Exercise and Real-world Training Using Pulse Rate Variability: A Systematic Review of Wearable Photoplethysmography Technology and Real-world Interventions

Michael R. Coggins^*
*Correspondence: Michael R. Coggins, Replicate International Inc., Replicate International Inc., USA, Tel: +1 9177155967,

Keywords

Wearable devices • Photoplethysmography • PPG • PPG technology • Athletes • Training • Pulse rate variability • prv • Heart rate variability • Hrv • Exercise • Real-world data

Introduction

Training of athletes of all experience levels has a rich history of using noninvasive quantitative measurements of human physiology to direct exercise programs, inform recovery programs, and achieve performance milestones. Traditionally, the technology and methodology for these quantitative analytics has been developed first, or co-operatively, through clinical lab testing, followed by adoption and adaptation for usage by coaches, athletes, and practitioners. Here I compare and contrast findings with these devices operating in lab conditions versus real world conditions.

Academic physiology labs began experimenting with non-invasive measurement of athlete Heart Rates (HRs) to indirectly measure physiological effects of different exercise modalities in the 1950s [1]. These lab tests enabled HR measurement to move from the clinic into the real-world training environment and inform other indirect metrics in the 1970-1980s. One example metric, VO₂max, could be correlated with HR to enable real-world usage of useful metabolic training methods [2,3].

Concurrently, medical clinicians began experimenting with Electrocardiogram (ECG) measured HR and Heart Rate Variability (HRV) in the 1960-70s [4]. HRV measurement is a non-invasive, complex and composite measurement of athlete physiological response to different modes, intensities, and durations of exercise and exercise programs [5]. In addition to athletic studies, HRV has been analyzed in clinical studies of mental load and cognitive tasks [6,7], prediction of cardiac mortality [8,9], and modulation of the autonomic system amongst other states and syndromes [10,11]. When technology evolved to allow for portable, low-cost single-lead ECG systems (eg. PolarTec), HRV measurement moved out of the lab and into further innovation in the field through real-world athletic testing in the 2000s [12].

Photoplethysmography (PPG) used for HR-related measurements of cardio-pulmonary-autonomic function was theorized as early as 1937 [13]. The technological advancement of low-power and inexpensive Light-Emitting Diodes (LEDs) by 2001 led to the development of compatible devices [14]. At present, technology for reflectance PPG is characterized by the usage of one or more LEDs and a light reflectance sensor to non-invasively probe changes in reflectance of light from underlying tissues as a proxy for increasing/decreasing density of blood in arterial vasculature at the fingertip, ring, earlobe, wrist, or proximal forearm near the elbow [15]. PPG measures light reflection changes due to density changes of vascular light-reflecting compounds presumably directly related to mechanical expansion and contraction of blood volume and is influenced by heart rate [16], resting blood volume [17], Respiratory Sinus Arrhythmia (RSA) [18] and likely other physiological properties [19,20].

Similar to ECG-based technology [21], PPG-based technology moved from initial lab experiments to real-world usage with the rapid commercialization of wearable devices (eg. Nike Fuel Band, Garmin, FitBit), including smartphones, containing PPG sensors. However, the rapidity with which PPG technology and consequent recommendations was embraced by commercial manufacturers, and the enormous potential real-world usage of PPG sensors to inform exercise and recovery decisions, raises a question as to how much clinical testing has been performed using PPG during exercise or recovery, and for training or performance concerns. These questions form the basis of this review.

The purpose for this literature review is to compile updated information from studies that used wearable devices containing Photoplethysmography (PPG) technology intended for athletes, athletics, and performance training in the real-world setting. Analogous to the purpose of Real World Data (RWD) or Real World Evidence (RWE) trials [22], as continuing, useful supplements to lab-based, controlled-environment, clinical trials for understanding health decisions, this review is meant to better understand the usage of PPG-based wearable devices for athletics or training continuing after and outside of the lab environment. For data that focus more on lab-based non-commercial PPG devices in comparison to ECG, or focused on non-exercise conditions, please refer to the excellent reviews by, El-Amrawy F and Nounou MI [23], and Schäfer A and Vagedes J [24].

