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  A look under the hood: analyzing engagement and usage data of a smartphone-based intervention

Siezenga, A., Mertens, E. C. A., & van Gelder, J.-L. (2023). A look under the hood: analyzing engagement and usage data of a smartphone-based intervention. BMC Digital Health, (1):. doi:10.1186/s44247-023-00048-7.

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アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-000D-F7AC-B 版のパーマリンク: https://hdl.handle.net/21.11116/0000-000D-F7AD-A
資料種別: 学術論文

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s44247-023-00048-7.pdf (全文テキスト(全般)), 2MB
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s44247-023-00048-7.pdf
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 作成者:
Siezenga, Aniek1, 著者           
Mertens, Esther C. A., 著者
van Gelder, Jean-Louis1, 著者           
所属:
1Criminology, Max Planck Institute for the Study of Crime, Security and Law, Max Planck Society, ou_2489695              

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 要旨: Background: Engagement with smartphone-based interventions stimulates adherence and improves the likelihood of gaining benefits from intervention content. Research often relies on system usage data to capture engagement. However, to what extent usage data reflect engagement is still an open empirical question. We studied how usage data relate to engagement, and how both relate to intervention outcomes.

Methods: We drew data from a randomized controlled trial (RCT) (N = 86) evaluating a smartphone-based intervention that aims to stimulate future self-identification (i.e., future self vividness, valence, relatedness). General app engagement and feature-specific engagement were retrospectively measured. Usage data (i.e., duration, number of logins, number of days used, exposure to intervention content) were unobtrusively registered.

Results: Engagement and usage data were not correlated. Multiple linear regression analyses revealed that general app engagement predicted future self vividness (p = .042) and relatedness (p = .004). Furthermore, engagement with several specific features also predicted aspects of future self-identification (p = .005 – .032). For usage data, the number of logins predicted future self vividness (p = .042) and exposure to intervention content predicted future self valence (p = .002).

Conclusions: Usage data did not reflect engagement and the latter was the better predictor of intervention outcomes. Thus, the relation between usage data and engagement is likely to be intervention-specific and the unqualified use of the former as an indicator of the latter may result in measurement error. We provide recommendations on how to capture engagement and app use in more valid ways.

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言語: eng - English
 日付: 2023
 出版の状態: 出版
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 識別子(DOI, ISBNなど): DOI: 10.1186/s44247-023-00048-7
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出版物 1

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出版物名: BMC Digital Health
種別: 学術雑誌
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