Social embeddedness in an online weight management programme is linked to greater weight loss

Abstract

The obesity epidemic is heightening chronic disease risk globally. Online weight management (OWM) communities could potentially promote weight loss among large numbers of people at low cost. Because little is known about the impact of these online communities, we examined the relationship between individual and social network variables, and weight loss in a large, international OWM programme. We studied the online activity and weight change of 22,419 members of an OWM system during a six-month period, focusing especially on the 2033 members with at least one friend within the community. Using Heckman's sample-selection procedure to account for potential selection bias and data censoring, we found that initial body mass index, adherence to self-monitoring and social networking were significantly correlated with weight loss. Remarkably, greater embeddedness in the network was the variable with the highest statistical significance in our model for weight loss. Average per cent weight loss at six months increased in a graded manner from 4.1% for non-networked members, to 5.2% for those with a few (two to nine) friends, to 6.8% for those connected to the giant component of the network, to 8.3% for those with high social embeddedness. Social networking within an OWM community, and particularly when highly embedded, may offer a potent, scalable way to curb the obesity epidemic and other disorders that could benefit from behavioural changes.

Keywords: complex networks; modelling; obesity; weight loss.

Figure 1.

Cumulative distribution of engagement time for all unique identifiers in the database (a) and for programme members with at least one friend (b), and their corresponding fits to a linear superposition of exponentials (Y = 0.47exp(−t/214) + 0.13exp(−t/17) and Y = 0.96exp(−t/288) + 0.04exp(−t/24), respectively). We defined engagement time as the number of days between signing up to the programme and the last day of recorded activity. We restrict the plotting to 180 days because our study focuses on that period of time. Note the extraordinarily good agreement between data (red or green symbols) and fit (black line). (Online version in colour.)

Figure 2.

Friendship connections among programme members. We denote members who lose weight by circles, and those who gain weight by triangles. We colour the symbols according to total per cent weight change. The vast majority of networked members (74%) belong to a single cluster, the so-called ‘giant component’. Fifty-four per cent of the members in the giant component lost 5% or more of their baseline body weight by their sixth month in the programme.

Figure 3.

Engagement in online social networking correlates with incremental weight loss at six months. (a) The networked members belonging to the giant component (and specially, the ones highly embedded in the network—k-shell higher or equal than 2) achieved the greatest weight loss. (b) Percentage weight loss increases by 0.7% for every unit increase in k-shell index (corr. coeff. = 0.91). Note that non-networked members have K(i) = 0. Bars represent standard errors, asterisks (***) denote p < 0.001 and n.s. denotes not significant. (Online version in colour.)