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

Drivers and Barriers of Acceptance of eHealth Interventions in Postpartum Mental Health Care: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Drivers and Barriers of Acceptance of eHealth Interventions in Postpartum Mental Health Care: A Cross-Sectional Study Lisa Maria Jahre, Anna-Lena Frewer, Heidi Meyer, Katja Koelkebeck, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4143017/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted 3 You are reading this latest preprint version Abstract 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, p adj < .001, d = .50, and with postpartum depression (present or past), t (395) = -4.54, p adj < .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. eHealth maternal mental health postpartum health UTAUT Women mental health Background Childbirth not only fundamentally effects the lives of the new parents, it also has a significant impact on the mental health of the childbearing mother. Psychological stress during pregnancy is thus common [ 1 ] and is, at the same time, a significant risk factor for postpartum mental illness [ 2 ]. The most common postpartum mental disorders are postpartum depression (PPD) and anxiety [ 3 ]. Whereas postpartum psychosis and post-traumatic stress disorder are less frequent, the severity of these disorders is highly relevant. In high-income countries, the point prevalence is 13% for PPD and around 10% for postpartum anxiety [ 3 ]. In the long term, burden of severe mental illness after childbirth, combined with other risk factors, may even lead to maternal suicide, which is described as the “leading cause of maternal death” [ 4 ]. PPD is also a crucial risk factor for negative outcomes in mothers as well as their children [ 5 , 6 ]. Maternal mental health, quality of life and relationships are negatively impacted by PPD. Further, the infants’ development and mother-child interactions are complicated or impaired by PPD. It is evident that prevention and treatment of postpartum mental illness takes an important role in women's care. Accordingly, the World Health Organization has developed recommendations for maternal and newborn care, which emphasizes the necessity of supportive, evidence-based mental health care after childbirth [ 7 ]. Early detection of PPD is essential for the implementation of appropriate treatment options [ 8 ]. 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. PPD can be treated effectively with psychotherapy [ 8 , 12 ]. In Germany, however, psychotherapeutic care for people with PPD is not ensured in practice [ 13 ]. In addition to a lack of available treatment options, another access barrier is stigma around mental health [ 14 ]. This existing gap in maternal mental health care must be adressed with high priority in order to ensure the well-being of mothers and their children. eHealth (electronic health) may provide an evidence-based addition to established mental health treatments. eHealth is a novel way to provide medical and psychological support services [ 15 ], and can be employed in different ways. For example, Internet-based cognitive behavioural therapy for depression has been proven to be as effective as face-to-face delivery [ 16 ]. Moreover, telehealth interventions are effective in decreasing symptoms of PPD, including anxiety and functional impairment [ 17 – 19 ]. Further, screening for PPD with eHealth tools seems to be a promising method, bypassing fears regarding openness about mental illness during the postpartum period [ 20 ]. On the other hand, there is a high number of available mHealth (mobile health) tools addressing postpartum mental health which are only of moderate quality and have not been clinically tested [ 21 ]. Users’ acceptance of such innovative eHealth services need to be investigated in an evidence-based manner. The successful implementation and patients’ adaption of eHealth services relies on the acceptance by its potential user base. According to the Unified Theory of Acceptance and Use of Technology (UTAUT), acceptance can be operationalized as behavioral intention to use such eHealth interventions [ 22 , 23 ]. Acceptance is determined by three core factors: social influence (SI), performance expectancy (PE) and effort expectancy (EE). SI is conceptualized as the extent to which family or friends would approve of the use of a technology. PE stands for the expectation that the technology will lead to a beneficial effect for the user. EE describes the amount of effort a person expects to expend when using the technology [ 22 , 23 ]. Acceptance of eHealth interventions has been investigated among different patient groups in a number of studies [ 24 – 27 ]. To this date, acceptance among women during the postpartum period has not been assessed. Further, studies suggest that, besides the three core predictors SI, PE and EE, additional factors should be integrated into the UTAUT model to obtain a full overview of relevant influences [ 22 ]. Therefore, it is important to examine the relevant drivers and barriers of eHealth use in postpartum mental health care. Only evidence-based evaluation of relevant factors influencing the use of eHealth interventions can ensure that effective health care services are actually adopted by their target group. Objectives The aim of this study was to determine the drivers and barriers of acceptance of eHealth interventions in postpartum mental health care. Differences in acceptance based on sociodemographic, obstetric, psychometric and medical data were investigated with a cross-sectional study in a group of postpartum women. Methods Study design and study population A cross-sectional, online-based questionnaire study was conducted to examine acceptance of eHealth interventions in postpartum mental health care among women who had experienced pregnancy. Participants were recruited via flyers from the Clinic for Gynecology and Obstetrics of the University Hospital Essen, from gynecological outpatient practices in Essen, self-help groups and counselors specializing in postpartum mental illness in Germany, and from posts on social media channels centered on pregnancy and postpartum mental illness (Facebook, Instagram). Study information was presented in form of posters and flyers. Participation was anonymous and voluntary. Participants did not receive compensation. Inclusion criteria were legal age, female gender, history of pregnancy, sufficient command of the German language and Internet access. Data was collected between October 2022 and June 2023 via the platform Unipark [ 28 ]. Digital informed consent was given before the start of the survey. Average completion time was M = 16.63 ( SD = 9.16) minutes. Initially, N = 558 participants started the questionnaire. N = 105 (18.82%) participants were excluded because they did not fulfill inclusion criteria or were missing data on the primary outcome (acceptance). Therefore, N = 453 participants (81.18%) were included in the final data analysis. The conductance of the study was approved by the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen (19-89-47-BO). Assessment instruments The survey encompassed sociodemographic, medical, psychometric, and eHealth data. Validated assessment instruments and self-generated items were used to acquire responses. Sociodemographic data included age, gender, marital status, educational level, occupational status and place of residence (population size). Medical data included presence of a somatic or mental illness and specifically the diagnosis of PPD. Regarding obstetric data, participants were asked about number of pregnancies, number of children, age of children, time since childbirth, whether the pregnancy was planned and if their pregnancy was considered high-risk. Perceived support during the last pregnancy was rated on a scale from 0 to 10 (0 = “I did not feel supported at all”, 10 = “I felt extremely supported.”). Current quality of life was also indicated on a scale from 0 to 10 (0 = “very low quality of life”, 10 = “very high quality of life”). Further, eHealth literacy was assessed using the revised German version of the eHealth Literacy Scale (GR-eHEALS; [ 29 ]). Responses were given on a five-point Likert scale (1 = “strongly disagree”, 5 = “strongly agree”). Internal consistency was excellent (Cronbach’s α = .92). Digital confidence [ 24 , 27 , 30 ] was assessed via three items (e.g. “How confident are you in using digital media?”) and responses were given on a five-point Likert scale (1 = “not very confident”, 5 = “very confident”). Internal consistency was high (Cronbach’s α = .89). Internet anxiety [ 26 , 30 , 31 ] and digital overload [ 24 , 26 , 32 ] were also assessed via three items each and responses were given on a five-point Likert scale (e.g. “I have concerns about using the Internet.”, “I feel burdened by the constant accessibility via cell phone or e-mail.”, 1 = “strongly disagree”, 5 = “strongly agree”). Internal consistency was good (Cronbach’s α = .75 for Internet anxiety and α = .82 for digital overload). In order to assess acceptance towards eHealth interventions in postpartum mental health care, the validated UTAUT model [ 22 , 23 ] was applied. Acceptance, which is operationalized as behavioral intention (BI), was determined by four items (e.g. “I would use such an eHealth intervention if it were offered to me.”). Internal consistency was high (Cronbach's α = .86). Of the three core predictors of the UTAUT model, social influence (SI) was assessed via three items (e.g. “People close to me would approve of the use of such an eHealth intervention.”). Internal consistency was high (Cronbach's α = .80). Performance expectancy (PE) consisted of four items (e.g. “Such an eHealth intervention could improve my overall well-being.”). Internal consistency was excellent (Cronbach's α = .91). Three items were used to assess effort expectancy (EE; e.g. “Using such an eHealth intervention would not be an additional burden for me.”). Internal consistency was good (Cronbach's α = .79). Responses were given on a five-point Likert scale (1 = “strongly disagree”, 5 = “strongly agree”). Statistical analysis Statistical analysis was performed using R (4.3.8). Sum scores (GR-eHEALS, PHQ-8) and mean scores (digital confidence, Internet anxiety, digital