Wearable Finger Pulse Oximetry for Continuous Oxygen Saturation Measurements During Daily Home Routines of Patients With Chronic Obstructive Pulmonary Disease (COPD) Over One Week: Observational Study

Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study

BACKGROUND

Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet.

OBJECTIVE

The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD.

METHODS

We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings.

RESULTS

In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively.

CONCLUSIONS

Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

The benefits and drawbacks of home oxygen therapy for COPD: what's next?

INTRODUCTION

Home oxygen therapy is one of the few interventions that can improve survival in patients with chronic obstructive pulmonary disease (COPD) when administered appropriately, although it may cause side effects and be an unnecessary burden for some patients.

AREAS COVERED

This narrative review summarizes the current literature on the assessment of hypoxemia, different types of home oxygen therapy, potential beneficial and adverse effects, and emerging research on home oxygen therapy in COPD. A literature search was performed using MEDLINE and EMBASE up to January 2024, with additional articles being identified through clinical guidelines.

EXPERT OPINION

Hypoxemia is common in patients with more severe COPD. Long-term oxygen therapy is established to prolong survival in patients with chronic severe resting hypoxemia. Conversely, in the absence of chronic severe resting hypoxemia, home oxygen therapy has an unclear or conflicting evidence base, including for palliation of breathlessness, and is generally not recommended. However, beneficial effects in some patients cannot be precluded. Evidence is emerging on the optimal daily duration of oxygen use, the role of high-flow and auto-titrated oxygen therapy, improved informed decision-making, and telemonitoring. Further research is needed to validate novel oxygen delivery systems and monitoring tools and establish long-term effects of ambulatory oxygen therapy in COPD.

Chronic hypoxia impairs skeletal muscle repair via HIF-2α stabilization

BACKGROUND

Chronic hypoxia and skeletal muscle atrophy commonly coexist in patients with COPD and CHF, yet the underlying physio-pathological mechanisms remain elusive. Muscle regeneration, driven by muscle stem cells (MuSCs), holds therapeutic potential for mitigating muscle atrophy. This study endeavours to investigate the influence of chronic hypoxia on muscle regeneration, unravel key molecular mechanisms, and explore potential therapeutic interventions.

METHODS

Experimental mice were exposed to prolonged normobaric hypoxic air (15% pO2, 1 atm, 2 weeks) to establish a chronic hypoxia model. The impact of chronic hypoxia on body composition, muscle mass, muscle strength, and the expression levels of hypoxia-inducible factors HIF-1α and HIF-2α in MuSC was examined. The influence of chronic hypoxia on muscle regeneration, MuSC proliferation, and the recovery of muscle mass and strength following cardiotoxin-induced injury were assessed. The muscle regeneration capacities under chronic hypoxia were compared between wildtype mice, MuSC-specific HIF-2α knockout mice, and mice treated with HIF-2α inhibitor PT2385, and angiotensin converting enzyme (ACE) inhibitor lisinopril. Transcriptomic analysis was performed to identify hypoxia- and HIF-2α-dependent molecular mechanisms. Statistical significance was determined using analysis of variance (ANOVA) and Mann-Whitney U tests.

RESULTS

Chronic hypoxia led to limb muscle atrophy (EDL: 17.7%, P < 0.001; Soleus: 11.5% reduction in weight, P < 0.001) and weakness (10.0% reduction in peak-isometric torque, P < 0.001), along with impaired muscle regeneration characterized by diminished myofibre cross-sectional areas, increased fibrosis (P < 0.001), and incomplete strength recovery (92.3% of pre-injury levels, P < 0.05). HIF-2α stabilization in MuSC under chronic hypoxia hindered MuSC proliferation (26.1% reduction of MuSC at 10 dpi, P < 0.01). HIF-2α ablation in MuSC mitigated the adverse effects of chronic hypoxia on muscle regeneration and MuSC proliferation (30.9% increase in MuSC numbers at 10 dpi, P < 0.01), while HIF-1α ablation did not have the same effect. HIF-2α stabilization under chronic hypoxia led to elevated local ACE, a novel direct target of HIF-2α. Notably, pharmacological interventions with PT2385 or lisinopril enhanced muscle regeneration under chronic hypoxia (PT2385: 81.3% increase, P < 0.001; lisinopril: 34.6% increase in MuSC numbers at 10 dpi, P < 0.05), suggesting their therapeutic potential for alleviating chronic hypoxia-associated muscle atrophy.

CONCLUSIONS

Chronic hypoxia detrimentally affects skeletal muscle regeneration by stabilizing HIF-2α in MuSC and thereby diminishing MuSC proliferation. HIF-2α increases local ACE levels in skeletal muscle, contributing to hypoxia-induced regenerative deficits. Administration of HIF-2α or ACE inhibitors may prove beneficial to ameliorate chronic hypoxia-associated muscle atrophy and weakness by improving muscle regeneration under chronic hypoxia.

Long-term effect of non-severe COVID-19 on pulmonary function, exercise capacities and physical activities: a cross-section study in Sakaka Aljouf

Background

COVID-19 has serious consequences on different body systems particularly the respiratory system with its impact on pulmonary function, exercise capacities, and physical activities. This study aimed to investigate the long-term effect of COVID-19 on pulmonary function, exercise capacities, and physical activities in patients with non-severe COVID-19.

Methods

160 individuals were selected to participate in a cross-section study. Group-I: 80 male and female patients with non-severe COVID-19 at least 3 months after the recovery time. Group-II: 80 male and female matched (non-infected with COVID-19) participants. The spirometer, six-minute walk test (6MWT), and International Physical Activity Questionnaire (IPAQ) were used to assess pulmonary function, exercise capacities, and physical activities respectively. The Kolmogorov-Smirnov test was used to test normality of data. The Mann-Whitney and independent t-tests were used to compare the significant differences between both groups.

Results

The results show significant differences in FVC & FEV 1 of the pulmonary function, exercise capacities, and physical activities of the work & transportations between both COVID-19 and matched groups p-value = (0.001 & 0.001, 0.001 and 0.005 & 0.012) respectively.

Conclusion

Pulmonary function, exercise capacities, and physical activities are negatively influenced by COVID-19 as long-term consequences indicating the need for extended health care, and prescription of proper rehabilitative training programs for non- severe COVID-19 patients whatever their severity degree of infection or history of hospitalization. Outcome reflections of the current results raise awareness of physical therapists to the importance of the proper rehabilitative training programs for non-severe COVID-19 patients.

Acceptance of the Apple Watch Series 6 for Telemonitoring of Older Adults With Chronic Obstructive Pulmonary Disease: Qualitative Descriptive Study Part 1

BACKGROUND

The Apple Watch is not a medical device per se; it is a smart wearable device that is increasingly being used for health monitoring. Evidence exists that the Apple Watch Series 6 can reliably measure blood oxygen saturation (SpO2) in patients with chronic obstructive pulmonary disease under controlled circumstances.

OBJECTIVE

This study aimed to better understand older adults' acceptance of the Watch as a part of telemonitoring, even with these advancements.

