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Non-Randomized Study Protocols and Methods (Non-eHealth) (543) Methods and New Tools in Mental Health Research (424) Users' and Patients' Needs for Mental Health Services (378) Perinatal Depression; Postpartum Depression; PPD (88) Pregnancy Information, Education and Lifestyle Interventions (281)

Published on in Vol 12 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49243, first published .
Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study

Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study

Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study

Authors of this article:

Charlie Rioux1 Author Orcid Image ;   Delaney C Fulp1 Author Orcid Image ;   Parker N Haley1 Author Orcid Image ;   Jenna L LaBelle1 Author Orcid Image ;   Mary E Aasted1 Author Orcid Image ;   Kasie K Lambert2 Author Orcid Image ;   Madison T Donohue1 Author Orcid Image ;   Nkatheko T Mafu1 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Protocol

    1Department of Psychology, University of Oklahoma, Norman, OK, United States

    2Jeannine Rainbolt College of Education, University of Oklahoma, Norman, OK, United States

    Corresponding Author:

    Charlie Rioux, PhD

    Department of Psychology

    University of Oklahoma

    455 W Lindsey Street

    Dale Hall Tower, Room 705

    Norman, OK, 73019-2007

    United States

    Phone: 1 405 325 4511

    Email: charlie.rioux@ou.edu


    Background: Mental health problems during pregnancy and post partum are common and associated with negative short- and long-term impacts on pregnant individuals, obstetric outcomes, and child socioemotional development. Socio-environmental factors are important predictors of perinatal mental health, but the effects of the environment on mental health are heterogeneous. The differential susceptibility theory and the environmental sensitivity framework suggest that individuals differ in their degree of sensitivity to positive and negative environments, which can be captured by individual phenotypes such as temperament and personality. While there is strong evidence for these models in childhood, few studies examined them in adults, and they were not examined in pregnancy.

    Objective: The primary objective of the Experiences of Pregnancy study is to explore whether childhood and current environments are associated with mental health and well-being in pregnancy and whether these effects depend on individual sensitivity phenotypes (personality). This study also aims to gather important psychosocial and health data for potential secondary data analyses and integrative data analyses.

    Methods: We will conduct a longitudinal cohort study. The study was not registered elsewhere, other than this protocol. Participants will be recruited through social media advertisements linking to the study website, followed by an eligibility call on Zoom (Zoom Video Communications). Participants must be aged 18 years or older, currently residing in the United States as citizens or permanent residents, and currently planning to continue the pregnancy. A minimum of 512 participants will be recruited based on power analyses for the main objectives. Since the data will also be a resource for secondary analyses, up to 1000 participants will be recruited based on the available budget. Participants will be in their first trimester of pregnancy, and they will be followed at each trimester and once post partum. Data will be obtained through self-reported questionnaires assessing demographic factors; pregnancy-related factors; delivery, labor, and birth outcomes; early infant feeding; individual personality factors; childhood and current environments; mental health and well-being; attachment; and infant temperament. A series of measures were taken to safeguard the study from web robots and fraudulent participants, as well as to reduce legal and social risks for participants following Dobbs v. Jackson.

    Results: The study received ethics approval in April 2023 from the University of Oklahoma-Norman Campus Institutional Review Board. Recruitment occurred from May to August 2023, with 3 follow-ups occurring over 10 months.

    Conclusions: The Experiences of Pregnancy study will extend theories of environmental sensitivity, mainly applied in children to the perinatal period. This will help better understand individual sensitivity factors associated with risk, resilience, plasticity, and receptivity to negative and positive environmental influences during pregnancy for pregnant individuals.

    International Registered Report Identifier (IRRID): PRR1-10.2196/49243

    JMIR Res Protoc 2023;12:e49243

    doi:10.2196/49243

    Keywords

    anxiety; biological sensitivity to context; depression; diathesis-stress; highly sensitive person; moderation; resilience; sensory processing reactivity; substance use; vantage sensitivity 


    Background

    Pregnancy is a period of increased risk for mental health problems, which are one of the leading preventable causes of morbidity and mortality during the perinatal period in high-income nations [1-3]. Among people who are pregnant, approximately 20% to 30% experience elevated symptoms of anxiety or depression [4-6]. In turn, these symptoms are associated with short- and long-term parental and child health and well-being. Elevated and sustained prenatal psychological distress can often lead to long-term disturbing emotional experiences such as anger, anxiety, and depression in the postpartum period and beyond [7-9]. Prenatal stress and distress may impair bonding with the fetus and child [10,11] and may lead to strained familial and social relationships. In infants, exposure to prenatal stress and mental health problems has been found to be associated with a more difficult temperament and impaired cognitive and motor development [12]. Impacts on children may be long-term, with prenatal stress and distress being associated with disorganized attachment, poorer socioemotional development, learning deficits, and mental health problems across the internalizing and externalizing spectrums in children and adolescents, including attention-deficit/hyperactivity disorder, anxiety, depression, and behavioral problems [12-15]. Finally, prenatal stress and distress are also associated with obstetric and birth outcomes, with research showing links with early gestational age, low weight at birth, preeclampsia, hypertension, and reduced lactation [8,12,16-19].

    Considering these broad negative impacts, understanding the risk and protective factors for mental health problems during pregnancy, as well as factors associated with well-being, is essential to promoting parental and child health. Environmental factors, both in early life and during pregnancy, have been found to be associated with perinatal mental health. For example, a meta-analysis found that adverse childhood experiences are a risk factor for prenatal and postpartum depression and anxiety, with the number of adverse childhood experiences being positively associated with symptom severity [20]. Sociodemographic factors are also important, with a meta-analysis showing that lower educational level and poor economic status are risk factors for perinatal depression [21]. Finally, research on current social environment factors shows that low social support from family members, friends, or community members [22], as well as poor romantic relationship satisfaction [23,24], are risk factors for perinatal depression, anxiety, and self-harm. The effects of the environment on mental health are, however, heterogeneous.