overload) were calculated. For acceptance (= BI) and its three predictors (EE, PE, SI) mean scores were calculated as well. Acceptance was further divided into categories, in accordance with prior research [ 24 , 25 , 30 ]: scores from 1 to 2.34 indicate low acceptance, scores from 2.35 to 3.67 indicate moderate acceptance and scores from 3.68 to 5 indicate high acceptance. Descriptive statistics were applied for sociodemographic, medical, obstetric, psychometric and eHealth data. Differences in acceptance based on educational level, somatic or mental illness, diagnosis of PPD, and risk during pregnancy were examined with independent t -tests and ANOVAs. P -values were adjusted for multiple comparisons via Bonferroni correction. Levene’s test indicated homoscedasticity. Due to the given sample size, normal distribution of residuals was assumed. Multiple hierarchical regression analysis was conducted to examine drivers and barriers of acceptance of eHealth interventions in postpartum mental health care. Predictors were included blockwise: 1) sociodemographic and obstetric data, 2) medical and psychometric data, 3) eHealth data, 4) UTAUT predictors. The variance inflation factor (VIF) was used to verify the absence of multicollinearity (all VIF values < 2.0). Visual inspection of qq-plots of the residuals showed no signs of violations against normality. Therefore, normal distribution of the residuals was assumed. Scatter plots of the standardized residuals and the adjusted predicted values verified homoscedasticity. The level of significance was set to α < .05 for all tests. Effect sizes were reported according to Cohen (1988 [ 33 ]), with values around 0.2, 0.5, and 0.8 indicating small, medium, and large effects, respectively. Results Study population Among n = 453 female participants who experienced pregnancy in the past the mean age was M = 35.97 ( SD = 6.55) years. The youngest participant was 18 years old and the oldest was 63 years old. The average number of pregnancies was M = 1.86 ( SD = 1.18), while the number of children was M = 1.53 ( SD = 0.76). The age of youngest child of the participants was M = 2.80 ( SD = 5.00) years, and the oldest M = 4.51 ( SD = 6.04) years. An average of M = 43.77 ( SD = 63.70) months had passed since the last childbirth. A high-risk pregnancy in the past was reported of 31.8% ( n = 144) of the participants. Among the participants, 79.5% ( n = 360) stated that their last pregnancy was planned. On a scale from 0 to 10, participants reported an average perceived support during pregnancy of M = 6.97 ( SD = 2.39) and their current quality of life of M = 7.10 ( SD = 1.95). In terms of eHealth, participants reported high eHealth literacy ( M = 31.98, SD = 5.90, range 8–40) and high digital confidence ( M = 4.20, SD = 0.79, range 1–5). Internet anxiety ( M = 1.54, SD = 0.65, range 1–5) was low and digital overload was moderate ( M = 2.70, SD = 1.03, range 1–5). Additional sample characteristics are given in Table 1 . Table 1 Sample characteristics N (%) Marital status Single 21 (4.6) In a relationship 79 (17.4) Married 330 (72.8) Divorced, separated 17 (3.8) Other 6 (1.3) Educational level No or lower secondary education/other 20 (4.4) Higher secondary education 48 (10.6) Higher education entrance qualification 114 (25.2) University education 271 (59.8) Occupational status Still in education, unemployed, unfit to work or other 30 (6.6) House wife, parenting 21 (4.6) Maternity leave 26 (5.7) Parental leave 168 (37.1) Part-time employed 143 (31.6) Employed 64 (14.1) Retired 1 (0.2) Place of residence (population size) Large city (> 100,000 residents) 287 (63.4) Medium sized city (> 20,000 residents) 78 (17.2) Small town (> 5,000 residents) 34 (7.5) Rural area (< 5,000 residents) 54 (11.9) Somatic illness No 298 (65.8) Yes 107 (23.6) Not available 48 (10.6) Mental illness No 260 (57.4) Yes 137 (30.2) Not available 56 (12.4) Diagnosis of PPD Currently 10 (2.2) In the past 46 (10.2) Currently and in the past 12 (2.6) No diagnosis, but suspected 102 (22.5) No 227 (50.1) Not available 56 (12.4) Total 453 (100.0) Note. PPD = Postpartum depression. +++ Insert Table 1 about here +++ Acceptance of eHealth Interventions in Postpartum Mental Health Care Overall, acceptance of eHealth interventions in postpartum mental health care was high ( M = 3.89, SD = 0.95, range 1–5). High acceptance was reported by 68.2% ( n = 309) of the participants and 23.0% ( n = 104) showed moderate acceptance, while only 8.8% ( n = 40) participants gave responses that indicated low acceptance. Acceptance of eHealth interventions in postpartum mental health care was significantly higher in mothers affected by mental illness, t (395) = -4.72, p adj < .001, d = .50. Moreover, women who were currently suffering from postpartum depression or had been in the past, including those without diagnosis, reported a significantly higher level of acceptance compared to participants without depressive symptoms, t (395) = -4.54, p adj .05. Predictors of Acceptance of eHealth Interventions in Postpartum Mental Health Care Multiple hierarchical regression analysis was applied to determine predictors of acceptance of eHealth interventions in postpartum mental health care. Only complete cases were included in the analysis, which is based on the data of n = 327 participants. First, sociodemographic and obstetric data were included ( R 2 = .024, R 2 adj = .014, F (3,323) = 2.59, p = .053). Perceived support during pregnancy (β = − .15, p = .009) was a significant predictor of acceptance. The explained variance of the first step was 2.4%. Psychometric and medical data, included in the second step ( R 2 = .086, R 2 adj = .069, F (6,320) = 5.02, p < .001), significantly increased the explained variance to 8.6% (∆ R 2 = .062, F (3,320) = 16.23, p < .001). Quality of life (β = − .13, p = .022) and PPD (β = .40, p = .001) were revealed as significant predictors of acceptance. In the third step, eHealth data ( R 2 = .123, R 2 adj = .095, F (10,316) = 4.41, p < .001), significantly increased the explained variance to 12.3% (∆ R 2 = .037, F (4,316) = 7.12, p < .001). Digital confidence (β = .18, p = .002) was a significant predictor of acceptance. In the final step, the three UTAUT predictors were included ( R 2 = .598, R 2 adj = .582, F (13,313) = 35.85, p < .001). Explained variance of the final model was significantly increased to 59.8% (∆ R 2 = .475, F (3,313) = 123.53, p < .001). EE (β = .10, p = .037), PE (β = .50, p < .001) and SI (β = .25, p < .001) were significant predictors. Table 2 contains the final UTAUT model of acceptance and its predictors. Table 2 Hierarchical Regression Model of Acceptance of eHealth Interventions in Postpartum Mental Health Care Predictors B β t R 2 ∆ R 2 p (Intercept) .62 − .09 1.39 .166 Step 1: Sociodemographic and obstetric data .024 .024 Age − .00 − .03 -0.53 .595 Time since birth .00 .02 0.33 .739 Perceived support during pregnancy − .00 − .00 -0.07 .942 Step 2: Psychometric and medical data .086 .062 Quality of life − .04 − .09 -2.23 .027 PPD .18 .21 2.58 .010 Somatic illness − .00 − .00 -0.03 .975 Step 3: eHealth data .123 .037 eHealth literacy − .00 − .03 -0.76 .448 Digital confidence .14 .12 3.08 .002 Internet anxiety − .01 − .01 -0.15 .877 Digital overload .02 .02 0.46 .643 Step 4: UTAUT predictors .598 .475 EE .10 .09 2.10 .037 PE .50 .51 10.72 < .001 SI .25 .23 5.47 < .001 Note. N = 327. In Step 2, 3, and 4 only the newly included variables are presented. B = Unstandardized beta. β = Standardized beta. t = Test statistic. R ² = Determination coefficient. ∆ R 2 = Changes in R 2 . EE = Effort expectancy, PE = Performance expectancy, PHQ-8 = Patient Health Questionnaire-8, PPD = Postpartum depression, SI = Social influence, UTAUT = Unified Theory of Acceptance and Use of Technology. +++ Insert Table 2 about here +++ Discussion This study aimed to determine acceptance of eHealth interventions in postpartum mental health care, as well as its drivers and barriers. To our knowledge, the UTAUT model has not been applied in the context of postpartum mental health care and relevant factors influencing the use of eHealth services has not yet been explored in this sample. Overall, women participating in this study reported a high level of acceptance of eHealth interventions in postpartum mental health care, with only a small number of participants indicating low levels of acceptance. Women with PPD or other mental illness in their medical history showed a higher level of acceptance than women who were not affected. There were no differences in acceptance based on level of education, occurrence of a high-risk pregnancy or diagnosis of a somatic disease. Significant predictors of acceptance were perceived support during pregnancy, quality of life, eHealth literacy, digital confidence, and the three core UTAUT predictors EE, PE, and SI. The extended UTAUT model, which included additional sociodemographic, medical, psychometric and eHealth characteristics, was able to explain a high level of variance in acceptance (60.1%). The high level of acceptance found in the present study is comparable to the evidence of good acceptance of telehealth programs for high-risk pregnancy [ 34 ] and eHealth tools for gestational diabetes mellitus [ 35 ]. Only a very small proportion of participants showed low acceptance. These results provide a promising basis for the implementation of eHealth services in postpartum mental health care. On the other hand, obstacles to the actual use of such offers must be minimized and potential users with low acceptance should be specifically targeted to raise awareness and uptake of innovative eHealth interventions. Acceptance of eHealth interventions was higher among participants with PPD or with other mental illness. This finding is in line with research among other patient groups [ 24 , 36 ], which shows that patients affected by mental illness demonstrate greater acceptance of eHealth offers. One explanation for this may be the greater burden of suffering and, as a result, the greater need for immediate, easily accessible treatment services. In particular, mothers affected by PPD, who might be limited by child care or bed rest, would be able to receive adequate mental health care through eHealth interventions. Similarly, a higher quality of life predicted