Department of Operations, Information and Decisions, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
Behavior Change for Good Initiative, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
Nature. 2021 Dec;600(7889):478-483. doi: 10.1038/s41586-021-04128-4. Epub 2021 Dec 8.
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens' decisions and outcomes. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time intervals. The lack of comparability of such individual investigations limits their potential to inform policy. Here, to address this limitation and accelerate the pace of discovery, we introduce the megastudy-a massive field experiment in which the effects of many different interventions are compared in the same population on the same objectively measured outcome for the same duration. In a megastudy targeting physical exercise among 61,293 members of an American fitness chain, 30 scientists from 15 different US universities worked in small independent teams to design a total of 54 different four-week digital programmes (or interventions) encouraging exercise. We show that 45% of these interventions significantly increased weekly gym visits by 9% to 27%; the top-performing intervention offered microrewards for returning to the gym after a missed workout. Only 8% of interventions induced behaviour change that was significant and measurable after the four-week intervention. Conditioning on the 45% of interventions that increased exercise during the intervention, we detected carry-over effects that were proportionally similar to those measured in previous research. Forecasts by impartial judges failed to predict which interventions would be most effective, underscoring the value of testing many ideas at once and, therefore, the potential for megastudies to improve the evidentiary value of behavioural science.
政策制定者越来越多地从行为科学中寻求如何改善公民决策和结果的洞见。通常,不同的科学家在不同的样本中使用不同的结果和不同的时间间隔测试不同的干预措施。这些单独调查的可比性缺乏限制了它们为政策提供信息的潜力。在这里,为了解决这一限制并加快发现的步伐,我们引入了“超级研究”——一项大规模的现场实验,在同一人群中、在同一客观测量的结果上、在相同的时间内比较许多不同干预措施的效果。在一项针对美国一家健身连锁店 61293 名成员的体育锻炼的超级研究中,来自 15 所美国不同大学的 30 名科学家以小的独立团队合作,总共设计了 54 种不同的为期四周的数字方案(或干预措施),以鼓励锻炼。我们表明,这些干预措施中有 45%显著增加了每周去健身房的次数,增加了 9%至 27%;表现最好的干预措施为错过锻炼后返回健身房提供微奖励。只有 8%的干预措施在四周的干预后产生了可衡量的行为改变。在干预期间增加锻炼的 45%的干预措施的基础上,我们检测到了延续效应,其比例与之前研究中测量的相似。公正的裁判的预测未能预测出哪些干预措施最有效,这突显了同时测试许多想法的价值,因此,超级研究有可能提高行为科学的证据价值。