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. 2021 Oct 29;16(10):e0259474. doi: 10.1371/journal.pone.0259474

Mobile interventions targeting common mental disorders among pregnant and postpartum women: An equity-focused systematic review

Ammar Saad 1,2, Olivia Magwood 2,3, Tim Aubry 4,5, Qasem Alkhateeb 6, Syeda Shanza Hashmi 7, Julie Hakim 8, Leanne Ford 9, Azaad Kassam 10,11,12, Peter Tugwell 1,13,14, Kevin Pottie 1,2,15,*
Editor: Eugene Demidenko16
PMCID: PMC8555821  PMID: 34714882

Abstract

Introduction

Pregnant and postpartum women face major psychological stressors that put them at higher risk of developing common mental disorders, such as depression and anxiety. Yet, their limited access to and uptake of traditional mental health care is inequitable, especially during the COVID-19 pandemic. Mobile interventions emerged as a potential solution to this discontinued healthcare access, but more knowledge is needed about their effectiveness and impact on health equity. This equity-focused systematic review examined the effectiveness and equity impact of mobile interventions targeting common mental disorders among pregnant and postpartum women.

Methods and results

We systematically searched MEDLINE, EMBASE, PsychINFO and 3 other databases, from date of database inception and until January 2021, for experimental studies on mobile interventions targeting pregnant and postpartum women. We used pooled and narrative synthesis methods to analyze effectiveness and equity data, critically appraised the methodological rigour of included studies using Cochrane tools, and assessed the certainty of evidence using the GRADE approach. Our search identified 6148 records, of which 18 randomized and non-randomized controlled trials were included. Mobile interventions had a clinically important impact on reducing the occurrence of depression (OR = 0.51 [95% CI 0.41 to 0.64]; absolute risk reduction RD: 7.14% [95% CI 4.92 to 9.36]; p<0.001) and preventing its severity perinatally (MD = -3.07; 95% CI -4.68 to -1.46; p<0.001). Mobile cognitive behavioural therapy (CBT) was effective in managing postpartum depression (MD = -6.87; 95% CI -7.92 to -5.82; p<0.001), whereas other support-based interventions had no added benefit. Results on anxiety outcomes and utilization of care were limited. Our equity-focused analyses showed that ethnicity, age, education, and being primiparous were characteristics of influence to the effectiveness of mobile interventions.

Conclusion

As the COVID-19 pandemic has increased the need for virtual mental health care, mobile interventions show promise in preventing and managing common mental disorders among pregnant and postpartum women. Such interventions carry the potential to address health inequity but more rigorous research that examines patients’ intersecting social identities is needed.

Introduction

Pregnancy is a life experience characterized by major physical and psychological stressors to the childbearing mother [1,2]. If left unaddressed, such stressors could have long-lasting mental health sequelae that affect the mother for years to come and disrupt the entire family ecosystem [35]. Common mental disorders (CMD) comprise a range of non-psychotic conditions defined by the presence of two symptom dimensions; depression and anxiety [6,7]. Research shows that depression and anxiety are the most common mental health conditions during pregnancy and postpartum [8,9]. Recent evidence also suggests that the social isolation and uncertainty surrounding the COVID-19 pandemic has increased the occurrence and worsened the severity of depression and anxiety among this population [1012].

Equity, in a broader sense, is an ethical value of fairness and social justice [13]. As such, health equity is defined as the absence of avoidable health disparities that are judged to be unfair and unjust [14]. The heightened levels of mental health needs among pregnant and postpartum women are often met with major barriers to accessing traditional mental health care [15,16], especially during the COVID-19 pandemic [17,18]. Our preliminary research shows that these barriers are heterogeneous across different subgroups of pregnant and postpartum women and may be linked to their characteristics and social identities [19], magnifying their vulnerability and worsening their outcomes [20].

Mobile interventions” are on the rise globally [21,22], providing a convenient and easily accessible approach to mental healthcare delivery [23]. These interventions utilize different mobile phone features, such as smartphone applications or text messages, to deliver asynchronous care and support without the need for direct communication with healthcare providers [24]. The mechanisms by which mobile interventions elicit change vary depending on their purpose [23]. Examples of such mechanisms include but are not limited to; peer and social support [25]; symptom tracking and monitoring [26]; health promotion and behavioural change [27]; and self-paced psychotherapy and stress management [28]. Evidence suggests that mobile interventions are acceptable for patients [29], and their mental healthcare providers [30], positioning them as a potential solution to the health inequity among pregnant and postpartum women. Yet more research is needed to understand the effectiveness of these interventions and the impact they carry on the health equity of this population.

Research objectives

The objective of this equity-focused systematic review is to synthesize quantitative evidence on the effectiveness and equity impact of prevention- and management-based mobile interventions targeting common mental disorders and stress among pregnant and postpartum women.

Materials and methods

This equity-focused systematic review was registered (PROSPERO ID: CRD42020200828), prepared according to a published protocol [19], and reported using the Preferred Reporting Items for Systematic Reviews (PRISMA) [31] and its equity extension (PRISMA-E) [32] (S1 and S2 Files). We followed a collaborative research approach whereby stakeholders, including pregnant and postpartum women with lived experience of depression and anxiety were engaged throughout different stages of the project [33]. Detailed description of this engagement is presented in S3 File.

Search strategy and selection criteria

We developed a comprehensive search strategy in consultation with a health sciences librarian (S4 File). We systematically searched Medline, Embase, PsychINFO, and Cochrane CENTRAL (via OVID), CINAHL (via EBSCO), PTSDPubs, as well as citation lists from Web of Science. We searched these databases from inception until June 2020 and updated the search in January 2021. We did not apply any date, language, or setting restrictions to our search. As well, we conducted a focused grey literature search (S4 File), hand-inspected the reference lists of all relevant systematic reviews, and backward-traced all potentially relevant trial registrations and protocols for publications. All records were uploaded to a systematic review management system “Covidence” [34], and two independent reviewers screened all records against our eligibility criteria, using their titles and abstracts, and then full text (Table 1).

Table 1. Eligibility criteria and outcomes of interest.

Study characteristic Description of the inclusion criteria
Population Pregnant women at any stage of the pregnancy experience (antenatal, perinatal, or postpartum), and regardless of the pregnancy outcome (e.g., full birth, miscarriage, medically induced abortion).
We defined the following pregnancy stages:
Antenatal: Any time from the onset of pregnancy and until the time point in which delivery is expected [0–36 weeks]
Perinatal: The period of time surrounding delivery and until it occurs [36 weeks-delivery]
Postpartum: The period of time after delivery and until 12 months after it occurs [delivery-12 months after]
Intervention Mobile interventions targeting common mental disorders and stress:
• Interventions that deliver care or support to pregnant women using features supported by mobile technology (e.g., mobile applications, text messaging programs) with the intention of managing or preventing mental health symptoms, psychological distress, or improving access to pregnancy-related or mental health care.
• These interventions operate “asynchronously”: independent of direct, face-to-face contact with a psychiatrist, psychologist, mental health professional, or primary healthcare provider, and can utilize different mechanisms (e.g., self-management of symptoms, self-management with supported care and peer support, improving cognition and thinking, improving skills and behaviours, providing psychoeducation and therapy or tracking symptoms).
Comparison • Usual or standard care
• Controlled intervention
• Placebo intervention
• No intervention
• Waitlisting
Outcomes • Severity of common mental health symptoms
• Changes in the occurrence of common mental health disorders
• Psychological wellbeing and distress
• Utilization of pregnancy related and mental healthcare services
Study design Using the recommendations of the Cochrane Effective Practice and Organization of Care (EPOC) group [35]:
• Randomized and quasi-randomized controlled trials (RCTs; qRCTs)
• Non-randomized controlled studies (NRS)
• Controlled before and after studies (CBA)
• Controlled interrupted time series (CITs) and repeated measures studies.
Time frame of follow-up • Short term: [post-intervention to 3 months]
• Medium term: [over 3 months to 6 months]
• Long term: [over 6 months of follow-up]

