Methods in Pediatric Sleep Research and Sleep Medicine

Mouthon, Anne-Laure; Huber, Reto

doi:10.1055/s-0035-1550232

Cited by 25 publications

(13 citation statements)

References 103 publications

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“…However, the instrumentation required in studies of this detail is only found in dedicated sleep laboratories (Grigg‐Damberger et al, ). Furthermore, polysomnography requires subjects to tolerate multiple electrodes and sensors along with sleeping in a foreign, intrusive unnatural sleeping environment which poses a challenge to normal sleep patterns and is a major limitation (Mouthon & Huber, ). Thus, polysomnography is not a practical option for most infant assessment protocols particularly in the newborn nursery or in home monitoring studies, (Montgomery‐Downs, Insana, & Bond, ).…”

Section: Introductionmentioning

confidence: 99%

An automated method for coding sleep states in human infants based on respiratory rate variability

Isler

Thai

Myers

et al. 2016

Developmental Psychobiology

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A novel quantitative method for coding epochs of active and quiet sleep in infants using respiration is reported. The approach uses the variance of the instantaneous breathing rate within brief epochs of sleep. Variances are normalized within subject by dividing by the 75th percentile variance across epochs. Then, a normalized variance active sleep threshold of 0.29 was determined to produce the highest concordance with a method based on visual inspection of respiratory variability (100% and 90% for quiet and active sleep, respectively). The method was independently validated by comparing to standard polysomnographic state coding (87% and 80% concordance for quiet and active sleep) as well as with behavioral state coding (92% and 78% for quiet and active sleep). Validity was also demonstrated by showing that sleep states identified by the method resulted in the expected state differences in infant heart rate variability and electrocortical activity.

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Section: Introductionmentioning

confidence: 99%

An automated method for coding sleep states in human infants based on respiratory rate variability

Isler

Thai

Myers

et al. 2016

Developmental Psychobiology

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“…& Future research in pediatric insomnia should also include an expanded repertoire of both subjective (e.g., validated questionnaires for all age groups and adapted for special populations) and objective outcome measures of sleep, including clinically meaningful biomarkers such as cardiovascular parameters, inflammatory markers, and physiologic stress measures [112,113].…”

Section: Intervention Outcomesmentioning

confidence: 99%

Behavioral Treatments for Pediatric Insomnia

Zhou

Owens

2016

Curr Sleep Medicine Rep

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Pediatric insomnia, defined as chronic and clinically significant difficulty with initiating and/or maintaining sleep in children and adolescents, can profoundly impact functioning and well-being on both the child and their family. Behavioral interventions for insomnia in pediatric populations have been shown to be highly effective. This review expands upon this growing literature by summarizing the studies that have examined behavioral treatments for insomnia in children and adolescents published since 2013. Three themes emerge: (1) how pediatric insomnia is being treated in health settings; (2) efforts to adapt treatment to unique patient populations; and (3) the development of novel delivery methods for effective dissemination of treatment. Recommendations for important future clinical research questions are discussed.

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“…However, there are substantial barriers to examining the relationship between asthma and sleep patterns. Such studies usually need to be carried out in a special environment (for capturing sleep patterns [ 17 - 20 ]) and over a relatively long time (to accumulate enough data), which incurs high costs. Specialized and expensive equipment is typically needed to collect sleep pattern data, and participants usually have to stay in an unfamiliar sleep study facility [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

Bian¹,

Guo²,

Xie³

et al. 2017

JMIR Mhealth Uhealth

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BackgroundSmart wearables such as the Fitbit wristband provide the opportunity to monitor patients more comprehensively, to track patients in a fashion that more closely follows the contours of their lives, and to derive a more complete dataset that enables precision medicine. However, the utility and efficacy of using wearable devices to monitor adolescent patients’ asthma outcomes have not been established.ObjectiveThe objective of this study was to explore the association between self‑reported sleep data, Fitbit sleep and physical activity data, and pediatric asthma impact (PAI).MethodsWe conducted an 8‑week pilot study with 22 adolescent asthma patients to collect: (1) weekly or biweekly patient‑reported data using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures of PAI, sleep disturbance (SD), and sleep‑related impairment (SRI) and (2) real-time Fitbit (ie, Fitbit Charge HR) data on physical activity (F-AM) and sleep quality (F‑SQ). To explore the relationship among the self-reported and Fitbit measures, we computed weekly Pearson correlations among these variables of interest.ResultsWe have shown that the Fitbit-derived sleep quality F-SQ measure has a moderate correlation with the PROMIS SD score (average r=−.31, P=.01) and a weak but significant correlation with the PROMIS PAI score (average r=−.18, P=.02). The Fitbit physical activity measure has a negligible correlation with PAI (average r=.04, P=.62).ConclusionsOur findings support the potential of using wrist-worn devices to continuously monitor two important factors—physical activity and sleep—associated with patients’ asthma outcomes and to develop a personalized asthma management platform.

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Methods in Pediatric Sleep Research and Sleep Medicine

Cited by 25 publications

References 103 publications

An automated method for coding sleep states in human infants based on respiratory rate variability

An automated method for coding sleep states in human infants based on respiratory rate variability

Behavioral Treatments for Pediatric Insomnia

Exploring the Association Between Self-Reported Asthma Impact and Fitbit-Derived Sleep Quality and Physical Activity Measures in Adolescents

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