App-based interventions for the prevention of postpartum depression: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-2431187/v1

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App-based interventions for the prevention of postpartum depression: a systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-2431187/v1

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Journal Publication

published 14 Jun, 2023

Read the published version in BMC Pregnancy and Childbirth →

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Background

This study aimed to assess whether automated apps are effective in preventing postpartum depression.

Methods

We conducted an article search on the electronic databases of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via Ovid, Scopus, PsycINFO, CINAHL, and ProQuest Dissertations & Theses A&I on March 26th, 2020. We also searched the International Clinical Trials Platform Search Portal (ICTRP), and Clinical Trials.

Results

We identified 1581 references, and seven studies were ultimately included in this review. Only one study has assessed the onset of postpartum depression as an outcome. This indicated that after the app intervention, the proportion of women who developed postpartum depression was significantly lower in the intervention group than in the control group (6 weeks postpartum risk ratio (RR)0.79, 95% confidence intervals (95%CI)0.58–1.06; 3 months postpartum RR0.74, 95%CI0.50–1.09; 6 months postpartum RR0.73, 95%CI0.49–1.11 RR0.73, 95%CI0.49–1.11). We performed a meta-analysis of Edinburgh Postnatal Depression Scale (EPDS) scores at each time point. During the immediate (0–8 weeks postpartum) period, the intervention group had significantly lower EPDS scores than the control group (mean differences (MD) -0.59; 95%CI -1.00 to -0.18; P = 0.005). In the short term (9–16 weeks postpartum), there was no significant difference between the intervention and control groups in terms of EPDS score (MD -0.32; 95%CI -10.82 to 1.17; P = 0.20).

Limitations:

Only one randomized controlled trial (RCT) measured the onset of postpartum depression as an outcome; we performed a meta-analysis only on the EPDS scores. Additionally, there was a high risk of incomplete outcome data due to the high attrition rates in the study.

Conclusion

The apps, including an automated component for the prevention of postpartum depression, improved the EPDS score; furthermore, they may prevent postpartum depression.

Postpartum depression is defined as depression that develops within the first year postpartum in clinical practice and research [1]. About 12% of mothers who give birth experience postpartum depression [1, 2]. There are many causes of perinatal deaths, but 5–20% of these deaths are due to suicide caused by postpartum depression [3–5]. Postpartum depression seriously effects the development of mothers and children [6]. Thus, prevention of postpartum depression is essential, but postpartum women are less likely to access prevention and treatment of postpartum depression due to various barriers [7, 8]. There are various physical and mental barriers, including the lack of time, stigma, and childcare issues.

Treatments without face-to-face contact have been developed to overcome these barriers, such as telemedicine and the use of short message services (SMS), phone calls, and video calls using smartphones. In recent years, digital health technologies have made remarkable progress, and fully automated apps for the treatment and prevention of mental illnesses have emerged. App interventions are effective in improving depressive symptoms and generalised anxiety in the general population [9]. Furthermore, the NICE recommends computerised cognitive behavioural therapy (CCBT) for mild to moderate depression in the general population. The leading commercial apps, Headspace, Youper, and Wysa, treat depression in the general population [10].

There are some previous systematic reviews (SR) on treating postpartum depression. Therapist interventions via the Internet significantly improve the symptoms of postpartum depression [11]. mHealth education significantly improved the Edinburgh Postnatal Depression Scale (EPDS) scores after the intervention [12]. mHealth education is an educational intervention that provides specific health information, social support, and advice through mobile technology such as SMS, phone calls, and video calls using smartphones and tablets [13]. Tsai et al. published an SR on treating postpartum depression with a fully automated app but concluded that this did not improve postpartum depression symptoms [14]. They also evaluated the quality of the apps and stated that a few were of high quality.

There has been an SR on treating postpartum depression with a fully automated app [14], but no SR on prevention has been conducted. Annette Bauer et al. indicated that the present value of total lifetime costs of perinatal depression was £75,728 per woman in the UK [15]; hence, prevention is more beneficial than treatment after getting sick. Therefore, we conducted an SR focusing on whether apps including an automated component are effective in preventing postpartum depression.

We conducted a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [16].