Data analysis

We used a standardized data collection form to extract data from included studies (S5 File). Two reviewers performed this activity, in duplicate and independently. Any discrepancies were resolved by discussion. We critically appraised the methodological rigour of included studies using the revised Cochrane Risk of Bias 2.0 tool (aka. ROB 2.0) for RCTs [36,37], and the Cochrane Risk of Bias in Non-Randomized Studies of Interventions (aka. ROBINS-I) tool for non-randomized studies [38,39]. Visual representations of these assessments were created using the ROBVIS platform [40].

Our statistical analysis plan aimed at calculating a uniform quantitative effect estimate from each included study to facilitate the narrative and pooled synthesis of results. For continuous outcomes (e.g., severity of symptoms), we chose the mean difference (MD) between study arms at follow-up, as this effect estimate measures the comparative effectiveness of the intervention relative to the control group after a period of intervention implementation. We retrieved mean differences from included studies or calculated them using the statistical algorithms presented by Deeks and Higgins on behalf of the Statistical Methods Group of The Cochrane Collaboration [41]. Calculation of mean differences comprised extracting the means, standard deviations, and sample sizes for each study arm from the included study and plotting these values into the RevMan 5.4 software. Calculated effect estimates were accompanied by measures of statistical significance (i.e., 95% confidence intervals and p-values). For categorical outcomes (e.g., occurrence of disease), we chose to retrieve a relative risk measure (odds ratio and/ or risk ratio), as well as an absolute risk measure (absolute risk reduction) for each effect estimate. Whenever these values were not reported in included studies, we aimed to calculate them using the same approach we used for continuous outcomes [41]. Similarly, effect estimates of categorical outcomes were accompanied by measures of statistical significance (i.e., 95% confidence intervals and p-values). To ensure reproducibility of our results, we reported the effect estimates that were retrieved, those that were calculated, and the values used to calculate them from each included study.

We assessed clinical homogeneity across studies by standardizing the definitions of their populations, interventions, comparisons, outcomes, and timepoints [42], and comparing these criteria across studies. Whenever possible, we meta-analyzed data by pooling the mean differences at follow-up from clinically-homogeneous studies using a random effects model [43]. We utilized the inverse variance statistical methods embedded in RevMan 5.4 software to meta-analyze data [44]. We assessed statistical heterogeneity using the I2 (≥70%) and Chi Square tests of independence (p≤0.1) [45]. Whenever clinical heterogeneity prevented the pooling of results, we synthesized evidence using a narrative approach [46,47]. For our equity-focused analysis, we adapted the PROGRESS+ framework, which stands for place of residence; race, ethnicity, culture, and language; occupation; gender and sex; religion; education; socioeconomic status; social capital; as well as personal characteristics associated with discrimination [48]. We defined “equity evidence” as any effect estimate that can be linked to a PROGRESS+ characteristic, and “equity impact” as any gradient in effect estimates when adjusting for a PROGRESS+ characteristic. Our primary equity analysis focused on ethnicity and race; age; socioeconomic status; social capital; and experience of intimate partner violence [19]. Exploratory equity evidence was reported for other available PROGRESS+ characteristics. Statistical significance was set at the p<0.05 threshold, whereas clinical importance was assessed by reviewing the literature on minimal clinically important differences (MCID) of the tools measuring the outcomes, and by engaging providers and patients in the decision-making process [49]. Finally, we used the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology to assess the certainty of our evidence [50].

Results

Search results and characteristics of included studies

Our search yielded a total of 6476 records, of which 4210 records were screened independently after the removal of duplicates (Fig 1). The inter-rater reliability was assessed with a random sample of n = 100 records and Cohen’s kappa coefficient of 0.9 proved high screening reliability. We screened 48 studies using their full-text publications and included n = 18 in our quantitative analysis, representing a cumulative sample size of N = 7,181 pregnant or postpartum women. A total of n = 14 studies linked their results to one or more PROGRESS+ characteristic and were included in our equity-focused analysis.

Fig 1. PRISMA flow diagram of study screening and selection.

Fig 1

The characteristics of included studies are presented in Table 2. In summary, thirteen included studies followed a randomized controlled study design, four followed a non-randomized controlled study design, and one followed a quasi-randomized controlled study design. The geographical location of the studies was diverse, and two studies required translation. Ten studies examined the effectiveness of prevention-based mobile interventions [5160], and eight studies examined management-based interventions [6168]. Outcome domains, measurement tools, and timing of follow-up are presented in Table 2, along with the quantitative statistical analysis method used in each included study, and effect estimate values we calculated.

Table 2. Characteristics of included studies.