    A proper understanding of the associations between environmental factors and mental health requires studying for whom the environment is influential. In turn, this can inform better targeted prevention efforts when psychosocial resources are limited. A frequently used theoretical model in psychiatry is the diathesis-stress model of vulnerability and resilience [25,26]. According to this model, some pregnant individuals would be vulnerable to adverse environments, leading to higher levels of psychological distress, while others would be resilient to such environments, leading to low levels of psychological distress despite negative environmental conditions. An alternative model, the differential susceptibility model [27,28], suggests that pregnant individuals who are sensitive to their environment would have higher levels of psychological distress than their peers in adverse environments but also lower levels of psychological distress and higher levels of well-being than their peers in positive environments. Thus, rather than being only vulnerable to adverse environments, individuals could be sensitive to both positive and negative environments, “for better or for worse” [29]. Individuals could also, however, be sensitive only to positive environmental influences, which was proposed in the vantage sensitivity model [30].

    The environmental sensitivity framework [31] (Figure 1 [32]) comprehensively integrates these theoretical models of psychosocial person-environment interactions. According to this model, individuals differ in their degree of sensitivity to the environment. Based on an individual sensitivity factor, individuals may differ in their sensitivity to adverse or negative environments only (diathesis-stress), to enriched or positive environments only (vantage sensitivity), or to both (differential susceptibility) [31]. Furthermore, some evidence suggests that this sensitivity may be domain specific rather than domain general, meaning different individual characteristics may capture sensitivity to different environments [33]. It is thus important to further elucidate for whom, when, and under which environmental conditions diathesis-stress, vantage sensitivity, and differential susceptibility apply. This is important because individuals’ differences in their receptivity to environmental inputs can influence their response to treatment interventions [34]. A better understanding of environmental sensitivity can thus help determine the best intervention and treatment on an individual basis, as well as inform targeted prevention efforts and psychoeducation [34,35].

    The differential susceptibility and environmental sensitivity models were proposed by developmental psychologists and have been thoroughly studied in children, with several reviews showing that individual-level factors originally thought to be vulnerability factors to adverse environments supported differential susceptibility, reflecting sensitivity to certain positive and negative environments [27,28,36]. We still know very little, however, about how these models apply to adults. Theory suggests that some adults are sensitive to positive and negative environmental conditions in the prediction of mental health, but very few empirical studies have examined the patterns of interaction of the environmental sensitivity framework based on phenotype in adults [37-39]. Thus, research is needed to elucidate environmental sensitivity factors in adulthood, including during pregnancy, a key period of heightened risk for psychological distress [1,5,40].

    ‎
    Figure 1. Integrated model of environmental sensitivity. The diathesis-stress model captures individual sensitivity to negative environments only. The vantage sensitivity model captures individual sensitivity to positive environments only. The differential susceptibility captures sensitivity to both negative and positive environments within the same individuals (adapted from Belsky et al [32], which is published under Creative Commons Attribution 4.0 International License [41]).

    Objectives

    The primary objective of the Experiences of Pregnancy study is to explore whether childhood and current environments are associated with mental health and well-being in pregnancy and whether these effects depend on individual sensitivity phenotypes (personality) through a longitudinal cohort study design. Considering there are few empirical studies on this topic in adulthood in general and no data to our knowledge in pregnancy, the study aims to document a variety of individual, environmental, and mental health factors to clarify models of sensitivity in adults and pregnant people. Following epidemiological cohort principles, this study also aims to gather important psychosocial and health data for potential secondary data analyses [42] and integrative data analyses [43].


    Participants and Recruitment

    Figure 2 shows the recruitment process to occur from May to August 2023. Participants will be recruited on the web through advertisements (Figure 2) on Facebook, Instagram, Twitter (subsequently rebranded X), and Reddit, as well as posts on the Reddit forums “r/Maternity” and “r/lgbtstudies.” Participants will be directed to the study website [44], where they will find general information about the study, eligibility criteria, and a link to join the study. Through this link, interested participants will be directed to a REDCap (Research Electronic Data Capture; Vanderbilt University) [45,46] survey protected by a reCAPTCHA Inc (Google) verification and redirecting to a Microsoft Bookings (Microsoft Corporation) page to schedule their call, with availabilities open for the next 14 days. Zoom (Zoom Video Communications) calls with undergraduate and graduate research assistants (RAs) will confirm their eligibility and go over the study process. Eligible and interested participants will receive their personal survey link through email after their call.

    Participants are eligible to enroll if they are aged 18 years or older, currently live in the United States, are citizens or permanent residents of the United States, and can read and write in English. Pregnant people of all gender identities are eligible to enroll in the study [47]. Furthermore, participants have to be currently planning to continue the pregnancy (see the Ethical Considerations section) and be in their first pregnancy trimester to join the study. Eligibility is to be 10 weeks pregnant or less when they signal their interest in the study (make their appointment through Microsoft Bookings) to allow time for their Zoom call confirming eligibility and for filling out the first survey. They remain eligible if they are 12 weeks pregnant or less by the time of their Zoom call. Overall, this allows for 2 weeks to schedule the Zoom call and gives them at least 1 week to fill out the first survey.

    ‎
    Figure 2. Flowchart of the study procedure for participant enrollment for the Experiences of Pregnancy longitudinal cohort study, United States, planned for 2023-2024.

