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Pediatric digital phenotyping could add objective signal, but prior work has largely tested single modalities, leaving open which signals matter most and whether combining them helps. We analyzed electrodermal, cardiovascular, temperature, movement, and speech (acoustic and linguistic) data from 103 children aged 4\u20138 during a \u223c7-minute structured behavioral assessment. Machine-learning models trained against gold-standard clinical-interview diagnoses discriminated ADHD, anxiety, and depression (AUC 0.74\u20130.92), comparing modalities, body locations, and tasks to optimize performance. Combining model predictions with caregiver report raised sensitivity by 35\u201354 points over caregiver report alone while maintaining moderate-to-high specificity and detected 2\u20133\u00d7 more clinician-confirmed cases. An accompanying implementation-burden score showed near-best performance was achievable at low burden for some targets. Findings support brief multimodal wearable assessment as an objective complement to caregiver-reported screening.<\/jats:p>","DOI":"10.64898\/2026.08.07.26359979","type":"posted-content","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T16:05:13Z","timestamp":1786377913000},"source":"Crossref","is-referenced-by-count":0,"title":["Multimodal, multi-device wearable phenotyping for early childhood mental health: balancing predictive performance and implementation burden"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4597-0783","authenticated-orcid":false,"given":"Bryn C.","family":"Loftness","sequence":"first","affiliation":[{"name":"Vermont Complex Systems Institute, University of Vermont, Burlington, VT, USA"},{"name":"College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT, 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