What-We-Do-In-The-Shadows-SESNZ-Presentation-October-2016
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dlfoy@mortonlawson.com An overview of a 3 year long research into the uses of an exercise app, exercise outcomes and behavioural associations with a particular interest on gleaning persuasive design cues.
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What-We-Do-In-The-Shadows-SESNZ-Presentation-October-2016
- 1. FacultyofHealth Dr. Daryl Foy School of Human Life Sciences WHAT WE DO IN THE SHADOWS UNDERSTANDING BEHAVIOUR IN AN ONLINE EXERCISE COMMUNITY
- 2. Faculty of Health Science “The gap between recording information and changing behavior is substantial…..while these devices are increasing in popularity, little evidence suggests that they are bridging that gap.” (Patel, Asch & Volpp, 2015)
- 3. Faculty of Health Science “Only a little over 50% of most common exercise wearables implement Behaviour Change Techniques.” (Mercer et al., 2016)
- 4. How do user characteristics influence wearable usage and persistence? FacultyofHealthScience How do individual social factors influence wearable usage and persistence? Can we link existing health behaviour change scales to behaviour systems design models in a practical way? the STUDY
- 6. FacultyofHealthScience Method Descriptive analyses 23,000 users Survey subset of 500+ users
- 7. Faculty of Health Science METHOD
- 8. FacultyofHealthScience Some findings SESNZ DLFOY Infographics for Study One What’s with the whole Twitter thing? What can we learn about the persistent?
- 9. FacultyofHealthScience A bit more method….... The majority of people who are in the top 50 percent of the Average Fitness Index persistently exercise regardless of their online sociability inside and outside of the app. For users who are in the lowest 50 percent of Average Fitness Index, that do not have the most intense workouts and have no Followers, but who have a Twitter account persistently work out with the system.
- 10. Faculty of Health Science METHOD
- 11. Faculty of Health Science METHOD Regression coefficients for the regressions of the scales of SOCS (Β = 0.165) and SOID (Β = 0.187) on ROPAS respectively revealed a statistically significant positive relationship. SOID fully mediates the relationship between ROPAS & SOCS scores underscored mediator effect of the SOID, z = 3.340, p < 0.001
- 12. Faculty of Health Science From a between subject t-test with the average score on ROPAS items as the dependent variable and the dummy variable for Twitter publications was used to create groups. The results indicated that users who published their exercise sessions to Twitter have significantly higher ROPAS scores (M = 3.84, SE = 0.10) than the users who don't publish to Twitter; (M = 3.56, SE = 0.09, t(519) = -2.156, p < 0.05).
- 13. Faculty of Health Science • A proclivity for greater system usage from those users that chose to publish their uploaded exercise sessions to Twitter. • A positive association for the less fit users between their Twitter usage and their persistence for exercise using the system. • Demonstrated the use of an integrated, psychosocial measurement scale that combines the existing constructs used by BCSS theory to analyse the persuasive elements of a behaviour change system with standardised scales for assessing an individual’s perceived relatedness to others in physical activity Build & test additional SDT scales with BCSS
- 14. Faculty of Health Science References Centola, D. (2013). Social Media and the Science of Health Behavior. Circulation, 127(21), pp.2135-2144. Chang, R., Lu, H., Yang, P. and Luarn, P. (2016). Reciprocal Reinforcement Between Wearable Activity Trackers and Social Network Services in Influencing Physical Activity Behaviors. JMIR mHealth and uHealth, 4(3), p.e84. Farmer, A. and Tarassenko, L. (2015). Use of Wearable Monitoring Devices to Change Health Behavior. JAMA, 313(18), p.1864. Fogg, B. (2002). Persuasive technology. Ubiquity, 2002(December), p.2. Jacob, B. and Amirfar, V. (2014). Achieving better health with activity trackers. Pharmacy Today, 20(2), p.42. Mercer, K., Li, M., Giangregorio, L., Burns, C. and Grindrod, K. (2016). Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis. JMIR mHealth uHealth, 4(2), p.e40. Nelson, E., Verhagen, T. and Noordzij, M. (2016). Health empowerment through activity trackers: An empirical smart wristband study. Computers in Human Behavior, 62, pp.364- 374. Oinas-Kukkonen, H. (2012). A foundation for the study of behavior change support systems. Pers Ubiquit Comput, 17(6), pp.1223-1235. Patel, M., Asch, D. and Volpp, K. (2015). Wearable Devices as Facilitators, Not Drivers,
Notas del editor
- The aim of the work is to create an improved understanding of how individuals experience online exercise communities and how this experience may be improved with a view to assisting health behaviour change to enable sustained exercise. The work will draw on quantitative and qualitative analyses of a 30,000 strong cohort in conjunction with the application of existing and emerging health behavioural theories in order to formulate and rationalise descriptive and predictive models of exercise satisfaction behaviour in an online exercise community. The findings and the model will be used along with end-user interview content to elicit a conceptual model for online exercise communities that may serve to improve exercise satisfaction for users of the technology.