Study ID Study design Setting Participants Intervention Comparison
Chan et al. 2019 [51] Randomized controlled trial Kwong Wah Hospital (KWH)
Hong Kong, China
First-time pregnant Chinese women
Pregnancy stage: Antenatal
iParent app: Smartphone application providing pregnancy-related health promotion. The app provided a platform to allow asking pregnancy-related questions.
Purpose: Preventing postnatal depression.
Standard antenatal care including a 4-session nurse-led antenatal course
Cheng et al. 2016 [52] Randomized controlled trial Hospital affiliated obstetric clinic
Kaohsiung City, Taiwan
Expecting Taiwanese pregnant women
Pregnancy stage: Perinatal
Smartphone application providing women with the means to connect and talk with peers (postpartum women).
Purpose: Preventing postpartum depression and stress.
Standard perinatal care
Chyzzy 2019 [53] Randomized controlled trial Community- based young parents’ agencies
Toronto, Canada
Expecting pregnant adolescents 16–24 years
Pregnancy stage: Perinatal
Smartphone application providing women with the means to talk (via voice and messages) with peer mentors (postpartum women)
Purpose: Preventing postpartum depression
Standard community support and care services
Gong et al. 2020 [54] Non-randomized study design: Non-randomized controlled trial Three public hospitals.
Jiangmen City, Guangdong Province, China
Chinese pregnant women
Pregnancy stage: Antenatal
Text messaging program consists of one-way messages promoting health and behavioural changes, as well as reminding participants of their routine appointments
Purpose: Preventing depression during pregnancy
Standard antenatal care
Jareethum et al. 2008 [55] Randomized controlled trial The antenatal care unit of the Siriraj Hospital
Bangkok, Thailand
Thai Pregnant women planning to deliver at the hospital
Pregnancy stage: Antenatal
Text messaging program providing antenatal support and education about pregnancy symptoms
Purpose: Increasing satisfaction with care and preventing anxiety
Standard antenatal care from the hospital
Lee and Kim 2017 [56] Non-randomized study design: Non-randomized controlled trial Four maternal hospitals
Daegu, South Korea
First-time South Korean Postpartum mothers
Pregnancy stage: Postnatal
Smartphone application with an interactive platform and newsfeed feature providing postpartum women with postpartum-related health management education and emergency contacts.
Purpose: Preventing postpartum depression and increasing confidence
Standard postpartum discharge care from the hospital
Mauriello et al. 2016 [57] Randomized controlled trial Three federally funded health centre organizations
Connecticut, Rhode Island, and New York, United States
English and Spanish speaking pregnant women
Pregnancy stage: Antenatal
Healthy Pregnancy Step By Step: iPad-delivered application providing women with risk stage-matched and tailored guidance on theoretically and empirically determined behavioural change strategies.
Purpose: Preventing postpartum stress and promoting stress management
Standard antenatal care from community organizations and behavioural change brochures
Shorey et al. 2019 [58] Randomized controlled trial Postnatal ward of a local tertiary hospital
National University Hospital region, Singapore
Postpartum mothers at risk of depression
Pregnancy stage: Postnatal
Mobile-based program providing mothers with the means to communicate with a trained peer volunteer using text-messages, phone calls, or applications depending on the mother’s preference
Purpose: Preventing postnatal depression and buffering the negative effects of childbirth
Standard postnatal care from the hospital
Shorey et al. 2017 [59] Randomized controlled trial Maternity ward of a local tertiary hospital
National University Hospital region, Singapore
Postpartum mothers and their partners
Pregnancy stage: Postnatal
Home But Not Alone: Mobile application providing mothers and their partners with psychoeducation and health promotion and reminding them of their appointments
Purpose: Decrease the risk of postnatal depression and improve parenting self-efficacy
Standard postnatal care provided by the hospital
Tsai et al. 2018 [60] Non-randomized study design:
Comparative cohort study
Obstetrics outpatient clinic at a medical center
Tainan, Taiwan
Taiwanese pregnant women with low-risk pregnancy
Pregnancy stage: Antenatal
Smartphone application where pregnant women can upload and access their antenatal care records. The application provided women with health promotion and journals for the self-management of symptoms. Access was available through the internet as well as the smartphone.
Purpose: Improving self-efficacy and prevent psychological stress
Standard antenatal support and education
Baumel et al. 2018 [61] Non-randomized study design: Historically controlled study The Adult Outpatient Department in the Zucker Hillside Hospital
New York, United States
New mothers with postpartum depression
Pregnancy stage: Postnatal
7Cups: Smartphone application providing women with the means to contact community peers or “past survivors”, a personalized progress map, and psychotherapy, such as gratitude exercises, mindfulness, psychoeducation, exercises drawn from principles of acceptance and commitment therapy.
Purpose: Manage postpartum depression and support women with mood disorders
Standard care or “treatment as usual”
Carissoli et al. 2017 [62] Randomized controlled trial At antenatal childbirth classes organized at the obstetrics wards of Saronno, Gallarate and Busto Arsizio Hospitals
Province of Varese, Italy
First-time Italian pregnant women approaching birth
Pregnancy stage: Perinatal
BenEssere Mama: Smartphone application providing mothers with daily relaxation and guided imagery exercises alongside mood journaling.
Purpose: Help mothers manage their affective state and improve their psychological wellbeing
Standard perinatal care
Constant et al. 2014 [63] Randomized controlled trial Two non-governmental organizations (NGOs) and two public sector primary care clinics
Cape Town, South Africa
Pregnant women undergoing medical abortion
Pregnancy stage: Post-abortion
Uniform text messaging program with health information about managing symptoms and side effects, alongside routine abortion care.
Purpose: Managing anxiety and emotional discomfort
Routine abortion care (including the provision of 200-mg mifepristone on site and self-administration of 800-mcg misoprostol at home) was provided to women in both study arms
Dennis-Tiwary et al. 2017 [64] Randomized controlled trial Large urban hospital
New York, United States
Pregnant women
Pregnancy stage: Antenatal
Personal Zen: smartphone application that utilizes an attention bias modification training (ABMT) protocol with video game-like features such as animated characters and sound effects.
Purpose: Manage anxiety and stress
Controlled application providing placebo training (PT)
Hantsoo et al. 2018 [65] Randomized controlled trial Urban ambulatory prenatal clinic within an academic
medical center
Pennsylvania, United States
Pregnant women from racial-ethnic minority groups with low incomes who were experiencing depressive symptoms.
Pregnancy stage: Antenatal
GINGER.IO Mood tracking and alert app: Smartphone application that monitors participants’ mood through daily surveys and physical activity trends and alerts providers of care of worsened mood
Purpose: Enhancing management of mood symptoms by improving mental health care delivery
Participants in both study arms received standard of care that includes a controlled smartphone application that allows access to the patient portal (PP)
Jannati et al. 2020 [66] Randomized controlled trial Three health care centers affiliated with Kerman University of Medical Sciences
Kerman, Iran
New mothers with postpartum depression
Pregnancy stage: postnatal
Happy Mom: Smartphone application providing women with cognitive behavioural therapy in the form of 8 lessons that read like a story
Purpose: Managing postpartum depression symptoms
Standard postpartum care as needed by mothers
Prasad 2018 [67] Quasi-randomized controlled trial
Quasi-random allocation scheme: days of the week
Local physician clinics
Texas, United States
New mothers with postpartum depression
Pregnancy stage: postnatal
VeedaMom: Smartphone application providing mothers with mindfulness and meditation exercises, videos that provide psychoeducation based on acceptance and commitment therapy (ACT) and dialectical behavioural therapy (DBT), as well as social support, in-app journaling, and mood tracking.
Purpose: Managing symptoms of depression after delivery and improving psychological wellbeing
Standard postnatal care in the form of booklets and resources for postnatal mothers provided by the state
Sawyer et al. 2019 [68] Randomized controlled trial Child and Family Health Service (CaFHS) community clinics
Adelaide and South, Australia
New mothers with depression and parenting problems
Pregnancy stage: Postnatal
eMums Plus: Smartphone application that provides mothers with means to chat with mothers and post questions about their experience. A time-sensitive guidance and health reminders, as well as resources and contacts. All chats were monitored by a nurse
Purpose: Managing depression symptoms and improving parenting skills
Standard postnatal care that includes an in-home visit by the nurse
Study ID Sample size a Outcomes Outcome measurement tool Outcome measurement time b Quantitative analysis method used
Study results used in our analysis
[calculated results] c
Mobile interventions targeting the prevention of mental health disorders
Chan et al. 2019
[51]
660
IG: 330
CG: 330
Severity of depression symptoms The validated Chinese version of the 10-item Edinburgh Postnatal Depression Scale (EPDS) Short term Analysis of covariance (ANCOVA)
MD at follow-up; 95% CI; p-value 
Severity of anxiety symptoms The anxiety subscale of the Depression Anxiety Stress Scale (DASS)
Severity of psychological stress The stress subscale of the Depression Anxiety Stress Scale (DASS)
Cheng et al. 2016
[52]
140
IG: 70
CG: 70
Severity of depression symptoms The Edinburgh Postnatal Depression Scale—Chinese version Short term
Analysis of covariance (ANCOVA)
MD at follow-up; SDs; p-value
Severity of psychological stress  The Perceived Stress Scale—Chinese version
Chyzzy 2019
[53]
40
IG: 21
CG: 19
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS) Short term Independent two-sample t test
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Severity of anxiety symptoms The State-Trait Anxiety Inventory (STAI)
Pregnancy related or mental health service utilization Number of healthcare visits using a questionnaire
Gong et al. 2020
[54]
4501
IG: 1739
CG: 2762
Occurrence of depression A cut off = 9 on the Edinburgh Postnatal Depression Scale (EPDS)—Chinese version Short term Chi square test; univariate logistic regression
N and % of participants with depression at each study arm; OR of not having depression
[OR of having depression; RR; ARR; 95% CI; p-value]
Jareethum et al. 2008
[55]
68
IG: 34
CG: 34
Severity of anxiety symptoms Questionnaire [unspecified] Short term Student t-test
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Lee and Kim 2017
[56]
81
IG: 40
CG: 41
Severity of depression symptoms The Korean version of the Edinburgh Postnatal Depression Scale Short term Paired t-test
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Mauriello et al. 2016
[57]
335
IG: 169
CG: 166
Pregnancy related or mental health service utilization Time spent practicing stress management using a structured questionnaire Short term, medium term Generalized estimating equations (GEEs)
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Shorey et al. 2019
[58]
138
IG: 69
CG: 69
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS) Short term Repeated measures analysis using a linear mixed model
MD at follow-up; 95% CI; p-value
Severity of anxiety symptoms The State-Trait Anxiety Inventory (STAI)
Shorey et al. 2017
[59]
125
IG: 63
CG: 62
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS) Short term Linear mixed-effect model analyses
MD at follow-up; 95% CI; p value
Tsai et al. 2018
[60]
155
IG: 80
CG: 75
Severity of psychological stress The Chinese Pregnancy Stress Rating Scale-36 (PSRS-36)  Short term Analyses of covariance (ANCOVA)
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Mobile interventions targeting the management of mental health disorders
Baumel et al. 2018
[61]
36
IG: 19
CG: 17
Severity of depression symptoms The Edinburgh Postnatal
Depression Scale (EPDS)
Short term Paired t-tests
M and SD for each study arm
[MD at follow-up; 95% CI; p-value]
Carissoli et al. 2017
[62]
78
IG: 35
CG: 43
Psychological wellbeing The Psychological Wellbeing Questionnaire (PWB)—Italian version Short term Repeated measures analysis of variance (ANOVA)
M and SD for each study arm; p-value
[MD at follow-up; 95% CI; p-value]
Constant et al. 2014
[63]
469
IG: 234
CG: 235
Severity of anxiety symptoms The Hospital Anxiety and Depression Scale (HADS) Short term Linear regression models
M and SD for anxiety measures; Beta coefficients for stress measures
[MD at follow-up; 95% CI; p-value for anxiety measures; Beta coefficients were reported as is for stress measures]
Severity of psychological stress The Impact of Event Scale (IES)
Dennis- Tiwary et al. 2017
[64]
29
IG: 15
CG: 14
Severity of psychological stress Lab-acquired cortisol saliva sample (ug/dl) Short term Analysis of covariance (ANCOVA)
ANCOVA F-test; p-value
[Results reported as is]
Hantsoo et al. 2018
[65]
72
IG: 48
CG: 24
Pregnancy related or mental health service utilization Number of provider phone calls that addressed mental health using electronic health record review Short term Analysis of covariance (ANCOVA)
M and SD for each study arm
[MD at follow-up; 95% CI; p-value]
Jannati et al. 2020
[66]
78
IG: 39
CG: 39
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS)—Persian version Short term Paired t-test; linear regression analysis
M and SD for each study arm
[MD at follow-up; 95% CI; p-value]
Prasad 2018
[67]
43
IG: 23
CG: 20
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS) Short term Mixed analysis of variance (ANOVA)
M and SD for each study arm
[MD at follow-up; 95% CI; p-value]
Psychological wellbeing The World Health Organization Quality of Life-BREF (WHOQOL-BREF) tool
Sawyer et al. 2019
[68]
133
IG: 72
CG: 61
Severity of depression symptoms The Edinburgh Postnatal Depression Scale (EPDS) Short term Linear generalized estimating equations (GEEs)
M and SD for each study arm
[MD at follow-up; 95% CI; p-value]
Pregnancy related or mental health service utilization Percentage of women who visited the emergency department 2 or more times in the past 6 months using a standardized questionnaire

