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We investigated the impact of the colour of football outfits on localising football players and on the results of football matches. Two studies were conducted: an experimental study examining the effects of outfit colour on the assessment of the positions of computer-animated football players in a video set-up (study 1) and a retrospective study on professional football clubs’ performances dependent on their outfit colours (study 2). The studies were conducted with 18 human volunteers aged 15–18 years (study 1) and league results from 10 professional European football teams over 17 years (1995–2013) (study 2). We analysed the number of correct assessments of the positions of virtual football players with different outfit colours (study 1) and analysed the relationship between match results and outfits’ colours (study 2). Study 1 showed that the position of players wearing white outfits was better assessed in 5.2% of the trials compared to players wearing green outfits (P = 0.007). Study 2 showed that Manchester City conceded less goals against in away games in highly visible kits (r = 0.62;P = 0.024), while Newcastle United conceded less goals and won more points while playing in kits associated with low visibility (r = 0.63;P = 0.007;r = 0.50;P = 0.040, respectively). We conclude that the colour of football outfits affects evaluations of football players’ positions on the field, with white tricots resulting in the best location assessment. The outfit colour may indirectly influence football match results, warranting more attention to the home and away shirts by team managers and football scientists. [ABSTRACT FROM AUTHOR]

Background/Aims: To identify prodromal Alzheimer's disease (AD) subjects using a data-driven approach to determine cognitive profiles in mild cognitive impairment (MCI). Methods: A total of 881 MCI subjects were recruited from 20 memory clinics and followed for up to 5 years. Outcome measures included cognitive variables, conversion to AD, and biomarkers (e.g. CSF, and MRI markers). Two hierarchical cluster analyses (HCA) were performed to identify clusters of subjects with distinct cognitive profiles. The first HCA included all subjects with complete cognitive data, whereas the second one selected subjects with very mild MCI (MMSE ≥28). ANOVAs and ANCOVAs were computed to examine whether the clusters differed with regard to conversion to AD, and to AD-specific biomarkers. Results: The HCAs identified 4-cluster solutions that best reflected the sample structure. One cluster (aMCIsingle) had a significantly higher conversion rate (19%), compared to subjective cognitive impairment (SCI, p < 0.0001), and non-amnestic MCI (naMCI, p = 0.012). This cluster was the only one showing a significantly different biomarker profile (Aβ42, t-tau, APOE ε4, and medial temporal atrophy), compared to SCI or naMCI. Conclusion: In subjects with mild MCI, the single-domain amnestic MCI profile was associated with the highest risk of conversion, even if memory impairment did not necessarily cross specific cut-off points. A cognitive profile characterized by isolated memory deficits may be sufficient to warrant applying prevention strategies in MCI, whether or not memory performance lies below specific z-scores. This is supported by our preliminary biomarker analyses. However, further analyses with bigger samples are needed to corroborate these findings. Copyright © 2013 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]