Human beings routinely help strangers at costs to themselves. Sometimes the help offered is generous—offering more than the other expects. The proximate mechanisms supporting generosity are not well-understood, but several lines of research suggest a role for empathy. In this study, participants were infused with 40 IU oxytocin (OT) or placebo and engaged in a blinded, one-shot decision on how to split a sum of money with a stranger that could be rejected. Those on OT were 80% more generous than those given a placebo. OT had no effect on a unilateral monetary transfer task dissociating generosity from altruism. OT and altruism together predicted almost half the interpersonal variation in generosity. Notably, OT had twofold larger impact on generosity compared to altruism. This indicates that generosity is associated with both altruism as well as an emotional identification with another person.

How do human beings decide when to be selfish or selfless? In this study, we gave testosterone to 25 men to establish its impact on prosocial behaviors in a double-blind within-subjects design. We also confirmed participants' testosterone levels before and after treatment through blood draws. Using the Ultimatum Game from behavioral economics, we find that men with artificially raised T, compared to themselves on placebo, were 27% less generous towards strangers with money they controlled (95% CI placebo: (1.70, 2.72); 95% CI T: (.98, 2.30)). This effect scales with a man's level of total-, free-, and dihydro-testosterone (DHT). Men in the lowest decile of DHT were 560% more generous than men in the highest decile of DHT. We also found that men with elevated testosterone were more likely to use their own money punish those who were ungenerous toward them. Our results continue to hold after controlling for altruism. We conclude that elevated testosterone causes men to behave antisocially.

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Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.