Inclusion Criteria

We included all RCTs that evaluated the application, including an automated component aimed at preventing postpartum depression by providing psychosocial interventions. An automated component indicates that the app itself has a function aimed at preventing postpartum depression. In this study, we incorporated a combination of automated apps and human interventions (e.g. phone calls and other interventions outside the app). Psychosocial interventions are non-pharmacological interventions that focus on psychological and social aspects, such as counselling, educational programs, social support, cognitive-behavioural therapy, motivational interviewing, and supportive care. We included women from pregnancy to 1 year postpartum and excluded those already diagnosed with depression, taking antidepressants, or suffering from psychiatric disorders. Quasi-randomised trials were excluded.

Search Strategy

We conducted an article search of the electronic databases of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via Ovid, Scopus, PsycINFO, CINAHL, and ProQuest Dissertations & Theses A&I on March 26th, 2020. We also searched the International Clinical Trials Platform Search Portal (ICTRP), and Clinical Trials. The following terms were used: [postpartum depression] [computer software, mobile applications, and computer-assisted therapy]. The search strategy and terms have been presented in Table 1.

References cited in the identified RCTs cited references of the identified RCTs, and recent SRs were also searched.

Table 1 indicates the search strategies and terms of each electronic database. The electronic databases are the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via Ovid, Scopus, PsycINFO, CINAHL, ProQuest Dissertations & Theses A&I, the International Clinical Trials Platform Search Portal (ICTRP), and Clinical Trials.

Study Selection, Data Extraction, and Risk of Bias Assessment

Two independent reviewers screened the titles and abstracts of the identified studies (AS, KJ, TA, YO, and YM), after removing duplicates. All selected studies were subjected to a full-text review and evaluated for meeting the inclusion criteria by two reviewers (KK and YM). In cases of disagreement, the two reviewers discussed and resolved the issues. Disagreements were resolved via discussion with a third reviewer (YO). In these cases, we could not decide whether the study should be included after the full-text review; we contacted the original author.

Two reviewers independently assessed the quality of each study using the Cochrane Risk of Bias (ROB) for RCTs. We evaluated random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessments, incomplete outcome data, and selective outcome reporting to rate studies as ‘low risk’, ‘high risk’, or ‘unclear’. Disagreements between the two reviewers were resolved through discussions. Discussions were also held with a third reviewer, as needed.

Types of Outcome Measures

The primary outcome was postpartum depression onset. Each study defined the onset of postpartum depression by using a clinical diagnostic interview or screening tool. A clinical diagnostic interview for depression is an evaluation conducted by a trained examiner based on an official diagnostic system, such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5), the International Classification of Disease (ICD-10), or other standard methods such as the Research Diagnostic Criteria (RDC) [17]. For example, screening tools include the EPDS [18].

Secondary outcomes were scores on the EPDS, the Patient Health Questionnaire-9 (PHQ-9), other depression-related measures (e.g. the Centre for Epidemiologic Studies Depression Scale (CES-D)), and the onset of anxiety and scale scores. Outcomes were assessed in the immediate term (0–8 weeks postpartum), short-term (9–16 weeks postpartum), intermediate-term (17–24 weeks postpartum), and long-term (> 25 weeks postpartum).

Data Synthesis and Statistical Analysis

A random-effects model for the meta-analyses [19] was used, as there was clinical heterogeneity in the interventions in this review. Binary variables were calculated using risk ratios (RR) with 95% confidence intervals (95% CIs). Continuous variables were calculated using standardised mean differences (SMDs) with 95%CIs.

We analysed these data based on intention-to-treat (ITT). When ITT data were available, they were prioritised over per-protocol or complete data. The missing data were queried by the original authors.

Heterogeneity was visually assessed using forest plots. I-square statistics [19], chi-squared statistics, and their P values were used to measure statistical heterogeneity. Data analysis was performed using Review Manager version 5.4 (Nordic Cochrane Center, Cochrane Collaboration; Copenhagen, Denmark; http://ims.cochrane.org/revman).

Figure 1 shows the flow diagram of the study selection process. We conducted an article search on March 26, 2020. We identified 1581 references and removed 282 duplicates. The titles and abstracts were evaluated, and 135 references were included in the full-text review. A total of 123 references were excluded because they did not meet the eligibility criteria, five were ongoing studies, and seven were ultimately included in this review.