a IG: Intervention group; CG: Control group.

b Short term: post-intervention to 3 months (inclusive); medium term: > 3 month to 6 months (inclusive); long-term: > 6 months.

c M: Mean; SD: Standard deviation; MD: mean difference; N: Number of participants with outcome; OR: Odds ratio; RR: Risk ratio; ARR: Absolute risk reduction; 95% CI: 95% confidence interval.

The majority of included studies were judged to have high risk of bias (Figs 2 and 3). Visual representations of critical appraisal assessments categorized by outcome are presented in S6 File. As well, GRADE Evidence Profiles for each outcome are presented in S7 File and provided alongside effectiveness findings in text. In summary, effectiveness results of mobile interventions targeting the prevention of mental health disorders were judged to be of higher certainty relative to interventions that target the management of mental health disorders. Certainty assessments ranged from high to very low and depended on the outcome and comparison (S7 File).

Fig 2. Risk of bias assessment in randomized controlled trials of effectiveness (ROB 2.0).

Fig 2

Fig 3. Risk of bias in non-randomized controlled studies of effectiveness (ROBINS-I).

Fig 3

The effectiveness of prevention-based mobile interventions

Results on prevention-based mobile interventions showed their potential to prevent depression and psychological stress. Clinical heterogeneity between studies prevented pooling results except for one instance of two studies (Fig 4). Heterogeneity mainly arose due to variability in outcome measurement tools and the pregnancy stage in which the interventions were delivered. Results from studies examining prevention-based mobile interventions are presented in Table 3. In the antenatal stages of pregnancy, one study measured changes in the occurrence of depression and reported a statistically significant and clinically important decrease in the odds of being screened positive for depression among the intervention group compared to the usual care group in the short term (OR = 0.51 [95% CI 0.41 to 0.64]; RR = 0.56 [95% CI 0.46 to 0.68]; absolute risk reduction RD: 7.14% [95% CI 4.92 to 9.36]; p<0.001; GRADE certainty: low) [54]. Severity of depression was measured in one study that found statistically significant, but not clinically important, improvement at 4-weeks follow-up (GRADE certainty: high) [51]. As well, evidence on anxiety from two studies was limited and did not reach statistical significance nor clinical importance [51,55], whereas evidence on psychological stress was mixed but showed potential; one study among antenatal women showed a statistically significant and clinically important improvements in stress levels compared to usual care at 12 weeks follow-up (MD = -11.12 [95% CI -17.19 to -5.05]; p<0.001; GRADE certainty: very low) [60], whereas another study found no added benefit at 4 weeks of follow-up [51].

