Toward the Value Sensitive Design of eHealth Technologies to Support Self-management of Cardiovascular Diseases: Content Analysis.

cardiovascular diseases content analysis eHealth self-care self-management value sensitive design values

Journal

JMIR cardio
ISSN: 2561-1011
Titre abrégé: JMIR Cardio
Pays: Canada
ID NLM: 101718325

Informations de publication

Date de publication:
01 Dec 2021
Historique:
received: 12 07 2021
accepted: 03 10 2021
revised: 16 08 2021
entrez: 2 12 2021
pubmed: 3 12 2021
medline: 3 12 2021
Statut: epublish

Résumé

eHealth can revolutionize the way self-management support is offered to chronically ill individuals such as those with a cardiovascular disease (CVD). However, patients' fluctuating motivation to actually perform self-management is an important factor for which to account. Tailoring and personalizing eHealth to fit with the values of individuals promises to be an effective motivational strategy. Nevertheless, how specific eHealth technologies and design features could potentially contribute to values of individuals with a CVD has not been explicitly studied before. This study sought to connect a set of empirically validated, health-related values of individuals with a CVD with existing eHealth technologies and their design features. The study searched for potential connections between design features and values with the goal to advance knowledge about how eHealth technologies can actually be more meaningful and motivating for end users. Undertaking a technical investigation that fits with the value sensitive design framework, a content analysis of existing eHealth technologies was conducted. We matched 11 empirically validated values of CVD patients with 70 design features from 10 eHealth technologies that were previously identified in a systematic review. The analysis consisted mainly of a deductive coding stage performed independently by 3 members of the study team. In addition, researchers and developers of 6 of the 10 reviewed technologies provided input about potential feature-value connections. In total, 98 connections were made between eHealth design features and patient values. This meant that some design features could contribute to multiple values. Importantly, some values were more often addressed than others. CVD patients' values most often addressed were related to (1) having or maintaining a healthy lifestyle, (2) having an overview of personal health data, (3) having reliable information and advice, (4) having extrinsic motivators to accomplish goals or health-related activities, and (5) receiving personalized care. In contrast, values less often addressed concerned (6) perceiving low thresholds to access health care, (7) receiving social support, (8) preserving a sense of autonomy over life, and (9) not feeling fear, anxiety, or insecurity about health. Last, 2 largely unaddressed values were related to (10) having confidence and self-efficacy in the treatment or ability to achieve goals and (11) desiring to be seen as a person rather than a patient. Positively, existing eHealth technologies could be connected with CVD patients' values, largely through design features that relate to educational support, self-monitoring support, behavior change support, feedback, and motivational incentives. Other design features such as reminders, prompts or cues, peer-based or expert-based human support, and general system personalization were also connected with values but in narrower ways. In future studies, the inferred feature-value connections must be validated with empirical data from individuals with a CVD or similar chronic conditions.

Sections du résumé

BACKGROUND BACKGROUND
eHealth can revolutionize the way self-management support is offered to chronically ill individuals such as those with a cardiovascular disease (CVD). However, patients' fluctuating motivation to actually perform self-management is an important factor for which to account. Tailoring and personalizing eHealth to fit with the values of individuals promises to be an effective motivational strategy. Nevertheless, how specific eHealth technologies and design features could potentially contribute to values of individuals with a CVD has not been explicitly studied before.
OBJECTIVE OBJECTIVE
This study sought to connect a set of empirically validated, health-related values of individuals with a CVD with existing eHealth technologies and their design features. The study searched for potential connections between design features and values with the goal to advance knowledge about how eHealth technologies can actually be more meaningful and motivating for end users.
METHODS METHODS
Undertaking a technical investigation that fits with the value sensitive design framework, a content analysis of existing eHealth technologies was conducted. We matched 11 empirically validated values of CVD patients with 70 design features from 10 eHealth technologies that were previously identified in a systematic review. The analysis consisted mainly of a deductive coding stage performed independently by 3 members of the study team. In addition, researchers and developers of 6 of the 10 reviewed technologies provided input about potential feature-value connections.
RESULTS RESULTS
In total, 98 connections were made between eHealth design features and patient values. This meant that some design features could contribute to multiple values. Importantly, some values were more often addressed than others. CVD patients' values most often addressed were related to (1) having or maintaining a healthy lifestyle, (2) having an overview of personal health data, (3) having reliable information and advice, (4) having extrinsic motivators to accomplish goals or health-related activities, and (5) receiving personalized care. In contrast, values less often addressed concerned (6) perceiving low thresholds to access health care, (7) receiving social support, (8) preserving a sense of autonomy over life, and (9) not feeling fear, anxiety, or insecurity about health. Last, 2 largely unaddressed values were related to (10) having confidence and self-efficacy in the treatment or ability to achieve goals and (11) desiring to be seen as a person rather than a patient.
CONCLUSIONS CONCLUSIONS
Positively, existing eHealth technologies could be connected with CVD patients' values, largely through design features that relate to educational support, self-monitoring support, behavior change support, feedback, and motivational incentives. Other design features such as reminders, prompts or cues, peer-based or expert-based human support, and general system personalization were also connected with values but in narrower ways. In future studies, the inferred feature-value connections must be validated with empirical data from individuals with a CVD or similar chronic conditions.