Consistent with our purpose, and the challenges of RWD/RWE experiments, we chose to focus on the type and location of PPG-hardware/ software technology utilized, and the analytic techniques and experimental methods employed by researchers, rather than focus on meta-data analysis or results (for meta-data analyses, see Georgiou K, et al. [25]). To better identify the continuing usage of wearable PPG technology for athletics, training and performance, our search and presentation is meant to identify areas of opportunity for experiments and data collection for performance training during real-world usage of wearable devices with PPG technology.

Methods

Scope

The scope for this literature review is restricted to primary experimental data from PPG technology used to monitor Pulse Rate Variability (PRV) as an input to training and exercise decisions for athletes and athletics. This review only considers portable PPG devices as acceptable technology and requires the experiments to include some form of exercise or movement designed to be significantly different in some physiologically meaningful measure from a resting state. We do allow the definition of athlete and athletics to be broad in order to encompass any subject, of any age, gender, or background as a potential athlete, as long as there is some condition used by experimenters intended to replicate athletic-like endeavors by the subjects.

Search

Searches of online and hardcover manuscripts consisted of two epochs: first, beginning in April, 2019 and continued through January, 2020, with a second epoch of Jan 1, 2024 continuing through Feb 2, 2024. Online searches were performed using PubMed, MEDLINE and the New York University (NYU) Search Portal. Physical searches of uncatalogued or missing manuscripts utilized the NYU Library system. Search terms included the following as an initial probe of the literature:

• ((("ppg" or "photoplethysmography") AND ("exercise" or "sport" or "sports" or "athletics" or "athletic") AND ("wearable" or "worn") and (hrv or "heart rate variability" or "PRV" or "pulse rate variability")))

· 192 records found

• ((((("wearable" or "wristworn" or "wrist-worn" or "watch technology" or "ppg")) AND ("hrv" or "heart rate variability")) AND ("time domain" or "time-domain" or "frequency domain" or "frequency-domain")) AND ("fft" or "hf/lf" or "lf/hf" or "sdnn" or "rmssd"))

· 19 records found

• ((("ppg" or "photoplethysmography")) AND ("wrist-worn" or "wrist worn" or "wrist")) AND ("exercise" or "sport" or "sports" or "athletics" or "athletic")

· 33 records found

Inclusion and exclusion criteria

Initial pruning of our search was based on duplicate data. If manuscripts contained the same data or utilized a previously created/used public dataset, the original paper with the most data using identical subjects, methods, etc. or the most recent paper was included if:

a) The paper(s) directly used human subjects, and

b) Contained the most data from the human subject sample population.

Review papers were utilized to find additional primary data not discovered by our search terms. However, review papers are not included in the results for discussing technology, experimental methods, or analytic techniques. Followon papers were excluded. Secondary pruning consisted of removing single subject case studies, studies involving no movement reasonably described as exercise (e.g. sleep studies) and manuscripts not written in the English language.

Results

Twenty-seven published papers were discovered utilizing wearable devices containing Photoplethysmography (PPG) as data collection technology for physiological adaptations during or after exercise. Significantly more primary literature has been produced using Electrocardiographic technology (ECG/EKG and variations) to understand subject responses during or postexercise; for reviews please see Plews DJ, et al. [26].

Therefore, instead of continued pruning and removing papers for consideration, I chose to categorize the manuscripts according to two hardware/methodological criteria:

1. PPG-based technology intended for lab usage (non-commercial usage or custom devices) vs. PPG-based technology intended for real-world usage (commercial or field usage)

2. Smartphone-based PPG measurement vs. Arm-wearable device PPG measurement

The first categorization of papers using wearable devices containing Photoplethysmography (PPG) and exercise, was based on whether the wearable device could be used for data collection, analysis, and recommendations outside of the lab environment by an athlete. Several, early papers utilized fingertip-based PPG, custom-built PPG devices, or products difficult to obtain in the commercial environment (eg. PulseOn) that would not be expected to be used by athletes outside of a lab environment. These papers are presented in (Table 1).