Fig 4. Forest plot of comparison: peer support mobile applications vs standard of care, outcome: severity of depression symptoms measured using the Edinburgh Postnatal Depression Scale (EPDS)—short term.

Fig 4

IV, Random: Inverse Variance methods, random effects model; CI: confidence interval.

Table 3. Effectiveness results of prevention-based mobile interventions.

Prevention-based mobile interventions
Outcome and outcome domain Study ID Effect estimate [95% CI or p value] Statistical significance Clinical importance GRADE certainty
Antenatal interventions
Depression severity Chan 2019 [51] MD = -0.65 [-1.29 to 0.0] Yes No High
Depression occurrence Gong 2020 [54] OR = 0.51 [0.41 to 0.64] Yes Yes Low
Anxiety severity Chan 2019 [51] MD = 0.01 [-0.30 to 0.32] No No Moderate
Anxiety severity Jareethum 2008 [55] MD = -1.01 [-2.28 to 0.26] No No Very low
Psychological stress Chan 2019 [51] MD = 0.07 [-0.35 to 0.50] No No Moderate
Tsai 2018 [60] MD = —11.12 [-17.19 to -5.05] Yes Yes Very low
Utilization of care Mauriello 2016 [57] MD = 15.77 [0.12 to 31.42] No No Very low
Perinatal interventions
Depression severity Cheng 2016 [52]
Chyzzy 2020 [53]
MD = -3.07 [-4.68 to -1.46] Yes Yes Very low
Anxiety severity Chyzzy et al. 2020 [53] MD = -2.0 [-7.71 to 3.71] No No Low
Psychological stress Cheng et al. 2016 [52] MD = -3.52 [-4.95 to -2.09] Yes No Very low
Utilization of care Chyzzy et al. 2020 [53] MD = 5.3 [-6.97 to 17.57] No No Low
Postpartum interventions
Depression severity Shorey 2019 [58] MD = -2.11 [-4.0 to -0.3] Yes No Moderate
Depression severity Shorey 2017 [59] MD = -0.69 [-1.66 to 0.29] No No Moderate
Depression severity Lee 2017 [56] MD = -2.68 [-4.86 to -0.5] Yes No Very low
Anxiety severity Shorey 2019 [58] MD = -2.45 [-9.9 to 5.0] No No Moderate

In the perinatal stages of pregnancy, a meta-analysis of two studies providing peer support mobile applications (Fig 4) showed short term improvements in depression severity that were statistically significant and clinically important (MD = -3.07 [95% CI -4.68 to -1.46]; p<0.001; GRADE certainty: very low) [52,53]. Pooled results were judged to be statistically homogeneous (I2 = 30%; Chi2 p = 0.23). Although one study showed statistically significant improvements in psychological stress associated with a mobile intervention compared to usual care [52], this improvement was not clinically important (Table 3). Further, the impact of mobile interventions on anxiety and utilization of care [53] from one study was not statistically significant nor clinically important.

Results of mobile interventions delivered to postpartum women showed their limited effectiveness on depression and anxiety symptoms. Three studies showed non-clinically important improvements in depression severity [56,58,59], albeit two were statistically significant [56,58]. Clinical heterogeneity prevented pooling of results and GRADE evidence certainty ranged from moderate to very low. Similarly, one study showed improvements in anxiety symptoms that were not statistically significant nor clinically important [58].

The effectiveness of management-based mobile interventions

Results on management-based mobile interventions showed their potential only among postpartum women. Clinical heterogeneity, arising from variability in intervention design and pregnancy stage in which the inteventions were delivered prevented pooling results, except for one instance of two studies on support-based mobile interventions (Fig 5). Results of studies examining management-based mobile interventions are presented in Table 4. Evidence on the impact of mobile interventions delivered antenatally or perinatally was limited and showed no added benefits on psychological stress [62,64], or seeking mental healthcare [65]. GRADE certainty of antenatal and perinatal evidence ranged from low to very low.

Fig 5. Forest plot of comparison: support-based psychotherapy applications vs standard of care, outcome: severity of depression symptoms measured using the Edinburgh Postnatal Depression Scale (EPDS)—short term.

Fig 5

IV, Random: Inverse Variance methods, random effects model; CI: confidence interval.

Table 4. Effectiveness results of management-based mobile interventions.

Management-based mobile interventions
Outcome and outcome domain Study ID Effect estimate [95% CI or p value] Statistical significance Clinical importance GRADE certainty
Antenatal interventions
Biological stress Dennis-Tiwary 2017 [64] F 1,22 = 4.96 [p = 0.037] Yes No Low
Utilization of care Hantsoo 2018 [65] MD = 0.88 [-1.08 to 2.84] No No Very low
Perinatal interventions
Psychological wellbeing Carissoli 2017 [62] Autonomy F = 5.725 [p<0.05] Yes No Very low
Postpartum interventions
Depression severity Prasad 2018 [67]
Sawyer 2019 [68]
MD = -0.93 [-2.08 to 0.21] No No Very low
Depression severity Baumel 2018 [61] MD = 2.82 [-0.49 to 6.13] No No Low
Depression severity Jannati 2020 [66] MD = -6.87 [-7.92 to -5.82] Yes Yes Very low
Anxiety severity Constant 2014 [63] MD = -1.30 [-2.33 to -0.27] Yes No Very low
Psychological wellbeing Prasad 2018 [67] MD = 0.23 [-2.73 to 0.73] Yes No Very low
Psychological stress Constant 2014 [63] Avoidance B = -1.8 [-3.2 to -0.4]
Intrusion B = -1.4 [-2.9 to 0.2]
Yes
No
No
No
Very low
Utilization of care Sawyer 2019 [68] [intervention vs control; p value]
GP visits: 78% vs 63% [p = 0.09]
A&E visits: 15% vs 4% [p = 0.04]
Online care: 67% vs 46% [p = 0.03]
No
Yes
Yes
No
No
No
Very low

Evidence on postpartum mobile interventions was mixed; a meta-analysis of two support-based mobile applications (Fig 5) showed no statistically significant nor clinically important improvements in the severity of depression symptoms compared to standard care in the short term (MD = -0.93 [95% CI -2.08 to 0.21]; p = 0.11; GRADE certainty: very low) [67,68]. Pooled results were judged to be statistically homogeneous (I2 = 0%; Chi2 p = 0.92). This result was supported by another non-randomized study of a similar intervention that showed trivial benefits compared to usual care [61]. However, one study of a cognitive behavioural therapy (CBT) mobile application among postpartum women with depression showed statistically significant and clinically important improvement in the severity of their symptoms at 2-month follow-up (MD = -6.87 [95% CI -7.92 to -5.82]; p<0.001; GRADE certainty: very low) [66].

