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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BACKGROUND 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 OBJECTIVE 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 METHODS 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 RESULTS 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 PUnder 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 CONCLUSIONS 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 TRIAL REGISTRATION ClinicalTrials gov NCT01280812 https clinicaltrials gov ct2 show NCT01280812 Archived by WebCite at http www webcitation org 70IkGagAJ