How self-tracking and engagement with personalized health content shape self-reported menstrual health experiences in a women’s health app
Journal
Research Square
Type
working paper
Date Issued
2025-07-08
Author(s)
Sou, Davinny
;
Fuchs, Magdalena
;
Meegahapola, Lakmal
;
Peintner, Andreas
;
Jutzeler, Catherine
;
;
Ivanova, Olena
;
Abstract
Menstrual health remains underrecognized and underserved in public health, despite its fundamental role in the overall well-being of nearly half the global population. Limited menstrual health literacy further contributes to suboptimal symptom management and awareness. While the growing adoption of menstrual health tracking apps offers promising opportunities to support symptom management and improve well-being, prior research has largely focused on cross-sectional analyses or self-tracking behaviors alone. However, empirical knowledge remains limited regarding how engagement with in-app health content – such as educational articles tailored to users’ self-tracked symptoms – relates to menstrual health experiences over time.
To address this gap, we conducted a longitudinal observational study employing linear mixed-effects modeling to examine associations between users’ health tracking consistency, their interactions with personalized health content recommended via an artificial intelligence (AI)-based system, and their self-reported menstrual health experiences. We analyzed data from 2,015 menstruating individuals (aged 18–49 years) collected over five months via a women’s health app. Individuals who tracked their health more consistently across distinct months reported, on average, lower levels of negative menstrual health experiences (e.g., low energy) (β = -0.06, pbonferroni = 0.003, 95% CI [-0.08, -0.03]). However, individuals who engaged more frequently with personalized health content reported, on average, higher levels of positive menstrual health experiences (e.g., high energy) (β = 0.15, pbonferroni < 0.001, 95% CI [0.13, 0.18]). While our work offers important real-world insights, future work should leverage experimental designs or randomized controlled trials to establish which intervention components (e.g., personalized educational articles, consistent self-tracking) effectively support menstrual health and well-being at scale.
To address this gap, we conducted a longitudinal observational study employing linear mixed-effects modeling to examine associations between users’ health tracking consistency, their interactions with personalized health content recommended via an artificial intelligence (AI)-based system, and their self-reported menstrual health experiences. We analyzed data from 2,015 menstruating individuals (aged 18–49 years) collected over five months via a women’s health app. Individuals who tracked their health more consistently across distinct months reported, on average, lower levels of negative menstrual health experiences (e.g., low energy) (β = -0.06, pbonferroni = 0.003, 95% CI [-0.08, -0.03]). However, individuals who engaged more frequently with personalized health content reported, on average, higher levels of positive menstrual health experiences (e.g., high energy) (β = 0.15, pbonferroni < 0.001, 95% CI [0.13, 0.18]). While our work offers important real-world insights, future work should leverage experimental designs or randomized controlled trials to establish which intervention components (e.g., personalized educational articles, consistent self-tracking) effectively support menstrual health and well-being at scale.
Start page
1
End page
28