Evidence on postpartum anxiety, psychological stress, and utilization of care showed a trend of improvements that were statistically significant but not clinically important; among women who underwent medically-induced abortion, one study of a text messaging intervention found statistically significant but not clinically important decrease in levels of anxiety relative to usual discharge care at 2–3 weeks after abortion (MD = -1.30 [95% CI -2.33 to -0.27]; p = 0.01; GRADE certainty: very low) [63]. When measuring subjective stress in the same study investigators found that women receiving the intervention reported lower levels of avoidance-based stress but not intrusive-based stress after adjusting for baseline anxiety (β = -1.8 [95% CI-3.2 to -0.4]; p = 0.015; GRADE certainty: very low) [63]. Another study of a mobile application showed statistically significant improvements on psychological stress that, similarly, did not reach clinical importance levels (GRADE certainty: very low) [67]. Finally, one study of a peer support mobile application found that postpartum women receiving the intervention were more likely to visit their general practitioner, visit the A&E department, and seek pregnancy-related online resources when needed. These findings were not clinically important, and lack of data prevented synthesizing results [68].

The health equity impact of mobile interventions

Table 5 represents a heatmap of our equity evidence. Overall, women’s ethnicity was the most examined characteristic, followed by age, and being primiparous (first-time mother).

Table 5. Heatmap of equity evidence.

Equity findings Severity of symptoms Psychological wellbeing and distress Occurrence of psychiatric illnesses Utilization of pregnancy and psychiatric care
Place of residence 1 0 0 0
Race, ethnicity, culture 9 9 1 3
Occupation 1 0 1 0
Gender, sex 0 0 0 0
Religion 0 0 0 0
Education 4 0 1 0
Socioeconomic status 1 0 1 3
Social capital 3 0 1 0
+ Age 8 0 1 5
+ Disability 0 0 0 0
+ Time-dependent: Primiparous 3 7 0 0
+ Time-dependent: IPV 0 0 0 0
+ Discrimination 1 0 0 0

Hues of the colour red increased in darkness with an increase in the number of equity evidence.

Details about our equity results are presented in a compartmentalized (Outcome x PROGRESS+ characteristic) table (S8 File). In summary, evidence showed that mobile interventions elicited significant improvements in mental health outcomes across East and South East Asian ethnicities, such as Chinese [51,54], Taiwanese [52,60], South Korean [56], and Thai [55], as well as West Asian ethnicities, such as Persian [66]. These improvements were variable across ethnicities, but clinical heterogeneity prevented subgroup analyses. Furthermore, exploring evidence that is linked to age, education, and being primiparous (first-time mothers), showed that these characteristics had mixed associations to the degree of statistical significance and clinical importance of the effectiveness of mobile interventions (S8 File). Finally, evidence on the association of socioeconomic status, social capital, and experience of intimate partner violence to the effectiveness of mobile interventions was limited.

Discussion

COVID-19 and the public health restrictions that ensued have shifted the approach by which mental health care is accessed and delivered. Exploring novel approaches to mental healthcare delivery requires a comprehensive understanding of their risks and benefits, as well as their equity impact among different populations. Our equity-focused systematic review aimed to provide knowledge users with an equity-focused evidence base on the impact of mobile interventions targeting the prevention and management of common mental disorders among pregnant and postpartum women.

Our findings highlight the clinical impact of prevention-based mobile interventions on lowering the severity and occurrence of depression throughout pregnancy [5154], as well as the potential they carried postpartum [56,58,59]. While findings on preventing psychological stress showed promise [52,60], evidence on preventing anxiety and promoting utilization of care was inconclusive. Furthermore, findings on management-based mobile interventions were limited during pregnancy and only showed promise postpartum; while a smartphone application delivering cognitive behavioral therapy (CBT) was clinically effective in managing the severity of postpartum depression [66], interventions utilizing other mechanisms, such as peer support and psychoeducation, did not show added benefit [61,67,68]. These findings highlight two characteristics of mobile interventions that require further investigation; timing of intervention delivery and the mechanisms under which the intervention operates. Scientific realism is well-positioned to explore these characteristics and shed light on the actors of change in the context of implementing mobile interventions [69].

Our equity evidence highlights ethnicity as a characteristic of influence among pregnant and postpartum women using mobile interventions. Findings suggest that these interventions carry the potential to elicit change across East and West Asian ethnicities [51,52,5456,60,66]. However, study heterogeneity prevented synthesizing results and conducting credible subgroup analyses. Of note, we recognize that ethnicity is a social identity that comprises multiple intersecting constructs of a shared culture [70], such as values and principles, practices and social norms, and religion and culture [71]. Understanding the influence of ethnicity, therefore, requires applying an intersectionality lens to our analysis to gain a deeper understanding of these constructs and how they mutually intersect and influence social disadvantage [72]. Moreover, our findings highlight other PROGRESS+ characteristics that require further investigation, such as age, education, and being primiparous (first time mothers).

This review is unique in that it used a comprehensive search strategy to capture records from different information sources, such as electronic bibliographic databases (i.e., Medline, Embase, PsycINFO, Cochrane CENTRAL, PTSDPubs, and Web of Science), trial registries, study protocols, reference lists of relevant studies and systematic reviews, as well as the grey literature. We used rigorous methodology to screen search records, extract data from included studies, critically appraise each of our results, and assess the certainty of our evidence. To the best of our knowledge, this review is the first to apply an equity lens and explore the health equity impact of mobile interventions among pregnant and postpartum women. Our findings were reported transparently and presented using innovative data presentation techniques (i.e., heat maps and compartmentalized tables), to facilitate future dissemination to and interpretation by knowledge users, such as providers of care, policy makers, and patients. Furthermore, the robustness of our methods and clarity in our reporting positions this review to serve as the starting point for future work that can replicate our methods and build upon our findings to advance knowledge and use of mobile interventions among pregnant and postpartum women. Finally, we engaged pregnant and postpartum women with lived experience of common mental disorders in interpreting our evidence, deciding on clinically important findings, and developing our knowledge translation strategies. This work, however, is not without limitations; Firstly, while our search strategy was iteratively developed in consultation with a health sciences librarian and experts in the field of knowledge synthesis, we did not use a structured process to peer review its components in duplicate [73]. Secondly, restricting our eligibility criteria to controlled studies have allowed us to synthesize results on the comparative effectiveness of mobile interventions relative to controlled conditions, but have also precluded the inclusion of longitudinal single-arm studies, which may have enriched our findings. Expanding our inclusion criteria to curate evidence from other study designs is, therefore, needed in future updates of this review. Thirdly, while our quantitative findings shed light on certain characteristics and social identities that may impact the health equity of mobile interventions, more comprehensive research that examines qualitative evidence may complement our findings and solidify our conclusions [74,75]. We recommend that future updates of this review expand the search strategy to consider other nursing and social sciences databases which are abundant with qualitative evidence on the subject matter.

Our findings show the potential mobile interventions have for preventing perinatal depression and psychological stress and managing postpartum depression symptoms once they arise. Future research should focus on examining outcomes of anxiety and utilization of care using more rigorous methods and sufficient sample sizes to gain higher certainty evidence on what works in the field of mobile interventions. Furthermore, our equity findings highlight multiple social characteristics of influence upon the effectiveness of mobile interventions; ethnicity, age, education, and being a first-time mother (primiparous). These findings provide future investigators with the opportunity to focus their research on exploring the impact of such characteristics using an intersectionality lens before implementing mobile interventions in different contexts and settings.