Identifiants

pubmed: 34855608
pii: v5i2e31985
doi: 10.2196/31985
pmc: PMC8686487
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e31985

Informations de copyright

©Roberto Rafael Cruz-Martínez, Jobke Wentzel, Britt Elise Bente, Robbert Sanderman, Julia EWC van Gemert-Pijnen. Originally published in JMIR Cardio (https://cardio.jmir.org), 01.12.2021.

Références

Int J Med Inform. 2019 Dec;132:103984
pubmed: 31605884
Games Health J. 2019 Apr;8(2):65-73
pubmed: 30199275
Transl Behav Med. 2019 Jan 1;9(1):76-98
pubmed: 29554380
JMIR Nurs. 2019 Sep 23;2(1):e14633
pubmed: 34345774
Comput Methods Programs Biomed. 2018 Aug;162:1-10
pubmed: 29903475
J Med Internet Res. 2011 Dec 05;13(4):e111
pubmed: 22155738
Implement Sci. 2017 Feb 23;12(1):25
pubmed: 28231840
JMIR Res Protoc. 2013 Sep 04;2(2):e32
pubmed: 24004517
Comput Inform Nurs. 2018 Feb;36(2):90-97
pubmed: 28901967
JMIR Rehabil Assist Technol. 2016 Jan 07;3(1):e1
pubmed: 28582250
Disabil Rehabil Assist Technol. 2014 Nov;9(6):529-38
pubmed: 24131369
West J Nurs Res. 2017 Dec;39(12):1606-1623
pubmed: 27881811
J Diabetes Sci Technol. 2017 Sep;11(5):1015-1027
pubmed: 28560898
J Am Coll Cardiol. 2017 Jul 4;70(1):1-25
pubmed: 28527533
BMJ Open. 2018 May 8;8(5):e020843
pubmed: 29739782
J Pers Med. 2015 Nov 17;5(4):389-405
pubmed: 26593951
BMC Fam Pract. 2017 Mar 20;18(1):40
pubmed: 28320330
Mind Cult Act. 2018;25(1):22-39
pubmed: 31105419
J Med Internet Res. 2013 Jan 08;15(1):e6
pubmed: 23305649
ANS Adv Nurs Sci. 2012 Jul-Sep;35(3):194-204
pubmed: 22739426
J Cardiovasc Nurs. 2020 Jan/Feb;35(1):74-85
pubmed: 31738217
J Clin Nurs. 2019 May;28(9-10):1782-1793
pubmed: 30667120
Appl Ergon. 2015 Mar;47:133-50
pubmed: 25479983
Digit Health. 2016 Oct 10;2:2055207616671461
pubmed: 29942568
J Clin Epidemiol. 2017 Mar;83:48-56
pubmed: 28126599
Contemp Clin Trials. 2017 Apr;55:34-38
pubmed: 28185994
PLoS One. 2018 May 3;13(5):e0196868
pubmed: 29723262
JMIR Hum Factors. 2019 May 02;6(2):e13009
pubmed: 31045504
Int J Med Inform. 2019 Oct;130:103941
pubmed: 31437618
JMIR Mhealth Uhealth. 2019 Jul 10;7(7):e13817
pubmed: 31293246
Nurs Res. 2013 Jan-Feb;62(1):2-9
pubmed: 23052421
J Med Internet Res. 2017 May 17;19(5):e172
pubmed: 28526671
AMIA Annu Symp Proc. 2018 Apr 16;2017:430-439
pubmed: 29854107
BMC Med Inform Decis Mak. 2017 Jan 9;17(1):5
pubmed: 28069041
DIS (Des Interact Syst Conf). 2017 Jun;2017:1165-1174
pubmed: 28890950
Patient Educ Couns. 2002 Oct -Nov;48(2):177-87
pubmed: 12401421
J Med Internet Res. 2017 Jun 29;19(6):e232
pubmed: 28663162
J Med Internet Res. 2020 Nov 11;22(11):e18025
pubmed: 33174847
BMC Med Inform Decis Mak. 2014 Jun 05;14:46
pubmed: 24903401
