Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention.
Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention.
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DOI：
10.1177/1090198113496787
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发表时间：
2013-10
期刊：
影响因子：
--
通讯作者：
Giacobbi PR Jr
中科院分区：
文献类型：
--
作者：
Hekler EB;Buman MP;Poothakandiyil N;Rivera DE;Dzierzewski JM;Morgan AA;McCrae CS;Roberts BL;Marsiske M;Giacobbi PR Jr
有效的干预措施，以促进长期维持体力活动还没有很好地理解。工程师们已经开发出创建用于建模具体（即，人与人之间的关系。在行为研究中，动态系统建模可以帮助分解干预效果，并确定可能促进行为维持的关键行为模式。积极的成人指导计划（AAMP）是一项为期16周的随机对照试验，以老年人为目标，以小组为基础，同伴提供身体活动干预。时间密集型（即，每天）的身体活动报告在整个干预过程中被收集。我们探讨了接受积极干预的参与者（N=34; 88%为女性，64.1±8.3岁）的行为模式差异，并在干预期后18个月时保持150分钟/周的中等至剧烈强度的体力活动（MVPA; n=10）或没有（n=24）。我们使用动态系统建模来探索关键干预组件（即，自我监测、进入锻炼设施、行为起始训练、行为维持训练）和理论上合理的行为协变量（即，室内与室外活动）预测保持者和非保持者之间的行为模式差异。我们发现维护者需要更长的时间才能达到MVPA的稳定状态。在干预的第10周，非维护者开始下降，而维护者增加MVPA。自我监控，行为启动培训，%户外活动，行为维护培训，但不访问一个运动设施，是关键变量，解释模式的变化之间的维护。未来的研究应进行系统地探讨这些概念的先验具体（即，N-of-1）实验设计。
Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program (AAMP) was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Time intensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N=34; 88% women, 64.1±8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; n=10) or did not (n=24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and non-maintainers. We found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, non-maintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, % outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.
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DOI：
10.1109/tcst.2011.2177525
发表时间：
2013-01-01
期刊：
IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society
影响因子：
--
作者：
Nandola NN;Rivera DE
通讯作者：
Rivera DE
影响因子：
3.1
作者：
Buman, Matthew P.;Giacobbi, Peter R., Jr.;Marsiske, Michael
通讯作者：
Marsiske, Michael
影响因子：
7
作者：
Smith, Justin D.
通讯作者：
Smith, Justin D.
影响因子：
5.5
作者：
Collins, Linda M.;Murphy, Susan A.;Strecher, Victor
通讯作者：
Strecher, Victor
影响因子：
4.2
作者：
Rivera, Daniel E.;Pew, Michael D.;Collins, Linda A.
通讯作者：
Collins, Linda A.