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Record W4406219392 · doi:10.1002/alz.091761

Childhood attention‐deficit hyperactivity disorder (ADHD) and cognitive function in later life: Analysis of HRS data

2024· article· en· W4406219392 on OpenAlexaff
Arne Stinchcombe, D. Hanes

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsPublic Health OntarioUniversity of TorontoBruyèreUniversity of Ottawa
Fundersnot available
KeywordsAttention deficit hyperactivity disorderCognitionPsychologyAttention deficitDevelopmental psychologyClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Attention‐deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition marked by cognitive deficits (e.g., challenges sustaining attention, distractibility). Symptoms of ADHD typically manifest in childhood and can interfere with engagement in school activities, chores, and interpersonal relationships. In many cases, symptoms endure into adulthood and older adulthood. ADHD in adulthood and mild cognitive impairment (MCI) share many overlapping cognitive (e.g., impaired executive functions) and non‐cognitive (e.g., depression, poor sleep) symptoms, complicating differential diagnosis. Currently, the association between ADHD and cognitive impairment associated with aging, such as MCI, remains unclear. The purpose of this present study was to examine differences in cognitive function by ADHD status in an aging sample. Method Data were drawn from the Health and Retirement Study, a longitudinal survey of U.S. residents aged ≥51 years. Waves 4‐14 (1998‐2018) were used in the present analysis. Of the 10,804 participants included for analysis, 99 participants (i.e., 0.9% of the sample) reported having an ADHD diagnosis in childhood. Cognitive function was measured on a 27‐point scale. The primary analysis consisted of a series of multiple linear regression models, treating cognition as the outcome, and adjusting for age, sex/gender, wealth, education, and race. Result Descriptive analysis showed that participants with ADHD reported lower levels of education and were more likely to have experienced homelessness and depression. Participants with ADHD showed lower cognitive scores at baseline (m = 14.52) compared to non‐ADHD peers (m = 17.01, t = 5.90, p<.001). Multivariable analysis revealed that after adjusting for covariates, ADHD status was associated with lower cognitive scores in the study (B = ‐1.88, p<.001). ADHD participants showed a similar slope of decline to non‐ADHD peers over time; however, cognitive scores were consistently lower at each time point for ADHD participants. Conclusion Individuals with self‐reported childhood ADHD diagnoses demonstrated lower cognitive scores compared to their non‐ADHD counterparts, suggesting a persistent impact of ADHD on cognitive function in later life and potential risk for cognitive impairment. These findings emphasize the need for further research to elucidate the complex relationship between ADHD and cognitive impairment and underscore the importance of considering ADHD in the broader context of cognitive health throughout the lifespan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.321
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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