Development and use of a co-produced short mood survey to collect ground truth in digital footprints research

Nina H Di Cara, Oliver Davis, Claire Haworth
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Abstract

Introduction & BackgroundTo use digital footprint data for mental health and well-being research we often need to collect concurrent, high-quality measures of ground truth. Delivering frequent surveys to participants using an ecological momentary assessment (EMA) methodology is one way to collect such data. However, existing surveys tend to be long, not focused on momentary states or rely on rating images which are not platform agnostic. Here we present a five-item test-based survey designed with participants and validated for use in EMA studies to collect data about momentary changes in mood. We describe its methodological development and how it has been used to investigate music listening on Spotify as a digital footprint of mood. Objectives & ApproachThe survey is based on the circumplex model of affect. It was co-produced with a participant advisory group (N=5), who gave feedback on the length, content and delivery of the survey. It was then piloted in a group of N=98 participants to assess statistical validity, and congruence with the 20-item Positive and Negative Affect Schedule (PANAS). Following this it was delivered in a wider sample (N=150) four times a day over a two-week period using an EMA app on participant’s phones. Relevance to Digital FootprintsEMA is an increasingly popular method for collecting ground truth to support the interpretation of digital footprint data. This newly developed and tested mood survey offers an opportunity to reduce participant burden for collecting mood data in EMA studies which will support the collection of high quality and high time-resolution ground truth for digital footprints research. ResultsTogether with participants we selected four emotions across the axes of arousal and valence, as well as rumination which participants considered important in their music listening behaviors. Factor analysis of pilot data showed that the questions represented two factors of positive and negative affect. The ratings on a 0-10 scale of the emotions ‘cheerful’ and ‘relaxed’ explained 44% of the variance in positive affect, and ratings of ‘worried’, ‘sad’ and ‘frustrated’ explained 40% of the variance in negative affect. Delivery of the questionnaire in a wider student sample (N=150) four times per day for two weeks allowed for the opportunity to assess typical response rates in a realistic EMA setting. On average participants completed 3 out of the 4 surveys a day. Conclusions & ImplicationsThe co-created, short mood survey for the collection of ground truth in digital footprint studies was validated across two independent samples, and shown to allow for good response rates in a two week study. Future testing on wider samples will provide opportunities to validate the survey and assess its effectiveness across demographic groups and different sample types.
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开发和使用共同制作的简短情绪调查,以收集数字足迹研究的基本真相
导言与背景要将数字足迹数据用于心理健康和幸福感研究,我们通常需要同时收集高质量的基本真实测量数据。使用生态瞬时评估(EMA)方法对参与者进行频繁调查是收集此类数据的一种方法。然而,现有的调查往往时间较长,不侧重于瞬间状态,或者依赖于评级图像,而这些都与平台无关。在此,我们介绍一种基于测试的五项调查,该调查由参与者共同设计,并经过验证,可用于 EMA 研究,以收集有关情绪瞬间变化的数据。我们将介绍其方法论的发展,以及如何将其用于调查 Spotify 上的音乐聆听作为情绪的数字足迹。目标与方法该调查基于情绪的圆周模型。它是与一个参与者咨询小组(N=5)共同制作的,该小组就调查问卷的长度、内容和交付方式提供了反馈意见。然后在一组 N=98 名参与者中进行试点,以评估统计有效性以及与 20 个项目的积极和消极情绪表(PANAS)的一致性。之后,在更广泛的样本中(样本数=150),使用参与者手机上的 EMA 应用程序,在两周内每天进行四次问卷调查。与数字足迹的相关性 EMA 是一种日益流行的收集基本事实的方法,可为数字足迹数据的解释提供支持。这项新开发和测试的情绪调查为减轻 EMA 研究中收集情绪数据的参与者负担提供了机会,这将有助于为数字足迹研究收集高质量和高时间分辨率的基本事实。结果我们与参与者一起选择了四种情绪,它们横跨唤醒轴、情绪轴以及反刍轴,参与者认为这些情绪对他们的音乐聆听行为很重要。对试验数据进行的因子分析显示,这些问题代表了积极情绪和消极情绪两个因子。对 "愉快 "和 "放松 "这两种情绪的 0-10 级评分解释了 44% 的积极情绪变异,而对 "担忧"、"悲伤 "和 "沮丧 "这三种情绪的评分解释了 40% 的消极情绪变异。在更广泛的学生样本中(样本数=150),连续两周每天发放四次问卷,从而有机会在现实的 EMA 环境中评估典型的回复率。参与者平均每天完成 4 次调查中的 3 次。结论与启示这项用于收集数字足迹研究基本事实的共同制作的简短情绪调查在两个独立样本中得到了验证,并表明在为期两周的研究中响应率较高。未来将在更广泛的样本中进行测试,以验证该调查问卷,并评估其在不同人口群体和不同样本类型中的有效性。
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