Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention
Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention
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DOI：
10.1016/j.jbi.2018.01.010
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发表时间：
2018-03-01
影响因子：
4.5
通讯作者：
Hekler, Eric B.
中科院分区：
文献类型：
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作者：
Phatak, Sayali S.;Freigoun, Mohammad T.;Hekler, Eric B.
背景：控制系统工程方法，特别是系统识别（系统ID），提供了一个具体的（即，个人特定的）方法来开发身体活动（PA）的动态模型，该模型可用于以系统的、可扩展的方式进行个性化干预。这项工作的目的是：（1）在社会认知理论告知的目标设定和积极强化干预的背景下，应用系统ID开发PA的个体动态模型（使用Fitbit Zip测量的步数/天）;以及（2）比较对潜在定制变量的见解（即，预期影响步骤并因此调节建议的步骤目标和目标实现的点的预测因子）与通过规则选择的那些（即，方法：采用个性化目标设定和积极强化干预，为期14周。第1-2周测量的基线PA用于告知第3-14周提供的个性化每日步数目标。使用来自系统ID的技术伪随机地分配目标和预期奖励点（在目标实现时授予），其中目标范围从其基线中值步数/天到高达2.5 X基线中值步数/天，并且点范围从100到500（即，$0.20-$1.00）。参与者完成了一系列日常自我报告措施。结果：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 ± 6.82 kg/m2）的成年人。ARX建模的结果表明，个体在因素方面存在差异（例如，感知压力，工作日/周末），影响他们观察到的步骤/天。相反，MLM的规则模型表明，目标和工作日/周末是预测步骤的关键变量。假设的ARX模型是更加个性化的，所获得的nomothetic模型将导致识别相同的预测为5的20名参与者，这表明一个不匹配的合理的剪裁变量使用的75%的sample.Conclusion：具体的方法，揭示了人的特定的预测超出了传统的MLM分析和解开PA的固有复杂性;即人与人是不同的，环境很重要。系统ID提供了一种可行的方法来开发个性化的PA的动态模型，并通知个人特定的剪裁变量选择用于自适应行为干预。
Background: 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.Method: 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 x 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.Results: Participants (N = 20, mean age = 47.25 +/- 6.16 years, 90% female) were insufficiently active, overweight (mean BMI = 33.79 +/- 6.82 kg/m(2)) 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.Conclusion: 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.