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Peloton baseline screening post-ride survey

Citation

Schneider, Margaret et al. (2022), Peloton baseline screening post-ride survey, Dryad, Dataset, https://doi.org/10.7280/D1R97W

Abstract

Technology-mediated interventions to promote physical activity are growing in popularity and appear to be effective for supporting continued adherence for some people. Some of this efficacy may be related to the cultivation of motivation that is self-determined (i.e., autonomous), which is posited to arise from the satisfaction of three basic psychological needs: competence, relatedness, and autonomy. The Peloton Instructor Needs-Supportive Coaching (PINC) tool was used to code 80 Peloton cycling classes across 4 different class types (Beginner, Power Zone, Groove, and High-Intensity Interval Training) to quantify the frequency of needs-supportive and needs-indifferent coaching within a class. We also provide an example of a pilot study using the tool to test the association between needs-supportive coaching and intrinsic motivation. This dataset provides the valid survey data used in the pilot study analysis to test the association between needs-supportive coaching and intrinsic motivation.  

Methods

The Peloton Bike is a technology-mediated product, which integrates on-demand cycling classes with instructor-delivered coaching and a social media-like interface. The Peloton Bike offers live classes which are then stored in an on-demand library. Cycling classes were chosen from the on-demand library to represent four types of classes: Beginner, High-Intensity Interval Training (HIIT), Power Zone, and Groove. The Peloton cycling classes were coded between 2020-09-17 and 2020-12-09 using the Peloton Instructor Needs-Supportive Coaching (PINC) tool.

To evaluate the predictive validity of the PINC, we carried out a study to examine the prospective association between the frequency of needs-relevant coaching and self-reported intrinsic motivation for Peloton cycling.  We utilized Facebook to recruit a sample of Peloton bike riders and randomly assigned them to complete a ride that had been previously coded using the PINC. Assigned rides were characterized as being high or low (relative to median scores from PINC coding) in coaching relevant to autonomy, competence, and relatedness, respectively. Rides representing all possible combinations of high and low coaching content were selected to create 8 possible conditions (e.g., high in all three coaching types, high in relatedness, low in competence and autonomy, etc.). Within each condition, riders were provided with three rides to choose from. Riders were instructed to complete an online survey immediately following their assigned ride (henceforth referred to as “post-ride survey”). Riders also provided their Peloton username and agreed to give the research team access to their online data so that we could verify both the ride completed and the timing of the survey completion in relation to the ride. Survey responses were used to examine the independent and combined contributions of the three types of coaching to self-reported intrinsic motivation in reference to the ride. All data were de-identified.

Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at the first author’s institution.  REDCap is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources.

Usage Notes

First look at the README file. The dataset was collected in the Research Electronic Data Capture (REDCap) tool and then exported and analyzed using SPSS version 27. A CSV file for the dataset is provided (Peloton_BaselineScreening_Postride_survey.csv). The data dictionary (Peloton_BaselineScreening_Postride_survey_DataDictionary.csv) explains the variables found in the dataset. 

For those with SPSS, the SPSS data file is "Peloton_BaselineScreening_Postride_survey.sav" and the SPSS syntax can be found in  "SPSS_syntax_Peloton_BaselineScreening_Postride_survey.sps" or "SPSS_syntax_Peloton_BaselineScreening_Postride_survey.txt".

Funding

National Center for Advancing Translational Sciences, Award: UL1 TR001414