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Aim To examine trends in public awareness and knowledge of drinking guidelines in the UK since their revision in 2016, which had moved from a daily to a weekly guideline, made the guideline the same for men and women, and reduced the guideline for men by around one-third. Method Data were from a representative, repeat cross-sectional survey. We analysed changes in awareness and knowledge of drinking guidelines among 8168 adult drinkers between 2016 and 2022 and associations with sociodemographic characteristics, smoking status and level of alcohol consumption. Results The proportion of drinkers aware of guidelines declined from 86.0% (95%CI 84.0–88.0%) in 2016 to 81.7% (79.5–84.0%) in 2019, then increased during the COVID-19 pandemic, peaking at 91.6% (90.1–93.1%) in 2020. The proportion who correctly identified the guideline as a maximum of exactly 14 units/week remained at around a quarter from 2016 (25.0%, 22.4–27.5%) to 2018 (25.8%, 23.2–28.3%), whereas the proportion who gave a figure of 14 units or fewer rose from 52.1 (49.2–55.0%) to 57.4% (54.6–60.3%). However, by 2022, guideline knowledge had worsened significantly, with these figures falling to 19.7 (17.4–21.9%) and 46.5% (43.6–49.4%), respectively. Changes over time were similar across subgroups. Odds of guideline awareness and knowledge were higher among drinkers who were aged ≥35, female, more educated and from more advantaged social grades. Conclusions The majority of adult drinkers in the UK are aware of low-risk drinking guidelines. However, 6 years since their announcement, knowledge of the revised drinking guidelines remains poor. Less than a quarter know the recommended weekly limit and only around half think it is 14 units or less. Inequalities have persisted over time, such that disadvantaged groups remain less likely to know the guidelines.

Conventional representation-based methods only consider the whole training set for representation and recognition. However, some of the training samples make little contribution to the representation of the test sample. In this paper, we propose to construct an optimal representation set for recognition. First, the nearest neighbour principle is used to initialize the optimal representation set. Then, it will be updated through adding a training sample which can work with the current representation set to represent the test sample with minimum error. With this scheme, we accelerate the computation procedure by the partitioned matrix technique. To fully validate the effectiveness of the proposed method, experiments have been conducted on public palmprint and face databases by comparing the proposed method with the state-of-the-art methods. The proposed competitive sample selection method shows promising results.