{"doi":"10.2196/78163","title":"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","abstract":"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.","journal":"JMIR Mental Health","year":2025,"id":575255,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.952,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1400285,"name":"Farzan Vahedifard","orcid":"0000-0002-0803-7831","position":1,"is_corresponding":false},{"id":18900,"name":"BORIS BIRMAHER","orcid":"0000-0001-9299-6519","position":2,"is_corresponding":false},{"id":608004,"name":"Satish Iyengar","orcid":"0000-0002-9329-6853","position":3,"is_corresponding":false},{"id":1483215,"name":"Maria Wolfe","orcid":"0009-0009-2385-4379","position":4,"is_corresponding":false},{"id":1483216,"name":"Brianna N Lepore","orcid":"0009-0000-6541-7948","position":5,"is_corresponding":false},{"id":1340085,"name":"Mariah Chobany","orcid":"0000-0003-3118-8432","position":6,"is_corresponding":false},{"id":727131,"name":"Halimah Abdul‐waalee","orcid":"0009-0005-6560-2816","position":7,"is_corresponding":false},{"id":1340086,"name":"Greeshma Malgireddy","orcid":"0000-0002-6641-6733","position":8,"is_corresponding":false},{"id":1340087,"name":"Jonathan Hart","orcid":"0009-0003-0825-2587","position":9,"is_corresponding":false},{"id":421936,"name":"Michele A. Bertocci","orcid":"0000-0002-3857-1226","position":10,"is_corresponding":false},{"id":609862,"name":"Rasim Somer Diler","orcid":"0000-0001-5895-6572","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-19T02:57:48.486077Z","pmid":"41343774","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}