OBJECTIVES The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. METHODS We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. RESULTS Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. CONCLUSION A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.

中文翻译：

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通过健康应用程序定价产生数据——作为游戏规则改变者的数字红利：离散选择实验。

目标本文的目的是研究在何种情况下可穿戴和健康应用程序用户会接受补偿付款，即数字红利，以共享其自我跟踪的健康数据。方法我们进行了离散选择实验替代方案，即分离的自适应双响应。我们选择这种方法来减少极端反应行为，考虑到健康数据销售的情绪化话题，并衡量接受意愿。以前在实验室环境中进行的实验导致了对高额货币补偿的要求。在第一次在线调查和两次预研究之后，我们验证了最终在线研究的四个属性：每月奖金支付、处理数据的利益相关者（例如，健康保险公司、制药或医疗设备公司、大学）、数据类型和数据销售给第三方。我们使用随机效用框架来评估个人选择偏好。为了测试主要研究的预期价格的稳健性，我们将受访者随机分配到两份价格范围不同的相同问卷中的一份。结果 在三周内，842 名受访者参加了主要调查，272 名受访者参加了第二次调查。参与者认为数据处理的透明度和不再向第三方出售数据对于与不同利益相关者共享数据的决定以及足够的货币补偿非常重要。实验产生的价格预期很高；制药和医疗设备公司必须为患者生成的所有类型的健康数据支付平均 237.30 欧元/月的数字红利。我们还观察到锚效应，这意味着人们在过程中而不是事前形成了价格预期。我们发现相对较低的价格预期和相对较高的价格预期之间存在双峰分布，这表明个人数据销售是一个分裂的社会问题。然而，结果表明，数字红利可能是一种公认​​的经济激励系统，可以为研究和开发目的收集大规模、自我跟踪的数据。在 COVID-19 危机之后，由于公众对对患者生成的健康数据进行大数据研究的必要性的敏感性，价格预期可能会发生变化。结论 现有数据捐赠模型持续成功的可能性很小。医疗保健部门需要提高数据处理的透明度和信任度。