METHODS

This study conducted content analysis on data collected from 10 older adults with chronic obstructive pulmonary disease who consented to wear the Watch.

RESULTS

Using the Extended Unified Theory of Acceptance and Use of Technology model, results showed that participants experienced potential health benefits; however, the inability of the Watch to reliably measure SpO2 when in respiratory distress was concerning. Participants' level of tech savviness varied, which caused some fear and frustration at the start, yet all felt supported by family and would have explored more features if they owned the Watch. All agreed that the Watch is mainly a medical tool and not a gadget.

CONCLUSIONS

To conclude, although the Watch may enhance their physical health and well-being, results indicated that participants are more likely to accept the Watch if it ultimately proves to be useful when experiencing respiratory distress.

Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases

INTRODUCTION

Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

AREAS COVERED

This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

EXPERT OPINION

Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study

BACKGROUND

Patients with chronic respiratory diseases and those in the postdischarge period following hospitalization because of COVID-19 are particularly vulnerable, and little is known about the changes in their symptoms and physiological parameters. Continuous remote monitoring of physiological parameters and symptom changes offers the potential for timely intervention, improved patient outcomes, and reduced health care costs.

OBJECTIVE

This study investigated whether a real-time multimodal program using commercially available wearable technology, home-based Bluetooth-enabled spirometers, finger pulse oximeters, and smartphone apps is feasible and acceptable for patients with chronic respiratory diseases, as well as the value of low-burden, long-term passive data collection.

METHODS

In a 3-arm prospective observational cohort feasibility study, we recruited 60 patients from the Royal Free Hospital and University College Hospital. These patients had been diagnosed with interstitial lung disease, chronic obstructive pulmonary disease, or post-COVID-19 condition (n=20 per group) and were followed for 180 days. This study used a comprehensive remote monitoring system designed to provide real-time and relevant data for both patients and clinicians. Data were collected using REDCap (Research Electronic Data Capture; Vanderbilt University) periodic surveys, Remote Assessment of Disease and Relapses-base active app questionnaires, wearables, finger pulse oximeters, smartphone apps, and Bluetooth home-based spirometry. The feasibility of remote monitoring was measured through adherence to the protocol, engagement during the follow-up period, retention rate, acceptability, and data integrity.

RESULTS

Lowest-burden passive data collection methods, via wearables, demonstrated superior adherence, engagement, and retention compared with active data collection methods, with an average wearable use of 18.66 (SD 4.69) hours daily (77.8% of the day), 123.91 (SD 33.73) hours weekly (72.6% of the week), and 463.82 (SD 156.70) hours monthly (64.4% of the month). Highest-burden spirometry tasks and high-burden active app tasks had the lowest adherence, engagement, and retention, followed by low-burden questionnaires. Spirometry and active questionnaires had the lowest retention at 0.5 survival probability, indicating that they were the most burdensome. Adherence to and quality of home spirometry were analyzed; of the 7200 sessions requested, 4248 (59%) were performed. Of these, 90.3% (3836/4248) were of acceptable quality according to American Thoracic Society grading. Inclusion of protocol holidays improved retention measures. The technologies used were generally well received.

CONCLUSIONS

Our findings provide evidence supporting the feasibility and acceptability of remote monitoring for capturing both subjective and objective data from various sources for respiratory diseases. The high engagement level observed with passively collected data suggests the potential of wearables for long-term, user-friendly remote monitoring in respiratory disease management. The unique piloting of certain features such as protocol holidays, alert notifications for missing data, and flexible support from the study team provides a reference for future studies in this field.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/28873.

Evaluating blood oxygen saturation measurements by popular fitness trackers in postoperative patients: A prospective clinical trial

Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could-at least in theory-help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients.

Experiences of Patients With Chronic Obstructive Pulmonary Disease Using the Apple Watch Series 6 Versus the Traditional Finger Pulse Oximeter for Home SpO2 Self-Monitoring: Qualitative Study Part 2

BACKGROUND

Amid the rise in mobile health, the Apple Watch now has the capability to measure peripheral blood oxygen saturation (SpO2). Although the company indicated that the Watch is not a medical device, evidence suggests that SpO2 measurements among patients with chronic obstructive pulmonary disease (COPD) are accurate in controlled settings. Yet, to our knowledge, the SpO2 function has not been validated for patients with COPD in naturalistic settings.

OBJECTIVE

This qualitative study explored the experiences of patients with COPD using the Apple Watch Series 6 versus a traditional finger pulse oximeter for home SpO2 self-monitoring.

METHODS

We conducted individual semistructured interviews with 8 female and 2 male participants with moderate to severe COPD, and transcripts were qualitatively analyzed. All received a watch to monitor their SpO2 for 5 months.

RESULTS

Due to respiratory distress, the watch was unable to collect reliable SpO2 measurements, as it requires the patient to remain in a stable position. However, despite the physical limitations and lack of reliable SpO2 values, participants expressed a preference toward the watch. Moreover, participants' health needs and their unique accessibility experiences influenced which device was more appropriate for self-monitoring purposes. Overall, all shared the perceived importance of prioritizing their physical COPD symptoms over device selection to manage their disease.

CONCLUSIONS

Differing results between participant preferences and smartwatch limitations warrant further investigation into the reliability and accuracy of the SpO2 function of the watch and the balance among self-management, medical judgment, and dependence on self-monitoring technology.

Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpO2 Monitoring

Flexible and wearable biosensors are the next-generation healthcare devices that can efficiently monitor human health conditions in day-to-day life. Moreover, the rapid growth and technological advancements in wearable optoelectronics have promoted the development of flexible organic photoplethysmography (PPG) biosensor systems that can be implanted directly onto the human body without any additional interface for efficient bio-signal monitoring. As an example, the pulse oximeter utilizes PPG signals to monitor the oxygen saturation (SpO2 ) in the blood volume using two distinct wavelengths with organic light emitting diode (OLED) as light source and an organic photodiode (OPD) as light sensor. Utilizing the flexible and soft properties of organic semiconductors, pulse oximeter can be both flexible and conformal when fabricated on thin polymeric substrates. It can also provide highly efficient human-machine interface systems that can allow for long-time biological integration and flawless measurement of signal data. In this work, a clear and systematic overview of the latest progress and updates in flexible and wearable all-organic pulse oximetry sensors for SpO2 monitoring, including design and geometry, processing techniques and materials, encapsulation and various factors affecting the device performance, and limitations are provided. Finally, some of the research challenges and future opportunities in the field are mentioned.