- Worldwide shipments of wearable devices are expected to reach 101.9 million units by the end of 2016, representing 29.0% growth over 2015. According to the International Data Corporation (IDC) Worldwide Quarterly Wearable Device Tracker, the market for wearable devices will experience a compound annual growth rate (CAGR) of 20.3%, culminating in 213.6 million units shipped in 2020.
- Worldwide shipments of wearable devices are expected to reach 101.9 million units by the end of 2016, representing 29.0% growth over 2015. According to the International Data Corporation (IDC) Worldwide Quarterly Wearable Device Tracker, the market for wearable devices will experience a compound annual growth rate (CAGR) of 20.3%, culminating in 213.6 million units shipped in 2020.
- EVIDENCE GAP Are BMI + ACTIVITY LEVEL + ACTIVITY TYPE associated with EXERCISE OUTPUT (duration+peak training effect+caloric expenditure). How valid are the algorithms used by the system to calculate and present earned exercise outcomes? How does EXERCISE OUTPUT + RELATEDNESS to OTHERS (likes+shouts+groups) effect EXERCISE-SYSTEM_USAGE Is EXERCISE OUTPUT + POSITIVE SELF (feelings) + SELF-THUMBS associated with EXERCISE-SYSTEM_USAGE Is EXERCISE OUTPUT + RELATEDNESS to OTHERS (likes+shouts+groups) associated with EXERCISE-SYSTEM_LIFETIME Is EXERCISE OUTPUT + POSITIVE SELF (feelings) associated with EXERCISE-SYSTEM_LIFETIME What is also under question is whether or not EXERCISE OUTPUT + RELATEDNESS to OTHERS is associated with POSITIVE SELF (feelings) SYSTEM USABILITY affects EX_SYSTEM_LIFETIME and USAGE?
- The study draws upon a number of disciplines. How do we make best use of established health behaviour change models and new technology to provide effective self management systems that could potentially guide and report on condition-specific exercise interventions in free-living populations? There is scant evidence as to the behavioural mechanisms at play in the explosion of online exercise data communities. We aim to elicit some fundamental statistical analysis of the available data and formulate appropriate models for better understanding and predicting individual exercise performance, satisfaction, participation and interaction levels. Once candidate models are derived we will have a framework for working with symptomatic populations and optimising any potential benefits of the technology for prescriptive exercise interventions. In plain English – does this stuff work? What are the benefits? What are the limitations? How do people use the technology? How does it affect their exercise motivation self efficacy, relatedness to others? How often do they use the technology and how long do they persist in its use?
- How to define a meaningful “sociability” measure using system functionality as input.
- Persistence as a measure here in our study is defined as a user having at least one move within 60 days of the end of the file cut-off date of January 20th, 2013.
