Data analytic plan
weight gain
Data were analyzed using SPSS Version 26 (IBM [Time-varying predictors were separated into between-person effects which were grand mean centered (mean aggregate across the sessions) and within-person effects which were person-mean centered (deviation from participant’s mean at each session; raw—mean). Time (session number or assessment point) was included in each model as a predictor.We examined whether fear of weight gain changed throughout treatment using session-by-session data. We conducted a MLM with session number (i.e., 1–12) as a fixed effect predicting four-item GFFS scores. Additionally, we examined whether fear of weight gain using the full scale (10-item GFFS) changed across the pre-treatment, mid-treatment, and post-treatment assessment points. We conducted an MLM with assessment time point (i.e., 1, 2, 3) as the fixed effect predicting GFFS scores.We examined whether the change in fear of weight gain (4-item GFFS) session by session was moderated by diagnosis of BN-spectrum compared to BED. We conducted an MLM with session (i.e., 1–12), diagnosis (BN-spectrum, BED), and the interaction between session and diagnosis as fixed effects predicting session GFFS scores. We also examined whether the change in fear of weight gain (10-item GFFS) during treatment was moderated by diagnosis. We conducted an MLM with assessment point (i.e., 1, 2, 3), diagnosis (BN-spectrum, BED), and the interaction between assessment point and diagnosis as fixed effects predicting GFFS scores.We conducted an MLM to examine whether GFFS scores were predictive of LOC episode frequency. We used a lagging procedure such that GFFS scores at a session “t” predicted LOC episode frequency (patient-reported) at session “t + 1”. This lagging procedure allows for testing the direction of effects, in other words, fear of weight gain at a given week predicting the following week’s LOC episode frequency. This MLM included the fixed effect of GFFS scores at session “t” predicting LOC episodes at session “t + 1”.We conducted MLMs to investigate whether BMI predicted fear of weight gain. We lagged GFFS scores in order to test whether BMI at session “t” (fixed effect) predicted GFFS scores at session “t + 1”. Conversely, we examined whether GFFS scores at session “t” (fixed effect) predicted BMI at session “t + 1”.As a sensitivity analysis, we included computerized training condition (i.e., sham, inhibitory control training) in all models as a control, but findings remained consistent. Thus, we reported results of analyses without controlling for training condition. Cohen’s 