Fig 1.
Flowchart of inclusion/exclusion criteria for the primary analysis.
Table 1.
Population characteristics for the primary analysis: mean (standard deviation) [number not reported].
Fig 2.
Flowchart of inclusion/exclusion criteria for the secondary analysis.
Table 2.
Population characteristics for subjects in the secondary analysis: mean (standard deviation) [number not reported].
Table 3.
Distribution of number of periods per user and median period length per user for all three log types in the secondary analysis.
Table 4.
Primary analysis linear regression coefficient estimates, 95% confidence intervals, and p-values.
Table 5.
Amount of additional weight loss associated with increased logging rate, for each activity.
Fig 3.
Plot of the modeled association between weight change and activity tracking frequency for both genders, over all activities, and for different monitoring durations.
Note that that frequency on the x axis is log scaled. The 10, 50, and 90 percentiles of tracking duration were chosen to represent users who monitored their activity for short, medium, and long durations, and to demonstrate how the association between weight change and activity tracking frequency varied with monitoring duration. In general, increased monitoring duration is associated with decreased weight loss per month and a weaker association between tracking frequency and weight change. The confidence bands are 95% confidence intervals. We note that the three lines intersect in every graph at the point where F = −β3/β4, recalling that F is the mean-centered logarithm of recordings per week.
Table 6.
Regions of significance for the tracking frequency–weight loss association in the primary analysis.
Table 7.
Secondary analysis linear regression coefficient estimates, 95% confidence intervals, and p-values.
Fig 4.
Histogram of average weight change for users during adherent and non-adherent periods of activity tracking.
The histograms depict the association between logging adherence and weight change while controlling for all user variation (gender, age, weight, etc.). For each user in the secondary analysis, average weight change is computed first for their adherent tracking periods and then for their non-adherent periods. Both averages are histogrammed for each activity type. The graph shows a positive association between logging adherence and weight loss, as the adherent weight-change distribution is shifted left relative to the non-adherent weight-change distribution.
Table 8.
Mean per-user weight change during adherent and non-adherent periods in the secondary analysis: mean (standard deviation).
Fig 5.
Average weight change during adherent and non-adherent periods for various definitions of adherent period.
We observe that non-adherent periods have higher weight loss, regardless of the value of max gap or period length. The secondary analysis was performed with a max gap of 4 days, and including periods of 7–28 days in length.