Investigating When, Which, and Why Users Stop Using a Digital Health Intervention to Promote an Active Lifestyle: Secondary Analysis With A Focus on Health Action Process Approach-Based Psychological Determinants.
attrition
digital health
dropout
health action process approach
health behaviors
healthy life style
mobile health
physical activity
psychosocial determinants
sedentary behavior
Journal
JMIR mHealth and uHealth
ISSN: 2291-5222
Titre abrégé: JMIR Mhealth Uhealth
Pays: Canada
ID NLM: 101624439
Informations de publication
Date de publication:
31 01 2022
31 01 2022
Historique:
received:
20
05
2021
accepted:
20
12
2021
revised:
01
09
2021
entrez:
31
1
2022
pubmed:
1
2
2022
medline:
17
3
2022
Statut:
epublish
Résumé
Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored. The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition. The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used. The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%). This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance. ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271. RR2-10.1186/s13063-019-3456-7.
Sections du résumé
BACKGROUND
Digital health interventions have gained momentum to change health behaviors such as physical activity (PA) and sedentary behavior (SB). Although these interventions show promising results in terms of behavior change, they still suffer from high attrition rates, resulting in a lower potential and accessibility. To reduce attrition rates in the future, there is a need to investigate the reasons why individuals stop using the interventions. Certain demographic variables have already been related to attrition; however, the role of psychological determinants of behavior change as predictors of attrition has not yet been fully explored.
OBJECTIVE
The aim of this study was to examine when, which, and why users stopped using a digital health intervention. In particular, we aimed to investigate whether psychological determinants of behavior change were predictors for attrition.
METHODS
The sample consisted of 473 healthy adults who participated in the intervention MyPlan 2.0 to promote PA or reduce SB. The intervention was developed using the health action process approach (HAPA) model, which describes psychological determinants that guide individuals in changing their behavior. If participants stopped with the intervention, a questionnaire with 8 question concerning attrition was sent by email. To analyze when users stopped using the intervention, descriptive statistics were used per part of the intervention (including pre- and posttest measurements and the 5 website sessions). To analyze which users stopped using the intervention, demographic variables, behavioral status, and HAPA-based psychological determinants at pretest measurement were investigated as potential predictors of attrition using logistic regression models. To analyze why users stopped using the intervention, descriptive statistics of scores to the attrition-related questionnaire were used.
RESULTS
The study demonstrated that 47.9% (227/473) of participants stopped using the intervention, and drop out occurred mainly in the beginning of the intervention. The results seem to indicate that gender and participant scores on the psychological determinants action planning, coping planning, and self-monitoring were predictors of first session, third session, or whole intervention completion. The most endorsed reasons to stop using the intervention were the time-consuming nature of questionnaires (55%), not having time (50%), dissatisfaction with the content of the intervention (41%), technical problems (39%), already meeting the guidelines for PA/SB (31%), and, to a lesser extent, the experience of medical/emotional problems (16%).
CONCLUSIONS
This study provides some directions for future studies. To decrease attrition, it will be important to personalize interventions on different levels, questionnaires (either for research purposes or tailoring) should be kept to a minimum especially in the beginning of interventions by, for example, using objective monitoring devices, and technical aspects of digital health interventions should be thoroughly tested in advance.
TRIAL REGISTRATION
ClinicalTrials.gov NCT03274271; https://clinicaltrials.gov/ct2/show/NCT03274271.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.1186/s13063-019-3456-7.
Identifiants
pubmed: 35099400
pii: v10i1e30583
doi: 10.2196/30583
pmc: PMC8845016
doi:
Banques de données
ClinicalTrials.gov
['NCT03274271']
Types de publication
Clinical Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e30583Informations de copyright
©Helene Schroé, Geert Crombez, Ilse De Bourdeaudhuij, Delfien Van Dyck. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 31.01.2022.