    Follow-Ups and Study Design

    Participants will fill out 4 questionnaires: 1 questionnaire per trimester of pregnancy (3 first questionnaires) and 1 after birth (Figure 3). This follow-up schedule will allow collection of data during each key developmental period (trimester) of pregnancy. For this study, this follow-up schedule was also based on research showing individual differences in how mental health changes across each trimester [48-51] and highlighting the importance of repeated measurements at each pregnancy trimester in assessing prenatal mental health.

    At each follow-up survey, participants will first be asked if they are still pregnant. In the event that a participant experiences pregnancy loss or premature birth, they will remain eligible to fill out the current survey as their last assessment (ie, 1 survey after loss or after birth). Depending on group sample sizes, this may allow for the examination of mental health and resilience factors after a pregnancy loss. At the end of each survey, the “submit” button redirects participants to resources on the study website, with resource lists for pregnancy, pregnancy loss, and post partum that include crisis lines, mental health services, information, and networks.

    Based on an estimate of 4 to 7 items per minute for participants who do not regularly participate in survey studies [52], the first survey is expected to take between 50 and 70 minutes. The following 3 questionnaires are expected to take between 40 and 60 minutes; participants will be informed of these time estimates during their Zoom call. Based on the upper range of US minimum wages, participants will be compensated for their time, receiving US $15 of compensation for the first questionnaire (longer questionnaire) and US $10 for each subsequent questionnaire. As an incentive to complete the study and to reduce attrition, participants who complete all 4 questionnaires (or all questionnaires for which they are eligible in the event of a pregnancy loss or premature birth) will receive an additional US $15. Compensation will be in the form of Amazon or Giftogram e-gift cards sent by email within 2 weeks of each questionnaire’s completion. To receive compensation, participants must submit the survey but are free not to answer questions they are uncomfortable with or do not wish to answer.

    ‎
    Figure 3. The timeline of completion of the study questionnaires for the Experiences of Pregnancy longitudinal cohort study, United States, planned for 2023-2024.

    Study Measures

    All study data will be obtained through self-reported questionnaires. Table 1 summarizes study measures across assessments. All personality measures will be collected at survey 1 only as potential moderators. Childhood environments will be retrospectively self-reported once as potential predictors. Adverse and benevolent childhood experiences will be assessed at survey 1, but parental bonding (a retrospective measure of the parenting styles of participants’ parents in their childhood) will be assessed at survey 2 to reduce the burden of the longer first questionnaire. Current environments will be measured repeatedly at each assessment and include recent stressful experiences, everyday discrimination, social support, couple satisfaction, romantic intimate bonding, and social and digital media use. Mental health and well-being measures will be measured repeatedly at each assessment and include depressive symptoms, anxiety symptoms, anger, well-being, life satisfaction, sleep, disturbed dreaming, substance use (alcohol, tobacco, cannabis, and other drugs), and mental health service use. The initial (survey 1) substance use questionnaires will also assess substance use in the year before pregnancy as well as the age of onset for each substance. Attachment (prenatal or postnatal) will also be measured at all assessments. Finally, infant temperament will be measured after birth. Both postnatal attachment and infant temperament will be measured only for singleton pregnancies, as these questionnaires are specific to 1 baby, and the surveys would be much longer if asked for multiple babies.

    Table 1. Study measures at each assessment for the Experiences of Pregnancy longitudinal cohort study, United States, planned for 2023-2024.
    Study measuresSurvey 1Survey 2Survey 3Survey 4

    		First trimesterSecond trimesterThird trimesterPost partum
    Demographic, pregnancy, and delivery information

    		Demographic informationAlla



    		Relationship statusAllAllAllAll

    		Daily activities and responsibilitiesAllAllAllAll

    		Pregnancy status
    		AllAllAll

    		Previous pregnancies and due dateAll



    		Pregnancy intendedness [53]All



    		Health conditions before pregnancyAll



    		Health conditions during pregnancyAllPb and LDcP and LDLD

    		HeightAll



    		WeightAllP and LDP and LD

    		Prenatal careAllP and LDP and LD

    		Delivery and birth outcomes
    		LDLDLD

    		Early breastfeeding and formula feeding
    		LDLDLD

    		Labor and Delivery Index [54]
    		LDLDLD
    Individual and personality factors

    		10-item Big Five Inventory [55]All



    		Highly Sensitive Person 12-item Scale [56]All



    		Substance Use Risk Profile Scale [57]All


    Environmental factors

    		Expanded Adverse Childhood Experiences Scale [58]All



    		Benevolent Childhood Experiences Scale [59]All



    		Parental Bonding Instrument [60]
    		All


    		Recent Stressful Experiences Checklist [61]AllAllAllAll

    		Everyday Discrimination Scale [62]AllAllAllAll

    		Multidimensional Scale of Perceived Social Support [63]AllAllAllAll

    		Couple Satisfaction Index [64]AllAllAllAll

    		Intimate Bond Measure [65]AllAllAllAll

    		Social and digital media useAllAllAllAll
    Mental health and well-being measures

    		PROMISd Anger [66]AllAllAllAll

    		Generalized Anxiety Disorder 7-item scale (GAD-7) [67]AllAllAllAll

    		Depression: Patient Health Questionnaire (PHQ-9) [68]AllAllAllAll

    		Substance useAllAllAllAll

    		Psychological Well-Being Scale [69]AllAllAllAll

    		PROMIS General Life Satisfaction [70]AllAllAllAll

    		Sleep durationAllAllAllAll

    		PROMIS Sleep Disturbance [71]AllAllAllAll

    		PROMIS Sleep-Related Impairment [71]AllAllAllAll

    		Disturbing Dreams and Nightmare Severity Index [72]AllAllAllAll

    		Mental Health Service Use Questionnaire [73]AllAllAllAll
    Attachment

    		Maternal Antenatal Attachment Scale [74]AllPP

    		Maternal Postnatal Attachment Scale [75]
    		LDLDLD
    Infant temperament

    		Early Infancy Temperament Questionnaire [76]e
    		LDLDLD
    Other

    		Intention to participate in follow-up surveysfAllP

    aAll: study measure is included in regular follow-ups and shown to all participants regardless of pregnancy status.