Our second categorization of papers was based on whether the data were recorded from a smartphone device or an arm-wearable, commercial product. The location where measurement is taken, expected style of usage, physical sensors integrated in the device, and software data pre-processing could be expected to be different between smartphone sensors and arm-wearable devices. Therefore, we categorized papers using smartphones to record data (Table 2) and papers using arm-wearable PPG technology (Table 3) as distinct. Our search returned 11 papers using lab-based, or non-commercial PPG-based systems (Table 1), 4 papers using smartphone-based PPG hardware/software (Table 2) and 12 papers using arm-wearable PPG-based commercial devices (Table 3).

Studies utilizing non-commercial PPG-based systems generally consisted of either finger-mounted PPG-based devices using infrared, red, or green wavelengths [27-29] or wrist-mounted PPG-based devices [30,31]. The papers we located generally used healthy subjects [18-20,32-34] although two notable papers looked at subjects with cardiovascular disease, and hypertension [29]. All the studies were observational in nature with one exception using changing ambient temperature [19]. Experiments focused on comparing PPG-based device data with ECG data as a measure of accuracy and all but one used treadmill-based walking/jogging/running to evaluate exercise effects on Heart Rate (HR). Unfortunately, none of these studies evaluated PRV.

Smartphone-based exercise data collected from PPG was only recorded in four papers to my knowledge. In these papers, iOS compliant phones were used in a manner where the camera acted as the PPG sensor in contact with the subject’s fingertip [35,36]. In one early paper the subjects tested were healthy young adults including elite athletes, however this study was observational and only evaluated guided breathing rates and did not include any PRV measurements [37]. For a second paper, the subjects tested were healthy young adults including elite athletes, all of which used a commercial application “HRV4Training” [36,38-41]. However this study was observational and only evaluated a particular parameter - lnRMSSD - over short time intervals of 1 minute [37]. In a third paper, healthy subjects had time- and frequencydomain PRV measurements recorded in a 5-minute epoch while seated; no exercises were performed in this observational study [42]. Finally, for the fourth paper, the study population consisted of clinical patients with Cardiovascular Disease (CVD) [35]. Patients were tested according to two treadmill protocols (Bruce protocol I and II) as well as at rest before exercise and during a 3-minute recovery. Unfortunately, this paper did not provide an in-depth analysis of PRV through time-domain, frequency-domain or non-stationary analytics.

As smartphones utilizing PPG technology require software, likely multiple layers of software, to enable interpretation of the sensor(s) signal, we additionally performed a pilot search of currently available smartphone applications claiming the ability to input and interpret Heart Rate Variability (HRV although we use PRV as a more apt acronym) for Android or iOS devices through either the Play Store (Android) or Apple Store (Apple). The results of this search are presented in Table 4 (Android) and Table 5 (iOS), where we include applications with 500+ downloads only. Our search of smartphone applications resulted in at least 21 applications for Android OS and 21, mostly overlapping, applications for iOS, available as of September 2019. Some of these applications required payment to access HRV (PRV) data and interpretation and/or recommendations, and many applications claimed to not need a sensor, or implied equivalency to ECG or clinical measurements of HRV.

Discussion

Papers evaluating arm-wearable PPG-based devices generally used healthy and pre-middle aged to middle-aged subjects. Two papers described their included subject pool as having previously performed some type of exercising in general [43,44] although they did not mention using athletes or subjects using some type of training regimen, and one paper had no restriction on exercise [12]. The types of activities used for experimental purposes in these papers included treadmill-based activities [23,44,45], elliptical exercise, stair climbing, stationary cycling, and light weight-lifting [43] as well as - notably - activities of daily living [46] and outdoor running [47]. All the studies were observational in nature, and unfortunately, none evaluated PRV.

In five of the papers, the PPG signal was recorded from a device attached to a fingertip and connected to an iOS-using smartphone with a commercial software program to analyze data (iThlete). In these studies, subjects were young and healthy and were tested in states including pre-season and seasonal exercise - in four studies [38-41] - and cycle exercise with choicereaction time (considered to require a form of sustained attention) tasks in two parallel study arms [48]. PRV was measured using a single time domain calculation, Root Mean Square of Standard Deviation (lnRMSSD), during the morning before any athletic activity and was not used as a decision metric for any training or exercise selection or modification.