Photoplethysmography-based derivation of physiological information using the BioPoint

The BioPoint is a new wireless and wearable device, targeting both the ambulatory and on-site monitoring of biosignals. It is described as being capable of streaming and recording the i) electromyography, ii) electrocardiography, iii) electrodermal activity, iv) photoplethysmography, v) skin temperature and vi) actigraphy simultaneously, while making the raw signals recorded by the sensors readily available. However, an in-depth assessment of the biophysical signals recorded by this device, as well as its ability to derive vital signs and other health metrics, remains to be carried out. Consequently, this work proposes a preliminary study to evaluate the quality of the signals that can be acquired by this wearable with a focus on the derivation of heart rate and peripheral blood oxygenation via photoplethysmography. The device is quantitatively compared to the medical-grade pulse oximeter NoninConnect 3245, by Nonin inc. This study was performed with participants wearing the BioPoint at different positions on the body (finger, wrist, forearm, biceps and plantar arch), while the NoninConnect was worn on the fingertip and used as the ground truth. The results show that the BioPoint can accurately determine both heart rate and oxygen saturation from various locations on the body. However, as the BioPoint's photoplethysmograph is not calibrated it cannot be used for medical purposes (non-medical-grade).

Parental home-based pulse oximetry monitoring for adults with intellectual disabilities at risk of serious respiratory problems including COVID-19: a brief report

BACKGROUND

People with intellectual disabilities (ID) are at high risk of developing respiratory health issues. The COVID-19 pandemic has compounded this, with serious consequences, and for some, death. Despite home-based oxygen saturation monitoring being recommended for people with ID, there is a stark lack of evidence in the literature on its feasibility.

METHOD

We conducted 3-day baseline home-based oxygen saturation monitoring, using pulse oximeters, with eight parents of nine adults with ID in Scotland. Two eligible parents also completed a further 2 weeks of monitoring, and returned an evaluation questionnaire on its feasibility.

RESULTS

Baseline mean readings for eight adults with ID were within the normal range (%Sp0₂  ≥ 95), and for another one 94%. Fluctuations over the 3-day assessment period were experienced by six of these individuals. However, these variations were within limits which are not dangerous (lowest reading 92%), implying that parental home-based pulse oximetry monitoring is likely to be safe for adults with ID. The two parents who completed the evaluation found home-based pulse oximetry monitoring to be easy/very easy to do, and effective/very effective.

CONCLUSIONS

This is the first research study, albeit with a very small sample, to report on the potential feasibility of parental home-based pulse oximetry monitoring for adults with ID. Home-based pulse oximetry monitoring appears to be safe in adults with ID at risk of developing serious respiratory problems, and not difficult for their parents to do. There is an urgent need to replicate this work, using a larger sample, to promote home-based respiratory health monitoring more widely for people with ID.

Machine Learning Detects Intraventricular Haemorrhage in Extremely Preterm Infants

OBJECTIVE

To test the potential utility of applying machine learning methods to regional cerebral (rcSO₂) and peripheral oxygen saturation (SpO₂) signals to detect brain injury in extremely preterm infants.

STUDY DESIGN

A subset of infants enrolled in the Management of Hypotension in Preterm infants (HIP) trial were analysed (n = 46). All eligible infants were <28 weeks' gestational age and had continuous rcSO₂ measurements performed over the first 72 h and cranial ultrasounds performed during the first week after birth. SpO₂ data were available for 32 infants. The rcSO₂ and SpO₂ signals were preprocessed, and prolonged relative desaturations (PRDs; data-driven desaturation in the 2-to-15-min range) were extracted. Numerous quantitative features were extracted from the biosignals before and after the exclusion of the PRDs within the signals. PRDs were also evaluated as a stand-alone feature. A machine learning model was used to detect brain injury (intraventricular haemorrhage-IVH grade II-IV) using a leave-one-out cross-validation approach.

RESULTS

The area under the receiver operating characteristic curve (AUC) for the PRD rcSO₂ was 0.846 (95% CI: 0.720-0.948), outperforming the rcSO₂ threshold approach (AUC 0.593 95% CI 0.399-0.775). Neither the clinical model nor any of the SpO₂ models were significantly associated with brain injury.

CONCLUSION

There was a significant association between the data-driven definition of PRDs in rcSO₂ and brain injury. Automated analysis of PRDs of the cerebral NIRS signal in extremely preterm infants may aid in better prediction of IVH compared with a threshold-based approach. Further investigation of the definition of the extracted PRDs and an understanding of the physiology underlying these events are required.

Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients

Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.

Usability of a continuous oxygen saturation device for home telemonitoring

Background

The emergency for the COVID-19 pandemic has led to greater use of home telemonitoring devices. The aim of this study was to assess the usability of continuous home-monitoring care with an oxygen saturation device on post-COVID-19 patients.

Method

The system consists of a digital continuous pulse oximeter and a smartphone with an App, which were provided to patients. A survey composed of a standard Post-Study System Usability Questionnaire, and a satisfaction questionnaire was exploited to conduct a usability and feasibility analysis of the service.

Results

A total of 29 patients (17.2% female) with a mean age of 65 ± 11.5 years were enrolled: 20 patients were smartphone users (69%) with a mean age of 60.2 ± 9.5 years, and 9 patients (31%) did not own a smartphone (mean age 76.8 ± 5.9). The monitoring period was 1 month: a total of 444 recordings were conducted, 15 recordings per patient averagely. In total, 82% of the recordings performed did not require any intervention, while 18% led to the production of a report and subsequent intervention by a nurse who verified, together with the specialist, the need to intervene (i.e. the patient accessed the clinic for medical control and/or modification of oxygen therapy). A total of 17 patients compiled a usability questionnaire. The service was perceived as useful and well-structured, although it often required caregiver support.

Conclusions

Using continuous home-monitoring care with an oxygen saturation device seems feasible and useful for patients who could be followed at home avoiding going back to the hospital every time a trend oximetry is needed. Further improvements in connections, data flow processes, and simplifications, based on patients' feedback, are needed to scale up the service.

Heart rate and oxygen uptake kinetics obtained from continuous measurements with wearable devices during outdoor walks of patients with COPD

Objective

Continuous physiological measurements during a laboratory-based exercise test can provide physiological biomarkers, such as heart rate (HR) and oxygen uptake (V̇O₂) kinetics, that carry clinically relevant information. In contrast, it is not clear how continuous data generated by wearable devices during daily-life routines could provide meaningful biomarkers. We aimed to determine whether valid HR and V̇O₂ kinetics can be obtained from measurements with wearable devices during outdoor walks in patients with chronic obstructive pulmonary disease (COPD).

Methods

HR (Polar Belt) and V̇O₂(METAMAX3B) were measured during 93 physical activity transitions performed by eight patients with COPD during three different outdoor walks (n_tr= 77) and a 6-minute walk test (n_tr= 16). HR and V̇O₂ kinetics were calculated every time a participant started a walk, finished a walk or walked upstairs. HR and V̇O₂ kinetics were considered valid if the response magnitude and model fit were adequate, and model parameters were reliable.

Results

Continuous measurements with wearable devices provided valid HR kinetics when COPD patients started or finished (range 63%-100%) the different outdoor walks and valid V̇O₂ kinetics when they finished (range 63%-100%) an outdoor walk. The amount of valid kinetics and kinetic model performance was comparable between outdoor walks and a laboratory-based exercise test (p > .05).

