Posting behaviour patterns in an online smoking cessation social network: implications for intervention design and development.

OBJECTIVES

Online Cessation Support Networks (OCSNs) are associated with increased quit success rates, but few studies have examined their use over time. We identified usage patterns in New Zealand's largest OCSN over two years and explored implications for OCSN intervention design and evaluation.

METHODS

We analysed metadata relating to 133,096 OCSN interactions during 2011 and 2012. Metrics covered aggregate network activity, user posting activity and longevity, and between-user commenting. Binary logistic regression models were estimated to investigate the feasibility of predicting low user engagement using early interaction data.

RESULTS

Repeating periodic peaks and troughs in aggregate activity related not only to seasonality (e.g., New Year), but also to day of the week. Out of 2,062 unique users, 69 Highly Engaged Users (180+ interactions each) contributed 69% of all OCSN interactions in 2012 compared to 1.3% contributed by 864 Minimally Engaged Users (< = 2 items each). The proportion of Highly Engaged Users increased with network growth between 2011 and 2012 (with marginal significance), but the proportion of Minimally Engaged Users did not decline substantively. First week interaction data enabled identification of Minimally Engaged Users with high specificity and sensitivity (AUROC= 0.94).

IMPLICATIONS

Results suggest future research should develop and test interventions that promote activity, and hence cessation support, amongst specific user groups or at key time points. For example, early usage information could help identify Minimally Engaged Users for tests of targeted messaging designed to improve their integration into, or re-engagement with, the OCSN. Furthermore, although we observed strong growth over time on varied metrics including posts and comments, this change did not coincide with large gains in first-time user persistence. Researchers assessing intervention effects should therefore examine multiple measures when evaluating changes in network dynamics over time.