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

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.

Adolescents at clinical-high risk for psychosis: Circadian rhythm disturbances predict worsened prognosis at 1-year follow-up

“…Third, our study was only 5 days in duration. Although this duration is consistent with prior studies of circadian rhythms in youth with severe mental illness (e.g., Faedda et al, 2016), longer studies would optimize consistency of sampling across participants (e.g., equal proportion of weekend/weekdays assessed). Future studies using larger samples, multiple time points (i.e., past the typical window for conversion), and longer circadian rhythm assessments (7+ days) will further clarify the sleep/wake disturbances experienced by CHR youth and their role in the emergence of psychosis.…”

Young children with Down syndrome show normal development of circadian rhythms, but poor sleep efficiency: a cross-sectional study across the first 60 months of life

“…It predicts sleep behavior in infants when directly compared to PSG [43] and has been used in several pediatric populations for the study of sleep-circadian disorders [44], including studies conducted in people with DS [45], [46]. Sleep variables were assessed at the medium sensitivity threshold (40 counts/epoch) and analyzed with Actiware software 6.08 (Philips Respironics, USA, Koninklijke Philips N.V.).…”

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

“…In those studies, actigraphy data were used as naturalistic observations of hyperactivity symptoms. Finally, a recent article made use of actigraphy readings of activity, sleep, and circadian rhythms to differentiate ADHD from bipolar disorder in a pediatric population (Faedda et al, 2016). …”