Data Privacy Concerns Using mHealth Apps and Smart Speakers: Comparative Interview Study Among Mature Adults.

data privacy concerns mHealth app mature adults privacy paradox smart speaker smartphone

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
13 Jun 2022
Historique:
received: 17 02 2021
accepted: 16 04 2022
revised: 30 06 2021
entrez: 14 6 2022
pubmed: 15 6 2022
medline: 15 6 2022
Statut: epublish

Résumé

New technologies such as mobile health (mHealth) apps and smart speakers make intensive use of sensitive personal data. Users are typically aware of this and express concerns about their data privacy. However, many people use these technologies although they think their data are not well protected. This raises specific concerns for sensitive health data. This study aimed to contribute to a better understanding of data privacy concerns of mature adults using new technologies and provide insights into their data privacy expectations and associated risks and the corresponding actions of users in 2 different data contexts: mHealth apps and smart speakers. This exploratory research adopted a qualitative approach, engaging with 20 mature adults (aged >45 years). In a 6-month test period, 10 (50%) participants used a smart speaker and 10 (50%) participants used an mHealth app. In interviews conducted before and after the test period, we assessed the influence of data privacy concerns on technology acceptance, use behavior, and continued use intention. Our results show that although participants are generally aware of the need to protect their data privacy, they accept the risk of misuse of their private data when using the technology. Surprisingly, the most frequently stated risk was not the misuse of personal health data but the fear of receiving more personalized advertisements. Similarly, surprisingly, our results indicate that participants value recorded verbal data higher than personal health data. Older adults are initially concerned about risks to their data privacy associated with using data-intensive technologies, but those concerns diminish fairly quickly, culminating in resignation. We find that participants do not differentiate between risky behaviors, depending on the type of private data used by different technologies.

Sections du résumé

BACKGROUND BACKGROUND
New technologies such as mobile health (mHealth) apps and smart speakers make intensive use of sensitive personal data. Users are typically aware of this and express concerns about their data privacy. However, many people use these technologies although they think their data are not well protected. This raises specific concerns for sensitive health data.
OBJECTIVE OBJECTIVE
This study aimed to contribute to a better understanding of data privacy concerns of mature adults using new technologies and provide insights into their data privacy expectations and associated risks and the corresponding actions of users in 2 different data contexts: mHealth apps and smart speakers.
METHODS METHODS
This exploratory research adopted a qualitative approach, engaging with 20 mature adults (aged >45 years). In a 6-month test period, 10 (50%) participants used a smart speaker and 10 (50%) participants used an mHealth app. In interviews conducted before and after the test period, we assessed the influence of data privacy concerns on technology acceptance, use behavior, and continued use intention.
RESULTS RESULTS
Our results show that although participants are generally aware of the need to protect their data privacy, they accept the risk of misuse of their private data when using the technology. Surprisingly, the most frequently stated risk was not the misuse of personal health data but the fear of receiving more personalized advertisements. Similarly, surprisingly, our results indicate that participants value recorded verbal data higher than personal health data.
CONCLUSIONS CONCLUSIONS
Older adults are initially concerned about risks to their data privacy associated with using data-intensive technologies, but those concerns diminish fairly quickly, culminating in resignation. We find that participants do not differentiate between risky behaviors, depending on the type of private data used by different technologies.

Identifiants

pubmed: 35699993
pii: v6i6e28025
doi: 10.2196/28025
pmc: PMC9237761
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e28025

Informations de copyright

©Tanja Schroeder, Maximilian Haug, Heiko Gewald. Originally published in JMIR Formative Research (https://formative.jmir.org), 13.06.2022.

Références

JMIR Mhealth Uhealth. 2021 Jan 13;9(1):e20427
pubmed: 33439130
JMIR Mhealth Uhealth. 2015 Nov 04;3(4):e101
pubmed: 26537656
J Med Internet Res. 2010 Dec 19;12(5):e64
pubmed: 21169174
JMIR Public Health Surveill. 2015 Nov 16;1(2):e18
pubmed: 27227136
JMIR Mhealth Uhealth. 2015 Jan 19;3(1):e8
pubmed: 25599627
PM R. 2017 May;9(5S):S106-S115
pubmed: 28527495
JMIR Mhealth Uhealth. 2014 Oct 29;2(4):e47
pubmed: 25355249
Psychol Health. 2010 Sep;25(7):873-87
pubmed: 20204963
Healthcare (Basel). 2020 May 13;8(2):
pubmed: 32414183
JMIR Mhealth Uhealth. 2018 Jan 23;6(1):e26
pubmed: 29362211
Computer (Long Beach Calif). 2016 Jun;49(6):22-30
pubmed: 28344359
J Health Commun. 2015;20(6):673-9
pubmed: 25868685
J Med Internet Res. 2013 Apr 25;15(4):e66
pubmed: 23624056
Stud Health Technol Inform. 2019;257:218-222
pubmed: 30741199
JMIR Mhealth Uhealth. 2018 Mar 21;6(3):e53
pubmed: 29563080
JMIR Mhealth Uhealth. 2017 Oct 18;5(10):e147
pubmed: 29046271
JMIR Mhealth Uhealth. 2018 Dec 14;6(12):e201
pubmed: 30552085
JMIR Mhealth Uhealth. 2019 Apr 16;7(4):e11223
pubmed: 30990458
Online J Public Health Inform. 2014 Feb 05;5(3):229
pubmed: 24683442
Health Informatics J. 2016 Jun;22(2):265-75
pubmed: 25385165
Proc ACM Int Conf Ubiquitous Comput. 2016 Sep;2016:706-717
pubmed: 28058408
JMIR Ment Health. 2018 Aug 29;5(3):e56
pubmed: 30158102

Auteurs

Tanja Schroeder (T)

Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Center for Research on Service Sciences, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.

Maximilian Haug (M)

Center for Research on Service Sciences, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.

Heiko Gewald (H)

Center for Research on Service Sciences, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.

Classifications MeSH