This search of primary academic manuscripts using Photoplethysmography (PPG) technology employed on a wearable device for athletes, athletics, or training, identified a small number of papers (n=16). This outcome was surprising given the number of years since PPG was introduced as a technology able to record and calculate Pulse Rate (PR) and Pulse Rate Variability (PRV). Previous papers and reviews using ECG/EKG have shown sustained interest and experiments utilizing ECG/EKG technology for athletics, athletes and training that have developed ideas relating to training adaptation [26,49,50], recovery [51,52], detraining [53], exercise cycle programming [54], predicting athletic performance [55], or overtraining [5,56,57].

In addition, the rapid growth in sold wearable devices containing PPG, claiming the ability to sense, interpret, and analyze HRV (PRV), led us to anticipate that significantly more research had been presented:

a) Validating PPG as used by current, commercial devices

b) Validating comparisons of PRV from PPG signals from commercial devices to ECG signals, and

c) Employing PRV to better elucidate recommendations for athletic exercise.

The number of papers we uncovered was even lower when considering wearable devices available commercially rather than recording machines/ software custom built or meant for the lab environment [5,58,59]. A selection of these commercially available wearable devices claiming the ability to measure and analyze HRV (PRV), as of February 2024, is presented in Table 6.

Conclusion

In conclusion, this review did not identify a significant body of scientific literature using commercially available PPG devices to measure PRV and inform decisions for athletes, athletics, or exercise training. Academic, labbased studies offer an important class of research to validate technology, analytics, and utilized statistics, explore treatment variable responses, probe causation of perturbations, and inform reasonable recommendations based on data. Without a clinical body of literature, there is concern over the usage and reliability of recommendations that are currently available to the general public through wearable devices or smartphone applications. The easy availability and breadth of devices and applications for use by athletes outside of the lab, raises particularly important questions as these products claim PPG is a valid and equivalent measurement tool as HRV which has a longstanding and robust medical literature built upon its particular technology and underlying physiology.

Caution is required in evaluating commercial PPG-based PRV and drawing inferences in comparison to ECG-based HRV treatment conclusions or recommendations, particularly outside of the lab environment. Field studies (sometimes termed RWRD) are a complementary class of studies to lab-based Randomized Control Trials (RCTs) that may help address real-world usage, clinical decisions, adherence/compliance, potential benefits, and risks. Our literature search identified only observational trials and no controlled trials. Very few field studies have been attempted using a smartphone and of those, the device was used to record 1 minute HRV (PRV). There is currently a lack of data evaluating PPG for PRV, what PRV means physiologically in relation to athletics and exercise training, or how to use it over time to affect positive performance adaptations.

Recommendations for further studies

Recommendations for further studies include three broad themes

a) Further lab-based clinical trials to evaluate commercially available wearable devices using PPG technology to record PRV. These trials shouldn’t simply compare PRV to HRV, as there is reason to expect different responses based on different technology (ECG field electrical measurement vs. PPG diode light reflectance), different underlying physical processes (ECG-recorded electrical properties of the heart nerve cells vs. PPG-recorded light-reflecting compound density changes in distal blood vessels), and different effector physiological systems (ECG influenced by respiration/electrical properties/ endocrine system/thermoregulation/blood pressure/other(?) vs. PPG influenced by respiration [60]/blood pressure(?)/endocrine system(?)/ thermoregulation(?)/dehydration(?)/other(?)). These trials should be designed to take advantage of the best capabilities of lab-based clinical experimental design to probe what physiological processes affect PPG measurements from commercial devices.

b) Further Real-world Real Data Studies (RWRD, or field studies) to inform usage characteristics and capabilities for PPG-technology in commercially available wearable devices. These studies should incorporate not only observational analysis, but interventions and experiments designed to probe adherence/compliance, effectiveness, limitations and risks for an athlete in the real-world setting. In addition, the concept of exercise and training requires time for measurable effects. However, the vast majority of PPG technology studies to date appear to reflect on single episodes of contiguous time for data collection rather than repeated measures at frequent intervals to judge effectiveness of interventions and recommendations.

c) Usage and exploration of alternative, specialized measurements and analytic statistics to acknowledge the probable complimentary, rather than supplementary, nature of PRV to HRV and the intra-individual training needs for athletes as opposed to comparisons to a generalized sample population. Indeed, cases of extreme variation in measured HRV values [26] and diametrically opposed anticipated performance results after ECG-based HRV measurements [61] have already been noted with healthy, younger subjects and elite-level athletes.

