Objective: Weight loss interventions have begun to receive increasing attention in primary care. Motivational interviewing (MI) is compatible with primary care because it requires relatively limited time and resources. Few studies, however, have examined the long‐term impact of MI for weight loss in primary care, and none have used attention‐control comparisons. This study was the first randomized controlled trial with a 12‐month follow‐up of two Web‐supported interventions: motivational interviewing and internet condition (MIC) and nutrition psychoeducation and internet condition (NPC).Methods: Fifty‐nine patients with overweight or obesity, with and without binge‐eating disorder (BED), were randomized to treatments and assessed at 12‐month follow‐up after completing 3‐month treatments in primary care (15 months total).Results: Mixed models examining weight loss at 12 months revealed a group and time interaction effect trend (P = 0.054; d′ = 0.57). Secondary end point analysis showed a decrease (−1.7%) versus an increase (1.3%) in weight at 12 months among NPC and MIC patients, respectively (P = 0.056; d′ = 0.57). Overall, 5 of 44 (11.4%) participants lost or maintained 5% weight losses; differences between treatments were not significant. BED status did not impact weight loss.Conclusions: Two brief and scalable weight loss interventions resulted in small effect sizes for weight loss 12 months following treatment conclusion. Because MIC required significantly more resources for adequate implementation, NPC may be more cost‐effective.

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. This paper presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP). We take advantage of evolutionary algorithms, in particular CGP, to evolve mathematical functions that act as classifiers. These classifiers are used to identify piano notes' pitches in an audio signal. For a first approach, the obtained results are very promising: our error rate outperforms two of three state-of-the-art pitch estimators.