Polycystic ovary syndrome (PCOS) is associated with poor quality of life (QoL) and high levels of depression. Existing research is confounded by small sample sizes and inconsistent use of control groups. Depression and QoL were assessed in women with PCOS and healthy controls (n = 1359). The polycystic ovary syndrome health-related QoL questionnaire (PCOSQ) was modified to include an acne subscale. Seventy-one percentage of women with PCOS who were taking anti-androgen (AA) medication and 67% not taking AA medication were classified as depressed. Women with PCOS had lower QoL on all seven factors of the modified PCOSQ (emotional disturbance, weight, infertility, acne, menstrual symptoms, menstrual predictability and hirsutism). Weight was the largest contributor to poor QoL for women taking and not taking AA medication. Women taking AA medication, independent of diagnosis, generally had better QoL than women not taking them. This large study refines our understanding of depression and QoL in PCOS and demonstrates the need to regularly review the psychological health of women with PCOS.

Exposure to the sight and smell of food influences our momentary desire to consume it. This study explored the process by which cue exposure promotes greater consumption of food. Three hypotheses were explored, cue exposure: (i) increases the planned consumption of food; (ii) increases tolerance of larger portion sizes; (iii) arrests the development of satiety. Female participants (n50) were each tested in two conditions. In a ‘cue condition’ they were exposed to the sight and smell of pizza for 60 s. Before and after this period they provided information about prospective and maximum tolerated portion sizes and their desire to eat pizza and other non-cued foods. Participants then consumed a fixed portion of pizza, rated their hunger and were finally offeredad libitumaccess to pizza. In the ‘no-cue condition’, cue exposure was replaced with a cognitive task. Cueing had little effect on tolerance of larger portion sizes or on hunger after consuming the fixed portion. Instead, it increased prospective pizza portion size and subsequent intake of pizza. Together, these results suggest that cueing increases the amount of food that people actively plan to eat. This plan is then executed, leading to greater intake. Pizza cueing also increased prospective portion size of other foods. Thus, contrary to previous reports, effects of exposure may generalise to other foods. Finally, we found evidence that restrained eaters are less ‘cue reactive’ than unrestrained eaters. In future, our approach might be adapted to consider whether heightened ‘cue reactivity’ represents a risk factor for obesity.

Laboratory studies have demonstrated that experimental manipulations of oral processing can have a marked effect on energy intake. Here, we explored whether variations in oral processing across a range of unmodified everyday meals could affect post-meal fullness and meal size. In Study 1, female participants (N = 12) attended the laboratory over 20 lunchtime sessions to consume a 400-kcal portion of a different commercially available pre-packaged meal. Prior to consumption, expected satiation was assessed. During each meal, oral processing was characterised using: (i) video-recordings of the mouth and (ii) real-time measures of plate weight. Hunger and fullness ratings were elicited pre- and post-consumption, and for a further three hours. Foods that were eaten slowly had higher expected satiation and delivered more satiation and satiety. Building on these findings, in Study 2 we selected two meals (identical energy density) from Study 1 that were equally liked but maximised differences in oral processing. On separate days, male and female participants (N = 24) consumed a 400-kcal portion of either the “fast” or “slow” meal followed by an ad libitum meal (either the same food or a dessert). When continuing with the same food, participants consumed less of the slow meal. Further, differences in food intake during the ad libitum meal were not compensated at a subsequent snacking opportunity an hour later. Together, these findings suggest that variations in oral processing across a range of unmodified everyday meals can affect fullness after consuming a fixed portion and can also impact meal size. Modifying food form to encourage increased oral processing (albeit to a lesser extent than in experimental manipulations) might represent a viable target for food manufacturers to help to nudge consumers to manage their weight.

Slowing eating rate appears to be an effective strategy for reducing food intake. This feasibility study investigated the effect of eating rate on post-meal responses using functional magnetic resonance imaging (fMRI), plasma gastrointestinal hormone concentrations, appetite ratings, memory for recent eating, and snack consumption. Twenty-one participants (mean age 23 years with healthy body mass index) were randomly assigned to consume a 600 kcal meal at either a “normal” or “slow” rate (6 vs. 24 min). Immediately afterwards, participants rated meal enjoyment and satisfaction. FMRI was performed 2-h post-meal during a memory task about the meal. Appetite, peptide YY, and ghrelin were measured at baseline and every 30 min for 3 h. Participants were given an ad-libitum snack three hours post-meal. Results were reported as effect sizes (Cohen’s d) due to the feasibility sample size. The normal rate group found the meal more enjoyable (effect size = 0.5) and satisfying (effect size = 0.6). Two hours post-meal, the slow rate group reported greater fullness (effect size = 0.7) and more accurate portion size memory (effect sizes = 0.4), with a linear relationship between time taken to make portion size decisions and the BOLD response in satiety and reward brain regions. Ghrelin suppression post-meal was greater in the slow rate group (effect size = 0.8). Three hours post-meal, the slow rate group consumed on average 25% less energy from snacks (effect size = 0.5). These data offer novel insights about mechanisms underlying how eating rate affects food intake and have implications for the design of effective weight-management interventions.