer, and cycling, which also showed higher artifact ratios. Thus, we suggest that the excessive amount of arm movement in these activities could affect HR recording, as previously shown [37]. Badminton and tennis are characterized by “sharp” movements and rotations of the nondominant arm, whereas soccer can induce some instability of the sensor due to random arm and wrist actions, which can result in watches sliding over skin and the transient loss of HR signals (Figure 2). Furthermore, although cycling shows an overall good CCC (0.971), the ICC (0.658), MAPE (8.1%, SD 11.0%), and LOA width (64.03 bpm) indicate a lack of reliability during this activity. Since cycling remains a lower limb cyclic activity, there are little to no arm movements that may affect sensor placement. However, wrist position on handlebars (eg, during road cycling) and the contractility of wrist muscle flexors and extensors could alter vascular arteriovenous system detection and lower signal quality while enhancing compression forces [6,8,16,17]. ICCs and CCCs were calculated for pooled activities—running sports (running and orienteering running) and racket sports (tennis and badminton). These activities mostly rely on the same corporal pattern, and grouping them allowed us to equilibrate the time of practice between other activities. Our data showed no differences in ICC or CCC parameters, with those for running sports and racket sports showing excellent and poor reliability, respectively (ICC: 0.926 vs 0.435; CCC: 0.996 vs 0.883; Table 4).

Overall, artifacts and random movements could highly affect a sensor’s precision. Some studies tried to reduce motion artifacts by using novel techniques, such as accelerometry coupling or algorithm-based processing, but there is no consensus yet on which one should be used [38-42]. Even by varying numbers of diodes, lights colors, and algorithms, the technical aspect for measuring HR remains similar, suggesting that some FC4 characteristics could increase the presence of motion artifacts. For example, the materials used for the strap was slippery on skin, amplified by exercise-induced sudation. In addition, from a general standpoint, algorithms used by manufacturers can also affect the recordings, but we did not have access to these proprietary processing scripts.

We observed no influence of skin tone on bias, unlike previous works that highlighted some effects on HR error rates [21]. However, our study is in line with another paper that was based on analyses of the signal to noise ratio, which showed no effect of skin tone when using a proper PPG wavelength (520 nm) [15]. More recently, another study did not find an effect of skin tone on beat-to-beat interval quality while separating skin types into two major groups (group 1: 1 to 4 on the Fitzpatrick Skin Scale; group 2: 5 and 6 on the Fitzpatrick Skin Scale) [43]. Even if our study findings are consistent with these results, no generalization can be made, since participants’ skin types ranged from 2 (n=6) to 4 (n=3) on the Fitzpatrick Skin Scale. Moreover, we did not take into account physiological and environment factors, such as local temperature, humidity, and sudation, which may impact peripheral vasomotricity and therefore increase or decrease PPG signal intensity [44-46]. It would also have been relevant to assess the influence of motion parameters, such as acceleration measured at the wrist, on HR accuracy and reliability of the FC4; however, we could not access raw data produced by the proprietary processes for further analyses, and it was not possible to use inertial units because these could have resulted in discomfort for the participants, and inertial units are not a commodity among the public. Furthermore, we chose to place the FC4 on the wrist of the nondominant arm, as this placement is recurrent in daily life and research, although we knew that the movements would be inferior to those of the dominant wrist and therefore would reduce the artifact rate. Finally, the low number of participants included for the running activity (n=3) should be considered while interpreting our results, although the pooled analysis lowered this potential bias while providing similar reliability and accuracy results.

A recent study evaluated the accuracy of the FC4 HR sensor against an ECG Holter monitor for activities of daily living (sitting, walking, typing, lying down, etc) and showed acceptable HR measurement capabilities [47]. The added value of our protocol comes from the ecological approach to the gear sensor validation, which included additional physical activities with various intensities and limb movements. To our knowledge, no study has evaluated the FC4 in this manner during multiple sports or activities yet. However, further studies should be conducted to measure the reliability of the FC4 and its parameters for activities of daily living (number of steps, number of stairs climbed, number of calories burned, and quality of sleep), especially among persons or patients whose physical activities are restricted, as observed in sedentary individuals, people with obesity, people with heart failure, etc.

Considering our results (ie, the lack of precision and reliability in specific activities), the FC4 should not be used for research or athlete training purposes, including those related to running, which showed the lowest LOA width and artifact ratio. However, the FC4 could be useful for tracking HR during daily activities, which does not require such accurate monitoring, and it may be considered for patients’ reeducation. However, for the latter, further studies should inspect the FC4’s reliability according to the characteristics of the population. For example, obesity affects the physiological factors necessary for proper PPG signal intensity and quality, such as capillary density and recruitment, blood flow, and skin thickness [44].

The FC4 shows excellent reliability for measuring HR during activities with slow and predictive arms movements, such as running. However, it should not be used for activities with “sharp” and random arm and wrist movements, such as soccer and racket sports, due to its sensitivity to motion artifacts. Hence, in ecological conditions, this device should not be used for research or training purposes due to the high artifact rate and LOA width.