Adherence to hydroxyurea, health-related quality of life domains and attitudes towards a smartphone app among Irish adolescents and young adults with sickle cell disease
Bibliographic record
Abstract
INTRODUCTION: SCD patients experience declines in health-related quality of life (HRQOL) domains compared with healthy controls. Despite evidence supporting the benefits of hydroxyurea, medication non-adherence remains problematic, especially in adolescents and young adults (AYA). Adherence barriers include forgetfulness and lack of knowledge. Recently, increased interest in technology-based strategies to improve medication adherence has emerged. No data currently exists on hydroxyurea adherence, HRQOL or perceptions of technology-based tools in the Irish SCD population. METHODS: In order to interrogate these domains among Irish AYA SCD patients we administered an anonymous survey at two tertiary referral centres in Dublin, Ireland, in July 2019. RESULTS: Sixty-three patients participated; 63% female and 37% male, with a median and mean age of 17 and 19 years, respectively. Average monthly adherence was 76% using a visual analogue scale. Recall barriers were present in 62% while 26% omit hydroxyurea for reasons other than forgetting. Reviewing HRQOL; only 36.5% felt always physically able to engage in recreational activities, while 51% experienced disruption to school/college/work due to pain. Eighty-one percent reported that anxiety about health interferes with their lives and non-adherence correlated with worse HRQOL outcomes. Interest in a smartphone app was expressed by the majority, with daily medication reminders being the most popular feature. Sharing adherence data with doctors and discussion forums were less appealing. CONCLUSIONS: Representing over 10% of the Irish SCD population, our survey provides novel and valuable insights into medication adherence and HRQOL domains. Preferred app features may inform future technology-based interventions to improve medication adherence in SCD and other chronic health conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".