App-based interventions for the prevention of postpartum depression: a systematic review and meta-analysis
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App-based interventions for the prevention of
postpartum depression: a systematic review and
meta-analysis
Yumika Miura
Hamamatsu Satocho Clinic
Yusuke Ogawa
Kyoto University
Ayako Shibata
Yodogawa Christian Hospital
Kyosuke Kamijo
Nagano Municipal Hospital
Ken Joko
Kikugawa General Hospital
Takuya Aoki ( taoki@jikei.ac.jp )
Jikei University School of Medicine
Research Article
Keywords:
Posted Date: January 17th, 2023
DOI: https://doi.org/10.21203/rs.3.rs-2431187/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Page 1/17Abstract
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.
Background
Page 2/17Postpartum 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.
Methods
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
Page 3/17incorporated 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
Page 4/17depression 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).
Results
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
Page 5/17automated 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
Page 6/17intervention, 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].
Page 7/17Chan 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].
Discussion
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
Page 8/17women 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.
Conclusions
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.
Abbreviations
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
Page 9/17MDE: 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
Declarations
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.
Page 10/17Author’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
Tables are available in Supplementary Files section.
Figures
Page 13/17Figure 1
Study flow diagram
Page 14/17Figure 2
Risk of bias graph.
Page 15/17Figure 3
Risk of bias summary.
Page 16/17Figure 4
Effectiveness for EPDS score in the immediate term.
Figure 5
Effectiveness for EPDS score in the short term
Supplementary Files
This is a list of supplementary files associated with this preprint. Click to download.
Table1.xlsx
Table2.xlsx
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