Assessment of physiological signs associated with COVID-19 measured using wearable devices.

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic. Data on 2745 subjects diagnosed with COVID-19 (active infection, PCR test) were collected from May 21 to September 11, 2020, consisting of PCR positive tests conducted between February 16 and September 9. Considering male (female) participants, 11.9% (11.2%) of the participants were asymptomatic, 48.3% (47.8%) recovered at home by themselves, 29.7% (33.7%) recovered at home with the help of someone else, 9.3% (6.6%) required hospitalization without ventilation, and 0.5% (0.4%) required ventilation. There were a total of 21 symptoms reported, and the prevalence of symptoms varies by sex. Fever was present in 59.4% of male subjects and in 52% of female subjects. Based on self-reported symptoms alone, we obtained an AUC of 0.82 ± 0.017 for the prediction of the need for hospitalization. Based on physiological signs, we obtained an AUC of 0.77 ± 0.018 for the prediction of illness on a specific day. Respiration rate and heart rate are typically elevated by illness, while HRV is decreased. Measuring these metrics, taken in conjunction with molecular-based diagnostics, may lead to better early detection and monitoring of COVID-19.

A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects.

PURPOSE: This paper aims to compare the absolute performance of three noncontact sleep measurement devices for measuring sleep parameters in normal subjects against polysomnography and to assess their relative performance. METHODS: The devices investigated were two noncontact radio-frequency biomotion sensors (SleepMinder (SM) and SleepDesign (HSL-101)) and an actigraphy-based system (Actiwatch). Overnight polysomnography measurements were carried out in 20 normal subjects, with simultaneous assessment of sleep parameters using the three devices. The parameters measured included total sleep time (TST), sleep efficiency (SE), sleep-onset latency (SOL), and wake-after-sleep onset (WASO). The per-epoch agreement level for sleep/wake distinction was evaluated. RESULTS: The TSTs reported by the three devices were 426 ± 34, 434 ± 22, and 441 ± 16 min, for the SM, HSL-101, and Actiwatch, respectively, against polysomnogram (PSG)-reported TST of 391 ± 49 min. The SOLs were 10 ± 10, 5 ± 6, and 3 ± 2 min for the SM, HSL-101 and Actiwatch, respectively against PSG SOL of 19 ± 13 min. The WASO times were 46 ± 33, 43 ± 22, and 38 ± 17 min, as against PSG-reported 69 ± 46 min. All three devices had a statistically significant bias to overestimate sleep time and underestimate WASO and SOL compared with PSG. The performance of the three devices was basically equivalent, with only minor interdevice differences. The overall per-epoch agreement levels were 86 % for the SM, 86 % for the HSL-101, and 85 % for the Actiwatch. CONCLUSIONS: Noncontact biomotion approaches to sleep measurement provided reasonable estimates of TST, but with a bias to over-estimation of sleep. The radio-frequency biomotion sensors provided similar accuracies for sleep/wake determination in normal subjects as the actigraph used in this study and slightly improved estimates of TST, SOL, and WASO.

Assessment of sleep-disordered breathing using a non-contact bio-motion sensor.

Obstructive sleep apnoea is a highly prevalent but under-diagnosed disorder. The gold standard for diagnosis of obstructive sleep apnoea is inpatient polysomnography. This is resource intensive and inconvenient for the patient, and the development of ambulatory diagnostic modalities has been identified as a key research priority. SleepMinder (BiancaMed, NovaUCD, Ireland) is a novel, non-contact, bedside sensor, which uses radio-waves to measure respiration and movement. Previous studies have shown it to be effective in measuring sleep and respiration. We sought to assess its utility in the diagnosis of obstructive sleep apnoea. SleepMinder and polysomnographic assessment of sleep-disordered breathing were performed simultaneously on consecutive subjects recruited prospectively from our sleep clinic. We assessed the diagnostic accuracy of SleepMinder in identifying obstructive sleep apnoea, and how SleepMinder assessment of the apnoea-hypopnoea index correlated with polysomnography. Seventy-four subjects were recruited. The apnoea-hypopnoea index as measured by SleepMinder correlated strongly with polysomnographic measurement (r = 0.90; P ≤ 0.0001). When a diagnostic threshold of moderate-severe (apnoea-hypopnoea index ≥15 events h(-1) ) obstructive sleep apnoea was used, SleepMinder displayed a sensitivity of 90%, a specificity of 92% and an accuracy of 91% in the diagnosis of sleep-disordered breathing. The area under the curve for the receiver operator characteristic was 0.97. SleepMinder correctly classified obstructive sleep apnoea severity in the majority of cases, with only one case different from equivalent polysomnography by more than one diagnostic class. We conclude that in an unselected clinical population undergoing investigation for suspected obstructive sleep apnoea, SleepMinder measurement of sleep-disordered breathing correlates significantly with polysomnography.

Objective and subjective assessment of sleep in chronic low back pain patients compared with healthy age and gender matched controls: a pilot study.

