Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

Zinkhan, Melanie; Kantelhardt, Jan W.

doi:10.1016/j.jsmc.2016.08.006

Cited by 16 publications

(15 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our initial observation of a peak in the 0.3 Hz range (corresponding to breaths per minute) in nocturnal three-axis accelerometry data recorded at the wrist 32 was the starting point for our approach towards respiration proxies. After we had found that the high amplitude resolution of modern accelerometers (down to ) can resolve tiny motions caused by respiratory activity 10 , 32 , 33 , we have systematically studied if this effect can be used for a practical derivation of respiration proxies.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Leube

Zschocke

Kluge

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Respiratory rate and changes in respiratory activity provide important markers of health and fitness. Assessing the breathing signal without direct respiratory sensors can be very helpful in large cohort studies and for screening purposes. In this paper, we demonstrate that long-term nocturnal acceleration measurements from the wrist yield significantly better respiration proxies than four standard approaches of ECG (electrocardiogram) derived respiration. We validate our approach by comparison with flow-derived respiration as standard reference signal, studying the full-night data of 223 subjects in a clinical sleep laboratory. Specifically, we find that phase synchronization indices between respiration proxies and the flow signal are large for five suggested acceleration-derived proxies with for males and for females (means ± standard deviations), while ECG-derived proxies yield only for males and for females. Similarly, respiratory rates can be determined more precisely by wrist-worn acceleration devices compared with a derivation from the ECG. As limitation we must mention that acceleration-derived respiration proxies are only available during episodes of non-physical activity (especially during sleep).

show abstract

Section: Methodsmentioning

confidence: 99%

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Leube

Zschocke

Kluge

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…There is a large body of literature to support the reliability and validity of sleep assessment using research‐grade, wrist‐worn accelerometers 151 . Outputs include the amount of time awake after sleep onset, sleep efficiency (ie, the percentage of time in bed spent sleeping), and total sleep time.…”

Section: Digital Phenotyping: Mobile Technology To Collect Pghdmentioning

confidence: 99%

Innovations in research and clinical care using patient‐generated health data

Jim

Hoogland

Brownstein

et al. 2020

CA A Cancer J Clinicians

View full text Add to dashboard Cite

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

show abstract

“…The new generation of accelerometers provides high temporal and intensity resolution, allowing the detection of tiny body movements and identification of pulse waves when attached to the wrist ( Zschocke et al, 2019 ). Even when worn on the wrist or hip, these devices provide data that can be used to track respiratory rate throughout the night ( Zinkhan & Kantelhardt, 2016 ). As such, these high-resolution accelerometers should detect tiny motions, even when attached to the neck, and triaxial recordings may be able to distinguish whether the monkey is in an upright position or lying down.…”

Section: Dear Editormentioning

confidence: 99%

“…At present, however, most algorithms have been developed and validated for wrist measurements. When accelerometers are placed at the hip, mean PSG bias is larger, and there is a distinct dependency in differences of magnitude of respective sleep measures compared to wrist measurements ( Zinkhan & Kantelhardt, 2016 ). Thus, new algorithms need to be developed to adapt to the different placements of accelerometers.…”

Section: Dear Editormentioning

confidence: 99%

Potential use of actigraphy to measure sleep in monkeys: comparison with behavioral analysis from videography

Qin

Feng

Zhang

et al. 2020

Zoological Research

View full text Add to dashboard Cite

A: Mean locomotor activity for each monkey measured by Actical monitors ( n =10). Activity data were collected over a seven-day period and are presented as means over that period. Lights were on at 0800 h and off at 2000 h. B: Correlations between sleep scores from actigraphy and videography. Four states were scored by actigraphy (X-axis: Actigraphy) and videography analysis (Y-axis: Videography), respectively, including wake, sleep, transitional sleep, and relaxed sleep. C: Bland-Altman plots for actigraphy vs. videography. X axes represent average of two methods ((actigraphy+videography)/2) and Y axes show differences between two paired measurements (actigraphy–videography), including wake, sleep, transitional sleep, and relaxed sleep. Solid lines show mean differences between actigraphy and videography measurements; dotted lines represent 95% limits of agreement, from –1.96 SD (standard deviation) to +1.96 SD .

show abstract

Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

Cited by 16 publications

References 99 publications

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Reconstruction of the respiratory signal through ECG and wrist accelerometer data

Innovations in research and clinical care using patient‐generated health data

Potential use of actigraphy to measure sleep in monkeys: comparison with behavioral analysis from videography

Contact Info

Product

Resources

About