Application of an Adapted Health Action Process Approach Model to Predict Engagement With a Digital Mental Health Website: Cross-Sectional Study

Overview

Journal JMIR Hum Factors

Specialty Health Services

Date 2024 Aug 7

PMID 39110965

Authors

Julien Rouvere

Brittany E Blanchard

Morgan Johnson

Isabell Griffith Fillipo

Brittany Mosser

Meghan Romanelli

Theresa Nguyen

Kevin Rushton

John Marion

Tim Althoff

Patricia A Arean

Michael D Pullmann

Affiliations

Soon will be listed here.

Abstract

Background: Digital Mental Health (DMH) tools are an effective, readily accessible, and affordable form of mental health support. However, sustained engagement with DMH is suboptimal, with limited research on DMH engagement. The Health Action Process Approach (HAPA) is an empirically supported theory of health behavior adoption and maintenance. Whether this model also explains DMH tool engagement remains unknown.

Objective: This study examined whether an adapted HAPA model predicted engagement with DMH via a self-guided website.

Methods: Visitors to the Mental Health America (MHA) website were invited to complete a brief survey measuring HAPA constructs. This cross-sectional study tested the adapted HAPA model with data collected using voluntary response sampling from 16,078 sessions (15,619 unique IP addresses from United States residents) on the MHA website from October 2021 through February 2022. Model fit was examined via structural equation modeling in predicting two engagement outcomes: (1) choice to engage with DMH (ie, spending 3 or more seconds on an MHA page, excluding screening pages) and (2) level of engagement (ie, time spent on MHA pages and number of pages visited, both excluding screening pages).

Results: Participants chose to engage with the MHA website in 94.3% (15,161/16,078) of the sessions. Perceived need (β=.66; P<.001), outcome expectancies (β=.49; P<.001), self-efficacy (β=.44; P<.001), and perceived risk (β=.17-.18; P<.001) significantly predicted intention, and intention (β=.77; P<.001) significantly predicted planning. Planning was not significantly associated with choice to engage (β=.03; P=.18). Within participants who chose to engage, the association between planning with level of engagement was statistically significant (β=.12; P<.001). Model fit indices for both engagement outcomes were poor, with the adapted HAPA model accounting for only 0.1% and 1.4% of the variance in choice to engage and level of engagement, respectively.

Conclusions: Our data suggest that the HAPA model did not predict engagement with DMH via a self-guided website. More research is needed to identify appropriate theoretical frameworks and practical strategies (eg, digital design) to optimize DMH tool engagement.

References

Kasten S, van Osch L, Candel M, de Vries H . The influence of pre-motivational factors on behavior via motivational factors: a test of the I-Change model. BMC Psychol. 2019; 7(1):7. PMC: 6381617. DOI: 10.1186/s40359-019-0283-2. View

Parschau L, Barz M, Richert J, Knoll N, Lippke S, Schwarzer R . Physical activity among adults with obesity: testing the Health Action Process Approach. Rehabil Psychol. 2014; 59(1):42-9. DOI: 10.1037/a0035290. View

Yeager C, Benight C . If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery. Mhealth. 2018; 4:37. PMC: 6182033. DOI: 10.21037/mhealth.2018.08.04. View

Yom-Tov E, Lekkas D, Heinz M, Nguyen T, Barr P, Jacobson N . Digitally filling the access gap in mental health care: An investigation of the association between rurality and online engagement with validated self-report screens across the United States. J Psychiatr Res. 2022; 157:112-118. PMC: 9898139. DOI: 10.1016/j.jpsychires.2022.11.024. View

Merle J, Cook C, Pullmann M, Larson M, Hamlin C, Hugh M . Longitudinal Effects of a Motivationally Focused Strategy to Increase the Yield of Training and Consultation on Teachers' Adoption and Fidelity of a Universal Program. School Ment Health. 2022; 15(1):105-122. PMC: 9343567. DOI: 10.1007/s12310-022-09536-z. View