    bP: pregnant; this indicates study measure is included for participants who are still pregnant at the time of that questionnaire.

    cLD: live delivery; this indicates study measure is included for participants who experienced a (term or preterm) live delivery at the time of that questionnaire. Participants who experience a pregnancy loss or neonatal death are only shown instruments labeled “All” and are not shown instruments related to pregnancy, labor, delivery, or the child.

    dPROMIS: Patient-Reported Outcomes Measurement Information System.

    eOnly the subscales “Rhythmicity,” “Intensity,” and “Threshold” are included.

    fSee the Missing Data Mitigation section for details.

    Sample Size Considerations

    We aim to recruit between 512 and 1000 participants. This study’s main objective is to explore person-environment interactions in the prediction of perinatal mental health and well-being. We examined power for the simplest model to meet these objectives (1 interaction effect in a multiple regression model). Power analyses were run using the InteractionPoweR Shiny app [77], with moderate main effect sizes and small interaction effect sizes based on our previous differential susceptibility research [78-80]. Measurement reliability was integrated into the power analyses, with good (environmental and personality variables) and excellent (mental health) reliabilities [23,56,80,81]. This analysis indicated that the minimum sample size to detect the interaction effects with 80% power is 341. Simulation studies suggest that for tests of differential susceptibility, 50% more cases are required to achieve 80% power compared with power for the interaction effect [82], yielding a minimum sample size of 512. This study is also designed to serve as a secondary data analyses related to mental health and well-being during pregnancy and post partum. To maximize possibilities for longitudinal modeling, we will be recruiting up to 1000 participants, with the maximum being based on budgetary considerations.

    Web-Based Data Integrity

    Measures were used to safeguard the study against web robots (bots). To prevent enrollment from bots and fraudulent participants, participants will be individually screened through Zoom calls. Individually screening participants has been deemed one of the most reliable methods to protect web-based data collections [83,84]. Considering recent advances in artificial intelligence (AI) language, calls were deemed more secure than emails for participant screening. Zoom calls were easier to implement administratively than phone calls, as there were already organizational accounts set up for Zoom, but phone accounts had to be purchased. Finally, appointments were expected to be more efficient than trying to reach participants through phone calls after they provided their information without a set time.

    To safeguard the Zoom call bookings from being targeted by bots, which could prevent genuine participants from enrolling due to appointments being unavailable, a total of 4 measures recommended to deter bots from studies were used. First, because bots are particularly interested in studies with financial incentives [83], monetary compensation was included in the images of the social media advertisements but will not be mentioned in any of the text accompanying the advertisements. Second, a total of 2 links will have to be accessed to get to the call bookings (the social media advertisement link and the link to join the study on the website). Direct links to Microsoft Bookings will not be posted on social media [83,85]. Third, a reCAPTCHA verification will protect the REDCap survey leading to the bookings page [83,85]. Finally, Microsoft Bookings settings will include 1-time passwords, where a numeric code will be sent to each participant’s email address to complete the booking.

    RAs conducting Zoom calls will complete 2 trainings. The first training (in person) will go over the study protocol, the steps for confirming calls with participants through email, conducting the call, and sending surveys, including how to use Microsoft Bookings, the study email account, and REDCap. Each RA will have a printed copy of a manual covering all these steps, along with access to a digital version of the manual. RAs will also practice all steps, including the calls, during the first training. The second training will be on Zoom, where RAs will practice calls further in a more similar setting to calls with participants. The principal investigator (CR) will have weekly scheduled meetings with RAs to discuss progress, questions, and any perceived issues during the calls, including potential fraud. RAs will also be able to share any perceived issues with a call in a rapid manner through the laboratory’s communication platform, Slack (Slack Technologies). This will allow the identification of any unforeseen issues during recruitment and the adaptation of call protocols if needed.

    Missing Data Mitigation

    To reduce attrition rates, an incentive of US $15 is offered for completing all 4 surveys (or all surveys a participant is eligible to in the event of a pregnancy loss or premature birth; see the Follow-Ups and Study Design section). Because participants are actively recruited and join the study through a Zoom call instead of only filling out the survey on the web, we expect more participant investment in the study than traditional web-based questionnaires.

    In analyses with missing data rates up to 10%, deletion techniques may be used as they are not associated with increased bias [86]. In analyses above this missing data rate (which can be expected for longitudinal analyses), recommended missing data treatments, including full information maximum likelihood (FIML) and multiple imputation, will be used [87]. These missing data treatments are most effective at preventing biased results when predictors of missing data (ie, predictors of the missing at random [MAR] mechanism) are included as auxiliary variables [87]. At the first 2 assessments, we provided a study timeline of the next surveys and their associated compensation and included 1 question asking participants “Currently, how likely do you think you are to complete the follow-up surveys?” with answers ranging from “very unlikely” (score 1) to “very likely” (score 4), presented in Table 1. This question was included to serve as a potential predictor of the MAR mechanism (ie, potential auxiliary variable) in addition to the main survey questions.

