Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA.
J Behav Med. 2010 Aug;33(4):315-25. doi: 10.1007/s10865-010-9257-9. Epub 2010 Mar 16.
Knowledge of factors associated with the use of technology could inform the design of technology-based behavioral interventions. This study examined modifiable and nonmodifiable factors associated with technology-based self-monitoring. 123 participants with type 2 diabetes self-monitored diet using a personal digital assistant in a 6-month behavioral intervention. Multinomial logistic regression was used to examine probability of nonadherent and suboptimally adherent behavior relative to adherent behavior. Sociodemographic characteristics were not associated with probability of self-monitoring. Probability of adherence generally was greater in the weeks preceding no group session, and lower in the weeks following no group session or following skipped sessions. Non-modifiable factors suggested by the literature to be associated with poorer access to technology (lower income, older age, minority race, and lower education) were not associated with probability of self-monitoring in this population.
了解与技术使用相关的因素可以为基于技术的行为干预措施的设计提供信息。本研究调查了与基于技术的自我监测相关的可改变和不可改变的因素。123 名 2 型糖尿病患者在 6 个月的行为干预中使用个人数字助理监测饮食。使用多项逻辑回归分析来检查与依从行为相比，非依从和不充分依从行为的概率。社会人口统计学特征与自我监测的可能性无关。与不参加团体会议的前几周相比，参加团体会议的前几周的依从性概率通常更高，而在不参加团体会议或跳过会议的后几周则更低。文献中提出的与较差的技术获取相关的不可改变因素（较低的收入、较年长、少数族裔和较低的教育程度）与该人群的自我监测概率无关。
