Pilot Study of a Wearable Hydration Monitor in Haemodialysis Patients: Haemodialysis Outcomes & Patient Empowerment Study 02.

Introduction: We aimed to assess the validity and reproducibility of a wearable hydration device in a cohort of maintenance dialysis patients. Methods: We conducted a prospective, single-arm observational study on 20 haemodialysis patients between January and June 2021 in a single centre. A prototype wearable infrared spectroscopy device, termed the Sixty device, was worn on the forearm during dialysis sessions and nocturnally. Bioimpedance measurements were performed 4 times using the body composition monitor (BCM) over 3 weeks. Measurements from the Sixty device were compared with the BCM overhydration index (litres) pre- and post-dialysis and with standard haemodialysis parameters. Results: 12 out of 20 patients had useable data. Mean age was 52 ± 12.4 years. The overall accuracy for predicting pre-dialysis categories of fluid status using Sixty device was 0.55 [K = 0.00; 95% CI: -0.39-0.42]. The accuracy for the prediction of post-dialysis categories of volume status was low [accuracy = 0.34, K = 0.08; 95% CI: -0.13-0.3]. Sixty outputs at the start and end of dialysis were weakly correlated with pre- and post-dialysis weights (r = 0.27 and r = 0.27, respectively), as well as weight loss during dialysis (r = 0.31), but not ultrafiltration volume (r = 0.12). There was no difference between the change in Sixty readings overnight and the change in Sixty readings during dialysis (mean difference 0.09 ± 1.5 kg), [t(39) = 0.38, p = 0.71]. Conclusion: A prototype wearable infrared spectroscopy device was unable to accurately assess changes in fluid status during or between dialysis sessions. In the future, hardware development and advances in photonics may enable the tracking of interdialytic fluid status.

Protocol of a pilot-scale, single-arm, observational study to assess the utility and acceptability of a wearable hydration monitor in haemodialysis patients.

BACKGROUND: Fluid overload has a high prevalence in haemodialysis patients and is an important risk factor for excess mortality and hospitalisations. Despite the risks associated with chronic fluid overload, it is clinically difficult to assess and maintain fluid status adequately. Current methods of fluid status assessment are either imprecise or time intensive. In particular, to date, no method exists to accurately assess fluid status during the interdialytic interval. OBJECTIVES: This pilot study aimed to evaluate whether a prototype wearable hydration monitor can accurately and reproducibly detect fluid overload in the haemodialysis population when compared to haemodialysis and bioimpedance data. METHODS: A prospective, open-label, single-arm observational trial of 20 patients commenced in January 2021 in a single haemodialysis centre in Ireland, with a wearable hydration monitor, the Sixty device. The Sixty device uses diffuse reflectance spectroscopy to measure fluid levels at the level of the subdermis and uses machine learning to develop an algorithm that can determine fluid status. The Sixty device was worn at every dialysis session and nocturnally over a three-week observational period. Haemodialysis parameters including interdialytic weight gain, ultrafiltration volume, blood pressure, and relative blood volume were collected from each session, and bioimpedance measurements using the Fresenius body composition monitor were performed on 4 occasions as a comparator. The primary objective of this trial was to determine the accuracy and reproducibility of the Sixty device compared to bioimpedance measurements. CONCLUSION: If the accuracy of the wearable hydration monitor is validated, further studies will be conducted to integrate the device output into a multi-parameter machine learning algorithm that can provide patients with actionable insights to manage fluid overload in the interdialytic period. TRIAL REGISTRATION: www.clinicaltrials.gov NCT04623281 . Registered November 10th, 2020.

Ambulatory detection of sleep apnea using a non-contact biomotion sensor.