Acknowledgement

Authors would like to extend gratitude to Aaron McGee PhD, David Mordecai PhD, and Samantha Kappagoda for critical reading of this manuscript. In addition, authors would like to thank Lawler Watkins for significant help in manuscript preparation.

Conflict of Interest

The author states no conflicts of interests. M. R. Coggins currently works for Replicate International Inc and Equinox Holdings Inc. Replicate International acts in advisory, technological review and educational roles related to technology, health and wellness, and athletics and performance. Neither Replicate International nor Equinox Holdings have current business relationship to any companies manufacturing or selling wearable technology. All work in preparation of this manuscript was completed under the auspices of Replicate International, solely completed by M. R. Coggins, and does not represent the views or work of Numerati Partners nor Equinox Holdings Inc.

References

1. Rowell, Loring B., Henry L. Taylor and Yang Wang. "Limitations to prediction of maximal oxygen intake." J Appl Physiol 19 (1964): 919-927.

   Google Scholar, Crossref, Indexed at

2. Jetté, Maurice, John Campbell, Jean Mongeon and Richard Routhier. "The Canadian home fitness test as a predictor of aerobic capacity." Can Med Assoc J 114 (1976): 680.

   Google Scholar, Indexed at

3. Shephard, RJ. “Article Sports Medicine I.” (1984).
4. Billman, George E. "Heart rate variability-a historical perspective." Front Physiol 2 (2011): 16984.

   Google Scholar, Crossref, Indexed at

5. Aubert, André E., Bert Seps and Frank Beckers. "Heart rate variability in athletes." Sports Med 33 (2003): 889-919.

   Google Scholar, Crossref, Indexed at

6. Mulder, G and W. R. E. H. Mulder-Hajonides van der Meulen. "Mental load and the measurement of heart rate variability." Ergon 16 (1973): 69-83.

   Google Scholar, Crossref, Indexed at

7. Zwaga, Harm Jitse Gerard. "Psychophysiological reactions to mental tasks: Effort or stress?." Ergon 16 (1973): 61-67.

   Google Scholar, Crossref, Indexed at

8. Camm, A. John, Marek Malik, J. Thomas Bigger and Günter Breithardt, et al. "Heart rate variability: Standards of measurement, physiological interpretation and clinical use. Task force of the European society of cardiology and the north American society of pacing and electrophysiology." Circ 93 (1996): 1043-1065.

   Google Scholar, Crossref, Indexed at

9. Kleiger, Robert E., J. Philip Miller, J. Thomas Bigger Jr and Arthur J. Moss. "Decreased heart rate variability and its association with increased mortality after acute myocardial infarction." Am J Card 59 (1987): 256-262.

   Google Scholar, Crossref, Indexed at

10. Chapleau, Mark W. and Rasna Sabharwal. "Methods of assessing vagus nerve activity and reflexes." Heart Fail Rev 16 (2011): 109-127.

    Google Scholar, Crossref, Indexed at

11. Freeman, Roy and Mark W. Chapleau. "Testing the autonomic nervous system." Handb Clin Neurol 115 (2013): 115-136.

    Google Scholar, Crossref, Indexed at

12. Dong, Jin-Guo. "The role of heart rate variability in sports physiology." Exp Ther Med 11 (2016): 1531-1536.

    Google Scholar, Crossref, Indexed at

13. Abramson, DI, K. Jochim and E.A. Ctjller. Downloaded from www.physiology.org/journal/ajplegacy at New York Univ (128.122.149.092) on [Internet].
14. Antink, Christoph Hoog, Steffen Leonhardt and Marian Walter. "Local interval estimation improves accuracy and robustness of heart rate variability derivation from photoplethysmography." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE (2018): 3558-3561.

    Google Scholar, Crossref, Indexed at

15. Allen, John. "Photoplethysmography and its application in clinical physiological measurement." Physiol Meas 28 (2007): R1.

    Google Scholar, Crossref, Indexed at

16. Hayano, Junichiro, Allan Kardec Barros, Atsunori Kamiya and Nobuyuki Ohte, et al. "Assessment of pulse rate variability by the method of pulse frequency demodulation." Biomed Eng Online 4 (2005): 1-12.

