Methods for Comparing and Improving the Design of Physical Activity Data Visualizations
TimeFriday, April 1611:20am - 11:40am EDT
DescriptionThis research seeks to improve the visualization of patient-generated health data (PGHD) from wearable devices. More specifically, we seek to gain insight onto what visualizations of physical activity data are most understandable and useful to patients.
Exercise prescription refers to the practice of outlining a plan of fitness-related activities which an individual performs to reap some health benefit . In recent decades, exercise prescription has become an effective means of managing chronic conditions and aiding with cardiac rehabilitation [2-4]; it is now a common treatment plan for such conditions [5-7]. In parallel, the past decade has seen rapid growth in the use of wearable devices to track physical activity and considerations of using this technology in the clinical setting [8-10]. The physical activity collected via these wearable devices could aid in successful execution of exercise prescription and in promoting general physical wellness.
Many visualizations of physical activity data generated by wearable devices are available via mobile apps and web browsers. However, it is not clear how current data visualizations are interpreted by users. This study seeks to identify what design features make physical activity data visualizations more useful to and usable by end users, by experimentally testing the efficacy of four existing data visualizations on three activity tracking platforms (Fitbit, Strava, Endomondo) and one new data visualization based on best practices in data visualization design.
Each visualization is populated with the same data, based on one year of physical activity data. Participants complete 12 tasks using one of the existing or newly designed data visualizations. Task success and time to complete each task are used as measures of user performance. After completing the tasks, participants provide subjective feedback on the usability of the visualizations, and then complete a design feedback process where they assess all six visualizations and provide feedback on an ideal visualization design.
This presentation will describe: 1) our design of the new physical activity data visualization based on visualization design best practices, 2) our methodology for quantitatively evaluating user performance across visualizations, 3) our methodology for evaluating user satisfaction with the visualizations and for gleaning formative design guidance to inform revisions to the data visualizations, and 4) preliminary study findings.
 Steven N. Blair (1995) Exercise Prescription for Health, Quest, 47:3, 338-353, DOI: 10.1080/00336297.1995.10484162
 Heran BS, Chen JM, Ebrahim S, Moxham T, Oldridge N, Rees K, Thompson DR, Taylor RS. Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev. 2011;(7):CD001800. doi: 10.1002/14651858.CD001800.pub2.http://europepmc.org/abstract/MED/21735386.
 Wong WP, Feng J, Pwee KH, Lim J. A systematic review of economic evaluations of cardiac rehabilitation. BMC Health Serv Res. 2012 Aug 08;12:243. doi: 10.1186/1472-6963-12-243.https://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-12-243.
 Taylor RS, Sagar VA, Davies EJ, Briscoe S, Coats AJ, Dalal H, Lough F, Rees K, Singh S. Exercise-based rehabilitation for heart failure. Cochrane Database Syst Rev. 2014;(4):CD003331. doi: 10.1002/14651858.CD003331.pub4.
 Balady, Gary, MD, FAHA, Williams, Mark, PhD, Co-Chair, Ades, Philip, Bittner, Vera, et al. (2007). Core Components of Cardiac Rehabilitation/Secondary Prevention Programs: 2007 Update: A Scientific Statement From the American Heart Association Exercise, Cardiac Rehabilitation, and Prevention Committee, the Council on Clinical Cardiology; the Councils on Cardiovascular Nursing, Epidemiology and Prevention, and Nutrition, Physical Activity, and Metabolism; and the American Association of Cardiovascular and Pulmonary Rehabilitation. Circulation, 115, 2675-2682. https://doi.org/10.1161/CIRCULATIONAHA.106.180945
 Graham I, Atar D, Borch-Johnsen K, et al. (2007). Fourth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (Constituted by representatives of nine societies and by invited experts). European Journal of Cardiovascular Prevention & Rehabilitation, 14(2_suppl), E1–E40. https://doi.org/10.1097/01.hjr.0000277984.31558.c4
 Donna K. Arnett, Roger S. Blumenthal, Michelle A. Albert, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines: Journal of the American College of Cardiology. JACC Journals, www.jacc.org/action/showCitFormats?doi=10.1016%2Fj.jacc.2019.03.009.
 Page, T. (2018). A forecast of the adoption of wearable technology. Wearable Technologies: Concepts, Methodologies, Tools, and Applications, 1370–1388. https://doi.org/10.4018/978-1-5225-5484-4.ch063
 Gao, Y., Li, H. and Luo, Y. (2015), "An empirical study of wearable technology acceptance in healthcare", Industrial Management & Data Systems, Vol. 115 No. 9, pp. 1704-1723. https://doi.org/10.1108/IMDS-03-2015-0087
 Hannan, A.L., Harders, M.P., Hing, W. et al. Impact of wearable physical activity monitoring devices with exercise prescription or advice in the maintenance phase of cardiac rehabilitation: systematic review and meta-analysis. BMC Sports Sci Med Rehabil 11, 14 (2019). https://doi.org/10.1186/s13102-019-0126-8