Residual Effect of Texting to Promote Medication Adherence for Villagers with Schizophrenia in China: 18-Month Follow-up Survey After the Randomized Controlled Trial Discontinuation
Bibliographic record
Abstract
BACKGROUND: Reducing the treatment gap for mental health in low- and middle-income countries is a high priority. Even with treatment, adherence to antipsychotics is rather low. Our integrated intervention package significantly improved medication adherence within 6 months for villagers with schizophrenia in resource-poor communities in rural China. However, considering the resource constraint, we need to test whether the effect of those behavior-shaping interventions may be maintained even after the suspension of the intervention. OBJECTIVE: The aim of this study is to explore the primary outcome of adherence and other outcomes at an 18-month follow-up after the intervention had been suspended. METHODS: In a 6-month randomized trial, 277 villagers with schizophrenia were randomized to receive either a government community mental health program (686 Program) or the 686 Program plus Lay health supporters, e-platform, award, and integration (LEAN), which included health supporters for medication or care supervision, e-platform access for sending mobile SMS text messaging reminders and education message, a token gift for positive behavior changes (eg, continuing taking medicine), and integrating the e-platform with the existing 686 Program. After the 6-month intervention, both groups received only the 686 Program for 18 months (phase 2). Outcomes at both phases included antipsychotic medication adherence, functioning, symptoms, number of rehospitalization, suicide, and violent behaviors. The adherence and functioning were assessed at the home visit by trained assessors. We calculated the adherence in the past 30 days by counting the percentage of dosages taken from November to December 2018 by unannounced home-based pill counts. The functioning was assessed using the World Health Organization Disability Assessment Schedule 2.0. The symptoms were evaluated using the Clinical Global Impression-Schizophrenia during their visits to the 686 Program psychiatrists. Other outcomes were routinely collected in the 686 Program system. We used intention-to-treat analysis, and missing data were dealt with using multiple imputation. The generalized estimating equation model was used to assess program effects on adherence, functioning, and symptoms. RESULTS: In phase 1, antipsychotic adherence and rehospitalization incidence improved significantly. However, in phase 2, the difference of the mean of antipsychotic adherence (adjusted mean difference 0.05, 95% CI -0.06 to 0.16; P=.41; Cohen d effect size=0.11) and rehospitalization incidence (relative risk 0.65, 95% CI 0.32-1.33; P=.24; number needed to treat 21.83, 95% CI 8.30-34.69) was no longer statistically significant, and there was no improvement in other outcomes in either phase (P≥.05). CONCLUSIONS: The simple community-based LEAN intervention could not continually improve adherence and reduce the rehospitalization of people with schizophrenia. Our study inclined to suggest that prompts for medication may be necessary to maintain medication adherence for people with schizophrenia, although we cannot definitively exclude other alternative interpretations.
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 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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".