Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial.
Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial.
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DOI：
10.2196/10042
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发表时间：
2018-06-20
影响因子：
5
通讯作者：
Aswani A
中科院分区：
文献类型：
--
作者：
Fukuoka Y;Lindgren TG;Mintz YD;Hooper J;Aswani A
定期的身体活动与降低慢性疾病的风险有关。尽管有各种类型的成功的身体活动干预措施，但长期保持活动是极具挑战性的。这篇原始论文的目的是：1）描述干预后的身体活动参与，2）使用自然语言处理（NLP）和聚类技术在完成身体活动干预的女性样本中识别动机概况，3）比较这些识别出的聚类组之间的社会人口统计学和临床数据。在这项横断面分析中，203名妇女完成了为期12个月的研究退出（电话）采访中的移动的电话为基础的体育活动教育研究进行了检查。这项基于移动的手机的体育活动教育研究是一项随机对照试验，旨在测试应用程序和加速计干预的有效性及其在9个月内的可持续性。所有受试者在最后9个月研究办公室访视时归还了加速计并停止访问应用程序。通过封闭式和开放式问题评估身体参与和动机特征，例如“自9个月研究访视以来，您的身体活动是否增加、减少或大致相同（与研究的前9个月相比）？”以及“什么最能激励你进行体育锻炼？”采用NLP和聚类分析对动机进行分类。描述性统计被用来比较参与者的基线特征之间确定的群体。2个干预组（常规组和附加组）中约有一半的人报告称，他们仍然佩戴加速计，并在干预阶段按照指导进行快走。两个干预组的这些数字远高于对照组（总体P= 0.01和P= 0.003）。通过NLP确定了三个聚类，并命名为减肥组（n=19），疾病预防组（n=138）和健康促进组（n=46）。减肥组明显比疾病预防和健康促进组年轻（总体P<.001）。与减肥组相比，疾病预防组有更多的白人（P= 0.001），减肥组主要由非裔美国人，西班牙裔或混血儿组成。此外，与疾病预防组相比，健康促进组的BMI评分往往较低（总体P=.02）。然而，3组之间基线中度至剧烈强度活动水平无差异（总体P> 0.05）。这些发现可能与调整体力活动维持干预有关。此外，NLP和聚类分析的结果是有用的方法来分析短的自由文本，以区分动机概况。随着更复杂的NL工具在未来的发展，NLP在行为研究中的应用潜力将扩大。ClinicalTrials.gov NCT 01280812; https://clinicaltrials.gov/ct2/show/NCT01280812（由WebCite在http：//www.webcitation.org/70IkGagAJ上存档）
Regular physical activity is associated with reduced risk of chronic illnesses. Despite various types of successful physical activity interventions, maintenance of activity over the long term is extremely challenging. The aims of this original paper are to 1) describe physical activity engagement post intervention, 2) identify motivational profiles using natural language processing (NLP) and clustering techniques in a sample of women who completed the physical activity intervention, and 3) compare sociodemographic and clinical data among these identified cluster groups. In this cross-sectional analysis of 203 women completing a 12-month study exit (telephone) interview in the mobile phone-based physical activity education study were examined. The mobile phone-based physical activity education study was a randomized, controlled trial to test the efficacy of the app and accelerometer intervention and its sustainability over a 9-month period. All subjects returned the accelerometer and stopped accessing the app at the last 9-month research office visit. Physical engagement and motivational profiles were assessed by both closed and open-ended questions, such as “Since your 9-month study visit, has your physical activity been more, less, or about the same (compared to the first 9 months of the study)?” and, “What motivates you the most to be physically active?” NLP and cluster analysis were used to classify motivational profiles. Descriptive statistics were used to compare participants’ baseline characteristics among identified groups. Approximately half of the 2 intervention groups (Regular and Plus) reported that they were still wearing an accelerometer and engaging in brisk walking as they were directed during the intervention phases. These numbers in the 2 intervention groups were much higher than the control group (overall P=.01 and P=.003, respectively). Three clusters were identified through NLP and named as the Weight Loss group (n=19), the Illness Prevention group (n=138), and the Health Promotion group (n=46). The Weight Loss group was significantly younger than the Illness Prevention and Health Promotion groups (overall P<.001). The Illness Prevention group had a larger number of Caucasians as compared to the Weight Loss group (P=.001), which was composed mostly of those who identified as African American, Hispanic, or mixed race. Additionally, the Health Promotion group tended to have lower BMI scores compared to the Illness Prevention group (overall P=.02). However, no difference was noted in the baseline moderate-to-vigorous intensity activity level among the 3 groups (overall P>.05). The findings could be relevant to tailoring a physical activity maintenance intervention. Furthermore, the findings from NLP and cluster analysis are useful methods to analyze short free text to differentiate motivational profiles. As more sophisticated NL tools are developed in the future, the potential of NLP application in behavioral research will broaden. ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/70IkGagAJ)
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