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Record W4362588097 · doi:10.2196/46578

Using Digital Measurement–Based Care to Address Symptoms of Inattention, Hyperactivity, and Opposition in Youth: Retrospective Analysis of Bend Health

2023· article· en· W4362588097 on OpenAlexvenueno aff
Darian Lawrence‐Sidebottom, Landry Goodgame Huffman, Jennifer Huberty, Clare Beatty, Monika Roots, Kurt Roots, Amit Parikh, Rachael Guerra, Jaclyn Weiser

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAttention deficit hyperactivity disorderPsychological interventionMental healthPsychiatryMedicineHealth careCollaborative CareClinical psychologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) and associated behavioral disorders are highly prevalent in children and adolescents, yet many of them do not receive the care they need. Digital mental health interventions (DMHIs) may address this need by providing accessible and high-quality care. Given the necessity for high levels of caregiver and primary care practitioner involvement in addressing ADHD symptoms and behavioral problems, collaborative care interventions that adopt a whole-family approach may be particularly well suited to reduce symptoms of inattention, hyperactivity, and opposition in children and adolescents. OBJECTIVE: The purpose of this study is to use member (ie, child and adolescent) data from Bend Health, Inc, a collaborative care DMHI that uses a whole-family approach to address child and adolescent mental health concerns, to (1) determine the effects of a collaborative care DMHI on inattention, hyperactivity, and oppositional symptoms in children and adolescents and (2) assess whether the effects of a collaborative care DMHI vary across ADHD subtypes and demographic factors. METHODS: Caregivers of children and adolescents with elevated symptoms of inattention, hyperactivity, or opposition assessed their children's symptom severity approximately every 30 days while participating in Bend Health, Inc. Data from 107 children and adolescents aged 6-17 years who exhibited clinically elevated symptoms at baseline were used to assess symptom severity across monthly assessments (inattention symptom group: n=91, 85.0%; hyperactivity symptom group: n=48, 44.9%; oppositional symptom group: n=70, 65.4%). The majority of the sample exhibited elevated symptoms of at least 2 symptom types at baseline (n=67, 62.6%). RESULTS: Members received care for up to 5.52 months and attended between 0 and 10 coaching, therapy, or psychiatry sessions through Bend Health, Inc. For those with at least 2 assessments, 71.0% (n=22) showed improvements in inattention symptoms, 60.0% (n=9) showed improvements in hyperactivity symptoms, and 60.0% (n=12) showed improvements in oppositional symptoms. When considering group-level change over time, symptom severity decreased over the course of treatment with Bend Health, Inc, for inattention (average decrease=3.51 points, P=.001) and hyperactivity (average decrease=3.07 points, P=.049) but not for oppositional symptoms (average decrease=0.70 points, P=.26). There was a main effect of the duration of care on symptom severity (P<.001) such that each additional month of care was associated with lower symptom scores. CONCLUSIONS: This study offers promising early evidence that collaborative care DHMIs may facilitate improvements in ADHD symptoms among children and adolescents, addressing the growing need for accessible and high-quality care for behavioral health problems in the United States. However, additional follow-up studies bolstered by larger samples and control groups are necessary to further establish the robustness of these findings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.206
GPT teacher head0.472
Teacher spread0.265 · 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 teacher head, 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

Citations23
Published2023
Admission routes1
Has abstractyes

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