Computer-Assisted School-Based Asthma Management: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: The high prevalence of asthma among children continues to be a major public health issue. In particular, low-income African-American and Hispanic children often receive asthma care in the emergency department and lack access to continuity of care. OBJECTIVE: The aim of the current study was to test the feasibility of implementing a computerized program for empowering low-income children with asthma to manage their own disease. This pilot program consisted of a guided, personalized, Web-based computer program as the main component of a school-based asthma intervention. METHODS: The Automated Live E-Health Response Tracking System (ALERTS), a computer-assisted, Web-based tracking program, was tested for implementation in a school in East Harlem, New York. The program required children with asthma, assisted by trained researchers, to routinely measure their peak flow meter readings and answer a symptom questionnaire. The program provided individualized feedback on their disease status based on peak flow meter input. The computer program sent reports to the child's physician and the nurse practitioner at the on-site school health center. The children were also encouraged to bring the reports home to their parents. A pre/post study design was employed such that each participant acted as his/her own control. Comparisons of preintervention and postintervention outcomes were calculated using the paired t-test and the McNemar test for dichotomous data. RESULTS: Twenty-four children (6 to 12 years) participated in the program over 2 to 15 months. Improvements in health outcomes showed the greatest significance among the group of participants who were enrolled for 8 months or longer. Statistically significant improvements were seen in the average physical health score of the children (from 65.64 preintervention to 76.28 postintervention, P = .045). There was a significant decrease in the number of participants experiencing wheezing episodes (n = 9 to n = 2, P = .03), and in the average number of wheezing episodes per child (1.86 to 0.43, P = .02). Although not statistically significant, decreases were also seen in the number of children experiencing an asthma attack and in the average number of asthma attacks among participants. There was also a significant decrease in the average number of visits to doctors' offices or clinics (1.23 to 0.38, P = .04). There were no overnight hospitalizations in the two-week period following the end of the pilot program, a nonsignificant reduction from an average of 0.21 per child. CONCLUSION: This individualized, computer-assisted intervention resulted in improvements in some health outcomes among low-income children in an urban, public school-based setting. Consistent peak flow meter self-measurements, management of medication usage, and a computerized approach to symptom tracking resulted in fewer asthma exacerbations and improved overall physical health among this pediatric population with asthma.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".