Photoplethysmography Detection of Overreaching

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Sleep Deprivation Deteriorates Heart Rate Variability and Photoplethysmography

Introduction

Sleep deprivation has deleterious effects on cardiovascular health. Using wearable health trackers, non-invasive physiological signals, such as heart rate variability (HRV), photoplethysmography (PPG), and baroreflex sensitivity (BRS) can be analyzed for detection of the effects of partial sleep deprivation on cardiovascular responses.

Methods

Fifteen participants underwent 1 week of baseline recording (BSL, usual day activity and sleep) followed by 3 days with 3 h of sleep per night (SDP), followed by 1 week of recovery with sleep ad lib (RCV). HRV was recorded using an orthostatic test every morning [root mean square of the successive differences (RMSSD), power in the low-frequency (LF) and high-frequency (HF) bands, and normalized power nLF and nHF were computed]; PPG and polysomnography (PSG) were recorded overnight. Continuous blood pressure and psychomotor vigilance task were also recorded. A questionnaire of subjective fatigue, sleepiness, and mood states was filled regularly.

Results

RMSSD and HF decreased while nLF increased during SDP, indicating a decrease in parasympathetic activity and a potential increase in sympathetic activity. PPG parameters indicated a decrease in amplitude and duration of the waveforms of the systolic and diastolic periods, which is compatible with increases in sympathetic activity and vascular tone. PSG showed a rebound of sleep duration, efficiency, and deep sleep in RCV compared to BSL. BRS remained unchanged while vigilance decreased during SDP. Questionnaires showed an increased subjective fatigue and sleepiness during SDP.

Conclusion

HRV and PPG are two markers easily measured with wearable devices and modified by partial sleep deprivation, contradictory to BRS. Both markers showed a decrease in parasympathetic activity, known as detrimental to cardiovascular health.

Functional Overreaching in Endurance Athletes: A Necessity or Cause for Concern?

There are variable responses to short-term periods of increased training load in endurance athletes, whereby some athletes improve without deleterious effects on performance, while others show diminished exercise performance for a period of days to months. The time course of the decrement in performance and subsequent restoration, or super compensation, has been used to distinguish between the different stages of the fitness-fatigue adaptive continuum termed functional overreaching (FOR), non-functional overreaching (NFOR) or overtraining syndrome. The short-term transient training-induced decrements in performance elicited by increases in training load (i.e. FOR) are thought be a sufficient and necessary component of a training program and are often deliberately induced in training to promote meaningful physiological adaptations and performance super-compensation. Despite the supposition that deliberately inducing FOR in athletes may be necessary to achieve performance super-compensation, FOR has been associated with various negative cardiovascular, hormonal and metabolic consequences. Furthermore, recent studies have demonstrated dampened training and performance adaptations in FOR athletes compared to non-overreached athletes who completed the same training program or the same relative increase in training load. However, this is not always the case and a number of studies have also demonstrated substantial performance super-compensation in athletes who were classified as being FOR. It is possible that there are a number of contextual factors that may influence the metabolic consequences associated with FOR and classifying this training-induced state of fatigue based purely on a decrement in performance may be an oversimplification. Here, the most recent research on FOR in endurance athletes will be critically evaluated to determine (1) if there is sufficient evidence to indicate that inducing a state of FOR is necessary and required to induce a performance super-compensation; (2) the metabolic consequences that are associated with FOR; (3) strategies that may prevent the negative consequences of overreaching.

Muscle fiber typology is associated with the incidence of overreaching in response to overload training

Variability in the performance responses following an overload training period and subsequent taper was associated with the variation in the muscle fiber typology of the gastrocnemius. Runners with an estimated higher proportion of type I fibers (i.e., lower carnosine z-score) were able to maintain performance in response to an overload training period and subsequently achieve a superior performance supercompensation. These findings show that muscle fiber typology contributes to the variability in performance responses following training.