lower acceptance, which underlines the interpretation that less burdened individuals see no necessity in using eHealth interventions, while highly burdened individuals may especially profit from supplement eHealth offers. Perceived social and medical support during pregnancy was negatively associated with acceptance of eHealth interventions in postpartum mental health care. A lack of social support during pregnancy is an important risk factor for PPD [ 37 , 38 ]. It is plausible that women who already receive sufficient support during this unique phase of their lives have less need to use supportive eHealth services. An app targeting women affected by PPD has proven to be effective in not only treating depressive symptoms but also increasing perceived social support [ 39 ]. eHealth offers may therefore especially support people who lack a strong social network in coping with the burden of PPD. Higher digital confidence was related to higher acceptance of eHealth interventions. This finding is in line with previous research [ 27 , 30 , 36 , 40 ]. Overall, digital confidence was high in this sample of young women. This correlation shows that people who feel comfortable using the Internet and use it frequently also report a higher level of openness towards eHealth. While highly confident Internet users may be easily approachable when implementing new eHealth interventions, those with lower confidence should be especially targeted. It might be helpful to offer specific support in the practical use of eHealth services to overcome this barrier. Acceptance of eHealth interventions in postpartum mental health care was significantly predicted by the three UTAUT predictors EE, PE and SI, as reported in previous research [ 26 , 27 , 30 , 36 , 40 ]. EE describes the effort that a person has to invest in order to use eHealth [ 23 ]. PE comprises the expectation that the eHealth offer is able to positively influence general well-being, mental and physical health, and coping with psychological stress. SI, which conceptualizes the extent to which related parties, the general practitioner or the gynecologist would approve of the use of the eHealth offer. E-health offers that are expected to involve minimal effort, offer a high benefit and receive positive approval from others are highly accepted by their users. In accordance with previous research [ 26 , 27 , 30 , 36 ], the high level of explained variance in acceptance shows that the UTAUT model, which includes the three core predictors and additional psychometric, medical and eHealth data, is a valid model to investigate acceptance of eHealth technology. Limitations The following limitations need to be considered when interpreting the results of the present study. Since the study was conducted online, sampling bias may have influenced the composition of the sample. The form of the questionnaire might have particularly appealed to people who are already online-savvy and feel comfortable using digital media. People who dislike technology may therefore be underrepresented. Further, a sizable proportion of participants reported a high level of education. It is therefore important, especially in the context of eHealth offers, to address these subgroups in a particularly targeted manner. There was no limitation for participation regarding how much time has passed since pregnancy. Memory bias must therefore be taken into account. In regards to the UTAUT model, which operationalizes behavioral intention as acceptance, the intention-behavior gap needs to be considered [ 41 , 42 ]. This concept describes the fact that not every intention is translated into action. Therefore, the actual usage behavior of specific eHealth intervention requires dedicated research. Conclusions In conclusion, acceptance towards eHealth interventions in postpartum mental health care was high and relevant drivers and barriers such as mental illness, perceived support, digital confidence, and the UTAUT predictors could be revealed. Based on the findings of this study, efforts should be made to implement eHealth offers that complement and enhance existing routine care. To accomplish this in a successful way, the identified drivers and barriers should be taken into account and specifically targeted. Mental health care in a particularly vulnerable and high-need group could thereby be improved by increasing acceptance and utilization. Abbreviations EE Effort expectancy GR-eHEALS Revised German version of the eHealth Literacy Scale PE Performance expectancy PHQ-8 Patient Health Questionnaire-8 PPD Postpartum depression SI Social influence UTAUT Unified Theory of Acceptance and Use of Technology VIF Variance inflation factor Declarations Ethics approval and consent to participate The conductance of the study was approved by the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen (19-89-47-BO). Digital informed consent was mandatory to participate in this study. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Non declared. Authors' contributions Conceptualization, AB, KK, LMJ, A-LF and HM; data curation, LMJ; formal analysis, LMJ; methodology, AB and LMJ; project administration, AB, KK and LMJ; supervision, AB and KK; writing—original draft, LMJ, HM, KK, and AB; writing—review and editing, LMJ, JM, A-LF, KK, MT, E-MS, AI and AB. All authors have read and agreed to the published version of the manuscript. Acknowledgements We are thankful to all women who participated in the online questionnaire. Moreover, we thank the Support association (Foerderverein) of the University of Duisburg-Essen for the support of our project as well as the funding of HM by a junior research fellowship by the Faculty of Medicine of the University of Duisburg-Essen (UMEA). References Bjelica A, Cetkovic N, Trninic-Pjevic A, Mladenovic-Segedi L. The phenomenon of pregnancy - a psychological view. Ginekol Pol. 2018;89(2):102-6. Wang D, Li YL, Qiu D, Xiao SY. 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Evaluating the effectiveness and quality of mobile applications for perinatal depression and anxiety: A systematic review and meta-analysis. J Affect Disord. 2022;296:443-53. Philippi P, Baumeister H, Apolinario-Hagen J, Ebert DD, Hennemann S, Kott L, et al. Acceptance towards digital health interventions - Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interv. 2021;26:100459. Venkatesh, Morris, Davis, Davis. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 2003;27(3). Bäuerle A, Mallien C, Rassaf T, Jahre L, Rammos C, Skoda EM, et al. Determining the Acceptance of Digital Cardiac Rehabilitation and Its Influencing Factors among Patients Affected by Cardiac Diseases. J Cardiovasc Dev Dis. 2023;10(4). Hennemann S, Beutel ME, Zwerenz R. Drivers and Barriers to Acceptance of Web-Based Aftercare of Patients in Inpatient Routine Care: A Cross-Sectional Survey. J Med Internet Res. 2016;18(12):e337. Schröder J, Bäuerle A, Jahre LM, Skoda EM, Stettner M, Kleinschnitz C, et al. Acceptance, drivers, and barriers to use eHealth interventions in patients with post-COVID-19 syndrome for management of post-COVID-19 symptoms: a cross-sectional study. Ther Adv Neurol Disord. 2023;16:17562864231175730. Stoppok P, Teufel M, Jahre L, Rometsch C, Mussgens D, Bingel U, et al. Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions Among Patients With Chronic Pain Using the Unified Theory of Acceptance and Use of Technology: Cross-sectional Study. JMIR Form Res. 2022;6(8):e37682. Tivian XI GmbH. Unipark. 2023. Marsall M, Engelmann G, Skoda EM, Teufel M, Bäuerle A. Measuring Electronic Health Literacy: Development, Validation, and Test of Measurement Invariance of a Revised German Version of the eHealth Literacy Scale. J Med Internet Res. 2022;24(2):e28252. Nurtsch A, Teufel M, Jahre LM, Esber A, Rausch R, Tewes M, et al. Drivers and barriers of patients' acceptance of video consultation in cancer care. Digit Health. 2024;10:20552076231222108. Zobeidi T, Homayoon SB, Yazdanpanah M, Komendantova N, Warner LA. Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic. Front Psychol. 2023;14:1104653. Rasool T, Warraich NF, Sajid M. Examining the Impact of Technology Overload at the Workplace: A Systematic Review. SAGE Open. 2022;12(3). Cohen J. Statistical power analysis for the behavioral sciences. Academic press. 1988. Valencia SA, Barrientos Gomez JG, Gomez Ramirez MC, Luna IF, Caicedo HA, Torres-Silva EA, et al. Evaluation of a telehealth program for high-risk pregnancy in a health service provider institution. Int J Med Inform. 2023;179:105234. Fiska BS, Pay ASD, Staff AC, Sugulle M. Gestational diabetes mellitus, follow-up of future maternal risk of cardiovascular disease and the use of eHealth technologies-a scoping review. Syst Rev. 2023;12(1):178. Rentrop V, Damerau M, Schweda A, Steinbach J, Schuren LC, Niedergethmann M, et al. Predicting Acceptance of e-Mental Health Interventions in Patients With Obesity by Using an Extended Unified Theory of Acceptance Model: Cross-sectional Study. JMIR Form Res. 2022;6(3):e31229. Morikawa M, Okada T, Ando M, Aleksic B, Kunimoto S, Nakamura Y, et al. Relationship between social support during pregnancy and postpartum depressive state: a prospective cohort study. Sci Rep. 2015;5:10520. Verreault N, Da Costa D, Marchand A, Ireland K, Dritsa M, Khalife S. Rates and risk factors associated with depressive symptoms during pregnancy and with postpartum onset. J Psychosom Obstet Gynaecol. 2014;35(3):84-91. Liu C, Chen H, Zhou F, Long Q, Wu K, Lo LM, et al. Positive intervention effect of mobile health application based on mindfulness and social support theory on postpartum depression symptoms of puerperae. BMC Womens Health. 2022;22(1):413. Lin J, Faust B, Ebert DD, Kramer L, Baumeister H. A Web-Based Acceptance-Facilitating Intervention for Identifying Patients' Acceptance, Uptake, and Adherence of Internet- and Mobile-Based Pain Interventions: Randomized Controlled Trial. J Med Internet Res. 2018;20(8):e244. Faries MD. Why We Don't "Just Do It": Understanding the Intention-Behavior Gap in Lifestyle Medicine. Am J Lifestyle Med. 2016;10(5):322-9. Sheeran P, Webb TL. The Intention–Behavior Gap. Social and Personality Psychology Compass. 2016;10(9):503-18. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted Editor assigned by journal 18 Apr, 2024 Submission checks completed at journal 26 Mar, 2024 First submitted to journal 21 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4143017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284318022,"identity":"24944d6a-479b-4b7c-9419-9da5fe2f7e64","order_by":0,"name":"Lisa Maria Jahre","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"Maria","lastName":"Jahre","suffix":""},{"id":284318023,"identity":"68ee0161-7178-4562-b8e1-ac0e96cf4921","order_by":1,"name":"Anna-Lena Frewer","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anna-Lena","middleName":"","lastName":"Frewer","suffix":""},{"id":284318024,"identity":"79aca3c1-e25d-4ac8-b066-a09b53b2addb","order_by":2,"name":"Heidi Meyer","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Heidi","middleName":"","lastName":"Meyer","suffix":""},{"id":284318025,"identity":"0a221af8-bd79-444e-a664-b5d42d1c3336","order_by":3,"name":"Katja Koelkebeck","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Koelkebeck","suffix":""},{"id":284318026,"identity":"76bda53c-d186-466b-878d-421c1d5f7397","order_by":4,"name":"Antonella Iannaccone","email":"","orcid":"","institution":"University Hospital Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Antonella","middleName":"","lastName":"Iannaccone","suffix":""},{"id":284318027,"identity":"cd0d1032-0328-4376-b97a-db4116c50df9","order_by":5,"name":"Eva-Maria Skoda","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eva-Maria","middleName":"","lastName":"Skoda","suffix":""},{"id":284318028,"identity":"dad0da17-7560-44db-8a65-80fa06e81cee","order_by":6,"name":"Martin Teufel","email":"","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Teufel","suffix":""},{"id":284318029,"identity":"eb3fb58e-bd69-4dce-9bd7-57f36a62d534","order_by":7,"name":"Alexander Bäuerle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABPElEQVRIie2QPUvDQBiALxxclqtZc1TNX7gQUEJL81cSAulScRMHwUDhXFJcIxT8C52K45WDusTOdWsROkWoiyjS1qNqHRIzO9wDx73c8bxfACgU/xCkby8oj8G34R6AXIt//nFRMeBOwf5XEoD8SoV0fxX6rWBaqVABx+b7XePwOMbB8oU1T5GevJIUND3rqsdBfl6ioNDuZW1nnxuC3LDIZfhhSAYgCgbZxNf6WYmC7VmNiSCVs9RlQJF5MiTztfCp2aGwxgqKJ4zlaMXEpVScjxXbUGTlCzIDG8+6zaWyLquiPcnkvgnwUV1jXFbBSDYmFz3FUolLFOQ4B6xtpxBHbjIJKcKR46YglLN06Kg/Lir33QV5Zg3L1K/F9O2sRQ1dzB8T0JIby+xZflHc8g5Y9sgrBIVCoVD8zScctGYIq6FQ3gAAAABJRU5ErkJggg==","orcid":"","institution":"LVR-University Hospital Essen, University of Duisburg-Essen","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Bäuerle","suffix":""}],"badges":[],"createdAt":"2024-03-21 11:12:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4143017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4143017/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25297-1","type":"published","date":"2025-11-17T15:58:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96650373,"identity":"0cb6ae9e-9f81-45fb-b977-97d2c543bdb7","added_by":"auto","created_at":"2025-11-24 16:11:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4143017/v1/2dc8001d-b224-4fd7-afec-86e07779c504.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Drivers and Barriers of Acceptance of eHealth Interventions in Postpartum Mental Health Care: A Cross-Sectional Study","fulltext":[{"header":"Background","content":"\u003cp\u003eChildbirth not only fundamentally effects the lives of the new parents, it also has a significant impact on the mental health of the childbearing mother. Psychological stress during pregnancy is thus common [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and is, at the same time, a significant risk factor for postpartum mental illness [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most common postpartum mental disorders are postpartum depression (PPD) and anxiety [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Whereas postpartum psychosis and post-traumatic stress disorder are less frequent, the severity of these disorders is highly relevant. In high-income countries, the point prevalence is 13% for PPD and around 10% for postpartum anxiety [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the long term, burden of severe mental illness after childbirth, combined with other risk factors, may even lead to maternal suicide, which is described as the \u0026ldquo;leading cause of maternal death\u0026rdquo; [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. PPD is also a crucial risk factor for negative outcomes in mothers as well as their children [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Maternal mental health, quality of life and relationships are negatively impacted by PPD. Further, the infants\u0026rsquo; development and mother-child interactions are complicated or impaired by PPD. It is evident that prevention and treatment of postpartum mental illness takes an important role in women's care. Accordingly, the World Health Organization has developed recommendations for maternal and newborn care, which emphasizes the necessity of supportive, evidence-based mental health care after childbirth [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly detection of PPD is essential for the implementation of appropriate treatment options [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, screening rates for PPD are low [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. There is no established routine screening program in Germany [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], even though validated instruments recommended by treatment guidelines are available [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, screening alone is not enough to alleviate the symptoms of those affected; appropriate treatment is required. PPD can be treated effectively with psychotherapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In Germany, however, psychotherapeutic care for people with PPD is not ensured in practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to a lack of available treatment options, another access barrier is stigma around mental health [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This existing gap in maternal mental health care must be adressed with high priority in order to ensure the well-being of mothers and their children. eHealth (electronic health) may provide an evidence-based addition to established mental health treatments.\u003c/p\u003e \u003cp\u003eeHealth is a novel way to provide medical and psychological support services [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and can be employed in different ways. For example, Internet-based cognitive behavioural therapy for depression has been proven to be as effective as face-to-face delivery [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Moreover, telehealth interventions are effective in decreasing symptoms of PPD, including anxiety and functional impairment [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Further, screening for PPD with eHealth tools seems to be a promising method, bypassing fears regarding openness about mental illness during the postpartum period [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. On the other hand, there is a high number of available mHealth (mobile health) tools addressing postpartum mental health which are only of moderate quality and have not been clinically tested [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Users\u0026rsquo; acceptance of such innovative eHealth services need to be investigated in an evidence-based manner.\u003c/p\u003e \u003cp\u003eThe successful implementation and patients\u0026rsquo; adaption of eHealth services relies on the acceptance by its potential user base. According to the Unified Theory of Acceptance and Use of Technology (UTAUT), acceptance can be operationalized as behavioral intention to use such eHealth interventions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Acceptance is determined by three core factors: social influence (SI), performance expectancy (PE) and effort expectancy (EE). SI is conceptualized as the extent to which family or friends would approve of the use of a technology. PE stands for the expectation that the technology will lead to a beneficial effect for the user. EE describes the amount of effort a person expects to expend when using the technology [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Acceptance of eHealth interventions has been investigated among different patient groups in a number of studies [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To this date, acceptance among women during the postpartum period has not been assessed. Further, studies suggest that, besides the three core predictors SI, PE and EE, additional factors should be integrated into the UTAUT model to obtain a full overview of relevant influences [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, it is important to examine the relevant drivers and barriers of eHealth use in postpartum mental health care. Only evidence-based evaluation of relevant factors influencing the use of eHealth interventions can ensure that effective health care services are actually adopted by their target group.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe aim of this study was to determine the drivers and barriers of acceptance of eHealth interventions in postpartum mental health care. Differences in acceptance based on sociodemographic, obstetric, psychometric and medical data were investigated with a cross-sectional study in a group of postpartum women.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and study population\u003c/h2\u003e \u003cp\u003eA cross-sectional, online-based questionnaire study was conducted to examine acceptance of eHealth interventions in postpartum mental health care among women who had experienced pregnancy. Participants were recruited via flyers from the Clinic for Gynecology and Obstetrics of the University Hospital Essen, from gynecological outpatient practices in Essen, self-help groups and counselors specializing in postpartum mental illness in Germany, and from posts on social media channels centered on pregnancy and postpartum mental illness (Facebook, Instagram). Study information was presented in form of posters and flyers. Participation was anonymous and voluntary. Participants did not receive compensation. Inclusion criteria were legal age, female gender, history of pregnancy, sufficient command of the German language and Internet access. Data was collected between October 2022 and June 2023 via the platform Unipark [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Digital informed consent was given before the start of the survey. Average completion time was \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.63 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.16) minutes. Initially, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;558 participants started the questionnaire. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;105 (18.82%) participants were excluded because they did not fulfill inclusion criteria or were missing data on the primary outcome (acceptance). Therefore, \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;453 participants (81.18%) were included in the final data analysis. The conductance of the study was approved by the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen (19-89-47-BO).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment instruments\u003c/h2\u003e \u003cp\u003eThe survey encompassed sociodemographic, medical, psychometric, and eHealth data. Validated assessment instruments and self-generated items were used to acquire responses.\u003c/p\u003e \u003cp\u003eSociodemographic data included age, gender, marital status, educational level, occupational status and place of residence (population size). Medical data included presence of a somatic or mental illness and specifically the diagnosis of PPD.\u003c/p\u003e \u003cp\u003eRegarding obstetric data, participants were asked about number of pregnancies, number of children, age of children, time since childbirth, whether the pregnancy was planned and if their pregnancy was considered high-risk.\u003c/p\u003e \u003cp\u003ePerceived support during the last pregnancy was rated on a scale from 0 to 10 (0 = \u0026ldquo;I did not feel supported at all\u0026rdquo;, 10 = \u0026ldquo;I felt extremely supported.\u0026rdquo;). Current quality of life was also indicated on a scale from 0 to 10 (0 = \u0026ldquo;very low quality of life\u0026rdquo;, 10 = \u0026ldquo;very high quality of life\u0026rdquo;).\u003c/p\u003e \u003cp\u003eFurther, eHealth literacy was assessed using the revised German version of the eHealth Literacy Scale (GR-eHEALS; [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]). Responses were given on a five-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo;, 5 = \u0026ldquo;strongly agree\u0026rdquo;). Internal consistency was excellent (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.92). Digital confidence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] was assessed via three items (e.g. \u0026ldquo;How confident are you in using digital media?\u0026rdquo;) and responses were given on a five-point Likert scale (1 = \u0026ldquo;not very confident\u0026rdquo;, 5 = \u0026ldquo;very confident\u0026rdquo;). Internal consistency was high (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.89). Internet anxiety [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and digital overload [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] were also assessed via three items each and responses were given on a five-point Likert scale (e.g. \u0026ldquo;I have concerns about using the Internet.\u0026rdquo;, \u0026ldquo;I feel burdened by the constant accessibility via cell phone or e-mail.\u0026rdquo;, 1 = \u0026ldquo;strongly disagree\u0026rdquo;, 5 = \u0026ldquo;strongly agree\u0026rdquo;). Internal consistency was good (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.75 for Internet anxiety and α\u0026thinsp;=\u0026thinsp;.82 for digital overload).\u003c/p\u003e \u003cp\u003eIn order to assess acceptance towards eHealth interventions in postpartum mental health care, the validated UTAUT model [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was applied. Acceptance, which is operationalized as behavioral intention (BI), was determined by four items (e.g. \u0026ldquo;I would use such an eHealth intervention if it were offered to me.\u0026rdquo;). Internal consistency was high (Cronbach's α\u0026thinsp;=\u0026thinsp;.86). Of the three core predictors of the UTAUT model, social influence (SI) was assessed via three items (e.g. \u0026ldquo;People close to me would approve of the use of such an eHealth intervention.\u0026rdquo;). Internal consistency was high (Cronbach's α\u0026thinsp;=\u0026thinsp;.80). Performance expectancy (PE) consisted of four items (e.g. \u0026ldquo;Such an eHealth intervention could improve my overall well-being.\u0026rdquo;). Internal consistency was excellent (Cronbach's α\u0026thinsp;=\u0026thinsp;.91). Three items were used to assess effort expectancy (EE; e.g. \u0026ldquo;Using such an eHealth intervention would not be an additional burden for me.\u0026rdquo;). Internal consistency was good (Cronbach's α\u0026thinsp;=\u0026thinsp;.79). Responses were given on a five-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo;, 5 = \u0026ldquo;strongly agree\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using R (4.3.8). Sum scores (GR-eHEALS, PHQ-8) and mean scores (digital confidence, Internet anxiety, digital overload) were calculated. For acceptance (=\u0026thinsp;BI) and its three predictors (EE, PE, SI) mean scores were calculated as well. Acceptance was further divided into categories, in accordance with prior research [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]: scores from 1 to 2.34 indicate low acceptance, scores from 2.35 to 3.67 indicate moderate acceptance and scores from 3.68 to 5 indicate high acceptance. Descriptive statistics were applied for sociodemographic, medical, obstetric, psychometric and eHealth data. Differences in acceptance based on educational level, somatic or mental illness, diagnosis of PPD, and risk during pregnancy were examined with independent \u003cem\u003et\u003c/em\u003e-tests and ANOVAs. \u003cem\u003eP\u003c/em\u003e-values were adjusted for multiple comparisons via Bonferroni correction. Levene\u0026rsquo;s test indicated homoscedasticity. Due to the given sample size, normal distribution of residuals was assumed. Multiple hierarchical regression analysis was conducted to examine drivers and barriers of acceptance of eHealth interventions in postpartum mental health care. Predictors were included blockwise: 1) sociodemographic and obstetric data, 2) medical and psychometric data, 3) eHealth data, 4) UTAUT predictors. The variance inflation factor (VIF) was used to verify the absence of multicollinearity (all VIF values\u0026thinsp;\u0026lt;\u0026thinsp;2.0). Visual inspection of qq-plots of the residuals showed no signs of violations against normality. Therefore, normal distribution of the residuals was assumed. Scatter plots of the standardized residuals and the adjusted predicted values verified homoscedasticity. The level of significance was set to α\u0026thinsp;\u0026lt;\u0026thinsp;.05 for all tests. Effect sizes were reported according to Cohen (1988 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]), with values around 0.2, 0.5, and 0.8 indicating small, medium, and large effects, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eAmong \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;453 female participants who experienced pregnancy in the past the mean age was \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35.97 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.55) years. The youngest participant was 18 years old and the oldest was 63 years old. The average number of pregnancies was \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.86 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18), while the number of children was \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.53 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76). The age of youngest child of the participants was \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.80 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.00) years, and the oldest \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.51 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.04) years. An average of \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;43.77 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63.70) months had passed since the last childbirth. A high-risk pregnancy in the past was reported of 31.8% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;144) of the participants. Among the participants, 79.5% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;360) stated that their last pregnancy was planned. On a scale from 0 to 10, participants reported an average perceived support during pregnancy of \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.97 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.39) and their current quality of life of \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.10 (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.95).\u003c/p\u003e \u003cp\u003eIn terms of eHealth, participants reported high eHealth literacy (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.98, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.90, range 8\u0026ndash;40) and high digital confidence (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.20, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, range 1\u0026ndash;5). Internet anxiety (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65, range 1\u0026ndash;5) was low and digital overload was moderate (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.70, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03, range 1\u0026ndash;5). Additional sample characteristics are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330 (72.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced, separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo or lower secondary education/other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher education entrance qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271 (59.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupational status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStill in education, unemployed, unfit to work or other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse wife, parenting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternity leave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParental leave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168 (37.