    Data Management

    All data will be collected and managed using REDCap tools hosted at the University of Oklahoma Health Sciences Center [45,46]. REDCap is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for data integration and interoperability with external sources. Metadata will be submitted to the Maelstrom Catalogue [88] to support harmonization initiatives among epidemiological cohorts. The research team welcomes collaboration on the study; access to the data dictionary and data request form will be available upon reasonable request made to the corresponding author.

    Data Analyses

    Analyses for the primary aim will follow recommendations for testing person-environment interactions based on the environmental sensitivity framework and differential susceptibility. Linear moderated regressions will be conducted. To test for diathesis-stress, vantage sensitivity, and differential susceptibility, the nature of significant interactions will be further examined using regions of significance analyses, the proportion of interaction (PoI) index, and the proportion affected (PA) index [89]. The best-practice recommendations are to use a combination of these indices to confirm support for one of the environmental sensitivity models [82,89].

    Regions of significance analyses use the Johnson-Neyman technique to identify at what levels of the environmental variable individuals differ in their mental health based on their phenotype [89]. The PoI index will quantify the “proportion of the total area of the interaction” that is attributable to the positive environment. Prototypical models would have a PoI of 0.5 for differential susceptibility, 0.0 for diathesis-stress, and 1.0 for vantage sensitivity. Proposed criteria are that a PoI of 0.2-0.8 supports differential susceptibility [82], with a PoI <0.2 supporting diathesis-stress and a PoI >0.8 supporting vantage sensitivity. The PA index will quantify the “proportion of participants” who benefit from the positive environment. Similar to the PoI index, prototypical models would have a PA of 0.5 for differential susceptibility, 0.0 for diathesis-stress, and 1.0 for vantage sensitivity. Proposed criteria are that a PA value <0.16 is considered as indicative of diathesis–stress [89], making a PA value >0.84 indicative of vantage sensitivity.

    Ethical Considerations

    The study received ethics approval from the University of Oklahoma-Norman Campus Institutional Review Board (IRB #15839). All participants will sign an electronic informed consent form once during the study through the REDCap eConsent framework [90] before completing the first questionnaire.

    Special considerations were taken to reduce the legal and social risks and protect participants from their data being used in legal proceedings related to abortion. First, a National Institutes of Health Certificate of Confidentiality was obtained. Second, inclusion criteria explicitly include the intention to keep the pregnancy, so that pregnancy losses recorded in the study would not be assumed to be from intentional abortion. Third, while our pregnancy-information questionnaire would have typically included the following questions—(1) How many total times have you been pregnant? (2) Have you ever had a miscarriage? and (3) How many children do you currently have at home?—in order to avoid combined answers to these questions being used to deduce the possibility of a past abortion, questions 2 and 3 were removed, and question 1 was changed to “Is this your first pregnancy?” Fourth, while we initially planned an open-ended question following pregnancy losses where participants could tell us about their experience, we opted to remove that open-text field to avoid inadvertently recording liable information.


    The study received ethics approval in April 2023. Recruitment occurred from May to August 2023. After the first survey, participant follow-ups will occur over 10 months.


    The Experiences of Pregnancy study will assess individual personality factors, social environmental factors, and psychological distress and well-being factors throughout pregnancy and post partum. The Experiences of Pregnancy study will allow extending theories of environmental sensitivity mainly applied in children to the perinatal period. This will help better understand individual characteristics associated with risk, resilience, plasticity, and receptivity to negative and positive environmental influences during pregnancy. In turn, this could help inform intervention and prevention efforts tailored to peoples’ individual sensitivity to their environment [34,35]. While measures on individual phenotypes, social environments, and mental health outcomes were prioritized, the principal investigator’s (CR) expertise in longitudinal study designs, population studies, and advanced quantitative analyses allowed the integration of a broader set of measures that will allow secondary data analyses and could contribute to data harmonization efforts addressing related developmental and epidemiological questions.

    Recruitment for Experiences of Pregnancy is occurring nationwide, is completely web based, and will be carefully tracked. The infiltration of web-based data by bots and fraudulent participants has been a major concern in recent years [91,92]. In this study, we implemented many steps to ensure data integrity and protect the study from bots and fraudulent participants while ensuring feasibility, including not linking surveys directly on social media, reCAPTCHA verification, email verification, and a Zoom call. The study has the potential to contribute to the design of future studies looking to conduct reliable and safe web-based data collection. This could help future epidemiological and medical research since web-based data collection can have advantages in collecting data from minority populations and from hard-to-reach populations [93-96]. This is not limited to self-report surveys such as ours since many aspects of data collection, including, for example, some biological assays [97-100], neurobehavioral and cognitive assessments [101-103], stress tests [104,105], and parent-child observations [106], have been successfully adapted to be conducted remotely. Finally, our data on social media advertisement costs and success will also be documented and could help inform recruitment budgets for future web-based studies on pregnant populations.

    The main weakness of the Experiences of Pregnancy study at the design stage is that all data will be self-reported. However, initial insights into environmental sensitivity and differential susceptibility in pregnancy stemming from this study could inform future investments in research integrating other measurement methods, such as examining genotypic and endophenotypic sensitivity factors [28,107]; recruiting participants’ social network (eg, parents, siblings, friends, and romantic partners) to measure their social environment through multiple informants [108]; integrating objective stress measures; or accessing medical record information for participants’ health, mental health, and birth outcomes. The generalizability of the results stemming from this study will also be limited to English-speaking US residents and citizens.

    Overall, the Experiences of Pregnancy study will help improve our understanding of risk and protection factors for perinatal distress, as well as how different factors interact with each other to predict mental health and well-being. In turn, this can help inform efforts aiming to reduce the high prevalence rates of mental health problems during the perinatal period.