Characteristics of Included Studies

Table 2 presents the characteristics of the studies included. All studies were published after 2015. In five studies, the intervention started prenatally [20–23] and in two studies, the intervention started postpartum [24, 25]. Two studies had participants as married couples [20, 24]. In one study, only the results for women were available [20].

Three studies provided the app interventions based on cognitive behavioral therapy [21, 23, 25]. One study used an intervention with an app aimed at preventing perinatal depression [26]. The app was a fully automated Internet-based program with 44 sessions for preventive intervention for perinatal depressive symptoms. Two studies were interventions in which the app provided educational content like psychoeducation, newborn care, etc. [22, 24]. One study was an intervention that provided educational sessions via phone and follow-up sessions via the app [20].

Four studies provided fully automated apps [21, 23, 25, 26]. Three studies included an automated app and human intervention (e.g., by phone outside the app) [20, 22, 24]. Of these, the intervention by Shorey et al. allowed non-interactive communication with a healthcare professional, in addition to the automated app [24]. Another intervention by Shorey et al. had a telephone education session followed by an automated app education session [20]. Chan et al.’s intervention had an automated app plus an interactive Q and A platform available [22].

One study has assessed the development of postpartum depression [26]. Seven studies assessed the Edinburgh Postpartum Depression Scale (EPDS) [20–26]. One study evaluated the Centre for Epidemiologic Studies Depression Scale (CES-D) and Major Depressive Episode (MDE) [23].

Table 2 indicates these items of each RCT. The items are the country, the number of people included, mean age, the timing of intervention, the format of intervention, the content of interventions and controls, treatment frequency, the timing of follow-up from baseline, the complete rate in the intervention group, and the main outcome.

Risk of Bias in the Included Studies

The risk of bias graph is shown in Fig. 2, and the risk of bias summary is shown in Fig. 3. Random sequence allocation was judged to have a low risk of bias in all studies. Allocation concealment was considered at a low risk of bias in four studies and unclear in three studies. App-based psychosocial interventions are difficult to blind, and all studies rated the risk of bias in blinding participants and personnel high. Blinding of outcome assessment was judged to be at high risk of bias in two studies, unclear in two studies, and low in three studies. Incomplete outcome data were at high risk of bias in five studies and low risk of bias in two studies. Selective reporting bias was judged to be at high risk of bias in two studies, unclear in two studies, and low in three studies. Other biases were at high risk of bias in the study and low risk of bias in the other six studies.

Effects of Interventions

Primary Outcomes

Only one study assessed the onset of postpartum depression as an outcome [26]. Therefore, a meta-analysis could not be performed on the primary outcomes.

Haga et al. evaluated EPDS at 21–25 weeks gestation as a baseline, and at 38 weeks gestation, 6 weeks postpartum (immediate), 3 months postpartum (short-term), and 6 months postpartum (intermediate) [26]. They defined an EPDS score of 10 or more as the onset of postpartum depression. After the app intervention, the proportion of women who developed postpartum depression was lower in the intervention group than in the control group, but there was no significant difference between the two groups (6 weeks postpartum RR0.79, 95%CI0.58–1.06; 3 months postpartum RR0.74, 95%CI0.50–1.09; 6 months postpartum RR0.73, 95%CI0.49–1.11 RR0.73, 95%CI0.49–1.11).

Secondary Outcomes

Seven studies measured EPDS scores. Three studies measured the EPDS score in the immediate (0–8 weeks postpartum) and two in the short-term (9–16 weeks postpartum). We performed a meta-analysis of EPDS scores at each time point.

During the immediate (0–8 weeks postpartum) period, the intervention group had significantly lower EPDS scores than the control group (MD -0.59; 95%CI -1.00 to -0.18; P = 0.005; Fig. 4). There was no evidence of heterogeneity in the effects (I2 = 0%, Chi2 = 0.03, P = 0.99).

In the short term (9–16 weeks postpartum), there was no significant difference between the intervention and control groups in terms of EPDS score (MD -0.32; 95%CI -10.82 to 1.17; P = 0.20; Fig. 5). There was no evidence of heterogeneity in the effects (I2 = 0%, Chi2 = 0.43, P = 0.51).

