Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire
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Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire. / Brink-Kjaer, Andreas; Gupta, Niraj; Marin, Eric; Zitser, Jennifer; Sum-Ping, Oliver; Hekmat, Anahid; Bueno, Flavia; Cahuas, Ana; Langston, James; Jennum, Poul; Sorensen, Helge B. D.; Mignot, Emmanuel; During, Emmanuel.
In: Movement Disorders, Vol. 38, No. 1, 2023, p. 82-91.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Ambulatory Detection of Isolated Rapid-Eye-Movement Sleep Behavior Disorder Combining Actigraphy and Questionnaire
AU - Brink-Kjaer, Andreas
AU - Gupta, Niraj
AU - Marin, Eric
AU - Zitser, Jennifer
AU - Sum-Ping, Oliver
AU - Hekmat, Anahid
AU - Bueno, Flavia
AU - Cahuas, Ana
AU - Langston, James
AU - Jennum, Poul
AU - Sorensen, Helge B. D.
AU - Mignot, Emmanuel
AU - During, Emmanuel
N1 - Publisher Copyright: © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
PY - 2023
Y1 - 2023
N2 - Background: Isolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used. Objective: The aim was to develop a machine learning classifier using high-frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision. Methods: The method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls. Results: The actigraphy classifier achieved 95.2% (95% confidence interval [CI]: 88.3–98.7) sensitivity and 90.9% (95% CI: 82.1–95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7–100.0) with 88.1% sensitivity (95% CI: 79.2–94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms. Conclusions: Actigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large-scale screening of iRBD in the general population.
AB - Background: Isolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used. Objective: The aim was to develop a machine learning classifier using high-frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision. Methods: The method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls. Results: The actigraphy classifier achieved 95.2% (95% confidence interval [CI]: 88.3–98.7) sensitivity and 90.9% (95% CI: 82.1–95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7–100.0) with 88.1% sensitivity (95% CI: 79.2–94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms. Conclusions: Actigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large-scale screening of iRBD in the general population.
KW - actigraphy
KW - machine learning
KW - Parkinson's disease
KW - rapid-eye-movement sleep behavior disorder
U2 - 10.1002/mds.29249
DO - 10.1002/mds.29249
M3 - Journal article
C2 - 36258659
AN - SCOPUS:85146532069
VL - 38
SP - 82
EP - 91
JO - Movement Disorders
JF - Movement Disorders
SN - 0885-3185
IS - 1
ER -
ID: 366761033