    Acknowledgments

    The Experiences of Pregnancy Study was funded through start-up funds to CR. No external funding agencies or sponsors were involved in the study.

    We thank the research assistants who will be involved in the recruitment process of our study: Nicole Barton, Chloe Biggers, Emily Flesher, Riggs Gorman, Zoe Rockey, Jacob Stephens, Annika Testerman, Fily Wahidin, and Megan Waldron.

    Data Availability

    The data sets generated during this study are not publicly available as this study is not completed. Data will be available from the corresponding author on reasonable request upon conclusion of the study.

    Authors' Contributions

    CR conceptualized the study. All authors contributed to the design of the study. CR, DCF, and PNH programmed the study questionnaires. CR, DCF, JLL, MEA, KKL, and MTD are contributing to the investigation process through participant recruitment. CR, PNH, and NTM prepared visualizations and figures for the study and this paper. CR wrote the initial draft. All authors reviewed and edited the paper. CR provided resources, supervised the research team, and coordinated the project.

    Conflicts of Interest

    None declared.

    1. Austin MP, Kildea S, Sullivan E. Maternal mortality and psychiatric morbidity in the perinatal period: challenges and opportunities for prevention in the Australian setting. Med J Aust. 2007;186(7):364-367. [CrossRef] [Medline]
    2. O'Neil SS, Platt I, Vohra D, Pendl-Robinson E, Dehus E, Zephyrin L, et al. Societal cost of nine selected maternal morbidities in the United States. PLoS One. 2022;17(10):e0275656. [FREE Full text] [CrossRef] [Medline]
    3. Glazer KB, Howell EA. A way forward in the maternal mortality crisis: addressing maternal health disparities and mental health. Arch Womens Ment Health. 2021;24(5):823-830. [CrossRef] [Medline]
    4. Gavin NI, Gaynes BN, Lohr KN, Meltzer-Brody S, Gartlehner G, Swinson T. Perinatal depression: a systematic review of prevalence and incidence. Obstet Gynecol. 2005;106(5 Pt 1):1071-1083. [CrossRef] [Medline]
    5. Dennis CL, Falah-Hassani K, Shiri R. Prevalence of antenatal and postnatal anxiety: systematic review and meta-analysis. Br J Psychiatry. 2017;210(5):315-323. [FREE Full text] [CrossRef] [Medline]
    6. Tomfohr-Madsen LM, Racine N, Giesbrecht GF, Lebel C, Madigan S. Depression and anxiety in pregnancy during COVID-19: a rapid review and meta-analysis. Psychiatry Res. 2021;300:113912. [FREE Full text] [CrossRef] [Medline]
    7. Obrochta CA, Chambers C, Bandoli G. Psychological distress in pregnancy and postpartum. Women Birth. 2020;33(6):583-591. [CrossRef] [Medline]
    8. Meltzer-Brody S, Stuebe A. The long-term psychiatric and medical prognosis of perinatal mental illness. Best Pract Res Clin Obstet Gynaecol. 2014;28(1):49-60. [FREE Full text] [CrossRef] [Medline]
    9. Littlewood J, McHugh N. Maternal Distress and Postnatal Depression: The Myth of Madonna. London, UK. MacMillan Press LTD; 1997.
    10. Göbel A, Stuhrmann LY, Harder S, Schulte-Markwort M, Mudra S. The association between maternal-fetal bonding and prenatal anxiety: an explanatory analysis and systematic review. J Affect Disord. 2018;239:313-327. [CrossRef] [Medline]
    11. Henrichs J, de Kroon M, Walker A, Witteveen A, Westerneng M, van Baar A, et al. Maternal prenatal distress, maternal pre- and postnatal bonding and behavioral and emotional problems in toddlers. A secondary analysis of the IRIS study. J Child Fam Stud. 2023;32(7):2113-2126. [FREE Full text] [CrossRef]
    12. Graignic-Philippe R, Dayan J, Chokron S, Jacquet AY, Tordjman S. Effects of prenatal stress on fetal and child development: a critical literature review. Neurosci Biobehav Rev. 2014;43:137-162. [CrossRef] [Medline]
    13. Madigan S, Oatley H, Racine N, Fearon RMP, Schumacher L, Akbari E, et al. A meta-analysis of maternal prenatal depression and anxiety on child socioemotional development. J Am Acad Child Adolesc Psychiatry. 2018;57(9):645-657.e8. [CrossRef] [Medline]
    14. Skoluda N, Nater UM. Consequences of developmental stress in humans: prenatal stress. In: Laviola G, Macrì S, editors. Adaptive and Maladaptive Aspects of Developmental Stress. New York, NY. Springer; 2013;121-145.
    15. Stein A, Pearson RM, Goodman SH, Rapa E, Rahman A, McCallum M, et al. Effects of perinatal mental disorders on the fetus and child. Lancet. 2014;384(9956):1800-1819. [CrossRef] [Medline]
    16. Grote NK, Bridge JA, Gavin AR, Melville JL, Iyengar S, Katon WJ. A meta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction. Arch Gen Psychiatry. 2010;67(10):1012-1024. [FREE Full text] [CrossRef] [Medline]
    17. Staneva A, Bogossian F, Pritchard M, Wittkowski A. The effects of maternal depression, anxiety, and perceived stress during pregnancy on preterm birth: a systematic review. Women Birth. 2015;28(3):179-193. [CrossRef] [Medline]
    18. Hu R, Li Y, Zhang Z, Yan W. Antenatal depressive symptoms and the risk of preeclampsia or operative deliveries: a meta-analysis. PLoS One. 2015;10(3):e0119018. [FREE Full text] [CrossRef] [Medline]