    Google Scholar, Crossref, Indexed at

17. Lu, Guohua, F. Yang, J. Andrew Taylor and John F. Stein. "A comparison of photoplethysmography and ECG recording to analyse heart rate variability in healthy subjects." J Med Eng Technol 33 (2009): 634-641.

    Google Scholar, Crossref, Indexed at

18. Allen, John and Alan Murray. "Age-related changes in the characteristics of the photoplethysmographic pulse shape at various body sites." Physiol Meas 24 (2003): 297.

    Google Scholar, Crossref, Indexed at

19. Shin, Hangsik. "Ambient temperature effect on pulse rate variability as an alternative to heart rate variability in young adult." J Clin Monit Comput 30 (2016): 939-948.

    Google Scholar, Crossref, Indexed at

20. Gil, Eduardo, Michele Orini, Raquel Bailon and José María Vergara, et al. "Photoplethysmography pulse rate variability as a surrogate measurement of heart rate variability during non-stationary conditions." Physiol Meas 31 (2010): 1271.

    Google Scholar, Crossref, Indexed at

21. Institute of Electrical and Electronics Engineers. Kerala Section, IEEE Region 10, Institute of Electrical and Electronics Engineers. IEEE TENSYMP 2017 : IEEE International Symposium on Technologies for Smart Cities: 14-16 July, 2017, Kochi, Kerala, India.
22. Reyners, A. K. L., B. P. C. Hazenberg, W. D. Reitsma and A. J. Smit. "Heart rate variability as a predictor of mortality in patients with AA and AL amyloidosis." Eur Heart J 23 (2002): 157-161.

    Google Scholar, Crossref, Indexed at

23. El-Amrawy, Fatema and Mohamed Ismail Nounou. "Are currently available wearable devices for activity tracking and heart rate monitoring accurate, precise, and medically beneficial?." Healthc Inform Res 21 (2015): 315.

    Google Scholar, Crossref, Indexed at

24. Schäfer, Axel and Jan Vagedes. "How accurate is pulse rate variability as an estimate of heart rate variability?: A review on studies comparing photoplethysmographic technology with an electrocardiogram." Int J Cardiol 166 (2013): 15-29.

    Google Scholar, Crossref, Indexed at

25. Georgiou, Konstantinos, Andreas V. Larentzakis, Nehal N. Khamis and Ghadah I. Alsuhaibani, et al. "Can wearable devices accurately measure heart rate variability? A systematic review." Folia Medica 60 (2018): 7-20.

    Google Scholar, Crossref, Indexed at

26. Plews, Daniel J., Paul B. Laursen, Jamie Stanley and Andrew E. Kilding, et al. "Training adaptation and heart rate variability in elite endurance athletes: Opening the door to effective monitoring." Sports Med 43 (2013): 773-781.

    Google Scholar, Crossref, Indexed at

27. Shah, Mansoor Hussain, Syed Absar Kazmi, Khairul Azami Sidek and Sheroz Khan. "Power Spectrum Density based analysis of Photolythsmographic signal for different physiological conditions." In 2014 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), IEEE (2014): 1-6.

    Google Scholar, Crossref, Indexed at

28. IEEE Engineering in Medicine and Biology Society. Annual International Conference (38th : 2016 : Orlando Fla), IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 16-20 Aug. 2016.
29. Lan, Kun-chan, Paweeya Raknim, Wei-Fong Kao and Jyh-How Huang. "Toward hypertension prediction based on PPG-derived HRV signals: A feasibility study." J Med Syst 42 (2018): 1-7.

    Google Scholar, Crossref, Indexed at

30. Zhang, Zhilin, Zhouyue Pi and Benyuan Liu. "TROIKA: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise." IEEE Trans Biomed Eng 62 (2014): 522-531.

    Google Scholar, Crossref

31. Chriskos, P., J. Munro, V. Mygdalis and I. Pitas. "Hindering. In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP 2017): Proceedings of a meeting held 14-16 November 2017, Montreal, Quebec, Canada. Institute of Electrical and Electronics Engineers (IEEE)." (2017): 403-307

    Google Scholar

32. Selvaraj, Nandakumar, Ashok Jaryal, Jayashree Santhosh and Kishore K. Deepak, et al. "Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography." J Med Eng Technol 32 (2008): 479-484.

