Evidence map›Paper›PMID 42299226›Full record

ArticlePsychology research and behavior management2026

Clinical Phenotype Identification in Chinese Children with ADHD Using Latent Profile Analysis and Implications for Precision Nursing Pathways.

Yue Ma, Yong-Hua Cui, Jia-Jia Gao, Fang Wen, Jun-Juan Yan, Ming-Mei Sun, Hong-Mei Duan, Nan Song

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Article in Psychology research and behavior management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yue MaSchool of Nursing, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China.
Yong-Hua CuiDepartment of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100101, People's Republic of China.
Jia-Jia GaoDepartment of Nursing, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, Beijing, 101300, People's Republic of China.
Fang WenDepartment of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100101, People's Republic of China.
Jun-Juan YanDepartment of Psychiatry, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100101, People's Republic of China.
Ming-Mei SunDepartment of Nursing, Shunyi Maternal and Children's Hospital of Beijing Children's Hospital, Beijing, 101300, People's Republic of China.
Hong-Mei DuanSchool of Nursing, Beijing University of Chinese Medicine, Beijing, 100029, People's Republic of China.
Nan SongDepartment of Nursing, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100101, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is characterized by substantial clinical heterogeneity, underscoring the need for a shift toward precision nursing approaches. The present study aimed to identify relatively homogeneous subgroups (phenotypes) among children with ADHD based on multidimensional clinical characteristics and to validate their distinct cognitive profiles using objective cognitive assessments, thereby providing an empirical basis for the development of a precision nursing practice pathway. Methods: A cohort of 403 treatment-naïve children with newly diagnosed ADHD were included. Behavioral, emotional, and social functioning were evaluated using the Swanson, Nolan, and Pelham Version IV Scale, the Strengths and Difficulties Questionnaire, and the Weiss Functional Impairment Rating Scale-Parent Report. Cognitive validation was conducted using the Continuous Performance Test (CPT). The primary analytical methods included latent profile analysis (LPA) and between-group comparisons. Results: The optimal LPA model identified three distinct classes: the "functionally balanced type" (25.8%), the "impulsivity-predominant type" (55.6%), and the "emotional-cognitive dysregulation subtype" (18.6%). Cognitive validation using the CPT indicated that the impulsivity-predominant type demonstrated a significantly higher commission error rate, whereas the emotional-cognitive dysregulation subtype exhibited significantly greater reaction time variability. Conclusion: The "behavioral subtyping-cognitive validation" framework proposed in this study links subjective behavioral clustering with objective cognitive indicators relevant to nursing care. This approach provides an important empirical foundation for shifting ADHD management from a uniform model toward mechanism-informed precision intervention.

Indexed as

attention-deficit/hyperactivity disorderchildrencognitionnursingphenotype

Identifiers

PMID42299226
PMCPMC13264979

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.