Variable agreement between wearable heart rate monitors during exercise in cystic fibrosis

Impact of Digital Technologies on Clinical Care for Adults with Cystic Fibrosis

The coronavirus disease 2019 pandemic accelerated the implementation of digital technologies, which have now become embedded as essential tools for the management of chronic disease, including cystic fibrosis (CF). Despite subsequent easing of restrictions and because of improved clinical stability resulting from the introduction of highly effective modulator therapy, digital technologies including video and telephone consultations and remote monitoring are likely to remain integral to the future delivery of CF health care. In this article, we explore some of the key developments in digital technologies, barriers to their adoption, and how the CF community is likely to embrace lessons learned from the recent pandemic to help modernize and reshape the future of CF care.

Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis

BACKGROUND

Although it is widely recognized that physical activity is an important determinant of health, assessing this complex behavior is a considerable challenge.

OBJECTIVE

The purpose of this systematic review and meta-analysis is to examine, quantify, and report the current state of evidence for the validity of energy expenditure, heart rate, and steps measured by recent combined-sensing Fitbits.

METHODS

We conducted a systematic review and Bland-Altman meta-analysis of validation studies of combined-sensing Fitbits against reference measures of energy expenditure, heart rate, and steps.

RESULTS

A total of 52 studies were included in the systematic review. Among the 52 studies, 41 (79%) were included in the meta-analysis, representing 203 individual comparisons between Fitbit devices and a criterion measure (ie, n=117, 57.6% for heart rate; n=49, 24.1% for energy expenditure; and n=37, 18.2% for steps). Overall, most authors of the included studies concluded that recent Fitbit models underestimate heart rate, energy expenditure, and steps compared with criterion measures. These independent conclusions aligned with the results of the pooled meta-analyses showing an average underestimation of -2.99 beats per minute (k comparison=74), -2.77 kcal per minute (k comparison=29), and -3.11 steps per minute (k comparison=19), respectively, of the Fitbit compared with the criterion measure (results obtained after removing the high risk of bias studies; population limit of agreements for heart rate, energy expenditure, and steps: -23.99 to 18.01, -12.75 to 7.41, and -13.07 to 6.86, respectively).

CONCLUSIONS

Fitbit devices are likely to underestimate heart rate, energy expenditure, and steps. The estimation of these measurements varied by the quality of the study, age of the participants, type of activities, and the model of Fitbit. The qualitative conclusions of most studies aligned with the results of the meta-analysis. Although the expected level of accuracy might vary from one context to another, this underestimation can be acceptable, on average, for steps and heart rate. However, the measurement of energy expenditure may be inaccurate for some research purposes.

Evaluating the Reproducibility of Physiological Stress Detection Models

Recent advances in wearable sensor technologies have led to a variety of approaches for detecting physiological stress. Even with over a decade of research in the domain, there still exist many significant challenges, including a near-total lack of reproducibility across studies. Researchers often use some physiological sensors (custom-made or off-the-shelf), conduct a study to collect data, and build machine-learning models to detect stress. There is little effort to test the applicability of the model with similar physiological data collected from different devices, or the efficacy of the model on data collected from different studies, populations, or demographics. This paper takes the first step towards testing reproducibility and validity of methods and machine-learning models for stress detection. To this end, we analyzed data from 90 participants, from four independent controlled studies, using two different types of sensors, with different study protocols and research goals. We started by evaluating the performance of models built using data from one study and tested on data from other studies. Next, we evaluated new methods to improve the performance of stress-detection models and found that our methods led to a consistent increase in performance across all studies, irrespective of the device type, sensor type, or the type of stressor. Finally, we developed and evaluated a clustering approach to determine the stressed/not-stressed classification when applying models on data from different studies, and found that our approach performed better than selecting a threshold based on training data. This paper's thorough exploration of reproducibility in a controlled environment provides a critical foundation for deeper study of such methods, and is a prerequisite for tackling reproducibility in free-living conditions.

Heart Rate Behavior in Speed Climbing

Speed climbing is an Olympic discipline within the combined sport climbing event in 2020 for the first time. Speed climbing is a high-speed and anaerobic exercise against gravity over a few seconds with extreme psychological pressure. Although there is some literature on heart rate (HR) when lead climbing, there is no literature on the behavior of the HR when speed climbing. The HR of seven near-elite participants was measured with a Polar HR monitor while climbing a 10- and 15-m wall, respectively, three times each, with pauses of 5 min between the first and last three climbs and a 20-min pause between the third and fourth climb. The average climbing times on the 10- and 15-m walls were 9.16 ± 3.06 s and 14.95 ± 3.14 s, respectively (data pooled between climbing heights). The peak HR on the 10- and 15-m walls were 164.57 ± 7.45 bpm and 176.43 ± 8.09 bpm. The rates of change in HR were as follows: average HR acceleration before peak HR, 2.53 ± 0.80 bpm/s; peak HR acceleration before peak HR, 4.16 ± 1.08 bpm/s; and average HR deceleration after peak HR, −0.98 ± 0.30 bpm/s. The average HR during the pauses ranged from 105.80 to 117.89 bpm. From the results, in comparison to the literature, we conclude that athletes, trained for sustaining high physical exertion and psychological pressure, have a far smaller HR acceleration than untrained people during light and unstressful exercises. Furthermore, the current rule that athletes shall have a minimum resting time of 5 min between climbing attempts during a speed climbing competition seems justified as sufficient time for HR recovery.

Validity of Wrist-Worn photoplethysmography devices to measure heart rate: A systematic review and meta-analysis

Heart rate (HR), when combined with accelerometry, can dramatically improve estimates of energy expenditure and sleep. Advancements in technology, via the development and introduction of small, low-cost photoplethysmography devices embedded within wrist-worn consumer wearables, have made the collection of heart rate (HR) under free-living conditions more feasible. This systematic review and meta-analysis compared the validity of wrist-worn HR estimates to a criterion measure of HR (electrocardiography ECG or chest strap). Searches of PubMed/Medline, Web of Science, EBSCOhost, PsycINFO, and EMBASE resulted in a total of 44 articles representing 738 effect sizes across 15 different brands. Multi-level random effects meta-analyses resulted in a small mean difference (beats per min, bpm) of -0.40 bpm (95 confidence interval (CI) -1.64 to 0.83) during sleep, -0.01 bpm (-0.02 to 0.00) during rest, -0.51 bpm (-1.60 to 0.58) during treadmill activities (walking to running), while the mean difference was larger during resistance training (-7.26 bpm, -10.46 to -4.07) and cycling (-4.55 bpm, -7.24 to -1.87). Mean difference increased by 3 bpm (2.5 to 3.5) per 10 bpm increase of HR for resistance training. Wrist-worn devices that measure HR demonstrate acceptable validity compared to a criterion measure of HR for most common activities.