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Record W4390956586 · doi:10.2196/53931

A Blended Intervention Targeting Emotion Dysregulation in Adults With Attention-Deficit/Hyperactivity Disorder: Development and Feasibility Study

2024· article· en· W4390956586 on OpenAlexvenueno aff
Emilie S. Nordby, Frode Guribye, Viktor Schønning, S. Andersen, Jonna Kuntsi, Astri J. Lundervold

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
FundersHelse Vest
KeywordsIntervention (counseling)Emotional dysregulationPsychological interventionClinical psychologyPsychologyAttention deficit hyperactivity disorderImpulsivityAnxietyQuality of life (healthcare)PsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

BACKGROUND: Many adults with attention-deficit/hyperactivity disorder (ADHD) experience difficulties related to emotion regulation. Such difficulties are known to substantially impact quality of life and overall functioning. Yet, there is a lack of treatment interventions specifically designed to address these challenges. OBJECTIVE: This study aimed to describe the development and assess the feasibility, along with the initial clinical outcomes, of a novel blended intervention for adults with ADHD. The blended intervention combines both face-to-face and digital components and is specifically designed to address emotion dysregulation in ADHD. METHODS: This intervention was an 8-week blended intervention combining weekly face-to-face group sessions with a supplementary digital companion app. The intervention is based on elements from dialectic behavioral therapy skills training and positive psychology. To evaluate its feasibility, we performed a 10-week feasibility study with an uncontrolled pre-post study design, including 16 adults with ADHD and co-occurring emotion dysregulation. The feasibility measures encompassed adherence, satisfaction, and perceived credibility of the intervention. Clinical outcomes were evaluated by self-reported symptoms of emotion dysregulation, inattention, hyperactivity-impulsivity, executive function, depression, anxiety, and a measure of quality of life. Paired sample 2-tailed t tests were used to analyze clinical outcomes with a Bonferroni-corrected significance level. RESULTS: Both treatment credibility and treatment satisfaction were rated favorably by the majority of the participants. In particular, the participants emphasized meeting others with ADHD as beneficial. In terms of adherence, 3 participants withdrew before initiating the intervention, while another 4 participants did not complete the intervention. On average, the participants who enrolled in the intervention attended 6.2 of the 8 group sessions and completed 6.7 of the 8 skills training modules in the companion app. In terms of clinical outcomes, there was a reduction in symptoms of emotion dysregulation from before to after the intervention (d=2.0). Significant improvements were also observed in measures of inattention (d=1.1) and hyperactivity-impulsivity (d=0.9). However, no significant improvements were found in the domains of depression, anxiety, quality of life, and executive functioning. CONCLUSIONS: The results are encouraging, both in terms of feasibility and the preliminary clinical results on emotion dysregulation. The blended format, combining digital and face-to-face elements, may also seem to offer some advantages: the group-based format was valued as it facilitated peer interaction, while a rather high completion of modules in the companion app highlights its potential to enhance skills training between the group sessions. Future randomized controlled trials are called for to further evaluate the clinical effectiveness of the intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT05644028; https://clinicaltrials.gov/study/NCT05644028.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.412
Teacher spread0.355 · 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 designNon-randomized trial
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

Citations12
Published2024
Admission routes1
Has abstractyes

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