Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study.
Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study.
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DOI：
10.2196/17757
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发表时间：
2020-10-09
影响因子：
7.4
通讯作者：
Greeff J
中科院分区：
文献类型：
--
作者：
Kelders SM;Kip H;Greeff J
参与度是数字健康干预措施有效性的预测指标。然而，缺乏对参与的共同理解。因此，开发了一个新的量表，提出了一个明确的定义，并创建了一个工具来衡量它。TWente与电子健康技术的参与度量表（TWEETS）是基于对参与健康应用程序用户的系统性审查和访谈。它将参与定义为行为、认知和情感的结合。本文的目的是评估的心理测量特性的TWEETS。此外，与数字行为改变干预参与量表（DBCI-ES-Ex）的经验部分进行了比较，该量表在以前的心理测量分析中显示了一些问题。在这项研究中，288名参与者被要求使用智能手机上的任何计步器应用程序2周。他们在4个时间点完成在线问卷：T0=基线，T1= 1天后，T2=1周，T3=2周。在T0时，对人口统计学和个性（积极性和智力/想象力）进行了评估;在T1-T3时，参与、参与、享受、主观使用和感知行为变化被纳入为理论上与我们的参与定义相关的指标。分析集中在内部的一致性，可靠性，收敛，发散和预测有效性的参与规模。通过将参与量表与参与、享受和主观使用相关联来评估收敛效度;通过将参与量表与个性相关联来评估发散效度;通过回归分析使用参与来预测在以后时间点感知的行为变化来评估预测效度。T1、T2和T3时TWEETS的Cronbach α值分别为0.86、0.86和0.87。探索性因素分析表明，1-因素结构最适合的数据。TWEETS与参与度和享受度中度至高度相关（理论上分别与认知和情感参与度相关; P<.001）。TWEETS和使用频率之间的相关性不显著或很小，并且在TWEETS上，粘附者和非粘附者之间的差异是显著的（P<0.001）。个性与TWETS之间的相关性不显著。T1时的TWEETS可预测T3时的感知行为变化，解释方差为16%。TWEETS和DBCI-ES-Ex的心理测量学特性在某些方面（例如，内部一致性）似乎相当，在其他方面，TWEETS似乎有点上级（发散和预测效度）。TWEETS作为一种敬业度测量工具，具有较高的内部一致性、合理的重测信度和收敛效度、良好的发散效度和合理的预测效度。由于量表的心理测量质量反映了量表与概念化概念的匹配程度，本文也试图将敬业度概念化并定义为一个独特的概念，为定义和测量敬业度的可接受标准迈出了第一步。
Engagement emerges as a predictor for the effectiveness of digital health interventions. However, a shared understanding of engagement is missing. Therefore, a new scale has been developed that proposes a clear definition and creates a tool to measure it. The TWente Engagement with Ehealth Technologies Scale (TWEETS) is based on a systematic review and interviews with engaged health app users. It defines engagement as a combination of behavior, cognition, and affect. This paper aims to evaluate the psychometric properties of the TWEETS. In addition, a comparison is made with the experiential part of the Digital Behavior Change Intervention Engagement Scale (DBCI-ES-Ex), a scale that showed some issues in previous psychometric analyses. In this study, 288 participants were asked to use any step counter app on their smartphones for 2 weeks. They completed online questionnaires at 4 time points: T0=baseline, T1=after 1 day, T2=1 week, and T3=2 weeks. At T0, demographics and personality (conscientiousness and intellect/imagination) were assessed; at T1-T3, engagement, involvement, enjoyment, subjective usage, and perceived behavior change were included as measures that are theoretically related to our definition of engagement. Analyses focused on internal consistency, reliability, and the convergent, divergent, and predictive validity of both engagement scales. Convergent validity was assessed by correlating the engagement scales with involvement, enjoyment, and subjective usage; divergent validity was assessed by correlating the engagement scales with personality; and predictive validity was assessed by regression analyses using engagement to predict perceived behavior change at later time points. The Cronbach alpha values of the TWEETS were .86, .86, and .87 on T1, T2, and T3, respectively. Exploratory factor analyses indicated that a 1-factor structure best fits the data. The TWEETS is moderately to strongly correlated with involvement and enjoyment (theoretically related to cognitive and affective engagement, respectively; P<.001). Correlations between the TWEETS and frequency of use were nonsignificant or small, and differences between adherers and nonadherers on the TWEETS were significant (P<.001). Correlations between personality and the TWEETS were nonsignificant. The TWEETS at T1 was predictive of perceived behavior change at T3, with an explained variance of 16%. The psychometric properties of the TWEETS and the DBCI-ES-Ex seemed comparable in some aspects (eg, internal consistency), and in other aspects, the TWEETS seemed somewhat superior (divergent and predictive validity). The TWEETS performs quite well as an engagement measure with high internal consistency, reasonable test-retest reliability and convergent validity, good divergent validity, and reasonable predictive validity. As the psychometric quality of a scale is a reflection of how closely a scale matches the conceptualization of a concept, this paper is also an attempt to conceptualize and define engagement as a unique concept, providing a first step toward an acceptable standard of defining and measuring engagement.
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