An (un) healthy poster: When environmental cues affect consumers’ food choices at vending machines

Environmental cues can affect food decisions. There is growing evidence that environmental cues influence how much one consumes. This article demonstrates that environmental cues can similarly impact the healthiness of consumers’ food choices. Two field studies examined this effect with consumers of vending machine foods who were exposed to different posters. In field study 1, consumers with a health-evoking nature poster compared to a pleasure-evoking fun fair poster or no poster in their visual sight were more likely to opt for healthy snacks. Consumers were also more likely to buy healthy snacks when primed by an activity poster than when exposed to the fun fair poster. In field study 2, this consumer pattern recurred with a poster of skinny Giacometti sculptures. Overall, the results extend the mainly laboratory-based evidence by demonstrating the health-relevant impact of environmental cues on food decisions in the field. Results are discussed in light of priming literature emphasizing the relevance of preexisting associations, mental concepts and goals.

Minimized cardiopulmonary bypass combined with a smart suction device: the future of cardiopulmonary bypass?

OBJECTIVE: The standard heart-lung machine is a major trigger of systemic inflammatory response and the morbidity attributed to conventional extracorporeal circulation (CECC) is still significant. Reduction of blood-artificial surface contact and reduction of priming volume are principal aims in minimized extracorporeal circulation (MECC) cardiopulmonary bypass systems. The aim of this paper is to give an overview of the literature and to present our experience with the MECC-smart suction system. METHODS AND RESULTS: At our institution, 1799 patients underwent isolated coronary artery bypass grafting (CABG) surgery, 1372 with a MECC-smart suction system and 427 with CECC. All in-hospital data were assessed and the results were compared between the 2 groups. Patient characteristics and the distribution of EuroSCORE risk profile in our collective were similar between both groups. Average age in the MECC collective was 67.5 +/- 11.4 years and average EuroSCORE was 5.0 +/- 1.5. Average number of distal anastomoses was similar to the average number encountered in patients undergoing CABG surgery with CECC (3.3 +/- 1.0 for MECC versus 3.2 +/- 1.1 for CECC; P = ns). Myocardial protection is superior in MECC patients with lower postoperative maximal cTnI values (11.0 +/- 10.8 micromol/L for MECC versus 24.7 +/- 25.3 micromol/L for CECC; P < .05). Postoperative recovery was faster in patients operated on with the MECC-smart suction system and discharge from the hospital was earlier than for CECC patients (7.4 +/- 1.9 days for MECC versus 8.8 +/- 3.8 days for CECC; P < .05). CONCLUSIONS: The MECC-smart suction system is a safe perfusion technique for CABG surgery. In patients operated on with this system, the clinical outcome seems to be better than in patients operated on with CECC. This promising and less damaging perfusion technology has the potential to replace CECC systems in CABG surgery.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.