Conclusion

We envision that the presented approach could improve telemonitoring applications of patients with COPD by providing regular, unsupervised assessments of HR kinetics during daily-life routines. This could allow to early identify a decline in the patients' dynamic physiological functioning, physical fitness and/or health status.

Specificity for the correlation between the body surface and viscera in the pathological state of COPD: A prospective, controlled, and assessor-blinded trial

Background: The association between the body surface and viscera remains obscure, but a better understanding of the body surface-viscera correlation will maximize its diagnostic and therapeutic values in clinical practice. Therefore, this study aimed to investigate the specificity of body surface-viscera correlation in the pathological state. Methods: The study subjects included 40 participants with chronic obstructive pulmonary disease (COPD) in the COPD group and 40 age-matched healthy participants in the healthy control group. Laser Doppler flowmetry, infrared thermography, and functional near-infrared spectroscopy were respectively adopted to measure 1) the perfusion unit (PU), 2) temperature, and 3) regional oxygen saturation (rSO₂) of four specific sites distributed in the heart and lung meridians. These three outcome measures reflected the microcirculatory, thermal, and metabolic characteristics, respectively. Results: Regarding the microcirculatory and thermal characteristics of the body surface, the PU and temperature of specific sites on the body surface [i.e., Taiyuan (LU9) and Chize (LU5) in the lung meridian] in the COPD group were significantly increased compared with healthy controls (p < 0.05), whereas PU and temperature of other sites in the heart meridian [i.e., Shenmen (HT7) and Shaohai (HT3)] did not change significantly (p > 0.05). Regarding the metabolic characteristics, rSO₂ of specific sites in the lung meridian [i.e., Taiyuan (LU9) and Chize (LU5)] and Shaohai (HT3) of the heart meridian in the COPD group was significantly decreased compared with healthy controls (p < 0.01), whereas rSO₂ of Shenmen (HT7) in the heart meridian did not change significantly (p > 0.05). Conclusion: In the disease state of COPD, the microcirculatory, thermal, and metabolic characteristics of specific sites on the body surface in the lung meridian generally manifest more significant changes than those in the heart meridian, thereby supporting relative specificity for the body surface-viscera correlation in the pathological state.

Potential Usefulness of Tracking Head Movement via a Wearable Device for Equilibrium Function Testing at Home

Many studies have reported the use of wearable devices to acquire biological data for the diagnosis and treatment of various diseases. Balance dysfunction, however, is difficult to evaluate in real time because the equilibrium function is conventionally examined using a stabilometer installed on the ground. Here, we used a wearable accelerometer that measures head motion to evaluate balance and examined whether it performs comparably to a conventional stabilometer. We constructed a simplified physical head-feet model that simultaneously records “head” motion measured using an attached wearable accelerometer and center-of-gravity motion at the “feet”, which is measured using an attached stabilometer. Total trajectory length (r = 0.818, p -false discovery rate [FDR] = 0.004) and outer peripheral area (r = 0.691, p -FDR = 0.026) values measured using the wearable device and stabilometer were significantly positively correlated. Root mean square area values were not significantly correlated with wearable device stabilometry but were comparable. These results indicate that wearable, widely available, non-medical devices may be used to assess balance outside the hospital setting, and new approaches for testing balance function should be considered.

Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study

OBJECTIVE

We investigated how a commercially available smartwatch that measures peripheral blood oxygen saturation (SpO₂) can detect hypoxemia compared to a medical-grade pulse oximeter.

METHODS

We recruited 24 healthy participants. Each participant wore a smartwatch (Apple Watch Series 6) on the left wrist and a pulse oximeter sensor (Masimo Radical-7) on the left middle finger. The participants breathed via a breathing circuit with a three-way non-rebreathing valve in three phases. First, in the 2-minute initial stabilization phase, the participants inhaled the ambient air. Then in the 5-minute desaturation phase, the participants breathed the oxygen-reduced gas mixture (12% O₂), which temporarily reduced their blood oxygen saturation. In the final stabilization phase, the participants inhaled the ambient air again until SpO₂ returned to normal values. Measurements of SpO₂ were taken from the smartwatch and the pulse oximeter simultaneously in 30-s intervals.

RESULTS

There were 642 individual pairs of SpO₂ measurements. The bias in SpO₂ between the smartwatch and the oximeter was 0.0% for all the data points. The bias for SpO₂ less than 90% was 1.2%. The differences in individual measurements between the smartwatch and oximeter within 6% SpO₂ can be expected for SpO₂ readings 90%-100% and up to 8% for SpO₂ readings less than 90%.

CONCLUSIONS

Apple Watch Series 6 can reliably detect states of reduced blood oxygen saturation with SpO₂ below 90% when compared to a medical-grade pulse oximeter. The technology used in this smartwatch is sufficiently advanced for the indicative measurement of SpO₂ outside the clinic.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04780724.

Skin-Attachable Sensors for Biomedical Applications

With the growing concern about human health issues, especially during the outbreak of the COVID-19 pandemic, the demand for personalized healthcare regarding disease prevention and recovery is increasing. However, tremendous challenges lie in both limited public medical resources and costly medical diagnosis approaches. Recently, skin-attachable sensors have emerged as promising health monitoring platforms to overcome such difficulties. Owing to the advantages of good comfort and high signal-to-noise ratio, skin-attachable sensors enable household, real-time, and long-term detection of weak physiological signals to efficiently and accurately monitor human motion, heart rate, blood oxygen saturation, respiratory rate, lung and heart sound, glucose, and biomarkers in biomedical applications. To further improve the integration level of biomedical skin-attachable sensors, efforts have been made in combining multiple sensing techniques with elaborate structural designs. This review summarizes the recent advances in different functional skin-attachable sensors, which monitor physical and chemical indicators of the human body. The advantages, shortcomings, and integration strategies of different mechanisms are presented. Specially, we highlight sensors monitoring pulmonary function such as respiratory rate and blood oxygen saturation for their potential usage in the COVID-19 pandemic. Finally, the future development of skin-attachable sensors is envisioned.

Home-Based Respiratory Care for COPD Patients

Despite significant advances in pharmacological treatment over the last few decades, COPD remains a heavy burden on the health systems around the world, affecting approximately 210 million people, with elevated morbimortality and socioeconomic impact. Barriers to healthcare access were even more evident during the coronavirus disease 19 (COVID-19) pandemic and increased patients’ vulnerability to physical deconditioning, depression, and social isolation. Home-based respiratory care in patients with COPD provides a valuable contribution to effective disease management, with potential advantages for monitoring, treatment adherence, and cost reduction. Technological innovation allows clinical markers of interest, such as respiratory frequency, pulmonary function, and oxygen saturation, to be tracked remotely from the patients’ homes, providing a better understanding of their real needs. Home-based telerehabilitation can also be a viable alternative to hospital-based programs. Here, we highlight the full extent of health benefits of HRC in COPD, particularly for patients with a higher risk of exacerbations, multiple comorbidities, and limited access to health services.

MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

Experiences of patients with chronic obstructive pulmonary disease receiving integrated telehealth nursing services during COVID-19 lockdown

Objective

Even though telemonitoring existed prior to COVID-19, the need was accelerated for patients with COPD due to the limited medical consultations and the anxiety for being infected. To our knowledge, no qualitative study has explored the experiences of COPD patients and the potential benefits of receiving integrated telehealth nursing services during the lockdown.

Methods

Ten participants were interviewed after receiving telehealth nursing services for 3 months; data was analyzed thematically.

Results

Irrespective of COPD severity, all participants expressed that the existing fragmented follow-up care was intensified during COVID. The telenursing services provided them with the comfort and reassurance that a clinician is available for support, advice, and care during the times of isolation. Despite diverse opinions regarding the respiratory-related questions for follow-up, all were enthusiastic about the use of the oximeter in their daily lives. None perceived sharing data as an issue, instead they encouraged the exchange across levels of care.

Conclusion

Despite an appreciation for the service, it is imperative for sustainability reasons that standardized protocols are developed to balance patient preferences in terms of response frequency and the clinical data needed for a telenurse to provide appropriate interventions.

Open-source FlexNIRS: A low-cost, wireless and wearable cerebral health tracker

Currently, there is great interest in making neuroimaging widely accessible and thus expanding the sampling population for better understanding and preventing diseases. The use of wearable health devices has skyrocketed in recent years, allowing continuous assessment of physiological parameters in patients and research cohorts. While most health wearables monitor the heart, lungs and skeletal muscles, devices targeting the brain are currently lacking. To promote brain health in the general population, we developed a novel, low-cost wireless cerebral oximeter called FlexNIRS. The device has 4 LEDs and 3 photodiode detectors arranged in a symmetric geometry, which allows for a self-calibrated multi-distance method to recover cerebral hemoglobin oxygenation (SO2) at a rate of 100 Hz. The device is powered by a rechargeable battery and uses Bluetooth Low Energy (BLE) for wireless communication. We developed an Android application for portable data collection and real-time analysis and display. Characterization tests in phantoms and human participants show very low noise (noise-equivalent power <70 fW/√Hz) and robustness of SO2 quantification in vivo. The estimated cost is on the order of $50/unit for 1000 units, and our goal is to share the device with the research community following an open-source model. The low cost, ease-of-use, smart-phone readiness, accurate SO2 quantification, real time data quality feedback, and long battery life make prolonged monitoring feasible in low resource settings, including typically medically underserved communities, and enable new community and telehealth applications.

Clinical safety study of photobiomodulation in acute spinal cord injury by scattering fiber

The study aimed to design a reliable and straightforward PBM method by implanting a medical scattering fiber above surgically exposed spinal cord in SCI patients. Moreover, the safety of this method was examined. Twelve patients with acute SCI (ASIA B) requiring posterior decompression were recruited. The medical scattering fiber was implanted above the spinal cord, and was continuously irradiated at 810 nm, 300 mW, 30 min/day, once per day for 7 days. The vital signs (temperature, blood pressure, respiratory rate, heart rate, and oxygen saturation), infection indicators (WBC, NEUT, hs-CRP, and PCT), photo-allergic reaction indicators (Eosinophil and Basophil), coagulation function indicators (PT, APTT, TT) and neurological stability indicators (ASIA sensory and motor scores) were recorded to evaluate the safety of PBM. Three months after surgery, 12 patients completed follow-up. In our study, direct PBM on SCI site did not cause clinically pathologic changes in vital signs of the patients. All patients had higher WBC, NEUT, and hs-CRP at day 3 during irradiation than those before surgery, and returned to normal at day 7. The changes in Eosinophil and Basophil that were closely associated with allergic reactions were within normal limits throughout the course of irradiation. The coagulation function (PT, APTT, and TT) of patients were also in the normal range. The ASIA sensory and motor scores of all patients had no changes throughout the irradiation process. However, in the follow-up, both ASIA sensory and motor scores of all patients had minor improvement than those in pre-irradiation, and 7 patients had adverse events, but they were not considered to be related to PBM. Our study might firstly employ direct PBM in the SCI by using scattered optical fibers. In a limited sample size, our study concluded that direct PBM at the site of SCI would not produce adverse effects within the appropriate irradiation parameters. The method is safe, feasible, and does not add additional trauma to the patient. Our preliminary study might provide a new methodology for the clinical PBM treatment of acute SCI.

Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies

Background

Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data.

Summary

The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving.

Key Messages

We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.

Remote patient monitoring in the management of chronic obstructive pulmonary disease

Remote patient monitoring allows monitoring high-risk patients through implementation of an expanding number of technologies in coordination with a healthcare team to augment care, with the potential to provide early detection of exacerbation, prompt access to therapy and clinical services, and ultimately improved patient outcomes and decreased healthcare utilization.In this review, we describe the application of remote patient monitoring in chronic obstructive pulmonary disease including the potential benefits and possible barriers to implementation both for the individual and the healthcare system.

A machine-learning-based prediction method for easy COPD classification based on pulse oximetry clinical use

Chronic Obstructive Pulmonary Disease (COPD) is a progressive, obstructive lung disease that restricts airflow from the lungs. COPD patients are at risk of sudden and acute worsening of symptoms called exacerbations. Early identification and classification of COPD exacerbation can reduce COPD risks and improve patient’s healthcare and management. Pulse oximetry is a non-invasive technique used to assess patients with acutely worsening symptoms. As part of manual diagnosis based on pulse oximetry, clinicians examine three warning signs to classify COPD patients. This may lack high sensitivity and specificity which requires a blood test. However, laboratory tests require time, further delayed treatment and additional costs. This research proposes a prediction method for COPD patients’ classification based on pulse oximetry three manual warning signs and the resulting derived few key features that can be obtained in a short time. The model was developed on a robust physician labeled dataset with clinically diverse patient cases. Five classification algorithms were applied on the mentioned dataset and the results showed that the best algorithm is XGBoost with the accuracy of 91.04%, precision of 99.86%, recall of 82.19%, F1 measure value of 90.05% with an AUC value of 95.8%. Age, current and baseline heart rate, current and baseline pulse ox. (SPO2) were found the top most important predictors. These findings suggest the strength of XGBoost model together with the availability and the simplicity of input variables in classifying COPD daily living using a (wearable) pulse oximeter.

The Current and Future Role of Technology in Respiratory Care

Over the past few decades, technology and improvements in artificial intelligence have dramatically changed major sectors of our day-to-day lives, including the field of healthcare. E-health includes a wide range of subdomains, such as wearables, smart-inhalers, portable electronic spirometers, digital stethoscopes, and clinical decision support systems. E-health has been consistently shown to enhance the quality of care, improve adherence to therapy, and allow early detection of worsening in chronic pulmonary diseases. The present review addresses the current and potential future role of major e-health tools and approaches in respiratory medicine, with the aim of providing readers with trustful and updated evidence to increase their awareness of the topic, and to allow them to optimally benefit from the latest innovation technology. Collected literature evidence shows that the potential of technology tools in respiratory medicine mainly relies on three fundamental interactions: between clinicians, between clinician and patient, and between patient and health technology. However, it would be desirable to establish widely agreed and adopted standards for conducting trials and reporting results in this area, as well as to take into proper consideration potentially relevant pitfalls related to privacy protection and compliance with regulatory procedures.

