SHOUT SHOUT SHOUT it all OUT

The next in our series of research snippets with 23,000 exercise wearable users

Along with a thorough set of descriptive analyses of demographic and anthropometric data for the 23,000 users of the exercise wearable and its app there was a concert effort made to learn more about how the much heralded social support functions of the app were used.

The Comments function, fits into the interaction graph concept as explored by (Long et al., 2013) and affords the app user the opportunity to comment to another user either generally and or posting to an uploaded exercise session from the other party.  This is a far more active engagement with the system than simply indicating a LIKE, as it requires a concerted effort; typing a comment as opposed to simply clicking on an  icon. 

The analysis of the population sample revealed that a relatively small number of users make an average of 4.5 Comments each to a larger group of receivers who receive an average of 1.9 each.  Each of these distributions was extremely positively skewed, as is evidenced by more than half of the users making and receiving only one Comment.  This very modest use of the function was echoed in the subsequent survey of the population subset.  Here, we found that despite at least 75 percent of the respondents regularly using the digital exercise tracking device and logging into the system and uploading an exercise session, in terms of their remaining uses of the system (Following another User, Comments, Likes, Joining a Group), no more than 25 percent of users regularly or frequently engage in these activities, i.e., at least 75% of respondents report never or only occasionally engaging in these activities.  Of the possible uses of the device, the most popular when ranked by the mean is following another user (M = 1.55, SE = 0.03), followed by joining a group (M = 1.44, SE = 0.03). 

Regression analyses also determined that neither gender nor any of the anthropometric measures including BMI and self-reported Fitness Index were associated with the number of general Comments made by users; where these are online comments made from one user to another but not directed at an uploaded exercise session.  Descriptive analyses of Comments that WERE directed at the uploaded exercise sessions of other users indicated that 1,467 users received Comments, with an average of 3.5 Comments received by users.  As well, 3,913 different exercise sessions received Comments with each session receiving 1.3 Comments on average.  

Interestingly, better scores by users on the fitness measures, Minimum of Fitness Index and Median of BMI, are associated with an increased Number of Exercise Sessions Receiving Comments for that user as is the Number of Exercise Sessions.  On the face of it, the fitter, leaner users exercise more using the system and receive more Comments from other users.  To somewhat underline this phenomenon; in terms of the median of BMI, each increase of 4.5 to BMI leads to an average of one fewer exercise sessions-receiving Comments.  There is insufficient evidence to speculate as to the reasons for why these less lean individuals receive less interactive comments from others around their uploaded exercise efforts in this system.  

In terms of gender differences, in a sample, 340 males had an average of 2.9 exercise sessions uploaded that received comments, whereas 32 females had an average of 4.9 exercise sessions that received comments.  On the face of this data, it seems that female users garner more comments about their exercise uploaded to the system than male counterparts.  Kimbrough, Guadagno, Muscanell, & Dill (2013) found, in an analysis of online behaviour that women, compared to men, are generally more frequent mediated communication users. Compared to men, women prefer and more frequently use text messaging and social media.  In a survey-based study of the text messaging experiences of one hundred and fifty three 18 to 24-year-old students, it was found that women, compared to men, are generally more frequent mediated communication users, (Ceccucci, Peslak, Kruck, & Patricia, 2013).  Compared to men, women prefer and more frequently use text messaging and social media. This may perhaps explain a little of the gender differences for Comment frequency in the exercise app but the real reasons require further investigation. 

References

Ceccucci, W., Peslak, A., Kruck, S. E., & Patricia, S. (2013). Does Gender Play a Role in Text Messaging? Issues in Information Systems, 14(2), 186.

Kimbrough, A. M., Guadagno, R. E., Muscanell, N. L., & Dill, J. (2013). Gender Differences in Mediated Communication: Women Connect More Than Do Men. Comput. Hum. Behav., 29(3), 896–900. doi:10.1016/j.chb.2012.12.00

Long, J., Chen, Y., Wang, T., Hui, P., & Vasilakos, A. (2013). Understanding user behavior in online social networks: a survey. IEEE Communications Magazine, 51(9), N.PAG. doi:10.1109/MCOM.2013.658866

Dr Daryl Foy

Dr Daryl Foy is a Behavioural Scientist who specialises in the design of effective health behaviour change apps based on evidence including his own validated models for optimising persistent use. He consults to industry on how-to integrate persuasive design into LEAN product development as well as conversational UI. He can be contacted at dlfoy@mortonlawson.com