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143 (31.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64 (14.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence (population size)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge city (\u0026gt;\u0026thinsp;100,000 residents)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287 (63.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium sized city (\u0026gt;\u0026thinsp;20,000 residents)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall town (\u0026gt;\u0026thinsp;5,000 residents)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area (\u0026lt;\u0026thinsp;5,000 residents)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSomatic illness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e298 (65.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107 (23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMental illness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260 (57.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137 (30.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiagnosis of PPD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn the past\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (10.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrently and in the past\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo diagnosis, but suspected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102 (22.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e227 (50.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (12.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e453 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNote.\u003c/em\u003e PPD\u0026thinsp;=\u0026thinsp;Postpartum depression.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e+++ Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here +++\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAcceptance of eHealth Interventions in Postpartum Mental Health Care\u003c/h2\u003e \u003cp\u003eOverall, acceptance of eHealth interventions in postpartum mental health care was high (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.89, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95, range 1\u0026ndash;5). High acceptance was reported by 68.2% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;309) of the participants and 23.0% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104) showed moderate acceptance, while only 8.8% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;40) participants gave responses that indicated low acceptance.\u003c/p\u003e \u003cp\u003eAcceptance of eHealth interventions in postpartum mental health care was significantly higher in mothers affected by mental illness, \u003cem\u003et\u003c/em\u003e(395) = -4.72, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.50. Moreover, women who were currently suffering from postpartum depression or had been in the past, including those without diagnosis, reported a significantly higher level of acceptance compared to participants without depressive symptoms, \u003cem\u003et\u003c/em\u003e(395) = -4.54, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.46. Differences in acceptance were not dependent on different levels of education, occurrence of a high-risk pregnancy or presence of a somatic disease, all \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026gt; .05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of Acceptance of eHealth Interventions in Postpartum Mental Health Care\u003c/h2\u003e \u003cp\u003e Multiple hierarchical regression analysis was applied to determine predictors of acceptance of eHealth interventions in postpartum mental health care. Only complete cases were included in the analysis, which is based on the data of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;327 participants.\u003c/p\u003e \u003cp\u003eFirst, sociodemographic and obstetric data were included (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.024, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.014, \u003cem\u003eF\u003c/em\u003e(3,323)\u0026thinsp;=\u0026thinsp;2.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.053). \u003cem\u003ePerceived support during pregnancy\u003c/em\u003e (β = \u0026minus;\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009) was a significant predictor of acceptance. The explained variance of the first step was 2.4%.\u003c/p\u003e \u003cp\u003ePsychometric and medical data, included in the second step (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.086, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.069, \u003cem\u003eF\u003c/em\u003e(6,320)\u0026thinsp;=\u0026thinsp;5.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), significantly increased the explained variance to 8.6% (∆\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.062, \u003cem\u003eF\u003c/em\u003e(3,320)\u0026thinsp;=\u0026thinsp;16.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). \u003cem\u003eQuality of life\u003c/em\u003e (β = \u0026minus;\u0026thinsp;.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022) and \u003cem\u003ePPD\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) were revealed as significant predictors of acceptance.\u003c/p\u003e \u003cp\u003eIn the third step, eHealth data (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.123, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.095, \u003cem\u003eF\u003c/em\u003e(10,316)\u0026thinsp;=\u0026thinsp;4.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), significantly increased the explained variance to 12.3% (∆\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.037, \u003cem\u003eF\u003c/em\u003e(4,316)\u0026thinsp;=\u0026thinsp;7.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). \u003cem\u003eDigital confidence\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) was a significant predictor of acceptance.\u003c/p\u003e \u003cp\u003eIn the final step, the three UTAUT predictors were included (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.598, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e\u003cem\u003eadj\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;.582, \u003cem\u003eF\u003c/em\u003e(13,313)\u0026thinsp;=\u0026thinsp;35.85, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Explained variance of the final model was significantly increased to 59.8% (∆\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;.475, \u003cem\u003eF\u003c/em\u003e(3,313)\u0026thinsp;=\u0026thinsp;123.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). \u003cem\u003eEE\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.037), \u003cem\u003ePE\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;.50, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and \u003cem\u003eSI\u003c/em\u003e (β\u0026thinsp;=\u0026thinsp;.25, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) were significant predictors. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e contains the final UTAUT model of acceptance and its predictors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHierarchical Regression Model of Acceptance of eHealth Interventions in Postpartum Mental Health Care\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e∆\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 1: Sociodemographic and obstetric data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime since birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived support during pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 2: Psychometric and medical data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomatic illness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 3: eHealth data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeHealth literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital overload\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.643\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStep 4: UTAUT predictors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;327. In Step 2, 3, and 4 only the newly included variables are presented. \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Unstandardized beta. β\u0026thinsp;=\u0026thinsp;Standardized beta. \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Test statistic. \u003cem\u003eR\u003c/em\u003e\u0026sup2; = Determination coefficient. ∆\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;Changes in \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e. EE\u0026thinsp;=\u0026thinsp;Effort expectancy, PE\u0026thinsp;=\u0026thinsp;Performance expectancy, PHQ-8\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-8, PPD\u0026thinsp;=\u0026thinsp;Postpartum depression, SI\u0026thinsp;=\u0026thinsp;Social influence, UTAUT\u0026thinsp;=\u0026thinsp;Unified Theory of Acceptance and Use of Technology.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e+++ Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here +++\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to determine acceptance of eHealth interventions in postpartum mental health care, as well as its drivers and barriers. To our knowledge, the UTAUT model has not been applied in the context of postpartum mental health care and relevant factors influencing the use of eHealth services has not yet been explored in this sample.\u003c/p\u003e \u003cp\u003eOverall, women participating in this study reported a high level of acceptance of eHealth interventions in postpartum mental health care, with only a small number of participants indicating low levels of acceptance. Women with PPD or other mental illness in their medical history showed a higher level of acceptance than women who were not affected. There were no differences in acceptance based on level of education, occurrence of a high-risk pregnancy or diagnosis of a somatic disease. Significant predictors of acceptance were perceived support during pregnancy, quality of life, eHealth literacy, digital confidence, and the three core UTAUT predictors EE, PE, and SI. The extended UTAUT model, which included additional sociodemographic, medical, psychometric and eHealth characteristics, was able to explain a high level of variance in acceptance (60.1%).