BACKGROUND: While approximately 70% of chronic low back pain (CLBP) sufferers complain of sleep disturbance, current literature is based on self report measures which can be prone to bias and no objective data of sleep quality, based exclusively on CLBP are available. In accordance with the recommendations of The American Sleep Academy, when measuring sleep, both subjective and objective assessments should be considered as the two are only modestly correlated, suggesting that each modality assesses different aspects of an individual's sleep experience. Therefore, the purpose of this study was to expand previous research into sleep disturbance in CLBP by comparing objective and subjective sleep quality in participants with CLBP and healthy age and gender matched controls, to identify correlates of poor sleep and to test logistics and gather information prior to a larger study. METHODS: 15 CLBP participants (mean age = 43.8 years (SD = 11.5), 53% female) and 15 healthy controls (mean age = 41.5 years (SD = 10.6), 53% female) consented. All participants completed the Pittsburgh Sleep Quality Index, Insomnia Severity Index, Pittsburgh Sleep Diary and the SF36v2. CLBP participants also completed the Oswestry Disability Index. Sleep patterns were assessed over three consecutive nights using actigraphy. Total sleep time (TST), sleep efficiency (SE), sleep latency onset (SL) and number of awakenings after sleep onset (WASO) were derived. Statistical analysis was conducted using unrelated t-tests and Pearson's product moment correlation co-efficients. RESULTS: CLBP participants demonstrated significantly poorer overall sleep both objectively and subjectively. They demonstrated lower actigraphic SE (p = .002) and increased WASO (p = .027) but no significant differences were found in TST (p = .43) or SL (p = .97). Subjectively, they reported increased insomnia (p =< .001), lower SE (p =< .001) and increased SL (p =< .001) but no difference between TST (p = .827) and WASO (p = .055). Statistically significant associations were found between low back pain (p = .021, r = -.589), physical health (p = .003, r = -.713), disability levels (p = .025, r = .576), and subjective sleep quality in the CLBP participants but not with actigraphy. CONCLUSION: CLBP participants demonstrated poorer overall sleep, increased insomnia symptoms and less efficient sleep. Further investigation using a larger sample size and a longer period of sleep monitoring is ongoing.

A portable automated assessment tool for sleep apnea using a combined Holter-oximeter.

STUDY OBJECTIVES: Resource limitations have raised interest in portable monitoring systems that can be used by specialist sleep physicians as part of an overall strategy to improve access to the diagnosis of sleep apnea. This study validates a combined electrocardiogram and oximetry recorder (Holter-oximeter) against simultaneous polysomnography for detection of sleep apnea. DESIGN: Prospective study. SETTING: A dedicated sleep disorders unit. PARTICIPANTS: 59 adults presenting for evaluation of suspected sleep apnea. INTERVENTIONS: NA. MEASUREMENTS AND RESULTS: An automated algorithm previously developed for sleep apnea detection was applied to the electrocardiogram and oximetry measurements. The algorithm provides (a) epoch-by-epoch estimates of apnea occurrence and (b) estimates of overall per-subject AHI. Using separate thresholds of AHI > or =15 and AHI <5 for defining clinically significant and insignificant sleep apnea, sensitivity, specificity, and likelihood ratios, conditional on positive or negative (but not indeterminate) test results were used to assess agreement between the proposed system and polysomnography. Sensitivity of 95.8% and specificity of 100% was achieved. Positive and negative likelihood ratios were >20 and 0.04 respectively, with 16.7% of subjects having intermediate test results (AHI 5-14/h). Regardless ofAHI, 85.3% of respiratory events were correctly annotated on an epoch-by-epoch basis. AHI underestimation bias was 0.9/h, and the antilogs of log-transformed limits of agreement were 0.3 and 2.7. Correlation between estimated and reference AHI was 0.95 (P <0.001). CONCLUSION: Combined Holter-oximeter monitoring compares well with polysomnography for identifying sleep apnea in an attended setting and is potentially suitable for home-based automated assessment of sleep apnea in a population suspected of having sleep apnea.

Assessment of sleep/wake patterns using a non-contact biomotion sensor.

We evaluate a contact-less continuous measuring system measuring respiration and activity patterns system for identifying sleep/wake patterns in adult humans. The system is based on the use of a novel non-contact biomotion sensor, and an automated signal analysis and classification system. The sleep/wake detection algorithm combines information from respiratory frequency, magnitude, and movement to assign 30 s epochs to either wake or sleep. Comparison to a standard polysomnogram system utilizing manual sleep stage classification indicates excellent results. It has been validated on overnight studies from 12 subjects. Wake state was correctly identified 69% and sleep with 88%. Due to its ease-of-use and good performance, the device is an excellent tool for long term monitoring of sleep patterns in the home environment in an ultraconvenient fashion.

Non-episode-dependent assessment of paroxysmal atrial fibrillation through measurement of RR interval dynamics and atrial premature contractions.

A method is presented for classifying a single lead surface electrocardiogram recording from a Holter monitor as being from a subject with paroxysmal atrial fibrillation (PAF) or not. The technique is based on first assessing the likelihood of 30-min segments of electrocardiogram (ECG) being from a subject with PAF, and then combining these per-segment likelihoods to form a per-subject classification. The per-segment assessment is based on the output of a supervised linear discriminant classifier (LDC) which has been trained using known data from the Physionet Atrial Fibrillation Prediction Database (which consists of two hundred 30-min segments of Holter ECG, taken from 53 subjects with PAF, and 47 without). One of two LDCs is used depending on whether there is a significant correlation between observed low-frequency and high-frequency spectral power in the RR power spectral density over the 30-min segment. If there is high correlation, then the LDC uses spectral features calculated over a 10-min window; in the low-correlation case, both spectral features and atrial premature contractions are used as features. The classifier was tested for its ability to distinguish PAF and non-PAF segments using three independent data sets (representing a total of 1370 segments from 50 subjects). The cumulative sensitivity, specificity, and accuracy on a per-segment basis were 43.0, 99.3, and 80.5%, respectively on these independent test sets. By combining the results of segment classification, a per-subject classification into PAF and non-PAF classes was performed. For the 50 subjects in the independent data sets, the sensitivity and specificity of the per-subject classifier were 100%.