Barrera et al. measured the EPDS score and calculated the hazard ratio (HR) using EPDS > 10 as the cut-off [23]. They did not specify when the EPDS was measured, and the results were not available for EPDS as a continuous variable. We did not include their study in the meta-analysis. There was no statistically significant difference in the incidence of postpartum depression (EPDS score ≥ 10) between the intervention and comparison groups (HR0. 598; 95%CI0.339 to 1.022; P = 0.061).

Shorey et al. included couples as the participants. On enquiry, EPDS scores by sex were not recorded. We did not include this study in our meta-analysis [24]. There were no statistically significant differences between the intervention and comparison group (MD, 0.33; 95%CI -1.21 to 0.53; P = 0.45).

We did not include Shorey et al. in the meta-analysis because of the high heterogeneity [20]. The intervention group had a significantly better outcome score for postnatal EPDS than did the control group (MD, 0.91; 95%CI -1.34 to -0.49; P < 0.001).

PHQ-9 was not measured in either study.

The onset of Anxiety Disorders was not measured in either study.

The Hospital Anxiety and Depression Scale (HADS) [25], State-Trait Anxiety Inventory (STAI) [20], and Depression Anxiety Stress Scale) [22] were used as indicators of anxiety.

Fonseca et al. found no statistically significant difference in HADS between the intervention and control groups at the time of evaluation (t=-0.12, p = .903, d = 0.002) ( fixed effects with an autoregressive covariance matrix) [25].

Chan et al. found that anxiety items on the DASS were not significantly different between the intervention and control groups (MD0.01; 95%CI -0.30 to 0.50; P = 0.94) (LOCF: the last observation carried forward method) [22].

This systematic review and meta-analysis examined the effectiveness of apps, including automated components, in preventing postpartum depression. We included seven RCTs in the final analysis.

One study measured the development of postpartum depression as its primary outcome. This RCT defined an EPDS score of 10 or higher as the onset of postpartum depression, and the app intervention lowered the proportion of women who developed postpartum depression [26].

We conducted a meta-analysis of EPDS scores. The meta-analysis showed significantly lower EPDS scores in the Immediate (0–8 weeks postpartum) period, but no significant difference in EPDS scores in the short-term (9–16 weeks postpartum) period.

The difference from previous studies was in the purpose of the intervention in postpartum depression prevention. Tsai et al. conducted a systematic review and meta-analysis of the treatment of postpartum depression using a fully automated app [14]. A systematic review showed that apps are ineffective in treating postpartum depression. However, there was high heterogeneity in the incorporated studies; therefore, the results must be carefully interpreted. Our systematic review also differed from previous studies in that the intervention method was an app with an automated component. In a previous systematic review and meta-analysis by Mu et al., the use of the internet to prevent postpartum depression significantly improved the prevalence of postpartum depression [11]. mHealth education via online or apps significantly improved EPDS scores [12].

This systematic review showed that interventions using apps with automated components are effective in preventing postpartum depression. This indicates that pregnant and postpartum women may be able to voluntarily engage in the prevention of postpartum depression using a smartphone or tablet device, rather than face-to-face.

The recent coronavirus disease-19 (COVID-19) pandemic has had various impacts on pregnant and postpartum women, including fear of COVID-19, fear of infection, lifestyle changes due to lockdown, and social isolation. The decrease in physical activity during the COVID-19 pandemic has increased postpartum depression [27]. Indeed, the COVID-19 pandemic increased the prevalence of postpartum depression to 34% (95% CI 24–46%) [28], although the prevalence of postpartum depression was 20.8% (95% CI 17.9–33.8%) in middle-income countries and 25.8% (95% CI 18.4–23.1%) in low-income countries until 2017 [29]. The COVID-19 pandemic has forced us to limit face-to-face interventions for postpartum depression despite the increasing demand for such interventions. This is a barrier for interventions to prevent postpartum depression, but app-based interventions that include an automated component can be an intervention method that can overcome this barrier. Pregnant and postpartum women can use their smartphones or tablets to voluntarily engage in postpartum depression prevention, rather than face-to-face. During the COVID-19 pandemic, postpartum depression prevention interventions with apps that include an automated component will receive more attention and be utilised.