    Google Scholar, Crossref, Indexed at

33. IEEE Engineering in Medicine and Biology Society. Annual International Conference (36th : 2014 : Chicago Ill), IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE : date 26-30 Aug. 2014.
34. IEEE Engineering in Medicine and Biology Society. Annual International Conference (37th : 2015 : Milan I, IEEE Engineering in Medicine and Biology Society, Institute of Electrical and Electronics Engineers. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) : date 25-29 Aug. 2015.
35. Lee, Eun Sun, Jin Seok Lee, Min Cheol Joo and Ji Hee Kim, et al. "Accuracy of heart rate measurement using smartphones during treadmill exercise in male patients with ischemic heart disease."Ann Rehabil Med 41 (2017): 129.

    Google Scholar, Crossref, Indexed at

36. Altini, Marco and Oliver Amft. "HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application." In 2016 38th Annu Int Conf IEEE Eng Med Biol Soc (EMBC), IEEE (2016): 2610-2613.

    Google Scholar, Crossref, Indexed at

37. Plews, Daniel J., Ben Scott, Marco Altini and Matt Wood, et al. "Comparison of heart-rate-variability recording with smartphone photoplethysmography, polar H7 chest strap, and electrocardiography." Int J Sports Physiol Perform 12 (2017): 1324-1328.

    Google Scholar, Crossref, Indexed at

38. Flatt, Andrew A. and Michael R. Esco. "Evaluating individual training adaptation with smartphone-derived heart rate variability in a collegiate female soccer team."J Strength Cond Res 30 (2016): 378-385.

    Google Scholar, Crossref, Indexed at

39. Flatt, Andrew A., Michael R. Esco and Fábio Y. Nakamura. "Individual heart rate variability responses to preseason training in high level female soccer players." J Strength Cond Res 31 (2017): 531-538.

    Google Scholar, Crossref, Indexed at

40. Flatt, Andrew A., Michael R. Esco, Jeff R. Allen and James B. Robinson, et al. "Heart rate variability and training load among national collegiate athletic association division 1 college football players throughout spring camp."J Strength Cond Res 32 (2018): 3127-3134.

    Google Scholar, Crossref, Indexed at

41. Flatt, Andrew A., Bjoern Hornikel and Michael R. Esco. "Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers."J Sci Med Sport 20 (2017): 606-610.

    Google Scholar, Crossref, Indexed at

42. Bánhalmi, András, János Borbás, Márta Fidrich and Vilmos Bilicki, et al. "Analysis of a pulse rate variability measurement using a smartphone camera." J Healthc Eng 2018 (2018).

    Google Scholar, Crossref, Indexed at

43. Spierer, David K., Zohn Rosen, Leib L. Litman and Kenji Fujii. "Validation of photoplethysmography as a method to detect heart rate during rest and exercise." J Med Eng Technol 39 (2015): 264-271.

    Google Scholar, Crossref, Indexed at

44. Stahl, Sarah E., Hyun-Sung An, Danae M. Dinkel and John M. Noble, et al. "How accurate are the wrist-based heart rate monitors during walking and running activities? Are they accurate enough?."BMJ Open Sport Exerc Med 2 (2016): e000106.

    Google Scholar, Crossref, Indexed at

45. Dooley, Erin E., Natalie M. Golaszewski and John B. Bartholomew. "Estimating accuracy at exercise intensities: A comparative study of self-monitoring heart rate and physical activity wearable devices." JMIR Mhealth Uhealth 5 (2017): e7043.

    Google Scholar, Crossref, Indexed at

46. Gorny, Alexander Wilhelm, Seaw Jia Liew, Chuen Seng Tan and Falk Müller-Riemenschneider. "Fitbit charge HR wireless heart rate monitor: Validation study conducted under free-living conditions." JMIR Mhealth Uhealth 5 (2017): e8233.

    Google Scholar, Crossref, Indexed at

47. Parak, Jakub, Maria Uuskoski, Jan Machek and Ilkka Korhonen. "Estimating heart rate, energy expenditure, and physical performance with a wrist photoplethysmographic device during running."JMIR Mhealth Uhealth 5 (2017): e7437.