The high prevalence of obstructive sleep apnea has led to increasing interest in ambulatory diagnosis. The SleepMinder™ (SM) is a novel non-contact device that employs radiofrequency wave technology to assess the breathing pattern, and thereby estimate obstructive sleep apnea severity. We assessed the performance of SleepMinder™ in the home diagnosis of obstructive sleep apnea. One-hundred and twenty-two subjects were prospectively recruited in two protocols, one from an unselected sleep clinic cohort (n = 67, mean age 51 years) and a second from a hypertension clinic cohort (n = 55, mean age 58 years). All underwent 7 consecutive nights of home monitoring (SMHOME ) with the SleepMinder™ as well as inpatient-attended polysomnography in the sleep clinic cohort or cardiorespiratory polygraphy in the hypertension clinic cohort with simultaneous SleepMinder™ recordings (SMLAB ). In the sleep clinic cohort, median SMHOME apnea-hypopnea index correlated significantly with polysomnography apnea-hypopnea index (r = .68; p < .001), and in the hypertension clinic cohort with polygraphy apnea-hypopnea index (r = .7; p < .001). The median SMHOME performance against polysomnography in the sleep clinic cohort showed a sensitivity and specificity of 72% and 94% for apnea-hypopnea index ≥ 15. Device performance was inferior in females. In the hypertension clinic cohort, SMHOME showed a 50% sensitivity and 72% specificity for apnea-hypopnea index ≥ 15. SleepMinder™ classified 92% of cases correctly or within one severity class of the polygraphy classification. Night-to-night variability in home testing was relatively high, especially at lower apnea-hypopnea index levels. We conclude that the SleepMinder™ device provides a useful ambulatory screening tool, especially in a population suspected of obstructive sleep apnea, and is most accurate in moderate-severe obstructive sleep apnea.

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness.

BACKGROUND: Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity. OBJECTIVES: We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness. METHODS: We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone. RESULTS: During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning. CONCLUSIONS: This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

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.

Comparison of a novel non-contact biomotion sensor with wrist actigraphy in estimating sleep quality in patients with obstructive sleep apnoea.

Ambulatory monitoring is of major clinical interest in the diagnosis of obstructive sleep apnoea syndrome. We compared a novel non-contact biomotion sensor, which provides an estimate of both sleep time and sleep-disordered breathing, with wrist actigraphy in the assessment of total sleep time in adult humans suspected of obstructive sleep apnoea syndrome. Both systems were simultaneously evaluated against polysomnography in 103 patients undergoing assessment for obstructive sleep apnoea syndrome in a hospital-based sleep laboratory (84 male, aged 55 ± 14 years and apnoea-hypopnoea index 21 ± 23). The biomotion sensor demonstrated similar accuracy to wrist actigraphy for sleep/wake determination (77.3%: biomotion; 76.5%: actigraphy), and the biomotion sensor demonstrated higher specificity (52%: biomotion; 34%: actigraphy) and lower sensitivity (86%: biomotion; 94%: actigraphy). Notably, total sleep time estimation by the biomotion sensor was superior to actigraphy (average overestimate of 10 versus 57 min), especially at a higher apnoea-hypopnoea index. In post hoc analyses, we assessed the improved apnoea-hypopnoea index accuracy gained by combining respiratory measurements from polysomnography for total recording time (equivalent to respiratory polygraphy) with total sleep time derived from actigraphy or the biomotion sensor. Here, the number of misclassifications of obstructive sleep apnoea severity compared with full polysomnography was reduced from 10/103 (for total respiratory recording time alone) to 7/103 and 4/103 (for actigraphy and biomotion sensor total sleep time estimate, respectively). We conclude that the biomotion sensor provides a viable alternative to actigraphy for sleep estimation in the assessment of obstructive sleep apnoea syndrome. As a non-contact device, it is suited to longitudinal assessment of sleep, which could also be combined with polygraphy in ambulatory studies.

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.

Sleep/wake measurement using a non-contact biomotion sensor.

We studied a novel non-contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low-power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30-s epoch basis based on the measured movement signal. The sensor and software were evaluated against gold-standard polysomnography on a database of 113 subjects [94 male, 19 female, age 53±13years, apnoea-hypopnea index (AHI) 22±24] being assessed for sleep-disordered breathing at a hospital-based sleep laboratory. The overall per-subject accuracy was 78%, with a Cohen's kappa of 0.38. Lower accuracy was seen in a high AHI group (AHI >15, 63 subjects) than in a low AHI group (74.8% versus 81.3%); however, most of the change in accuracy can be explained by the lower sleep efficiency of the high AHI group. Averaged across subjects, the overall sleep sensitivity was 87.3% and the wake sensitivity was 50.1%. The automated algorithm slightly overestimated sleep efficiency (bias of +4.8%) and total sleep time (TST; bias of +19min on an average TST of 288min). We conclude that the non-contact biomotion sensor can provide a valid means of measuring sleep-wake patterns in this patient population, and also allows direct visualization of respiratory movement signals.

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.