Fukuoka Yoshimi, Lindgren Teri G, Mintz Yonatan Dov, Hooper Julie, Aswani Anil
Department of Physiological Nursing/Institute for Health & Aging, School of Nursing, University of California, San Francisco, San Francisco, CA, United States.
School of Nursing, Rutgers University, Newark, NJ, United States.
JMIR Mhealth Uhealth. 2018 Jun 20;6(6):e10042. doi: 10.2196/10042.
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).
规律的体育活动与降低慢性病风险相关。尽管有各种类型的成功体育活动干预措施，但长期维持活动极具挑战性。
本原创论文的目的是：1）描述干预后体育活动参与情况；2）在完成体育活动干预的女性样本中，使用自然语言处理（NLP）和聚类技术识别动机概况；3）比较这些已识别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，对203名完成了为期12个月的基于手机的体育活动教育研究的退出（电话）访谈的女性进行了检查。基于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效果及其在9个月期间的可持续性。所有受试者在最后一次9个月的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度和动机概况，例如“自您9个月的研究访视以来，您的体育活动是增加了、减少了还是大致相同（与研究的前9个月相比）？”以及“什么最能激励您进行体育活动？”使用NLP和聚类分析对动机概况进行分类。描述性统计用于比较已识别组之间参与者的基线特征。
两个干预组（常规组和加强组）中约有一半报告称，他们仍在佩戴加速计并按照干预阶段的指示进行快走。这两个干预组的这些数字远高于对照组（总体P值分别为0.01和0.003）。通过NLP识别出三个聚类，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组和健康促进组年轻（总体P < 0.001）。与减肥组相比，疾病预防组的白人数量更多（P = 0.001），减肥组主要由那些自认为是非洲裔美国人、西班牙裔或混血儿的人组成。此外，与疾病预防组相比，健康促进组的BMI得分往往较低（总体P = 0.02）。然而，三组之间的基线中度至剧烈强度活动水平没有差异（总体P > 0.05）。
这些发现可能与定制体育活动维持干预措施相关。此外，NLP和聚类分析的结果是分析简短自由文本以区分动机概况的有用方法。随着未来开发出更复杂的自然语言工具，NLP在行为研究中的应用潜力将得到拓宽。
ClinicalTrials.gov NCT01280812；https://clinicaltrials.gov/ct2/show/NCT01280812（由WebCite存档于http://www.webcitation.org/70IkGagAJ）。
