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Cited by 111Bipolar Disorder and TreatmentSleep and related disordersCircadian rhythm and melatonin

Actigraph measures discriminate pediatric bipolar disorder from attention‐deficit/hyperactivity disorder and typically developing controls

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Faedda, Gianni L., Ohashi, Kyoko, Hernandez, Mariely, McGreenery, Cynthia E., Grant, Marie C., Baroni, Argelinda, Polcari, Ann, Teicher, Martin H.·2016·Journal of Child Psychology and Psychiatry
DOI 10.1111/jcpp.12520PMC PMC4873411
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Abstract

Background: Distinguishing pediatric bipolar disorder ( BD ) from attention‐deficit hyperactivity disorder ( ADHD ) can be challenging. Hyperactivity is a core feature of both disorders, but severely disturbed sleep and circadian dysregulation are more characteristic of BD , at least in adults. We tested the hypothesis that objective measures of activity, sleep, and circadian rhythms would help differentiate pediatric subjects with BD from ADHD and typically developing controls. Methods: Unmedicated youths ( N = 155, 97 males, age 5–18) were diagnosed using DSM ‐ IV criteria with Kiddie‐ SADS PL /E. BD youths ( n = 48) were compared to typically developing controls ( n = 42) and children with ADHD ( n = 44) or ADHD plus comorbid depressive disorders ( n = 21). Three‐to‐five days of minute‐to‐minute belt‐worn actigraph data (Ambulatory Monitoring Inc.), collected during the school week, were processed to yield 28 metrics per subject, and assessed for group differences with analysis of covariance. Cross‐validated machine learning algorithms were used to determine the predictive accuracy of a four‐parameter model, with measures reflecting sleep, hyperactivity, and circadian dysregulation, plus Indic's bipolar vulnerability index ( VI ). Results: There were prominent group differences in several activity measures, notably mean 5 lowest hours of activity, skewness of diurnal activity, relative circadian amplitude, and VI . A predictive support vector machine model discriminated bipolar from non‐bipolar with mean accuracy of 83.1 ± 5.4%, ROC area of 0.781 ± 0.071, kappa of 0.587 ± 0.136, specificity of 91.7 ± 5.3%, and sensitivity of 64.4 ± 13.6%. Conclusions: Objective measures of sleep, circadian rhythmicity, and hyperactivity were abnormal in BD . Wearable sensor technology may provide bio‐behavioral markers that can help differentiate children with BD from ADHD and healthy controls.

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  • By tracking additional metrics like diurnal activity and circadian amplitude and dysregulation using wearable sensors, researchers could distinguish BD and non-BD conditions, such as ADHD, with 83% accuracy (92% specificity and 64% sensitivity).

    “By tracking additional metrics like diurnal activity and circadian amplitude and dysregulation using wearable sensors, researchers could distinguish BD and non-BD conditions, such as ADHD, with 83% accuracy (92% specificity and 64% sensitivity) [98].”

    From Wearables and the Medical Revolution (2018) · cites this paper as [98]

  • Actigraphy can reveal differences in circadian parameters diurnal skew, L5 and relative circadian amplitude that can be used to discriminate ADHD from bipolar disorder and control in children.

    “Faedda et al. (2016) report that actigraphy can reveal differences in circadian parameters diurnal skew, L5 and relative circadian amplitude that can be used to discriminate ADHD from bipolar disorder and control in children, indicating that changes in rest/activity patterns associated in ADHD may be different from those associated with other psychiatric populations.”

    From A systematic review of circadian function, chronotype and chronotherapy in attention deficit hyperactivity disorder (2017) · cites this paper as Faedda et al. (2016)

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Research works printed but not linked: 1

  1. NIMH Arousal and Regulatory Systems: Workshop Proceedings 2012 Rockville, MD http://www.nimh.nih.gov/research-priorities/rdoc/arousal-and-regulatory-systems-workshop-proceedings.shtml

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Authors
Faedda, G. L., Ohashi, K., Hernandez, M., McGreenery, C. E., Grant, M. C., Baroni, A., Polcari, A., & Teicher, M. H.
Affiliations
  • NYU Langone Health (US)
  • Harvard University (US)
  • Mood Disorders Center ‘Lucio Bini’ New York NY USA
  • McLean Hospital (US)
  • Northeastern University (US)
Corresponding author
Faedda, Gianni L.
ORCID
Venue
Journal of Child Psychology and Psychiatry
Publisher
Wiley
Published
2016-01-22
Volume
57
Issue
6
Pages
706-716
ISSN
  • 0021-9630 (ISSN-L)
  • 1469-7610
DOI
10.1111/jcpp.12520
PMC
PMC4873411
OpenAlex
W2302336355
Cited by
111
References
48
Citations by year
2026: 6 · 2025: 15 · 2024: 14 · 2023: 6 · 2022: 19 · 2021: 10 · 2020: 12 · 2019: 7 · 2018: 10 · 2017: 9 · 2016: 3
FWCI
8.59
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Journal of Child Psychology and Psychiatry (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
New Harbinger Publications Inc.
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
  • Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Circadian rhythm and melatonin
Keywords
pediatric bipolar disorder, attention deficit hyperactivity disorder, actigraphy, circadian rhythm, sleep, hyperactivity, circadian dysfunction, differential diagnosis, wearable sensors, support vector machine classification
MeSH terms
Adolescent, Attention Deficit Disorder with Hyperactivity, Bipolar Disorder, Child, Child Development, Child, Preschool, Circadian Rhythm, Depressive Disorder, Female, Humans, Male, Sensitivity and Specificity, Comorbidity, Adolescent Development, Actigraphy, Support Vector Machine

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