    19. Andersson L, Sundström-Poromaa I, Wulff M, Aström M, Bixo M. Implications of antenatal depression and anxiety for obstetric outcome. Obstet Gynecol. 2004;104(3):467-476. [CrossRef] [Medline]
    20. Racine N, Devereaux C, Cooke JE, Eirich R, Zhu J, Madigan S. Adverse childhood experiences and maternal anxiety and depression: a meta-analysis. BMC Psychiatry. 2021;21(1):28. [FREE Full text] [CrossRef] [Medline]
    21. Yang K, Wu J, Chen X. Risk factors of perinatal depression in women: a systematic review and meta-analysis. BMC Psychiatry. 2022;22(1):63. [FREE Full text] [CrossRef] [Medline]
    22. Bedaso A, Adams J, Peng W, Sibbritt D. The relationship between social support and mental health problems during pregnancy: a systematic review and meta-analysis. Reprod Health. 2021;18(1):162. [FREE Full text] [CrossRef] [Medline]
    23. Xie EB, Rioux C, Madsen JW, Lebel C, Giesbrecht GF, Tomfohr-Madsen L. Romantic relationship quality and mental health in pregnancy during the COVID-19 pandemic. J Soc Clin Psychol. 2022;41(5):463-490. [CrossRef]
    24. Tissera H, Auger E, Séguin L, Kramer MS, Lydon JE. Happy prenatal relationships, healthy postpartum mothers: a prospective study of relationship satisfaction, postpartum stress, and health. Psychol Health. 2021;36(4):461-477. [CrossRef] [Medline]
    25. Monroe SM, Simons AD. Diathesis-stress theories in the context of life stress research: implications for the depressive disorders. Psychol Bull. 1991;110(3):406-425. [CrossRef] [Medline]
    26. Zuckerman M. Vulnerability to Psychopathology: A Biosocial Model. Washington, DC. American Psychological Association; 1999.
    27. Belsky J, Pluess M. Beyond diathesis stress: differential susceptibility to environmental influences. Psychol Bull. 2009;135(6):885-908. [CrossRef] [Medline]
    28. Belsky J, Pluess M. Beyond risk, resilience, and dysregulation: phenotypic plasticity and human development. Dev Psychopathol. 2013;25(4 Pt 2):1243-1261. [CrossRef] [Medline]
    29. Belsky J, Bakermans-Kranenburg MJ, van IJzendoorn MH. For better and for worse: differential susceptibility to environmental influences. Curr Dir Psychol Sci. 2016;16(6):300-304. [CrossRef]
    30. Pluess M, Belsky J. Vantage sensitivity: individual differences in response to positive experiences. Psychol Bull. 2013;139(4):901-916. [CrossRef] [Medline]
    31. Pluess M. Individual differences in environmental sensitivity. Child Dev Perspect. 2015;9(3):138-143. [CrossRef]
    32. Bakermans-Kranenburg MJ, van IJzendoorn MH. The hidden efficacy of interventions: gene×environment experiments from a differential susceptibility perspective. Annu Rev Psychol. 2015;66:381-409. [FREE Full text] [CrossRef] [Medline]
    33. Belsky J, Zhang X, Sayler K. Differential susceptibility 2.0: are the same children affected by different experiences and exposures? Dev Psychopathol. 2022;34(3):1025-1033. [FREE Full text] [CrossRef] [Medline]
    34. de Villiers B, Lionetti F, Pluess M. Vantage sensitivity: a framework for individual differences in response to psychological intervention. Soc Psychiatry Psychiatr Epidemiol. 2018;53(6):545-554. [FREE Full text] [CrossRef] [Medline]
    35. Assary E, Krebs G, Eley TC. Practitioner review: differential susceptibility theory: might it help in understanding and treating mental health problems in youth? J Child Psychol Psychiatry. 2023;64(8):1104-1114. [FREE Full text] [CrossRef] [Medline]
    36. Rioux C, Castellanos-Ryan N, Parent S, Séguin JR. The interaction between temperament and the family environment in adolescent substance use and externalizing behaviors: support for diathesis-stress or differential susceptibility? Dev Rev. 2016;40:117-150. [FREE Full text] [CrossRef] [Medline]
    37. Aron EN, Aron A, Jagiellowicz J. Sensory processing sensitivity: a review in the light of the evolution of biological responsivity. Pers Soc Psychol Rev. 2012;16(3):262-282. [CrossRef] [Medline]
    38. Rioux C, Séguin JR, Paris J. Differential susceptibility to the environment and borderline personality disorder. Harv Rev Psychiatry. 2018;26(6):374-383. [CrossRef] [Medline]
    39. Greven CU, Lionetti F, Booth C, Aron EN, Fox E, Schendan HE, et al. Sensory processing sensitivity in the context of environmental sensitivity: a critical review and development of research agenda. Neurosci Biobehav Rev. 2019;98:287-305. [FREE Full text] [CrossRef] [Medline]
    40. Woody CA, Ferrari AJ, Siskind DJ, Whiteford HA, Harris MG. A systematic review and meta-regression of the prevalence and incidence of perinatal depression. J Affect Disord. 2017;219:86-92. [CrossRef] [Medline]
    41. Attribution 4.0 International (CC BY 4.0). Creative Commons. URL: https://creativecommons.org/licenses/by/4.0/ [accessed 2023-11-30]
    42. Castle JE. Maximizing research opportunities: secondary data analysis. J Neurosci Nurs. 2003;35(5):287-290. [CrossRef] [Medline]
    43. Curran PJ, Hussong AM. Integrative data analysis: the simultaneous analysis of multiple data sets. Psychol Methods. 2009;14(2):81-100. [FREE Full text] [CrossRef] [Medline]
    44. Experiences of pregnancy. PEANUTs Lab. 2023. URL: https://experiences-study.com/ [accessed 2023-11-11]
