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The impact of psychological user characteristics on adherence to an mHealth intervention among women in their postpartum period
Schoemann, Natalie; Hügel, Julia C.; Vogel, Lea; u. a. (2026): The impact of psychological user characteristics on adherence to an mHealth intervention among women in their postpartum period, in: Internet interventions : the application of information technology in mental and behavioural health, Amsterdam [u.a.]: Elsevier, Jg. 45, Nr. 100968, S. 1–18, doi: 10.1016/j.invent.2026.100968.
Faculty/Chair:
Title of the Journal:
Internet interventions : the application of information technology in mental and behavioural health
ISSN:
2214-7829
Publisher Information:
Year of publication:
2026
Volume:
45
Issue:
100968
Pages:
Language:
English
Abstract:
Until now, the impact of psychological factors on adherence to mHealth applications, especially among postpartum women, remains poorly understood, even though the efficacy depends on the regular use of the applications. The I-PREGNO application aims to promote maternal healthy weight management and improve mental health during the perinatal period by addressing core psychological skills like stress management and emotion regulation. This study examined whether depressive symptoms, parenting stress, emotion regulation difficulties, self-efficacy, and partnership status predicted adherence across four dimensions (length, breadth, depth, interaction) in a pooled sample of N = 154 postpartum women from two randomized controlled trials, using hierarchical regression analyses and latent profile analysis.
No psychological predictor survived correction for multiple testing, and the overall pattern of results does not support the conclusion that baseline psychological characteristics reliably determine adherence in this population. Before correction, isolated associations emerged: higher depressive symptoms with greater interaction (number of character input, IRR = 1.10, 95% CI [1.00, 1.21]), higher self-efficacy with lower depth (using the app at least three times per week, IRR = 0.64, 95% CI [0.40, 1.02]), and partnership status with longer usage (time spent in the app, B = 1.90, 95% CI [0.32, 3.47]) but these should be treated as preliminary signals, particularly given the limited statistical power. Latent profile analysis identified three adherence patterns: low (24.0%), moderate (60.4%), and high adherence (15.6%). Psychological factors did not significantly differentiate between adherence patterns. The findings do not provide robust evidence that baseline psychological characteristics predict adherence to mHealth interventions in postpartum women. Future research should consider more proximal determinants of adherence, including social, structural, and intervention-related factors.
No psychological predictor survived correction for multiple testing, and the overall pattern of results does not support the conclusion that baseline psychological characteristics reliably determine adherence in this population. Before correction, isolated associations emerged: higher depressive symptoms with greater interaction (number of character input, IRR = 1.10, 95% CI [1.00, 1.21]), higher self-efficacy with lower depth (using the app at least three times per week, IRR = 0.64, 95% CI [0.40, 1.02]), and partnership status with longer usage (time spent in the app, B = 1.90, 95% CI [0.32, 3.47]) but these should be treated as preliminary signals, particularly given the limited statistical power. Latent profile analysis identified three adherence patterns: low (24.0%), moderate (60.4%), and high adherence (15.6%). Psychological factors did not significantly differentiate between adherence patterns. The findings do not provide robust evidence that baseline psychological characteristics predict adherence to mHealth interventions in postpartum women. Future research should consider more proximal determinants of adherence, including social, structural, and intervention-related factors.
Keywords: ; ; ;
Adherence
mHealth
Postpartum
Mental health
Type:
Article
Activation date:
June 29, 2026
Project(s):
Versioning
Question on publication
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https://fis.uni-bamberg.de/handle/uniba/115801