School of Nutrition and Health Promotion, Arizona State University, Phoenix, AZ 85004, USA.
Control Systems Engineering Laboratory, School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe 85281, USA.
J Biomed Inform. 2018 Mar;79:82-97. doi: 10.1016/j.jbi.2018.01.010. Epub 2018 Feb 1.
Control systems engineering methods, particularly, system identification (system ID), offer an idiographic (i.e., person-specific) approach to develop dynamic models of physical activity (PA) that can be used to personalize interventions in a systematic, scalable way. The purpose of this work is to: (1) apply system ID to develop individual dynamical models of PA (steps/day measured using Fitbit Zip) in the context of a goal setting and positive reinforcement intervention informed by Social Cognitive Theory; and (2) compare insights on potential tailoring variables (i.e., predictors expected to influence steps and thus moderate the suggested step goal and points for goal achievement) selected using the idiographic models to those selected via a nomothetic (i.e., aggregated across individuals) approach.
A personalized goal setting and positive reinforcement intervention was deployed for 14 weeks. Baseline PA measured in weeks 1-2 was used to inform personalized daily step goals delivered in weeks 3-14. Goals and expected reward points (granted upon goal achievement) were pseudo-randomly assigned using techniques from system ID, with goals ranging from their baseline median steps/day up to 2.5× baseline median steps/day, and points ranging from 100 to 500 (i.e., $0.20-$1.00). Participants completed a series of daily self-report measures. Auto Regressive with eXogenous Input (ARX) modeling and multilevel modeling (MLM) were used as the idiographic and nomothetic approaches, respectively.
Participants (N = 20, mean age = 47.25 ± 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 ± 6.82 kg/m) adults. Results from ARX modeling suggest that individuals differ in the factors (e.g., perceived stress, weekday/weekend) that influence their observed steps/day. In contrast, the nomothetic model from MLM suggested that goals and weekday/weekend were the key variables that were predictive of steps. Assuming the ARX models are more personalized, the obtained nomothetic model would have led to the identification of the same predictors for 5 of the 20 participants, suggesting a mismatch of plausible tailoring variables to use for 75% of the sample.
The idiographic approach revealed person-specific predictors beyond traditional MLM analyses and unpacked the inherent complexity of PA; namely that people are different and context matters. System ID provides a feasible approach to develop personalized dynamical models of PA and inform person-specific tailoring variable selection for use in adaptive behavioral interventions.
控制系统工程方法，尤其是系统辨识（system ID），为开发物理活动（PA）的动态模型提供了一种具体的（即特定于个人的）方法，可用于以系统和可扩展的方式个性化干预措施。本研究的目的是：（1）应用系统辨识方法，在基于社会认知理论的目标设定和正强化干预的背景下，为使用 Fitbit Zip 测量的步数/天开发个体活动动态模型；（2）比较通过个体化模型选择的潜在调整变量（即预计会影响步数的预测因子，从而调节建议的步数目标和目标达成的分数）的见解与通过Nomothetic（即个体间汇总）方法选择的见解。
实施了个性化的目标设定和正强化干预措施，为期 14 周。第 1-2 周的基线 PA 用于为第 3-14 周提供个性化的每日步数目标。目标和预期奖励分数（在达到目标时授予）使用系统辨识技术随机分配，目标范围从基线中位数步数/天增加到基线中位数步数/天的 2.5 倍，分数范围从 100 到 500（即 0.20 美元至 1.00 美元）。参与者完成了一系列每日自我报告的措施。自回归外生输入（ARX）建模和多层次建模（MLM）分别作为个体化和 Nomothetic 方法。
参与者（N=20，平均年龄 47.25±6.16 岁，90%为女性）为活动不足、超重（平均 BMI=33.79±6.82kg/m）的成年人。ARX 模型的结果表明，个体在影响观察到的步数/天的因素（例如感知压力、工作日/周末）方面存在差异。相比之下，MLM 的 Nomothetic 模型表明，目标和工作日/周末是预测步数的关键变量。假设 ARX 模型更具个性化，那么 Nomothetic 模型将确定相同的预测因子，其中 20 名参与者中的 5 名，这表明有 75%的样本无法识别可能的调整变量。
个体化方法揭示了传统 MLM 分析之外的特定于个体的预测因子，并阐明了 PA 的内在复杂性；即人们是不同的，环境很重要。系统辨识为开发 PA 的个性化动态模型并为适应性行为干预中的个性化调整变量选择提供了一种可行的方法。