Effectiveness and safety of pulse oximetry in remote patient monitoring of patients with COVID-19: a systematic review

The COVID-19 pandemic has led health systems to increase the use of tools for monitoring and triaging patients remotely. In this systematic review, we aim to assess the effectiveness and safety of pulse oximetry in remote patient monitoring (RPM) of patients at home with COVID-19. We searched five databases (MEDLINE, Embase, Global Health, medRxiv, and bioRxiv) from database inception to April 15, 2021, and included feasibility studies, clinical trials, and observational studies, including preprints. We found 561 studies, of which 13 were included in our narrative synthesis. These 13 studies were all observational cohorts and involved a total of 2908 participants. A meta-analysis was not feasible owing to the heterogeneity of the outcomes reported in the included studies. Our systematic review substantiates the safety and potential of pulse oximetry for monitoring patients at home with COVID-19, identifying the risk of deterioration and the need for advanced care. The use of pulse oximetry can potentially save hospital resources for patients who might benefit the most from care escalation; however, we could not identify explicit evidence for the effect of RPM with pulse oximetry on health outcomes compared with other monitoring models such as virtual wards, regular monitoring consultations, and online or paper diaries to monitor changes in symptoms and vital signs. Based on our findings, we make 11 recommendations across the three Donabedian model domains and highlight three specific measurements for setting up an RPM system with pulse oximetry.

An integrated and conductive hydrogel-paper patch for simultaneous sensing of Chemical-Electrophysiological signals

Simultaneous monitoring of electrophysiological and biochemical signals is of great importance in healthcare and fitness management, while the fabrication of highly integrated and flexible devices is crucial to these applications. Herein, we devised a multifunctional and flexible hydrogel-paper patch (HPP) that was capable of simultaneously real-time monitoring of electrocardiogram (ECG) signal and biochemical signal (glucose content) in sweat during exercise. The self-assembly of the highly porous PEDOT:PSS hydrogel on paper fiber provided the HPP with good conductivity and hydrophilic wettability for efficient electron transmission and substance diffusion, thereby enabling it to serve as a low-impedance ECG electrode and a highly sensitive glucose sensor. Additionally, the spontaneous capillary flow effect allows the paper patch to be used as microfluidic channels for the collect and analysis of sweat. Moreover, the HPP is integrated with a flexible printed circuit board (FPCB) and works as a multifunctional wearable device mounted on the chest for real-time monitoring of electrophysiological and biochemical signals during exercise.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Application of oxygen saturation variability analysis for the detection of exacerbation in individuals with COPD: A proof-of-concept study

BACKGROUND

Individuals with chronic obstructive pulmonary disease (COPD) commonly experience exacerbations, which may require hospital admission. Early detection of exacerbations, and therefore early treatment, could be crucial in preventing admission and improving outcomes. Our previous research has demonstrated that the pattern analysis of peripheral oxygen saturation (Sp O2 ) fluctuations provides novel insights into the engagement of the respiratory control system in response to physiological stress (hypoxia). Therefore, this pilot study tested the hypothesis that the pattern of Sp O2 variations in overnight recordings of individuals with COPD would distinguish between stable and exacerbation phases of the disease.

METHODS

Overnight pulse oximetry data from 11 individuals with COPD, who exhibited exacerbation after a period of stable disease, were examined. Stable phase recordings were conducted overnight and one night prior to exacerbation recordings were also analyzed. Pattern analysis of Sp O2 variations was carried examined using sample entropy (for assessment of irregularity), the multiscale entropy (complexity), and detrended fluctuation analysis (self-similarity).

RESULTS

Sp O2 variations displayed a complex pattern in both stable and exacerbation phases of COPD. During an exacerbation, Sp O2 entropy increased (p = 0.029) and long-term fractal-like exponent (α2) decreased (p = 0.002) while the mean and standard deviation of Sp O2 time series remained unchanged. Through ROC analyses, Sp O2 entropy and α2 were both able to classify the COPD phases into either stable or exacerbation phase. With the best positive predictor value (PPV) for sample entropy (PPV = 70%) and a cut-off value of 0.454. While the best negative predictor value (NPV) was α2 (NPV = 78%) with a cut-off value of 1.00.

CONCLUSION

Alterations in Sp O2 entropy and the fractal-like exponent have the potential to detect exacerbations in COPD. Further research is warranted to examine if Sp O2 variability analysis could be used as a novel objective method of detecting exacerbations.

Oxygen Saturation Behavior by Pulse Oximetry in Female Athletes: Breaking Myths

The myths surrounding women’s participation in sport have been reflected in respiratory physiology. This study aims to demonstrate that continuous monitoring of blood oxygen saturation during a maximal exercise test in female athletes is highly correlated with the determination of the second ventilatory threshold (VT2) or anaerobic threshold (AnT). The measurements were performed using a pulse oximeter during a maximum effort test on a treadmill on a population of 27 healthy female athletes. A common behavior of the oxygen saturation evolution during the incremental exercise test characterized by a decrease in saturation before the aerobic threshold (AeT) followed by a second significant drop was observed. Decreases in peripheral oxygen saturation during physical exertion have been related to the athlete’s physical fitness condition. However, this drop should not be a limiting factor in women’s physical performance. We found statistically significant correlations between the maximum oxygen uptake and the appearance of the ventilatory thresholds (VT1 and VT2), the desaturation time, the total test time, and between the desaturation time and the VT2. We observed a relationship between the desaturation time and the VT2 appearance. Indeed, a linear regression model between the desaturation time and the VT2 appearance can predict 80% of the values in our sample. Besides, we suggest that pulse oximetry is a simple, fairly accurate, and non-invasive technique for studying the physical condition of athletes who perform physical exertion.

Validity of Peripheral Oxygen Saturation Measurements with the Garmin Fēnix® 5X Plus Wearable Device at 4559 m

Decreased oxygen saturation (SO₂) at high altitude is associated with potentially life-threatening diseases, e.g., high-altitude pulmonary edema. Wearable devices that allow continuous monitoring of peripheral oxygen saturation (SpO₂), such as the Garmin Fēnix^® 5X Plus (GAR), might provide early detection to prevent hypoxia-induced diseases. We therefore aimed to validate GAR-derived SpO₂ readings at 4559 m. SpO₂ was measured with GAR and the medically certified Covidien Nellcor SpO₂ monitor (COV) at six time points in 13 healthy lowlanders after a rapid ascent from 1130 m to 4559 m. Arterial blood gas (ABG) analysis served as the criterion measure and was conducted at four of the six time points with the Radiometer ABL 90 Flex. Validity was assessed by intraclass correlation coefficients (ICCs), mean absolute percentage error (MAPE), and Bland–Altman plots. Mean (±SD) SO₂, including all time points at 4559 m, was 85.2 ± 6.2% with GAR, 81.0 ± 9.4% with COV, and 75.0 ± 9.5% with ABG. Validity of GAR was low, as indicated by the ICC (0.549), the MAPE (9.77%), the mean SO₂ difference (7.0%), and the wide limits of agreement (−6.5; 20.5%) vs. ABG. Validity of COV was good, as indicated by the ICC (0.883), the MAPE (6.15%), and the mean SO₂ difference (0.1%) vs. ABG. The GAR device demonstrated poor validity and cannot be recommended for monitoring SpO₂ at high altitude.