\u003c/p\u003e \u003cp\u003eThe high level of acceptance found in the present study is comparable to the evidence of good acceptance of telehealth programs for high-risk pregnancy [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and eHealth tools for gestational diabetes mellitus [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Only a very small proportion of participants showed low acceptance. These results provide a promising basis for the implementation of eHealth services in postpartum mental health care. On the other hand, obstacles to the actual use of such offers must be minimized and potential users with low acceptance should be specifically targeted to raise awareness and uptake of innovative eHealth interventions.\u003c/p\u003e \u003cp\u003eAcceptance of eHealth interventions was higher among participants with PPD or with other mental illness. This finding is in line with research among other patient groups [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which shows that patients affected by mental illness demonstrate greater acceptance of eHealth offers. One explanation for this may be the greater burden of suffering and, as a result, the greater need for immediate, easily accessible treatment services. In particular, mothers affected by PPD, who might be limited by child care or bed rest, would be able to receive adequate mental health care through eHealth interventions. Similarly, a higher quality of life predicted lower acceptance, which underlines the interpretation that less burdened individuals see no necessity in using eHealth interventions, while highly burdened individuals may especially profit from supplement eHealth offers.\u003c/p\u003e \u003cp\u003ePerceived social and medical support during pregnancy was negatively associated with acceptance of eHealth interventions in postpartum mental health care. A lack of social support during pregnancy is an important risk factor for PPD [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is plausible that women who already receive sufficient support during this unique phase of their lives have less need to use supportive eHealth services. An app targeting women affected by PPD has proven to be effective in not only treating depressive symptoms but also increasing perceived social support [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. eHealth offers may therefore especially support people who lack a strong social network in coping with the burden of PPD.\u003c/p\u003e \u003cp\u003eHigher digital confidence was related to higher acceptance of eHealth interventions. This finding is in line with previous research [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Overall, digital confidence was high in this sample of young women. This correlation shows that people who feel comfortable using the Internet and use it frequently also report a higher level of openness towards eHealth. While highly confident Internet users may be easily approachable when implementing new eHealth interventions, those with lower confidence should be especially targeted. It might be helpful to offer specific support in the practical use of eHealth services to overcome this barrier.\u003c/p\u003e \u003cp\u003eAcceptance of eHealth interventions in postpartum mental health care was significantly predicted by the three UTAUT predictors EE, PE and SI, as reported in previous research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. EE describes the effort that a person has to invest in order to use eHealth [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. PE comprises the expectation that the eHealth offer is able to positively influence general well-being, mental and physical health, and coping with psychological stress. SI, which conceptualizes the extent to which related parties, the general practitioner or the gynecologist would approve of the use of the eHealth offer. E-health offers that are expected to involve minimal effort, offer a high benefit and receive positive approval from others are highly accepted by their users. In accordance with previous research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], the high level of explained variance in acceptance shows that the UTAUT model, which includes the three core predictors and additional psychometric, medical and eHealth data, is a valid model to investigate acceptance of eHealth technology.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe following limitations need to be considered when interpreting the results of the present study. Since the study was conducted online, sampling bias may have influenced the composition of the sample. The form of the questionnaire might have particularly appealed to people who are already online-savvy and feel comfortable using digital media. People who dislike technology may therefore be underrepresented. Further, a sizable proportion of participants reported a high level of education. It is therefore important, especially in the context of eHealth offers, to address these subgroups in a particularly targeted manner. There was no limitation for participation regarding how much time has passed since pregnancy. Memory bias must therefore be taken into account. In regards to the UTAUT model, which operationalizes behavioral intention as acceptance, the intention-behavior gap needs to be considered [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This concept describes the fact that not every intention is translated into action. Therefore, the actual usage behavior of specific eHealth intervention requires dedicated research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, acceptance towards eHealth interventions in postpartum mental health care was high and relevant drivers and barriers such as mental illness, perceived support,\u003c/p\u003e \u003cp\u003edigital confidence, and the UTAUT predictors could be revealed. Based on the findings of this study, efforts should be made to implement eHealth offers that complement and enhance existing routine care. To accomplish this in a successful way, the identified drivers and barriers should be taken into account and specifically targeted. Mental health care in a particularly vulnerable and high-need group could thereby be improved by increasing acceptance and utilization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffort expectancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGR-eHEALS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRevised German version of the eHealth Literacy Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerformance expectancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHQ-8\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Health Questionnaire-8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostpartum depression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial influence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTAUT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnified Theory of Acceptance and Use of Technology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance inflation factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe conductance of the study was approved by the Ethics Committee of the Medical Faculty of the University of Duisburg-Essen (19-89-47-BO). Digital informed consent was mandatory to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNon declared.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, AB, KK, LMJ, A-LF and HM; data curation, LMJ; formal analysis, LMJ; methodology, AB and LMJ; project administration, AB, KK and LMJ; supervision, AB and KK; writing\u0026mdash;original draft, LMJ, HM, KK, and AB; writing\u0026mdash;review and editing, LMJ, JM, A-LF, KK, MT, E-MS, AI and AB. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to all women who participated in the online questionnaire. Moreover, we thank the Support association (Foerderverein) of the University of Duisburg-Essen for the support of our project as well as the funding of HM by a junior research fellowship by the Faculty of Medicine of the University of Duisburg-Essen (UMEA).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBjelica A, Cetkovic N, Trninic-Pjevic A, Mladenovic-Segedi L. The phenomenon of pregnancy - a psychological view. Ginekol Pol. 2018;89(2):102-6.\u003c/li\u003e\n\u003cli\u003eWang D, Li YL, Qiu D, Xiao SY. Factors Influencing Paternal Postpartum Depression: A Systematic Review and Meta-Analysis. J Affect Disord. 2021;293:51-63.\u003c/li\u003e\n\u003cli\u003eMeltzer-Brody S, Howard LM, Bergink V, Vigod S, Jones I, Munk-Olsen T, et al. Postpartum psychiatric disorders. Nat Rev Dis Primers. 2018;4:18022.\u003c/li\u003e\n\u003cli\u003eReid HE, Pratt D, Edge D, Wittkowski A. Maternal Suicide Ideation and Behaviour During Pregnancy and the First Postpartum Year: A Systematic Review of Psychological and Psychosocial Risk Factors. Front Psychiatry. 2022;13:765118.\u003c/li\u003e\n\u003cli\u003eSlomian J, Honvo G, Emonts P, Reginster JY, Bruyere O. Consequences of maternal postpartum depression: A systematic review of maternal and infant outcomes. Womens Health (Lond). 2019;15:1745506519844044.\u003c/li\u003e\n\u003cli\u003eField T. Postpartum depression effects on early interactions, parenting, and safety practices: a review. Infant Behav Dev. 2010;33(1):1-6.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. WHO recommendations on maternal and newborn care for a positive postnatal experience. 2022. https://www.who.int/publications/i/item/9789240045989. Accessed 21 March 2024.\u003c/li\u003e\n\u003cli\u003eStewart DE, Vigod SN. Postpartum Depression: Pathophysiology, Treatment, and Emerging Therapeutics. Annu Rev Med. 2019;70:183-96.\u003c/li\u003e\n\u003cli\u003eGoldin Evans M, Phillippi S, Gee RE. Examining the Screening Practices of Physicians for Postpartum Depression: Implications for Improving Health Outcomes. Womens Health Issues. 2015;25(6):703-10.\u003c/li\u003e\n\u003cli\u003ePawils S, Kochen E, Weinbrenner N, Loew V, Doring K, Daehn D, et al. [Postpartum depression-who cares? Approaches to care via midwifery, gynaecology, paediatrics and general practice]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2022;65(6):658-67.\u003c/li\u003e\n\u003cli\u003ePark SH, Kim JI. Predictive validity of the Edinburgh postnatal depression scale and other tools for screening depression in pregnant and postpartum women: a systematic review and meta-analysis. Arch Gynecol Obstet. 