    Google Scholar, Crossref, Indexed at

48. Heathers, James AJ. "Smartphone-enabled pulse rate variability: An alternative methodology for the collection of heart rate variability in psychophysiological research."                 Int J Psychophysiol 89 (2013): 297-304.

    Google Scholar, Crossref, Indexed at

49. Heffernan, Kevin S., Christopher A. Fahs, Kevin K. Shinsako and Sae Young Jae, et al. "Heart rate recovery and heart rate complexity following resistance exercise training and detraining in young men."Am J Physiol Heart Circ Physiol 293 (2007): H3180-H3186.

    Google Scholar, Crossref, Indexed at

50. Prinsloo, Gabriell E., HG Laurie Rauch and Wayne E. Derman. "A brief review and clinical application of heart rate variability biofeedback in sports, exercise, and rehabilitation medicine."Phys Sportsmed 42 (2014): 88-99.

    Google Scholar, Crossref, Indexed at

51. Chen, Jui-Lien, Ding-Peng Yeh, Jo-Ping Lee and Chung-Yu Chen, et al. "Parasympathetic nervous activity mirrors recovery status in weightlifting performance after training."J Strength Cond Res 25 (2011): 1546-1552.

    Google Scholar, Crossref, Indexed at

52. Edmonds, R. C., W. H. Sinclair and A. S. Leicht. "Effect of a training week on heart rate variability in elite youth rugby league players." Int J Sports Med (2013): 1087-1092.

    Google Scholar, Crossref, Indexed at

53. Gamelin, Francois-Xavier, Serge Berthoin, H. Sayah and C. Libersa, et al. "Effect of training and detraining on heart rate variability in healthy young men." Int J Sports Med (2007): 564-570.

    Google Scholar, Crossref, Indexed at

54. Kiviniemi, Antti M., Arto J. Hautala, Hannu Kinnunen and Mikko P. Tulppo. "Endurance training guided individually by daily heart rate variability measurements." Eur J Appl Physiol 101 (2007): 743-751.

    Google Scholar, Crossref, Indexed at

55. Buchheit, M., M. B. Simpson, H. Al Haddad and P. C. Bourdon, et al. "Monitoring changes in physical performance with heart rate measures in young soccer players." Eur J Appl Physiol 112 (2012): 711-723.

    Google Scholar, Crossref, Indexed at

56. Hynynen, E. S. A., Arja Uusitalo, Niilo Konttinen and Heikki Rusko. "Heart rate variability during night sleep and after awakening in overtrained athletes." Med Sci Sports Exerc 38 (2006): 313-317.

    Google Scholar, Crossref, Indexed at

57. Baumert, Mathias, Lars Brechtel, Jürgen Lock and Mario Hermsdorf, et al. "Heart rate variability, blood pressure variability, and baroreflex sensitivity in overtrained athletes." Clin J Sport Med16 (2006): 412-417.

    Google Scholar, Crossref, Indexed at

58. Bellenger, Clint R., Dean Miller, Shona L. Halson and Gregory D. Roach, et al. "Evaluating the typical day-to-day variability of WHOOP-derived heart rate variability in olympic water polo athletes." Sensors 22 (2022): 6723.

    Google Scholar, Crossref, Indexed at

59. Chen, Xiang, Yuan-Yuan Huang, Feng Yun and Tian-Jun Chen, et al. "Effect of changes in sympathovagal balance on the accuracy of heart rate variability obtained from photoplethysmography." Exp Ther Med 10 (2015): 2311-2318.

    Google Scholar, Crossref, Indexed at

60. Plews, Daniel J., Paul B. Laursen, Andrew E. Kilding and Martin Buchheit. "Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison." Eur J Appl Physiol 112 (2012): 3729-3741.

    Google Scholar, Crossref, Indexed at

61. Yuda, Emi, Muneichi Shibata, Yuki Ogata and Norihiro Ueda, et al. "Pulse rate variability: A new biomarker, not a surrogate for heart rate variability." J Phys Anthropol 39 (2020): 1-4.

    Google Scholar, Crossref, Indexed at