Oren Eyal, Bell Melanie L, Garcia Francisco, Perez-Velez Carlos, Gerald Lynn B
Department of Epidemiology and Biostatistics, College of Public Health, University of Arizona, 1295 N. Martin Avenue, P.O. Box 245211, Tucson, AZ 85724 USA.
Asthma and Airways Disease Research Center, University of Arizona Health Sciences, Tucson, AZ USA.
Pilot Feasibility Stud. 2017 Mar 13;3:15. doi: 10.1186/s40814-017-0128-9. eCollection 2017.
An estimated two billion people, over one third of the world's population, have latent infection with (LTBI). Patient adherence to LTBI treatment is currently poor given that individuals show no symptoms of illness and may not feel that they are at risk of developing active tuberculosis (TB). Short text messages can serve as a simple reminder to take medications and address barriers to adherence such as forgetfulness and lack of social support.
METHODS/DESIGN: We aim to determine the feasibility and acceptability of text reminders for improving adherence in latent TB patients using a randomized controlled single-blinded trial, measuring adherence through an increase in treatment completion rates. Forty adult LTBI participants will be randomized to either text messages plus phone call reminders or phone call reminders only (usual care). Recruitment, retention, and study acceptability will be assessed as primary outcomes.
This pilot study will examine the feasibility of using text messaging for increasing adherence to treatment for latent tuberculosis infection. The study will allow for evaluation of process measures and challenges and development of a model for scaling up an effectiveness trial for increasing treatment adherence.
NCT02690818 (Clinical Trials.gov).
据估计，全球有20亿人感染了结核分枝杆菌潜伏感染（LTBI），占世界人口的三分之一以上。由于个体没有疾病症状，可能不觉得自己有患活动性结核病（TB）的风险，目前患者对LTBI治疗的依从性较差。简短的短信可以作为服药的简单提醒，并解决依从性的障碍，如健忘和缺乏社会支持。
方法/设计：我们旨在通过一项随机对照单盲试验，确定短信提醒对提高潜伏性结核患者依从性的可行性和可接受性，通过提高治疗完成率来衡量依从性。40名成年LTBI参与者将被随机分为短信加电话提醒组或仅电话提醒组（常规护理）。招募、保留率和研究可接受性将作为主要结果进行评估。
这项试点研究将检验使用短信提高潜伏性结核感染治疗依从性的可行性。该研究将允许评估过程指标和挑战，并开发一个扩大有效性试验以提高治疗依从性的模型。
NCT02690818（ClinicalTrials.gov）。
