Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

Dec 4, 2025JMIR mental health

Using Mood, Energy, and Activity Data to Distinguish Bipolar Disorder, ADHD, and Other Mental Health Conditions in Children

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Abstract

A receiver operating characteristic area under the curve (ROC-AUC) of 0.85 was achieved for predicting same-day severe mood in adolescents.

Pediatric bipolar disorder without attention-deficit/hyperactivity disorder (ADHD) displayed a strong connection between extreme activity and negative mood and energy.
ADHD without bipolar disorder showed a stronger relationship between activity levels and positive energy.
Energy variability and the average or peak activity levels were identified as the most important predictors of severe mood days.
Machine learning models demonstrated an accuracy of 0.79 for predicting same-day severe mood and a moderate performance for predicting next-day severe mood.

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BACKGROUND: Distinguishing pediatric bipolar disorder (BD) from attention-deficit/hyperactivity disorder (ADHD) is challenging due to overlapping fluctuations in mood, energy, and activity. Combining objective actigraphy with self-reported mood and energy data may aid differential diagnosis and risk monitoring.

OBJECTIVE: This study aimed to test same-day associations between actigraphy-derived activity extremes and self-reported mood and energy, and to evaluate whether these measures predict same-day and next-day severe mood in adolescents with BD, ADHD, and other diagnoses.

METHODS: We analyzed 209 inpatients (2148 patient-days) across 4 groups (ADHD without BD: n=54; BD with ADHD: n=42; BD without ADHD: n=34; other diagnoses: n=79). Actigraphy data (Philips Actiwatch 2) were summarized into daily maximum and minimum quartiles (Max1-Max4 and Min1-Min4). Mood and Energy Thermometer (-10 to +10) ratings were categorized as follows: OK (<3), mild (3-4), moderate (5-6), and severe (>6). Group differences used Kruskal-Wallis and Mann-Whitney U tests with Bonferroni correction (P<.004). Associations used chi-square tests with Cramér V. Leak-safe machine learning (patient-wise GroupKFold) classified SevereDay (same day) and SevereTomorrow (next day) using actigraphy, sleep, energy, and demographic data.

RESULTS: BD without ADHD showed the tightest coupling of extreme activity with negative mood and energy (Cramér V of up to 0.24; P<.004). ADHD without BD showed stronger links between activity and positive energy. Machine learning achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.85, an accuracy of 0.79, and an F1-score of 0.67 for SevereDay. SevereTomorrow performance was moderate (ROC-AUC=0.80; accuracy=0.79; F1-score=0.60). Energy variability and actigraphy averages/peaks were the top predictors.

CONCLUSIONS: Integrating actigraphy, sleep, and daily energy ratings identifies severe mood days and provides early next-day risk signals in hospitalized adolescents. The findings support wearable-based phenotyping for precision monitoring, with external validation needed in outpatients.

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Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

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