Conclusion

As the COVID-19 pandemic transitions mental health care delivery into a virtual reality, a knowledge base is needed to inform key knowledge users on what works among pregnant and postpartum women. Our review highlights the effectiveness of mobile interventions and directs future research towards certain characteristics and social identities that require further investigation, such as ethnicity, age, education, and being a first-time mother.

Supporting information

S1 File. PRISMA reporting checklist.

(DOCX)

S2 File. PRISMA-E reporting checklist.

(DOCX)

S3 File. Knowledge translation plan.

(DOCX)

S4 File. Search strategy and grey literature outputs.

(DOCX)

S5 File. Standardized data extraction form.

(DOCX)

S6 File. Critical appraisal visuals.

(DOCX)

S7 File. GRADE Evidence profiles.

(DOCX)

S8 File. Compartmentalized (Outcome x PROGRESS+) table of equity results.

(DOCX)

Acknowledgments

We would like to extend our appreciation to Dr. Vivian Welch and Dr. Melissa Brouwers for their revisions of this work.

Data Availability

All relevant data are within the manuscript and its Supporting Information files

Funding Statement

AS received the Bruyère Research Institute Graduate Studentship Award (April 2020). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Eugene Demidenko

2 Sep 2021

PONE-D-21-24609

Mobile interventions targeting common mental disorders among pregnant and postpartum women: An equity-focused systematic review

PLOS ONE

Dear Dr. Kevin Pottie,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Oct 17 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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We look forward to receiving your revised manuscript.

Kind regards,

Eugene Demidenko, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at 

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and 

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

The study reports on the meta-analysis of mobile interventions aimed to improve mental health of pregnant and postpartum women. I’m overly positive on the work done, accomplishments and findings, but at the same time, I’m frustrated at the lack of transparency of how the summary statistics have been obtained. The paper will become reader-friendly, much stronger and results more convincing upon expanding the quantitative aspect of the paper. I should remind the authors that scientific work requires complete transparency regarding derivation of the results so that they could be reproduced.

Specifically,

1. Unfortunately, the authors mostly report on the qualitative findings but make little effort in explaining how the major quantitative summary statistics such as OR, absolute risk reduction (ARR; why RD?), mean difference (MD) and have been derived.

2. Table 2 misses critical information on quantitative study-specific statistics, such as MD, RD or OR along with their CI, sample size (n) along with statistical method used (logistic regression, contingency table, t-test). When reporting the summary statistics (meta-analysis) from #1 the authors should indicate what data from individual studies have been used.

3. Why Figs 4 and 5 involves only two studies per figure? Why not all 18 studies? What IV, random means?

4. I’m curious why our paper on the mobile intervention “Measuring outcomes of digital technology-assisted nursing postpartum: A randomized controlled trial” by Deborah E McCarter, Eugene Demidenko, Mark T Hegel, PMID: 29772609 PMCID: PMC6240405 DOI: 10.1111/jan.13716 was not a part of the study.

I believe that the authors can address my comments without major revision. I’m looking forward to see the revised version.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I can’t comment on the high-level statistics, but as for how it was conceived and reviewed, it is quite thorough. It is important to have searched the nursing database CINAHL, although ultimately only one nursing journal was referenced, and while this may be due to the rigor of the studies, nursing journals are very focused on the clinical and qualitative results of interventions, and are often the ones best qualified to provide the intervention, so more nursing focus could have strengthened this.

The equity lens is excellent, and a great model for others to follow, and adds a significant part to the rigor and value of this research. Similarly, the chart with the risk of bias is quite valuable, but one needs to also consider how this kind of intervention is measured, and what results might be missed by prioritizing randomized controlled trials, which don't lend themselves as well to measuring outcomes of interventions designed to improve mood and mental health. Measuring mental health is limited by the measurement instruments used, and thus, the need for qualitative data, as mentioned in the discussion, is paramount. All in all, I think it was a great paper.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Deborah McCarter, PhD, RN

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2021 Oct 29;16(10):e0259474. doi: 10.1371/journal.pone.0259474.r002

Author response to Decision Letter 0


16 Oct 2021

Dear Dr. Eugene Demidenko,

We would like to thank you and the reviewers for your careful consideration of our manuscript. The fields of maternal mental health and virtual care are ever-evolving and we envision this project as the first equity-focused intersection between these two fields. The findings that we highlight carry the potential to improve pregnant and postpartum women’s access to timely and appropriate mental health care, and we value the positive comments we have received about the impact of our findings.

Our team has carefully reviewed your revisions and addressed your comments. In summary, we have expanded our methods section to better explain our statistical analysis plan, providing readers with more information on what data was obtained from studies, and how it was analyzed and reported to facilitate reproducibility of our results and ensure transparency of our reporting. We have also reviewed our reference list and ensured that our manuscript meets your journal’s style and submission requirements. All changes are accompanied by a response and marked with the page and line numbers in which changes were made (Please see Response to Reviewers file).

Should you have any further questions or require further clarifications, please do not hesitate to contact our team.

Kevin Pottie and Ammar Saad on behalf of authors

Response to editor/ reviewer comments:

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

We have ensured that our manuscript meets PLOS ONE’s style requirements and we named our files in accordance with your submission requirements.

2. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

We have reviewed our reference list to ensure all cited records were correct and no cited records were retracted. The following changes have been made to our reference list:

We have added reference [41] to our list as part of expanding our methods section [Page 22; Line 484]

We have fixed the following 5 references to ensure their completeness and easy accessibility:

Reference [36]: we have added a link to the guidance document [Page 22; Line 474-475]

Reference [39]: We have added a link to the guidance document [Page 22; Line 480-481]

Reference [42]: We have added book chapter page numbers and fixed referencing style [Page 22; Line 485-487]

Reference [53]: We have added a link to the full text manuscript [Page 23; Line 517-518]

Reference [67]: We have added publisher name and year [Page 24; Line 550]

Additional Editor Comments:

The study reports on the meta-analysis of mobile interventions aimed to improve mental health of pregnant and postpartum women. I’m overly positive on the work done, accomplishments and findings, but at the same time, I’m frustrated at the lack of transparency of how the summary statistics have been obtained. The paper will become reader-friendly, much stronger and results more convincing upon expanding the quantitative aspect of the paper. I should remind the authors that scientific work requires complete transparency regarding derivation of the results so that they could be reproduced.

Thank you for sharing our positivity around the findings of this novel equity-focused systematic review, and for highlighting the shortcoming of describing our quantitative data analysis plan. We have now addressed this shortcoming by adding sufficient details to our methods, clearly explaining our statistical analysis plan, including what data was extracted from included studies, and how such data was analyzed and presented [Page 5-6; Line 126-139].

Specifically,

1. Unfortunately, the authors mostly report on the qualitative findings but make little effort in explaining how the major quantitative summary statistics such as OR, absolute risk reduction (ARR; why RD?), mean difference (MD) and have been derived.

We have added to our methods section to explain how quantitative summary statistics were derived/ calculated for both continuous and categorical outcomes [Page 5-6; Line 126-139].