- They are a well-educated group, as more than half (53%) have at least some graduate school experience and more than 96 percent graduated high school. The mean age as of January 1, 2014 was 40.32 years old, with a standard deviation of 9.69 years and a range of 17 years old to 69 years old, consistent with the results from the first study. More than 85 percent of the sample (445/521) had exercised in the year preceding their purchase of the device. Of the 75 people who had not exercised in the year prior to purchasing the device, 80 percent had exercised at some point in their lives. Clearly, our survey sample was consistent with the database population used in study one in terms of their engagement in physical activity. At least 75 percent of the respondents reported regularly wearing the device, logging into the system and uploading an exercise session (move). The survey itself was a composite of an existing measurement instrument originally devised to investigate intention of use continuance for web sites providing weight-loss management, (Lehto & Oinas-Kukkonen, 2013), the Relatedness to Others in Physical Activity scale (ROPAS) originally published by (Wilson & Bengoechea, 2010) and a sub-scale for Online Sociability from (Johnson & Kulpa, 2007). The original ROPAS scale was designed to indicate the degree to which exercise is a social activity for an individual in a real world physical setting only, without any attempt to connect that to Internet usage. The current study looked to link ROPAS with user patronage of key online social functions of the app by examining the association of user ROPAS scores with the two key BCSS-PSD design constructs of Social Identification and Social Support as implemented in the system. The factorial analysis conducted mirrored results largely similar to the original work which found for weight loss management web-based system the intention to continue using (CONT) is determined by users’ perceptions of the effectiveness of the system (EFFE), the effort required to use the system (EFFO), the credibility of the system (CRED), and the social support offered by the system (SOCS). In turn, perceived effectiveness (EFFE) of the system and the perceived effort required to use the system (EFFO) are functions of primary task support (PRIM).
- The original BCSS instrument used an eight factor scale to cover the key PSD categories of (i) CONT or use continuance, the intent of the user to keep using the system; (ii) CRED or systems credibility based on the user’s perception of the system’s trust, reliability and believability; (iii) (DIAL) or system dialogue support for the user, which should take the form of feedback, prompts, suggestions and reminders to keep the user on task; (iv) (EFFE) or the perceived effectiveness of the system in assisting the user to perform key tasks; (v) (EFFO) the user’s perception of how much work is needed to use the system effectively; (vi) (PRIM) or primary task support is the user’s judgment on how well the system supports the user’s achievement of goals through facilitating goal setting and tracking, adapts to user’s needs and promotes the user’s self-efficacy; (vii) (SOCS) or the perceived social support the system affords the user in helping them reach their goals and (viii) (SOCID) or social identification is an assessment by the user of how well they identify with other system users and the extent to which they share interests and feel part of a user community. The factorial analysis conducted mirrored results largely similar to the original work which found for weight loss management web-based system the intention to continue using (CONT) is determined by users’ perceptions of the effectiveness of the system (EFFE), the effort required to use the system (EFFO), the credibility of the system (CRED), and the social support offered by the system (SOCS). In turn, perceived effectiveness (EFFE) of the system and the perceived effort required to use the system (EFFO) are functions of primary task support (PRIM). From our study we observe that there are especially strong correlations between SOCS and SOID (r = 0.745, p &lt; 0.001), PRIM and EFFE (r = 0.625, p &lt; 0.001) and PRIM and DIAL (r = 0.641, p &lt; 0.001) and DIAL and EFFE (r = 0.592, p &lt; 0.001). PRIM and CRED are also correlated at more than the 0.5 level. In the sample there is overwhelming support for the same positive relationship between the scales for PRIM and EFFE (B = 0.825, SE = 0.052, p &lt; 0.001) and PRIM and EFFO (B = 0.419, SE = 0.044, p &lt; 0.001 as has been found with weight loss management systems, (Lehto & Oinas-Kukkonen, 2013). This is an encouraging endorsement of the original BCSS measurement instrument and may indicate that it has application to the assessment of the persuasive design category associations in digital exercise systems such as movescount.com It’s at this point that our factor analysis with its inclusion of the additional ROPAS factor shows a fresh divergence from the literature and the emergence of an interesting association. Although, high scores on the Relatedness to Others in Physical Activity Scale (ROPAS) predicts high scores on perceived social support (SOCS), ROPAS is not specifically related to any exercise data system or online social network so much as it is a reflection of face-to-face contact during exercise. As a result it was reasoned that social identification with an online community that uses the device might be a prerequisite for ROPAS to have any effect on social support. This triggered an investigation of the hypothesis that users’ social identification scores (SOID) can mediate the relationship between ROPAS and SOCS scores.
- I wondered if there was a difference in the ROPAS scores between users who publish exercises sessions to Twitter and those users who do not?