Acta Inform Med. 2020 Jun;28(2):130-137
pubmed: 32742066
J Gen Intern Med. 2017 Dec;32(12):1278-1284
pubmed: 28849368
BMC Med Inform Decis Mak. 2014 Nov 25;14:109
pubmed: 25421307
Patient Educ Couns. 2017 Apr;100(4):616-635
pubmed: 28029572
Implement Sci. 2015 Nov 13;10:159
pubmed: 26566623
BMC Cardiovasc Disord. 2017 Jun 14;17(1):156
pubmed: 28615004
JMIR Res Protoc. 2013 Jun 24;2(1):e21
pubmed: 23796508
JMIR Mhealth Uhealth. 2020 Jul 21;8(7):e17703
pubmed: 32706745
Eur J Cardiovasc Nurs. 2018 Oct;17(7):598-604
pubmed: 29533083
JMIR Res Protoc. 2019 Jul 16;8(7):e13334
pubmed: 31313659
J Biomed Inform. 2017 Dec;76:1-8
pubmed: 28974460
JMIR Mhealth Uhealth. 2017 May 23;5(5):e69
pubmed: 28536089
JMIR Cardio. 2018 Feb 09;2(1):e3
pubmed: 31758783
J Cardiovasc Nurs. 2014 Jan-Feb;29(1):29-37
pubmed: 23416934
J Med Internet Res. 2019 Sep 06;21(9):11759
pubmed: 31493323
Eur Heart J Qual Care Clin Outcomes. 2015 Nov 1;1(2):66-71
pubmed: 29474596
Appl Nurs Res. 2016 Nov;32:156-163
pubmed: 27969021
J Med Internet Res. 2020 May 21;22(5):e16157
pubmed: 32436852
Int J Med Inform. 2015 Oct;84(10):743-53
pubmed: 26037921
BMC Med Inform Decis Mak. 2017 Jun 26;17(1):89
pubmed: 28651588
Int J Hum Comput Interact. 2017;33(4):298-312
pubmed: 30429638
JMIR Cardio. 2021 Oct 22;5(2):e33252
pubmed: 34677130
J Med Internet Res. 2017 Aug 01;19(8):e277
pubmed: 28765103
JMIR Form Res. 2018 Apr 27;2(1):e8
pubmed: 30684426
Health Technol Assess. 2015 Nov;19(99):1-188
pubmed: 26616119
Int J Environ Res Public Health. 2019 Apr 06;16(7):
pubmed: 30959858
J Am Heart Assoc. 2017 Aug 31;6(9):
pubmed: 28860232
Eur J Prev Cardiol. 2016 May;23(8):801-17
pubmed: 26490093
J Biomed Inform. 2015 Aug;56:406-17
pubmed: 26071681
Patient Educ Couns. 2017 Feb;100(2):283-288
pubmed: 27599712
Disabil Rehabil Assist Technol. 2014 Nov;9(6):521-8
pubmed: 24131371
BMJ Open. 2016 Nov 7;6(11):e012684
pubmed: 27821598

Auteurs

Roberto Rafael Cruz-Martínez (RR)

Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Netherlands.

Jobke Wentzel (J)

Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Netherlands.
Department of Health and Social Studies, Windesheim University of Applied Sciences, Zwolle, Netherlands.

Britt Elise Bente (BE)

Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Netherlands.

Robbert Sanderman (R)

Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Netherlands.
General Health Psychology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.

Julia Ewc van Gemert-Pijnen (JE)

Department of Psychology, Health and Technology, Faculty of Behavioural, Management and Social Sciences, Technical Medical Centre, University of Twente, Enschede, Netherlands.

Classifications MeSH