Is Wearable Technology Becoming Part of Us? Developing and Validating a Measurement Scale for Wearable Technology Embodiment
Is Wearable Technology Becoming Part of Us? Developing and Validating a Measurement Scale for Wearable Technology Embodiment
复制标题
DOI：
10.2196/12771
复制
发表时间：
2019-08-09
影响因子：
5
通讯作者：
Noordzij, Matthijs L.
中科院分区：
文献类型：
--
作者：
Nelson, Elizabeth C.;Verhagen, Tibert;Noordzij, Matthijs L.
背景：以这样一种方式体验外部物体，即它们被视为自己身体的一个组成部分，这被称为化身。可穿戴技术是一类物体，由于其固有的属性（如靠近身体、频繁互动、获取个人信息），很可能被具体化。这种现象在本文中被称为可穿戴技术的体现，在各个研究领域引起了广泛的概念思考。关于量化可穿戴技术实施方案的这些考虑和进一步的可能性对移动健康（mHealth）领域具有特别的价值。例如，预测移动医疗干预措施有效性的能力，以及了解人们体现这项技术的程度，可能对提高移动医疗的依从性至关重要。为了便于检查可穿戴技术的实施例，我们为此构建了一个测量量表。目的：本研究旨在概念化可穿戴技术体现，创建一种测量工具，并使用与技术采用相关的知名构念来测试量表的预测效度。所介绍的仪器有3个维度，包括9个测量项目。在身体延伸、认知延伸和自我延伸三个维度上，项目分布均匀。方法：通过基于小短文的调查收集数据（n=182）。每个受访者都有3个不同的小插曲，描述了一个假设的情况，使用不同类型的可穿戴技术（智能手机、智能腕带或智能手表），目的是跟踪日常活动。对量表各维度和项目信度进行效度和拟合优度指数（GFI）检验。结果：验证性因子分析因子负荷（>0.70）、平均方差提取值（>0.50）、最小项目与总相关性（>0.40）均超过设定的阈值，建立了3个维度的收敛效度及其信度。量表的信度也被证实为Cronbach alpha，复合信度超过0.70。GFI检验证实，这三个维度是相互关联的一阶因素。预测效度测试表明，这些维度显著增加了与预测新技术采用相关的多个构念（即信任、感知有用性、参与、态度和持续意图）。结论：可穿戴技术体现测量仪器有望成为测量个人身体、认知和自我延伸的工具，并预测技术采用的某些方面。这种三维仪器可以应用于混合方法研究，可穿戴技术开发人员可以使用它来改进未来的版本，比如适合度，提高生物反馈数据的准确性，以及可定制的功能或时尚，以连接用户的个人身份。建议进一步研究将该测量工具应用于多种场景和技术，以及更多样化的用户群体。
Background: To experience external objects in such a way that they are perceived as an integral part of one's own body is called embodiment. Wearable technology is a category of objects, which, due to its intrinsic properties (eg, close to the body, inviting frequent interaction, and access to personal information), is likely to be embodied. This phenomenon, which is referred to in this paper as wearable technology embodiment, has led to extensive conceptual considerations in various research fields. These considerations and further possibilities with regard to quantifying wearable technology embodiment are of particular value to the mobile health (mHealth) field. For example, the ability to predict the effectiveness of mHealth interventions and knowing the extent to which people embody the technology might be crucial for improving mHealth adherence. To facilitate examining wearable technology embodiment, we developed a measurement scale for this construct.Objective: This study aimed to conceptualize wearable technology embodiment, create an instrument to measure it, and test the predictive validity of the scale using well-known constructs related to technology adoption. The introduced instrument has 3 dimensions and includes 9 measurement items. The items are distributed evenly between the 3 dimensions, which include body extension, cognitive extension, and self-extension.Methods: Data were collected through a vignette based survey (n=182). Each respondent was given 3 different vignettes, describing a hypothetical situation using a different type of wearable technology (a smart phone, a smart wristband, or a smart watch) with the purpose of tracking daily activities. Scale dimensions and item reliability were tested for their validity and Goodness of Fit Index (GFI).Results: Convergent validity of the 3 dimensions and their reliability were established as confirmatory factor analysis factor loadings (>0.70), average variance extracted values (>0.50), and minimum item to total correlations (>0.40) exceeded established threshold values. The reliability of the dimensions was also confirmed as Cronbach alpha and composite reliability exceeded 0.70. GFI testing confirmed that the 3 dimensions function as intercorrelated first-order factors. Predictive validity testing showed that these dimensions significantly add to multiple constructs associated with predicting the adoption of new technologies (ie, trust, perceived usefulness, involvement, attitude, and continuous intention).Conclusions: The wearable technology embodiment measurement instrument has shown promise as a tool to measure the extension of an individual's body, cognition, and self, as well as predict certain aspects of technology adoption. This 3-dimensional instrument can be applied to mixed method research and used by wearable technology developers to improve future versions through such things as fit, improved accuracy of biofeedback data, and customizable features or fashion to connect to the users' personal identity. Further research is recommended to apply this measurement instrument to multiple scenarios and technologies, and more diverse user groups.