2023;307(5):1331-45.\u003c/li\u003e\n\u003cli\u003eKittel-Schneider S, Reif A. [Treatment of psychiatric disorders during pregnancy and the breast feeding : Psychotherapy and other nondrug therapies]. Nervenarzt. 2016;87(9):967-73.\u003c/li\u003e\n\u003cli\u003eJordan W, Bielau H, Cohrs S, Hauth I, Hornstein C, Marx A, et al. [Actual care and funding situation with regard to mother-child units for psychic disorders associated with pregnancy in Germany]. Psychiatr Prax. 2012;39(5):205-10.\u003c/li\u003e\n\u003cli\u003eModak A, Ronghe V, Gomase KP, Mahakalkar MG, Taksande V. A Comprehensive Review of Motherhood and Mental Health: Postpartum Mood Disorders in Focus. Cureus. 2023;15(9):e46209.\u003c/li\u003e\n\u003cli\u003eHussain-Shamsy N, Shah A, Vigod SN, Zaheer J, Seto E. Mobile Health for Perinatal Depression and Anxiety: Scoping Review. J Med Internet Res. 2020;22(4):e17011.\u003c/li\u003e\n\u003cli\u003eKambeitz-Ilankovic L, Rzayeva U, Volkel L, Wenzel J, Weiske J, Jessen F, et al. A systematic review of digital and face-to-face cognitive behavioral therapy for depression. NPJ Digit Med. 2022;5(1):144.\u003c/li\u003e\n\u003cli\u003eHanach N, de Vries N, Radwan H, Bissani N. The effectiveness of telemedicine interventions, delivered exclusively during the postnatal period, on postpartum depression in mothers without history or existing mental disorders: A systematic review and meta-analysis. Midwifery. 2021;94:102906.\u003c/li\u003e\n\u003cli\u003eLiu X, Huang S, Hu Y, Wang G. The effectiveness of telemedicine interventions on women with postpartum depression: A systematic review and meta-analysis. Worldviews Evid Based Nurs. 2022;19(3):175-90.\u003c/li\u003e\n\u003cli\u003eZhao L, Chen J, Lan L, Deng N, Liao Y, Yue L, et al. Effectiveness of Telehealth Interventions for Women With Postpartum Depression: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth. 2021;9(10):e32544.\u003c/li\u003e\n\u003cli\u003evan den Heuvel JF, Groenhof TK, Veerbeek JH, van Solinge WW, Lely AT, Franx A, et al. eHealth as the Next-Generation Perinatal Care: An Overview of the Literature. J Med Internet Res. 2018;20(6):e202.\u003c/li\u003e\n\u003cli\u003eTsai Z, Kiss A, Nadeem S, Sidhom K, Owais S, Faltyn M, et al. Evaluating the effectiveness and quality of mobile applications for perinatal depression and anxiety: A systematic review and meta-analysis. J Affect Disord. 2022;296:443-53.\u003c/li\u003e\n\u003cli\u003ePhilippi P, Baumeister H, Apolinario-Hagen J, Ebert DD, Hennemann S, Kott L, et al. Acceptance towards digital health interventions - Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interv. 2021;26:100459.\u003c/li\u003e\n\u003cli\u003eVenkatesh, Morris, Davis, Davis. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 2003;27(3).\u003c/li\u003e\n\u003cli\u003eB\u0026auml;uerle A, Mallien C, Rassaf T, Jahre L, Rammos C, Skoda EM, et al. Determining the Acceptance of Digital Cardiac Rehabilitation and Its Influencing Factors among Patients Affected by Cardiac Diseases. J Cardiovasc Dev Dis. 2023;10(4).\u003c/li\u003e\n\u003cli\u003eHennemann S, Beutel ME, Zwerenz R. Drivers and Barriers to Acceptance of Web-Based Aftercare of Patients in Inpatient Routine Care: A Cross-Sectional Survey. J Med Internet Res. 2016;18(12):e337.\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;der J, B\u0026auml;uerle A, Jahre LM, Skoda EM, Stettner M, Kleinschnitz C, et al. Acceptance, drivers, and barriers to use eHealth interventions in patients with post-COVID-19 syndrome for management of post-COVID-19 symptoms: a cross-sectional study. Ther Adv Neurol Disord. 2023;16:17562864231175730.\u003c/li\u003e\n\u003cli\u003eStoppok P, Teufel M, Jahre L, Rometsch C, Mussgens D, Bingel U, et al. Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions Among Patients With Chronic Pain Using the Unified Theory of Acceptance and Use of Technology: Cross-sectional Study. JMIR Form Res. 2022;6(8):e37682.\u003c/li\u003e\n\u003cli\u003eTivian XI GmbH. Unipark. 2023.\u003c/li\u003e\n\u003cli\u003eMarsall M, Engelmann G, Skoda EM, Teufel M, B\u0026auml;uerle A. Measuring Electronic Health Literacy: Development, Validation, and Test of Measurement Invariance of a Revised German Version of the eHealth Literacy Scale. J Med Internet Res. 2022;24(2):e28252.\u003c/li\u003e\n\u003cli\u003eNurtsch A, Teufel M, Jahre LM, Esber A, Rausch R, Tewes M, et al. Drivers and barriers of patients\u0026apos; acceptance of video consultation in cancer care. Digit Health. 2024;10:20552076231222108.\u003c/li\u003e\n\u003cli\u003eZobeidi T, Homayoon SB, Yazdanpanah M, Komendantova N, Warner LA. Employing the TAM in predicting the use of online learning during and beyond the COVID-19 pandemic. Front Psychol. 2023;14:1104653.\u003c/li\u003e\n\u003cli\u003eRasool T, Warraich NF, Sajid M. Examining the Impact of Technology Overload at the Workplace: A Systematic Review. SAGE Open. 2022;12(3).\u003c/li\u003e\n\u003cli\u003eCohen J. Statistical power analysis for the behavioral sciences. Academic press. 1988.\u003c/li\u003e\n\u003cli\u003eValencia SA, Barrientos Gomez JG, Gomez Ramirez MC, Luna IF, Caicedo HA, Torres-Silva EA, et al. Evaluation of a telehealth program for high-risk pregnancy in a health service provider institution. Int J Med Inform. 2023;179:105234.\u003c/li\u003e\n\u003cli\u003eFiska BS, Pay ASD, Staff AC, Sugulle M. Gestational diabetes mellitus, follow-up of future maternal risk of cardiovascular disease and the use of eHealth technologies-a scoping review. Syst Rev. 2023;12(1):178.\u003c/li\u003e\n\u003cli\u003eRentrop V, Damerau M, Schweda A, Steinbach J, Schuren LC, Niedergethmann M, et al. Predicting Acceptance of e-Mental Health Interventions in Patients With Obesity by Using an Extended Unified Theory of Acceptance Model: Cross-sectional Study. JMIR Form Res. 2022;6(3):e31229.\u003c/li\u003e\n\u003cli\u003eMorikawa M, Okada T, Ando M, Aleksic B, Kunimoto S, Nakamura Y, et al. Relationship between social support during pregnancy and postpartum depressive state: a prospective cohort study. Sci Rep. 2015;5:10520.\u003c/li\u003e\n\u003cli\u003eVerreault N, Da Costa D, Marchand A, Ireland K, Dritsa M, Khalife S. Rates and risk factors associated with depressive symptoms during pregnancy and with postpartum onset. J Psychosom Obstet Gynaecol. 2014;35(3):84-91.\u003c/li\u003e\n\u003cli\u003eLiu C, Chen H, Zhou F, Long Q, Wu K, Lo LM, et al. Positive intervention effect of mobile health application based on mindfulness and social support theory on postpartum depression symptoms of puerperae. BMC Womens Health. 2022;22(1):413.\u003c/li\u003e\n\u003cli\u003eLin J, Faust B, Ebert DD, Kramer L, Baumeister H. A Web-Based Acceptance-Facilitating Intervention for Identifying Patients\u0026apos; Acceptance, Uptake, and Adherence of Internet- and Mobile-Based Pain Interventions: Randomized Controlled Trial. J Med Internet Res. 2018;20(8):e244.\u003c/li\u003e\n\u003cli\u003eFaries MD. Why We Don\u0026apos;t \u0026quot;Just Do It\u0026quot;: Understanding the Intention-Behavior Gap in Lifestyle Medicine. Am J Lifestyle Med. 2016;10(5):322-9.\u003c/li\u003e\n\u003cli\u003eSheeran P, Webb TL. The Intention\u0026ndash;Behavior Gap. Social and Personality Psychology Compass. 2016;10(9):503-18.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"eHealth, maternal mental health, postpartum health, UTAUT, Women mental health","lastPublishedDoi":"10.21203/rs.3.rs-4143017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4143017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePostpartum 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHigh acceptance of eHealth interventions in postpartum mental health care was reported by 68.2% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;309) of postpartum women. Acceptance was significantly higher in women affected by mental illness, \u003cem\u003et\u003c/em\u003e(395) = -4.72, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.50, and with postpartum depression (present or past), \u003cem\u003et\u003c/em\u003e(395) = -4.54, \u003cem\u003ep\u003c/em\u003e\u003csub\u003eadj\u003c/sub\u003e \u0026lt; .001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.46. Significant predictors of acceptance were Perceived support during pregnancy (β = \u0026minus;\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.009), Quality of life (β = \u0026minus;\u0026thinsp;.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022), Postpartum depression (β\u0026thinsp;=\u0026thinsp;.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), Digital confidence (β\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002), and the UTAUT predictors Effort expectancy (β\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.037), Performance expectancy (β\u0026thinsp;=\u0026thinsp;.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Social influence (β\u0026thinsp;=\u0026thinsp;.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The extended UTAUT model was able to explain 59.8% of variance in acceptance.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis 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.\u003c/p\u003e","manuscriptTitle":"Drivers and Barriers of Acceptance of eHealth Interventions in Postpartum Mental Health Care: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 11:33:28","doi":"10.21203/rs.3.rs-4143017/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-04-18T07:48:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-27T02:44:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-03-21T11:09:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e908d46f-7ff2-41e7-b1b1-f9934d2b0273","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:07:08+00:00","versionOfRecord":{"articleIdentity":"rs-4143017","link":"https://doi.org/10.1186/s12889-025-25297-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-17 15:58:41","publishedOnDateReadable":"November 17th, 2025"},"versionCreatedAt":"2024-03-29 11:33:28","video":"","vorDoi":"10.1186/s12889-025-25297-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25297-1","workflowStages":[]},"version":"v1","identity":"rs-4143017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4143017","identity":"rs-4143017","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}