Efficacy of a Smartphone App in Enhancing Medication Adherence and Accuracy in Individuals With Schizophrenia During the COVID-19 Pandemic: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Poor medication adherence or inaccuracy in taking prescribed medications plays an important role in the recurrence or worsening of psychiatric symptoms in patients with schizophrenia, and the COVID-19 pandemic impacted their medication adherence with exacerbated symptoms or relapse. The use of mobile health services increased during the COVID-19 pandemic, and their role in improving mental health is becoming clearer. OBJECTIVE: This study aimed to explore the effectiveness of a smartphone app (MedAdhere) on medication adherence and accuracy among patients with schizophrenia and to measure their psychiatric symptoms and cognitive functions. METHODS: In this 12-week experimental study, participants were provided interventions with the MedAdhere app, and data were collected between June 2021 and September 2022. A total of 105 participants were randomly assigned to either the experimental or control groups. We used the Positive and Negative Syndrome Scale and Mini-Mental State Examination to measure the participants' psychiatric symptoms and cognitive functions. Generalized estimating equations were used for data analysis. RESULTS: A total of 94 participants met the inclusion criteria and completed the protocol, and the medication adherence rate of the experimental group was 94.72% (2785/2940) during the intervention. Psychotic symptoms (positive, negative, and general psychopathology symptoms) and cognitive functions (memory, language, and executive function) were significantly improved in the experimental group compared to the control group after the intervention. CONCLUSIONS: The MedAdhere app effectively and significantly improved medication adherence and, thereby, the psychiatric symptoms of patients with schizophrenia. This artificial intelligence assisted app could be extended to all patients who need to be reminded to take medication on schedule. TRIAL REGISTRATION: ClinicalTrials.gov NCT05892120; https://clinicaltrials.gov/study/NCT05892120.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".