Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies...

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Autores principales:	Noemi Festic, Michael Latzer, Svetlana Smirnova
Formato:	article
Lenguaje:	EN
Publicado:	Cogitatio 2021
Materias:	algorithmic selection coping strategies mhealth risk awareness self-tracking apps self-quantification societal risks user perception wearables Communication. Mass media P87-96
Acceso en línea:	https://doaj.org/article/4399468241b544aca16c825f6b54d570
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spelling	oai:doaj.org-article:4399468241b544aca16c825f6b54d5702021-11-18T11:14:12ZAlgorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies2183-243910.17645/mac.v9i4.4162https://doaj.org/article/4399468241b544aca16c825f6b54d5702021-11-01T00:00:00Zhttps://www.cogitatiopress.com/mediaandcommunication/article/view/4162https://doaj.org/toc/2183-2439Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies now make use of self-tracking data. Associated social risks such as privacy violations or measurement inaccuracies have been theoretically derived, although empirical evidence remains sparse. This article conceptualizes self-tracking as algorithmic-selection applications and empirically examines users’ risk awareness related to self-tracking applications as well as coping strategies as an option to deal with these risks. It draws on representative survey data collected in Switzerland. The results reveal that Swiss self-trackers’ awareness of risks related to the applications they use is generally low and only a small number of those who self-track apply coping strategies. We further find only a weak association between risk awareness and the application of coping strategies. This points to a cost-benefit calculation when deciding how to respond to perceived risks, a behavior explained as a privacy calculus in extant literature. The widespread willingness to pass on personal data to insurance companies despite associated risks provides further evidence for this interpretation. The conclusions—made even more pertinent by the potential of wearables’ track-and-trace systems and state-level health provision—raise questions about technical safeguarding, data and health literacies, and governance mechanisms that might be necessary considering the further popularization of self-tracking for health.Noemi FesticMichael LatzerSvetlana SmirnovaCogitatioarticlealgorithmic selectioncoping strategiesmhealthrisk awarenessself-tracking appsself-quantificationsocietal risksuser perceptionwearablesCommunication. Mass mediaP87-96ENMedia and Communication, Vol 9, Iss 4, Pp 145-157 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	algorithmic selection coping strategies mhealth risk awareness self-tracking apps self-quantification societal risks user perception wearables Communication. Mass media P87-96
spellingShingle	algorithmic selection coping strategies mhealth risk awareness self-tracking apps self-quantification societal risks user perception wearables Communication. Mass media P87-96 Noemi Festic Michael Latzer Svetlana Smirnova Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
description	Self-tracking with wearable devices and mobile applications is a popular practice that relies on automated data collection and algorithm-driven analytics. Initially designed as a tool for personal use, a variety of public and corporate actors such as commercial organizations and insurance companies now make use of self-tracking data. Associated social risks such as privacy violations or measurement inaccuracies have been theoretically derived, although empirical evidence remains sparse. This article conceptualizes self-tracking as algorithmic-selection applications and empirically examines users’ risk awareness related to self-tracking applications as well as coping strategies as an option to deal with these risks. It draws on representative survey data collected in Switzerland. The results reveal that Swiss self-trackers’ awareness of risks related to the applications they use is generally low and only a small number of those who self-track apply coping strategies. We further find only a weak association between risk awareness and the application of coping strategies. This points to a cost-benefit calculation when deciding how to respond to perceived risks, a behavior explained as a privacy calculus in extant literature. The widespread willingness to pass on personal data to insurance companies despite associated risks provides further evidence for this interpretation. The conclusions—made even more pertinent by the potential of wearables’ track-and-trace systems and state-level health provision—raise questions about technical safeguarding, data and health literacies, and governance mechanisms that might be necessary considering the further popularization of self-tracking for health.
format	article
author	Noemi Festic Michael Latzer Svetlana Smirnova
author_facet	Noemi Festic Michael Latzer Svetlana Smirnova
author_sort	Noemi Festic
title	Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
title_short	Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
title_full	Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
title_fullStr	Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
title_full_unstemmed	Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies
title_sort	algorithmic self-tracking for health: user perspectives on risk awareness and coping strategies
publisher	Cogitatio
publishDate	2021
url	https://doaj.org/article/4399468241b544aca16c825f6b54d570
work_keys_str_mv	AT noemifestic algorithmicselftrackingforhealthuserperspectivesonriskawarenessandcopingstrategies AT michaellatzer algorithmicselftrackingforhealthuserperspectivesonriskawarenessandcopingstrategies AT svetlanasmirnova algorithmicselftrackingforhealthuserperspectivesonriskawarenessandcopingstrategies
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Algorithmic Self-Tracking for Health: User Perspectives on Risk Awareness and Coping Strategies

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