This study had some limitations. Only one RCT measured the onset of postpartum depression as an outcome [26]. Therefore, we performed a meta-analysis only on EPDS scores. Additionally, there was a high risk of incomplete outcome data due to the high attrition rates in the study.

This study presents the results of current RCTs on interventions with apps, including an automated component for the prevention of postpartum depression that has been conducted. The apps, including an automated component for the prevention of postpartum depression, improved the EPDS score; furthermore, they may prevent postpartum depression. Additional RCTs like reducing attrition rates and setting the primary outcome onset of postpartum depression are needed.

95%CI: 95% Confidence Intervals

CCBT: Computerized Cognitive Behavioral Therapy

CENTRAL: the Cochrane Central Register of Controlled Trials

CES-D: the Center for Epidemiologic Studies Depression Scale

COVID-19: coronavirus disease

DASS: the Depression Anxiety Stress Scale

DSM-5: the Diagnostic and Statistical Manual of Mental Disorders fifth edition

EPDS: the Edinburgh Postnatal Depression Scale

HADS: the Hospital Anxiety and Depression Scale

HR: Hazard Ratio

ICD-10: the International Classification of Disease

ICTRP: International Clinical Trials Platform Search Portal

ITT: Intention to Treat

LOCF: the Lost Observation Carried Forward method

MDE: Major Depressive Episode

PHQ-9: the Patient Health Questionnaire-9

PRISMA: the Preferred Reporting Items for Systematic Reviews and Meta-Analysis

RCT: Randomized Controlled Trial

RDC: Research Diagnostic Criteria

ROB: Risk of Bias

RR: Risk Ration

SMD: Standardized Mean Difference

SMS: Short Message Service

SR: Systematic Review

STAI: State-Trait Anxiety Inventory

Ethics approval and consent to participate: Not applicable

Consent for publication: Not applicable

Availability of data and materials: All data analyzed in this study are included within the article and a list of references.

Authors' contributions: YM designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. YO designed the study; screened titles and abstracts; conducted a full-text review; assessed the quality of each study; interpreted the data and review the manuscript. AS screened titles and abstracts and reviewed the manuscript. KK conducted a full-text review; assessed the quality of each study and reviewed the manuscript. KJ screened titles and abstracts. TA designed the study; interpreted the data and reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgements: We would like to thank Editage (www.editage.com) for English language editing.

Funding: Not applicable

Competing interests: The authors have no competing interests to disclose.

Author’s Information: YM is a family physician at Hamamatsu Satocho Clinic. YO is an associate professor in the Department of Healthcare Epidemiology, School of Public Health, Kyoto University. AS is a medical director at the Department of Obstetrics Gynecology, Yodogawa Christian Hospital. KK is a medical director at the Department of Gynecology, Nagano Municipal Hospital. KJ is a medical director at the Department of Obstetrics & Gynecology, Kikugawa General Hospital. TA is a lecturer in the Division of Clinical Epidemiology, Research Center for Medical Sciences, The Jikei University School of Medicine and belongs to the Section of Clinical Epidemiology, Department of Community Medicine, Graduate School of Medicine, Kyoto University

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Tables are available in Supplementary Files section.

No competing interests reported.

Download PDF

Journal Publication

published 14 Jun, 2023

Read the published version in BMC Pregnancy and Childbirth →

Editorial decision: Major revision
10 Mar, 2023
Reviews received at journal
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Reviewers agreed at journal
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Reviews received at journal
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Reviewers agreed at journal
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First submitted to journal
31 Dec, 2022

You are reading this latest preprint version

App-based interventions for the prevention of postpartum depression: a systematic review and meta-analysis

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Limitations:

Conclusion

Figures

Background

Methods

Inclusion Criteria

Search Strategy

Study Selection, Data Extraction, and Risk of Bias Assessment

Types of Outcome Measures

Data Synthesis and Statistical Analysis

Results

Characteristics of Included Studies

Risk of Bias in the Included Studies

Effects of Interventions

Primary Outcomes

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1