The Japanese version of the Richards‐Campbell Sleep Questionnaire: Reliability and validity assessment

Murata, Hiroaki; Oono, Yutaka; Sanui, Masamitsu; Saito, Keita; Yamaguchi, Yoko; Takinami, Masanori; Richards, Kathy; Henker, Richard

doi:10.1002/nop2.252

Cited by 12 publications

(10 citation statements)

References 20 publications

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“…Indeed, our results were similar to results found in ICU patients (R = 0.33, P=0.03) with selfreported sleep. 13 Since the RCSQ has been found to correlate moderately well with polysomnography in ICU patients 18,29 , the discrepancy between our ndings and the previous results with Fitbits may suggest that sleep detection algorithms of Fitbits may be less accurate for hospitalized patients. Patients' conditions and the hospital environment may reduce the accuracy of Fitbit sleep algorithms.…”

Section: Discussioncontrasting

confidence: 63%

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

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Background: Wearable devices such as Fitbits may provide important insights about hospitalized patients that include data on low activity and poor sleep. Monitoring this information could spur interventions to improve mobility and sleep which may reduce the adverse effects associated with hospitalization. However, there is a lack of studies assessing the accuracy of wearables in hospitalized medical patients. The purpose of our study was to determine the accuracy of Fitbit heart rate, sleep and physical activity in hospitalized medical patients.Methods: We conducted a prospective cohort feasibility study enrolling 50 medical inpatients at two hospitals providing them with a wrist-worn Fitbit Charge. Our main measures were Fitbit heart rate, sleep and activity data as well as nurse recorded heart rates, patient reported sleep, and nurse assessments of activity.Results: Of the 50 patients who consented to the study, 47 patients wore the devices. Comparing pairs of heart rate data from Fitbit and nurse recorded vital signs for the same minute, there were 261 pairs available for comparison. The mean difference was 0.45 bpm (SD: 13.0, Pearson correlation: 0.68 P<0.001) and the 95% limits of agreement were -25 to 26 bpm. The association between the patient-reported sleep score and Fitbit total sleep duration was 0.19 (P=0.24) and between the self-reported hours of sleep and Fitbit total sleep duration was 0.21 (P=0.21). The correlation between nurse-recorded activity and Fitbit daily steps was 0.06 (P=0.52). Conclusions: Fitbit heart rates correlated well with nurse-recorded heart rate but did not correlate well with nurse assessments of activity nor with patient self-assessment of sleep. This study highlights limitations of the accuracy of current wearable wrist-worn device algorithms in activity and sleep detection in patients in hospital. The findings call into question the validity of Fitbits for assessment of patient activity and sleep in the hospital setting and suggest that they should not be routinely used without further validation.Trial Registration: ClinicalTrials.gov NCT03646435

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Section: Discussioncontrasting

confidence: 63%

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Indeed, our results were similar to results found in ICU patients (R = 0.33, P = 0.03) with self-reported sleep. 11 Since the RCSQ has been found to correlate well with polysomnography in ICU patients 14,25 , the discrepancy between our ndings and the previous results with Fitbits may suggest that sleep detection algorithms of Fitbits may be less accurate for hospitalized patients. Fitbit sleep detection algorithms using actigraphy have not been derived from hospitalized patients, and the algorithms are known to be inaccurate with disrupted or abnormal sleep, which is highly prevalent in hospital.…”

Section: Discussioncontrasting

confidence: 62%

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Patel

Moosa

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundWearable devices such as Fitbits may provide important insights about hospitalized patients that include data on low activity and poor sleep. Monitoring this information could spur interventions to improve mobility and sleep which may reduce the adverse effects associated with hospitalization. However, there is a lack of studies assessing the accuracy of wearables in hospitalized medical patients. The purpose of our study was to determine the accuracy of Fitbit heart rate, sleep and physical activity in hospitalized medical patients.MethodsWe conducted a prospective cohort feasibility study enrolling 50 medical inpatients at two hospitals providing them with a Fitbit Charge. Our main measures were Fitbit heart rate, sleep and activity data as well as nurse recorded heart rates, patient reported sleep, and nurse assessments of activity.ResultsOf the 50 patients who consented to the study, 47 patients wore the devices. Comparing pairs of heart rate data from Fitbit and nurse recorded vital signs for the same minute, there were 261 pairs available for comparison. The mean difference was 0.45 bpm (SD: 13.0, Pearson correlation: 0.68 P<0.001) and the 95% limits of agreement were -25 to 26 bpm. The association between the patient-reported sleep score and Fitbit total sleep duration was 0.19 (P=0.24) and between the self-reported hours of sleep and Fitbit total sleep duration was 0.21 (P=0.21). The correlation between nurse-recorded activity and Fitbit daily steps was 0.06 (P=0.52). ConclusionsFitbit heart rates correlated well with nurse-recorded heart rate but did not correlate well with nurse assessments of activity nor with patient self-assessment of sleep. This study highlights limitations of the accuracy of current wearable wrist-worn device algorithms in activity and sleep detection in patients in hospital. The findings call into question the validity of Fitbits for assessment of patient activity and sleep in the hospital setting and suggest that they should not be routinely used without further validation.Trial RegistrationClinicalTrials.gov NCT03646435

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“…The RCSQ has demonstrated content validity, criterion validity against PSG, and internal consistency reliability with Cronbach's alpha of 0.90 ( 2 ), and has been used in well over 1,000 ICU patients. The RCSQ has been translated and validated in other languages, including versions in Arabic ( 82 ), Chinese ( 83 ), German ( 84 ), Japanese ( 85 ), and Portuguese ( 86 ). Although the RCSQ was developed as a self-assessment tool, some studies explored its patient-nurse interrater agreement.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Sleep Measurement in Critically Ill Patients

et al. 2020

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Background: Clinical trialists and clinicians have used a number of sleep quality measures to determine the outcomes of interventions to improve sleep and ameliorate the neurobehavioral consequences of sleep deprivation in critically ill patients, but findings have not always been consistent. To elucidate the source of these consistencies, an important consideration is responsiveness of existing sleep measures. The purpose of an evaluative measure is to describe a construct of interest in a specific population, and to measure the extent of change in the construct over time. This systematic literature review identified measures of sleep quality in critically ill adults hospitalized in the Intensive Care Unit (ICU), and assessed their measurement properties, strengths and weaknesses, clinical usefulness, and responsiveness. We also recommended modifications, including new technology, that may improve clinical usefulness and responsiveness of the measures in research and practice.

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The Japanese version of the Richards‐Campbell Sleep Questionnaire: Reliability and validity assessment

Cited by 12 publications

References 20 publications

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

Fitbit data show poor correlation with measures of activity and sleep among hospitalized general medicine patients

A Systematic Review of Sleep Measurement in Critically Ill Patients

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