Références
J Nutr Educ Behav. 2016 Mar;48(3):219-228.e1
pubmed: 26965100
J Med Internet Res. 2009 Apr 24;11(2):e13
pubmed: 19403466
PLoS One. 2017 Mar 31;12(3):e0173403
pubmed: 28362821
NPJ Digit Med. 2020 Feb 17;3:21
pubmed: 32128451
J Med Internet Res. 2020 Sep 29;22(9):e20283
pubmed: 32990635
Am J Prev Med. 2016 Nov;51(5):833-842
pubmed: 27745683
Behav Modif. 2002 Apr;26(2):223-73
pubmed: 11961914
J Med Internet Res. 2019 Aug 02;21(8):e13363
pubmed: 31376274
J Med Internet Res. 2018 Apr 18;20(4):e122
pubmed: 29669703
J Med Internet Res. 2013 May 22;15(5):e96
pubmed: 23697614
Res Sports Med. 2019 Jan-Mar;27(1):34-49
pubmed: 30047785
Bull World Health Organ. 2020 Apr 1;98(4):231-231A
pubmed: 32284641
PLoS One. 2017 Dec 21;12(12):e0190020
pubmed: 29267396
Can J Sport Sci. 1992 Dec;17(4):338-45
pubmed: 1330274
J Med Internet Res. 2015 Oct 07;17(10):e228
pubmed: 26446779
Int J Behav Nutr Phys Act. 2018 Mar 14;15(1):23
pubmed: 29540227
Int J Behav Nutr Phys Act. 2020 Oct 7;17(1):127
pubmed: 33028335
Patient Educ Couns. 2001 Aug;44(2):119-27
pubmed: 11479052
Health Educ Res. 2010 Aug;25(4):585-95
pubmed: 19897515
J Med Internet Res. 2020 Jul 30;22(7):e18338
pubmed: 32729835
Am J Lifestyle Med. 2014 Jan;8(1):42-68
pubmed: 25045343
J Med Internet Res. 2018 Oct 01;20(10):e10412
pubmed: 30274961
Int J Behav Nutr Phys Act. 2016 Dec 7;13(1):127
pubmed: 27927218
J Med Internet Res. 2015 May 11;17(5):e115
pubmed: 25963607
Br J Health Psychol. 2014 May;19(2):240-57
pubmed: 24628841
Rehabil Psychol. 2011 Aug;56(3):161-70
pubmed: 21767036
J Med Internet Res. 2012 Nov 14;14(6):e152
pubmed: 23151820
Int J Behav Nutr Phys Act. 2012 Apr 30;9:52
pubmed: 22546283
J Sport Exerc Psychol. 2015 Feb;37(1):97-107
pubmed: 25730895
J Med Internet Res. 2005 Mar 31;7(1):e11
pubmed: 15829473
Patient Prefer Adherence. 2008 Feb 02;2:57-65
pubmed: 19920945
Int J Environ Res Public Health. 2018 May 10;15(5):
pubmed: 29748460
Med Sci Sports Exerc. 2014 Jun;46(6):1248-60
pubmed: 24492633
Int J Environ Res Public Health. 2018 Jan 30;15(2):
pubmed: 29385770
Qual Life Res. 2008 Dec;17(10):1239-46
pubmed: 18850327
Trials. 2019 Jun 10;20(1):340
pubmed: 31182147
JMIR Mhealth Uhealth. 2018 Dec 13;6(12):e10972
pubmed: 30545810
PLoS Med. 2013;10(1):e1001362
pubmed: 23349621
J Med Internet Res. 2007 May 09;9(2):e11
pubmed: 17513282
J Health Psychol. 2006 Jan;11(1):37-50
pubmed: 16314379
Br J Health Psychol. 2018 May;23(2):296-310
pubmed: 29265563
J Med Internet Res. 2017 Jul 11;19(7):e241
pubmed: 28698168
Health Educ Behav. 2010 Aug;37(4):533-46
pubmed: 20547760
Ann Behav Med. 1999 Fall;21(4):339-49
pubmed: 10721442
Health Psychol. 2019 Jul;38(7):623-637
pubmed: 30973747
J Med Internet Res. 2020 May 29;22(5):e17572
pubmed: 32348255
Obes Rev. 2021 Oct;22(10):e13295
pubmed: 34159684
J Med Internet Res. 2015 Jul 15;17(7):e176
pubmed: 26180040
J Med Internet Res. 2012 Dec 17;14(6):e179
pubmed: 23246790
J Med Internet Res. 2021 Mar 12;23(3):e24905
pubmed: 33709943
J Med Internet Res. 2018 Mar 22;20(3):e110
pubmed: 29567635
J Med Internet Res. 2007 Jan 22;9(1):e1
pubmed: 17478410
JMIR Res Protoc. 2017 Oct 23;6(10):e205
pubmed: 29061557
J Med Internet Res. 2011 Apr 14;13(2):e32
pubmed: 21493191
J Med Internet Res. 2013 Aug 30;15(8):e162
pubmed: 23996958
J Med Internet Res. 2020 Dec 9;22(12):e21687
pubmed: 33295292
J Med Internet Res. 2013 Jul 16;15(7):e146
pubmed: 23859884