Postpartale Depression – wer kümmert sich? Versorgungszugänge über Hebammen, Gynäkologie, Pädiatrie und Allgemeinmedizin

Pawils, Silke; Kochen, Eileen; Weinbrenner, Nora; Loew, Viola; Döring, Kornelia; Daehn, Daria; Martens, Claudia; Kaczmarek, Philip; Renneberg, Babette

doi:10.1007/s00103-022-03545-8

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…However, screening rates for PPD are low [9]. There is no established routine screening program in Germany [10], even though validated instruments recommended by treatment guidelines are available [11]. Moreover, screening alone is not enough to alleviate the symptoms of those affected; appropriate treatment is required.…”

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confidence: 99%

Drivers and Barriers of Acceptance of eHealth Interventions in Postpartum Mental Health Care: A Cross-Sectional Study

Jahre,

Frewer,

Meyer

et al. 2024

Preprint

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Background Postpartum mental health problems are common in women. Screening practice and treatment options are less common, which is a possible threat to health of mothers and children. eHealth interventions might bridge the gap but few validated programs are available. For developing relevant tools, an assessment of user behavior is a relevant step. Users acceptance of eHealth interventions can be examined via the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Methods A cross-sectional study was conducted between October 2022 and June 2023. Acceptance, sociodemographic, medical, psychometric, and eHealth data were assessed. This study included 453 postpartum women. Multiple hierarchical regression analysis and group comparisons (t-tests, ANOVA) were conducted. Results High acceptance of eHealth interventions in postpartum mental health care was reported by 68.2% (n = 309) of postpartum women. Acceptance was significantly higher in women affected by mental illness, t(395) = -4.72, padj < .001, d = .50, and with postpartum depression (present or past), t(395) = -4.54, padj < .001, d = .46. Significant predictors of acceptance were Perceived support during pregnancy (β = − .15, p = .009), Quality of life (β = − .13, p = .022), Postpartum depression (β = .40, p = .001), Digital confidence (β = .18, p = .002), and the UTAUT predictors Effort expectancy (β = .10, p = .037), Performance expectancy (β = .50, p < .001) and Social influence (β = .25, p < .001). The extended UTAUT model was able to explain 59.8% of variance in acceptance. Conclusions This study provides valuable insights into user behavior of postpartum women. High acceptance towards eHealth interventions in postpartum mental health care and identified drivers and barriers should be taken into account when implementing tailored eHealth interventions for this vulnerable target group. Specifically women with mental health issues report high acceptance and should therefore be addressed in a targeted manner.

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confidence: 99%