Comparison of SpO2 and heart rate values on Apple Watch and conventional commercial oximeters devices in patients with lung disease

Lung diseases have high mortality and morbidity, with an important impact on quality of life. Hypoxemic patients are advised to use oxygen therapy to prolong their survival, but high oxygen saturation (SpO2) levels can also have negative effects. Pulse oximeters are the most common way to assess oxygen levels and guide medical treatment. This study aims to assess whether wearable devices can provide precise SpO2 measurements when compared to commercial pulse oximeters. This is a cross-section study with 100 patients with chronic obstructive pulmonary disease and interstitial lung disease from an outpatient pneumology clinic. SpO2 and heart rate data were collected with an Apple Watch Series 6 (Apple) and compared to two commercial pulse oximeters. The Bland-Altman method and interclass correlation coefficient were used to compare their values. We observed strong positive correlations between the Apple Watch device and commercial oximeters when evaluating heart rate measurements (r = 0.995, p < 0.001) and oximetry measurements (r = 0.81, p < 0.001). There was no statistical difference in the evaluation of skin color, wrist circumference, presence of wrist hair, and enamel nail for SpO2 and heart rate measurements in Apple Watch or commercial oximeter devices (p > 0.05). Apple Watch 6 is a reliable way to obtain heart rate and SpO2 in patients with lung diseases in a controlled environment.

Accuracy and Reliability of Commercial Wrist-Worn Pulse Oximeter During Normobaric Hypoxia Exposure Under Resting Conditions

Purpose: The present study analyzed peripheral blood oxygen saturation (S_pO₂) and heart rate (HR) measurements taken on the Garmin fēnix® 5X Plus watch, comparing them to measurements taken on a standard medical-grade pulse oximeter during normobaric hypoxia exposure under resting conditions. Methods: Thirteen women (mean ± SD: Age 20 ± 1 years, height 165 ± 5 cm, mass, 67 ± 9 kg) and ten men (mean ± SD: Age 21 ± 3 years, height 177 ± 6 cm, mass 78 ± 11 kg) sat inside a customized environmental chamber while the fraction of inspired oxygen (F_IO₂) was adjusted to simulate altitudes of 12,000; 10,000; 8,000; 6,000; and 900 ft. The novel commercial device (Garmin fēnix®) and a medical-grade pulse oximeter (Nonin® 7500) were used to measure S_pO₂ and HR in triplicate at each simulated altitude. Bland-Altman analyses were used to assess differences between methods. Results: Bland-Altman analysis indicated 3.3% bias for S_pO₂ measurements taken on the Garmin fēnix® at 12,000 ft of simulated altitude (limits of agreement: -1.9-8.6%). Mean differences in S_pO₂ measurements were smaller at the remaining simulated altitudes, where bias measurements ranged from 0.7% to 0.8%. The Garmin fēnix® also underestimated heart rate, but those discrepancies were minimal (bias measurements at all simulated altitude exposures were < 1.0 bpm). Conclusions: With the exception of readings taken at 12,000 ft of simulated altitude, the Garmin fēnix® exhibits minimal overestimation of S_pO₂ and minimal underestimation of HR during simulated altitude exposure. These data suggest the Garmin fēnix® watch may be a viable method to monitor S_pO₂ and HR under most ambient environmental conditions.

Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment

Biometric monitoring technologies (BioMeTs) have attracted the attention of the health care community because of their user-friendly form factor and multi-sensor data-collection capabilities. The potential benefits of remote monitoring for collecting comprehensive, longitudinal, and contextual datasets span therapeutic areas, and both chronic and acute disease settings. Importantly, multimodal BioMeTs unlock the ability to generate rich contextual data to augment digital measures. Currently, the availability of devices is no longer the main factor limiting adoption but rather the ability to integrate fit-for-purpose BioMeTs reliably and safely into clinical care. We provide a critical review of the state of art for multimodal BioMeTs in clinical care and identify three unmet clinical needs: 1) expand the abilities of existing ambulatory unimodal BioMeTs; 2) adapt standardized clinical test protocols ("spot checks'') for use under free living conditions; and 3) develop novel applications to manage rehabilitation and chronic diseases. As the field is still in an early and quickly evolving state, we make practical recommendations: 1) to select appropriate BioMeTs; 2) to develop composite digital measures; and 3) to design interoperable software to ingest, process, delegate, and visualize the data when deploying novel clinical applications. Multimodal BioMeTs will drive the evolution from in-clinic assessments to at-home data collection with a focus on prevention, personalization, and long-term outcomes by empowering health care providers with knowledge, delivering convenience, and an improved standard of care to patients.

Telemonitoring of daily activities compared to the six-minute walk test further completes the puzzle of oximetry-guided interventions

Pulmonary rehabilitation is based on a thorough patient assessment, including peripheral oxygen saturation (SpO₂) and heart rate (HR) at rest and on exertion. To understand whether exercise-field tests identify patients who desaturate (SpO₂ < 90%) during physical activities, this study compared the six-minute walk test (6MWT) and daily-life telemonitoring. Cross-sectional study including 100 patients referred for pulmonary rehabilitation. The 6MWT was performed in hospital with continuous assessment of SpO₂, HR, walked distance and calculated metabolic equivalent of tasks (METs). Patients were also evaluated in real-life by SMARTREAB telemonitoring, a combined oximetry-accelerometery with remote continuous assessment of SpO₂, HR and METs. SMARTREAB telemonitoring identified 24% more desaturators compared with the 6MWT. Moreover, there were significant mean differences between 6MWT and SMARTREAB in lowest SpO₂ of 7.2 ± 8.4% (P < 0.0005), in peak HR of - 9.3 ± 15.5% (P < 0.0005) and also in activity intensity of - 0.3 ± 0.8 METs (P < 0.0005). The 6MWT underestimates the proportion of patients with exercise-induced oxygen desaturation compared to real-life telemonitoring. These results help defining oximetry-guided interventions, such as telemedicine algorithms, oxygen therapy titration and regular physical activity assessment in pulmonary rehabilitation.

Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers

Objective.Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic condition. COPD is a major cause of morbidity, mortality and healthcare costs globally. Spirometry is the gold standard test for a definitive diagnosis and severity grading of COPD. However, a large proportion of individuals with COPD are undiagnosed and untreated. Given the high prevalence of COPD and its clinical importance, it is critical to develop new algorithms to identify undiagnosed COPD. This is particularly true in specific disease groups in which the presence of concomitant COPD increases overall morbidity/mortality such as those with sleep-disordered breathing. To our knowledge, no research has looked at the feasibility of automated COPD diagnosis using a data-driven analysis of the nocturnal continuous oximetry time series. We hypothesize that patients with COPD will exert certain patterns and/or dynamics of their overnight oximetry time series that are unique to this condition and that may be captured using a data-driven approach.Approach.We introduce a novel approach to nocturnal COPD diagnosis using 44 oximetry digital biomarkers and five demographic features and assess its performance in a population sample at risk of sleep-disordered breathing. A total ofn=350 unique patients' polysomnography (PSG) recordings were used. A random forest (RF) classifier was trained using these features and evaluated using nested cross-validation.Main results.The RF classifier obtainedF1 = 0.86 ± 0.02 and AUROC = 0.93 ± 0.02 on the test sets. A total of 8 COPD individuals out of 70 were misclassified. No severe cases (GOLD 3-4) were misdiagnosed. Including additional non-oximetry derived PSG biomarkers resulted in minimal performance increase.Significance.We demonstrated for the first time, the feasibility of COPD diagnosis from nocturnal oximetry time series for a population sample at risk of sleep-disordered breathing. We also highlighted what set of digital oximetry biomarkers best reflect how COPD manifests overnight. The results motivate that overnight single channel oximetry can be a valuable modality for COPD diagnosis, in a population sample at risk of sleep-disordered breathing. Further data is needed to validate this approach on other population samples.

Accuracy and Reliability of Pulse O2 Saturation Measured by a Wrist-worn Oximeter

This study aims to evaluate the accuracy of the Garmin Forerunner 245 heart rate (HR) and pulse O₂ saturation (SpO₂) sensors compared with electrocardiogram and medical oximeter, from sea level to high altitude. Ten healthy subjects underwent five tests in normoxia and hypoxia (simulated altitudes from 3000 to 5500 m), consisting in a 5-min rest phase, followed by 5-min of mild exercise. Absolute error (±10 bpm for HR and ±3% for SpO₂, around criterion) and intraclass correlations (ICC) were calculated. Error rates for HR remained under 10%, except at 3000 m, and ICCs evidenced a good reliability between Garmin and criterion. Overall SpO₂ was higher than criterion (P<0.001) with a >50% error rate (>80% above 4800 m), and a poor reliability with criterion. The Garmin device displayed acceptable HR data at rest and exercise for all altitudes, but failed to provide trustworthy SpO₂ values, especially at high altitude, where a pronounced arterial O₂ desaturation could lead to acute mountain sickness in hypoxia-sensitive subjects, and its life-threatening complications; moreover, readings of overestimated SpO₂ values might induce trekkers into further hazardous behavior by pursuing an ascent while being already at risk. Therefore, its use to assess SpO₂ should be proscribed in altitude for acclimatization evaluation.

Remote Cardiac Safety Monitoring through the Lens of the FDA Biomarker Qualification Evidentiary Criteria Framework: A Case Study Analysis

Clinical safety findings remain one of the reasons for attrition of drug candidates during clinical development. Cardiovascular liabilities are not consistently detected in early-stage clinical trials and often become apparent when drugs are administered chronically for extended periods of time. Vital sign data collection outside of the clinic offers an opportunity for deeper physiological characterization of drug candidates and earlier safety signal detection. A working group representing expertise from biopharmaceutical and technology sectors, US Food and Drug Administration (FDA) public-private partnerships, academia, and regulators discussed and presented a remote cardiac monitoring case study at the FNIH Biomarkers Consortium Remote Digital Monitoring for Medical Product Development workshop to examine applicability of the biomarker qualification evidentiary framework by the FDA. This use case examined the components of the framework, including the statement of need, the context of use, the state of the evidence, and the benefit/risk profile. Examination of results from 2 clinical trials deploying 510(k)-cleared devices for remote cardiac data collection demonstrated the need for analytical and clinical validity irrespectively of the regulatory status of a device of interest, emphasizing the importance of data collection method assessment in the context of intended use. Additionally, collection of large amounts of ambulatory data also highlighted the need for new statistical methods and contextual information to enable data interpretation. A wider adoption of this approach for drug development purposes will require collaborations across industry, academia, and regulatory agencies to establish methodologies and supportive data sets to enable data interpretation and decision-making.

Monitoring Physical Activity with a Wearable Sensor in Patients with COPD during In-Hospital Pulmonary Rehabilitation Program: A Pilot Study

Accelerometers have become a standard method of monitoring physical activity in everyday life by measuring acceleration in one, two, or three axes. These devices provide reliable and objective measurements of the duration and intensity of physical activity. We aimed to investigate whether patients undertake physical activity during non-supervised days during stationary rehabilitation and whether patients adhere to the rigor of 24 h monitoring. The second objective was to analyze the strengths and weaknesses of such kinds of sensors. The research enrolled 13 randomly selected patients, qualified for in-patient, 3 week, high-intensity, 5 times a week pulmonary rehabilitation. The SenseWear armband was used for the assessment of physical activity. Participants wore the device 24 h a day for the next 4 days (Friday-Monday). The analysis of the number of steps per day, the time spent lying as well as undertaking moderate or vigorous physical activity (>3 metabolic equivalents of task (METs)), and the energy expenditure expressed in kcal showed no statistically significant difference between the training days and the days off. It seems beneficial to use available physical activity sensors in patients with chronic obstructive pulmonary disease (COPD); measurable parameters provide feedback that may increase the patient's motivation to be active to achieve health benefits.

Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index (BI), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily BI was linearly associated with respiratory tract viral load (p < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 in predicting clinical worsening events (sensitivity 94.1% and specificity 88.9%) and prolonged hospitalization (sensitivity 66.7% and specificity 72.7%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.

Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic with serious clinical manifestations including death. A pandemic at the large-scale like COVID-19 places extraordinary demands on the world's health systems, dramatically devastates vulnerable populations, and critically threatens the global communities in an unprecedented way. While tremendous efforts at the frontline are placed on detecting the virus, providing treatments and developing vaccines, it is also critically important to examine the technologies and systems for tackling disease emergence, arresting its spread and especially the strategy for diseases prevention. The objective of this article is to review enabling technologies and systems with various application scenarios for handling the COVID-19 crisis. The article will focus specifically on 1) wearable devices suitable for monitoring the populations at risk and those in quarantine, both for evaluating the health status of caregivers and management personnel, and for facilitating triage processes for admission to hospitals; 2) unobtrusive sensing systems for detecting the disease and for monitoring patients with relatively mild symptoms whose clinical situation could suddenly worsen in improvised hospitals; and 3) telehealth technologies for the remote monitoring and diagnosis of COVID-19 and related diseases. Finally, further challenges and opportunities for future directions of development are highlighted.

Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use

Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached [Formula: see text]. The resulting python OBM toolbox, denoted "pobm", was contributed to the open software PhysioZoo ( physiozoo.org ). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.