Lippke S, Schwarzer R, Ziegelmann J, Scholz U, Schuz B . Testing stage-specific effects of a stage-matched intervention: a randomized controlled trial targeting physical exercise and its predictors. Health Educ Behav. 2010; 37(4):533-46. DOI: 10.1177/1090198109359386. View

Flora D, Curran P . An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychol Methods. 2004; 9(4):466-91. PMC: 3153362. DOI: 10.1037/1082-989X.9.4.466. View

Kroenke K, Spitzer R, Williams J . The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001; 16(9):606-13. PMC: 1495268. DOI: 10.1046/j.1525-1497.2001.016009606.x. View

Labudek S, Fleig L, Jansen C, Kramer-Gmeiner F, Nerz C, Becker C . Applying Social Cognition Models to Explain Walking Duration in Older Adults: The Role of Intrinsic Motivation. J Aging Phys Act. 2021; 29(5):744-752. DOI: 10.1123/japa.2020-0296. View

10.

Tang C, Raat H, Yan M, Zhang Q, Li K, Jiang M . Application of the health action process approach model for reducing excessive internet use behaviors among rural adolescents in China: a school-based intervention pilot study. BMC Public Health. 2021; 21(1):986. PMC: 8152115. DOI: 10.1186/s12889-021-10999-z. View

11.

Ranjbaran S, Shojaeizadeh D, Dehdari T, Yaseri M, Shakibazadeh E . Determinants of medication adherence among Iranian patients with type 2 diabetes: An application of health action process approach. Heliyon. 2020; 6(7):e04442. PMC: 7364035. DOI: 10.1016/j.heliyon.2020.e04442. View

12.

Spitzer R, Kroenke K, Williams J, Lowe B . A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006; 166(10):1092-7. DOI: 10.1001/archinte.166.10.1092. View

13.

Garett R, Chiu J, Zhang L, Young S . A Literature Review: Website Design and User Engagement. Online J Commun Media Technol. 2016; 6(3):1-14. PMC: 4974011. View

14.

Chiu C, Lynch R, Chan F, Berven N . The Health Action Process Approach as a motivational model for physical activity self-management for people with multiple sclerosis: a path analysis. Rehabil Psychol. 2011; 56(3):171-81. DOI: 10.1037/a0024583. View

15.

Baumel A, Muench F, Edan S, Kane J . Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis. J Med Internet Res. 2019; 21(9):e14567. PMC: 6785720. DOI: 10.2196/14567. View

16.

Zech J, Johnson M, Pullmann M, Hull T, Althoff T, Munson S . An Integrative Engagement Model of Digital Psychotherapy: Exploratory Focus Group Findings. JMIR Form Res. 2023; 7:e41428. PMC: 10173033. DOI: 10.2196/41428. View

17.

Schwarzer R, Schuz B, Ziegelmann J, Lippke S, Luszczynska A, Scholz U . Adoption and maintenance of four health behaviors: theory-guided longitudinal studies on dental flossing, seat belt use, dietary behavior, and physical activity. Ann Behav Med. 2007; 33(2):156-66. DOI: 10.1007/BF02879897. View

18.

Ng M, Firth J, Minen M, Torous J . User Engagement in Mental Health Apps: A Review of Measurement, Reporting, and Validity. Psychiatr Serv. 2019; 70(7):538-544. PMC: 6839109. DOI: 10.1176/appi.ps.201800519. View

19.

Nussbeck F, Eid M, Lischetzke T . Analysing multitrait-multimethod data with structural equation models for ordinal variables applying the WLSMV estimator: what sample size is needed for valid results?. Br J Math Stat Psychol. 2006; 59(Pt 1):195-213. DOI: 10.1348/000711005X67490. View

20.

Duan Y, Wienert J, Hu C, Si G, Lippke S . Web-Based Intervention for Physical Activity and Fruit and Vegetable Intake Among Chinese University Students: A Randomized Controlled Trial. J Med Internet Res. 2017; 19(4):e106. PMC: 5404143. DOI: 10.2196/jmir.7152. View