The Detroit Young Adult Asthma Project: Proposal for a Multicomponent Technology Intervention for African American Emerging Adults With Asthma
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic minority youth have poorer asthma status than white youth, even after controlling for socioeconomic variables. Proper use of asthma controller medications is critical in reducing asthma mortality and morbidity. The clinical consequences of poor asthma management include increased illness complications, excessive functional morbidity, and fatal asthma attacks. There are significant limitations in research on interventions to improve asthma management in racial minority populations, particularly minority adolescents and young adults, although illness management tends to deteriorate after adolescence during emerging adulthood, the unique developmental period beyond adolescence but before adulthood. OBJECTIVE: The objective of the pilot study was to test the feasibility, acceptability, and signals of efficacy of an intervention targeting adherence to controller medication in African American youth (ages 18-29) with asthma. All elements of the protocol were piloted in a National Heart, Lung, and Blood Institute (NHLBI)-funded pilot study (1R34HL107664 MacDonell). Results suggested feasibility and acceptability of the protocol as well as proof of concept. We are now ready to test the intervention in a larger randomized clinical trial. METHODS: The proposed study will include 192 African American emerging adults with moderate to severe persistent asthma and low controller medication adherence recruited from clinic, emergency department, and community settings. Half of the sample will be randomized to receive a multicomponent technology-based intervention targeting adherence to daily controller medication. The multicomponent technology-based intervention consists of 2 components: (1) 2 sessions of computer-delivered motivational interviewing targeting medication adherence and (2) individualized text messaging focused on medication adherence between the sessions. Text messages will be individualized based on ecological momentary assessment. The remaining participants will complete a series of computer-delivered asthma education modules matched for length, location, and method of delivery of the intervention session. Control participants will also receive text messages between intervention sessions. Message content will be the same for all control participants and contain general facts about asthma (not tailored). RESULTS: It is hypothesized that youth randomized to multicomponent technology-based intervention will show improvements in medication adherence (primary outcome) and asthma control (secondary outcome) compared with comparison condition at all postintervention follow-ups (3, 6, 9, and 12 months). The proposed study was funded by NHLBI from September 1, 2016 through August 31, 2021. CONCLUSIONS: This project will test a brief, technology-based intervention specifically targeting adherence to asthma controller medications in an under-researched population, African American emerging adults. If successful, our multicomponent technology-based intervention aimed at improving adherence to asthma medications has the potential to improve quality of life of minority emerging adults with asthma at relatively low cost. It could eventually be integrated into clinical settings and practice to reach a large number of emerging adults with asthma. TRIAL REGISTRATION: ClinicalTrials.gov NCT03121157; https://clinicaltrials.gov/ct2/show/NCT03121157 (Archived by WebCite at http://www.webcitation.org/6wq4yWHPv).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".