2. Table 2 misses critical information on quantitative study-specific statistics, such as MD, RD or OR along with their CI, sample size (n) along with statistical method used (logistic regression, contingency table, t-test). When reporting the summary statistics (meta-analysis) from #1 the authors should indicate what data from individual studies have been used.

We have created a continuation of Table 2 to present information about study-specific statistics, such as the sample size, study-level statistical method used, study results retrieved from each included study, and effect estimates calculated for the purpose of our data synthesis [Page 12-13; Line 195].

Furthermore, we have added to our methods to explain what data was used from individual studies that contributed to our Meta-Analyses (i.e., mean differences at follow-up) [Page 6; Line 147-149]. The information provided in the continuation of Table 2 should provide readers of what values were used to calculate these mean differences from each included study [Page 12-13; Line 195].

3. Why Figs 4 and 5 involve only two studies per figure? Why not all 18 studies? What IV, random means?

While we recognize that meta-analyses with a larger number of studies provide more precise pooled results, ensuring that these meta-analyses are built on sound methods is paramount. Unfortunately, the clinical heterogeneity between studies prevented pooling data from more than the two studies in each of Fig 4 and 5. Clinical heterogeneity arose due to the different pregnancy stages that women were in when the intervention was delivered, different intervention designs, and different outcome measurement tools. We have added this explanation to our results to provide readers with more information about lack of studies in the two meta-analyses [Page13; Line 218-220 and Page 15; Line 255-258].

“IV, Random” is an automatic label created by our analysis software (RevMan 5.4) to indicate the statistical method used in pooling results (i.e., Inverse Variance) and analysis model (i.e., Random effects model). Thank you for pointing out the need to clarify that. We have added to the legends of Figs 5 and 6 to explain that label [Page 14; Line 243 and Page 16; Line 276].

4. I’m curious why our paper on the mobile intervention “Measuring outcomes of digital technology-assisted nursing postpartum: A randomized controlled trial” by Deborah E McCarter, Eugene Demidenko, Mark T Hegel, PMID: 29772609 PMCID: PMC6240405 DOI: 10.1111/jan.13716 was not a part of the study.

Our records show that your publication titled “Measuring outcomes of digital technology-assisted nursing postpartum: A randomized controlled trial” was indeed captured by our search and screened in duplicate by our team. However, Phases 1 and 2 were excluded from our final list of included studies due to “ineligible study design” (i.e., one-arm studies with no control group). While the third phase of the study is an open-label three-arm parallel RCT, the publication reports that follow-up and/ or analysis is still ongoing and only shares baseline demographics. Therefore, we have labelled your study as “ongoing”.

I believe that the authors can address my comments without major revision. I’m looking forward to see the revised version.

We thank you again for your comments and hope that we have addressed them to your standards. If you have any further comments or require any further clarifications, please do not hesitate to contact us.

Reviewers' comments:

Reviewer #1: I can’t comment on the high-level statistics, but as for how it was conceived and reviewed, it is quite thorough. It is important to have searched the nursing database CINAHL, although ultimately only one nursing journal was referenced, and while this may be due to the rigor of the studies, nursing journals are very focused on the clinical and qualitative results of interventions, and are often the ones best qualified to provide the intervention, so more nursing focus could have strengthened this.

We thank you, Dr. McCarter, for reviewing and highly appraising our work. We share your perspective on the importance of examining mobile interventions using an interdisciplinary lens that considers the field of nursing sciences, among others, in curating evidence on the subject matter. Our iterative process of developing the search strategy with the health sciences librarian at the University of Ottawa as well as experts in knowledge syntheses identified CINAHL as the database of choice for its inclusivity of nursing publications and records that meet our study design inclusion criteria following the Cochrane Effective Practice and Organization of Care guidelines (i.e., experiments with a controlled arm). You mentioned that the methodological rigour of our inclusion criteria may have hindered including more studies from the field of nursing and we agree, recognizing that the field of nursing sciences may include further publications of longitudinal one-arm studies as well as qualitative studies on the subject matter. We have added to our discussion [Page 19; Line 359-361] to recommend adding more nursing-specific databases and study designs to future updates of this review.

The equity lens is excellent, and a great model for others to follow, and adds a significant part to the rigor and value of this research. Similarly, the chart with the risk of bias is quite valuable, but one needs to also consider how this kind of intervention is measured, and what results might be missed by prioritizing randomized controlled trials, which don't lend themselves as well to measuring outcomes of interventions designed to improve mood and mental health. Measuring mental health is limited by the measurement instruments used, and thus, the need for qualitative data, as mentioned in the discussion, is paramount. All in all, I think it was a great paper.

Our vision for this review is to serve as a blueprint for future work that considers and addresses equity around maternal mental health interventions, specifically ones that utilize virtual or digital means of care delivery. We thank you for highlighting the significance of the equity lens. In regards to your comment about study designs and outcome measurements, one of the lengthy debates we have had among our team and with our equity experts is the balance between including evidence of higher certainty from studies with rigorous methodology (e.g., Randomized and non-randomized controlled trials) versus enriching our findings with broader but less certain evidence from other study designs (e.g., Observational and qualitative studies). While there is no right or wrong approach to either of these two arguments, we elected to consider this review as a first step, in which we present our knowledge users, such as healthcare providers, patients, and policy makers with higher certainty evidence that shows the effectiveness and equity potential of mobile interventions to raise awareness and draw attention to this field, while highlighting the importance of including broader evidence in future updates of this work. To highlight the need for future inclusion of other study design, we have highlighted, in our discussion, the need to expand the eligibility criteria in future updates of this review [Page 19; Line 353-357].

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Deborah McCarter, PhD, RN

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

We have uploaded our figures to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool and they have been adjusted according to your journal’s requirements. We have uploaded the adjusted figures to our resubmission.

Attachment

Submitted filename: Response to Reviewers.docx

Decision Letter 1

Eugene Demidenko

20 Oct 2021

Mobile interventions targeting common mental disorders among pregnant and postpartum women: An equity-focused systematic review

PONE-D-21-24609R1

Dear Dr. Pottie,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Eugene Demidenko, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The authors addressed the comments raised by the Editor, who acted as a reviewer, and another reviewer. The paper is in publishable form. Just an advice to make the paper reader-friendly: in the two tables where you indicate 'statistical significance' please remind that this means that p-value < 0.05 (correct?). 

Acceptance letter

Eugene Demidenko

22 Oct 2021

PONE-D-21-24609R1

Mobile interventions targeting common mental disorders among pregnant and postpartum women: An equity-focused systematic review

Dear Dr. Pottie:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Eugene Demidenko

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. PRISMA reporting checklist.

    (DOCX)

    S2 File. PRISMA-E reporting checklist.

    (DOCX)

    S3 File. Knowledge translation plan.

    (DOCX)

    S4 File. Search strategy and grey literature outputs.

    (DOCX)

    S5 File. Standardized data extraction form.

    (DOCX)

    S6 File. Critical appraisal visuals.

    (DOCX)

    S7 File. GRADE Evidence profiles.

    (DOCX)

    S8 File. Compartmentalized (Outcome x PROGRESS+) table of equity results.

    (DOCX)

    Attachment

    Submitted filename: Response to Reviewers.docx

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files


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