JMIR Publications

ABSTRACT

Background:

The therapeutic efficacy of acupuncture in treating Alzheimer’s disease (AD) largely depends on consistent treatment adherence. Therefore, identifying key factors influencing adherence and developing targeted interventions are crucial for enhancing clinical outcomes.

Objective:

To develop and validate a predictive model for identifying patients with AD who are likely to maintain good adherence to acupuncture treatment.

Methods:

This secondary analysis included 108 patients with probable AD, aged 50–85 years, from two independent randomized controlled trials conducted at Guang’anmen Hospital, China Academy of Chinese Medical Sciences. Sixty-six patients were assigned to the development cohort and 42 to the external validation cohort. Acupuncture adherence was defined as the proportion of completed sessions relative to scheduled sessions, with good adherence defined as ≥80% completion. Baseline data included demographic, clinical, cognitive, functional, psychological, and caregiving variables. Multivariable logistic regression with backward stepwise selection was used to identify significant predictors, and a nomogram was constructed based on the final model. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis, with external validation performed by ROC analysis.

Results:

The number of treatments in the first month, caregiving role, and disease duration were identified as significant predictors of adherence. The nomogram incorporating these variables demonstrated excellent discrimination in the development cohort (AUC = 0.914) and good performance in the external validation cohort (AUC = 0.838).

Conclusions:

This study is the first to develop and validate a predictive model for acupuncture adherence in patients with AD. The model offers valuable clinical and research implications. Early identification of patients at high risk for non-adherence enables the implementation of targeted interventions or the use of stratified analyses to reduce bias and improve study integrity. Moreover, the identified predictors provide actionable insights for clinicians to enhance adherence and optimize treatment outcomes.