    45. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208. [FREE Full text] [CrossRef] [Medline]
    46. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research Electronic Data Capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377-381. [FREE Full text] [CrossRef] [Medline]
    47. Rioux C, Weedon S, London-Nadeau K, Paré A, Juster RP, Roos LE, et al. Gender-inclusive writing for epidemiological research on pregnancy. J Epidemiol Community Health. 2022;76(9):823-827. [CrossRef] [Medline]
    48. Ou CH, Sedov I, Sanguino H, Holtzman S, Tomfohr-Madsen L. Trajectories and correlates of anger during the perinatal period. J Obstet Gynecol Neonatal Nurs. 2022;51(6):599-611. [CrossRef] [Medline]
    49. Bayrampour H, Tomfohr L, Tough S. Trajectories of perinatal depressive and anxiety symptoms in a community cohort. J Clin Psychiatry. 2016;77(11):e1467-e1473. [CrossRef] [Medline]
    50. Bai G, Raat H, Jaddoe VWV, Mautner E, Korfage IJ. Trajectories and predictors of women's health-related quality of life during pregnancy: a large longitudinal cohort study. PLoS One. 2018;13(4):e0194999. [FREE Full text] [CrossRef] [Medline]
    51. Boekhorst MGBM, Beerthuizen A, Endendijk JJ, van Broekhoven KEM, van Baar A, Bergink V, et al. Different trajectories of depressive symptoms during pregnancy. J Affect Disord. 2019;248:139-146. [CrossRef] [Medline]
    52. Rioux C, Parent S, Castellanos-Ryan N, Archambault I, Boivin M, Herba CM, et al. The 3D-transition study: objectives, methods, and implementation of an innovative planned missing-data design. Am J Epidemiol. 2021;190(11):2262-2274. [FREE Full text] [CrossRef] [Medline]
    53. Speizer IS, Santelli JS, Afable-Munsuz A, Kendall C. Measuring factors underlying intendedness of women's first and later pregnancies. Perspect Sex Reprod Health. 2004;36(5):198-205. [CrossRef] [Medline]
    54. Gärtner FR, de Miranda E, Rijnders ME, Freeman LM, Middeldorp JM, Bloemenkamp KWM, et al. Good reliability and validity for a new utility instrument measuring the birth experience, the labor and delivery index. J Clin Epidemiol. 2015;68(10):1184-1194. [CrossRef] [Medline]
    55. Rammstedt B, John OP. Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German. J Res Pers. 2007;41(1):203-212. [CrossRef]
    56. Pluess M, Lionetti F, Aron EN, Aron A. People differ in their sensitivity to the environment: an integrated theory, measurement and empirical evidence. J Res Pers. 2023;104:104377. [FREE Full text] [CrossRef]
    57. Woicik PA, Stewart SH, Pihl RO, Conrod PJ. The substance use risk profile scale: a scale measuring traits linked to reinforcement-specific substance use profiles. Addict Behav. 2009;34(12):1042-1055. [CrossRef] [Medline]
    58. Cronholm PF, Forke CM, Wade R, Bair-Merritt MH, Davis M, Harkins-Schwarz M, et al. Adverse childhood experiences: expanding the concept of adversity. Am J Prev Med. 2015;49(3):354-361. [CrossRef] [Medline]
    59. Narayan AJ, Rivera LM, Bernstein RE, Harris WW, Lieberman AF. Positive childhood experiences predict less psychopathology and stress in pregnant women with childhood adversity: a pilot study of the Benevolent Childhood Experiences (BCEs) scale. Child Abuse Negl. 2018;78:19-30. [CrossRef] [Medline]
    60. Parker G, Tupling H, Brown LB. A parental bonding instrument. Brit J Med Psychol. 2011;52(1):1-10. [CrossRef]
    61. Roos LE, Kaminski L, Stienwandt S, Hunter S, Giuliano R, Mota N, et al. The Building Regulation in Dual-Generations Program (BRIDGE): a mixed-methods feasibility pilot of a parenting program for depressed mothers of preschoolers, matched with dialectical behavior therapy skills. Child Psychiatry Hum Dev. 2023;54(1):34-50. [FREE Full text] [CrossRef] [Medline]
    62. Williams DR, Yu Y, Jackson JS, Anderson NB. Racial differences in physical and mental health: socio-economic status, stress and discrimination. J Health Psychol. 1997;2(3):335-351. [FREE Full text] [CrossRef] [Medline]
    63. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. J Pers Assess. 1988;52(1):30-41. [CrossRef]
    64. Funk JL, Rogge RD. Testing the ruler with item response theory: increasing precision of measurement for relationship satisfaction with the couples satisfaction index. J Fam Psychol. 2007;21(4):572-583. [CrossRef] [Medline]
    65. Wilhelm K, Parker G. The development of a measure of intimate bonds. Psychol Med. 1988;18(1):225-234. [CrossRef] [Medline]
    66. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D, et al. PROMIS Cooperative Group. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger. Assessment. 2011;18(3):263-283. [FREE Full text] [CrossRef] [Medline]
    67. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-1097. [FREE Full text] [CrossRef] [Medline]
    68. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613. [FREE Full text] [CrossRef] [Medline]
    69. Diener E, Wirtz D, Biswas-Diener R, Tov W, Kim-Prieto C, Choi DW, et al. New measures of well-being. In: Diener E, editor. Assessing Well-Being: The Collected Works of Ed Diener. Dordrecht, Netherlands. Springer; 2009;247-266.
    70. Vaughan B, Mulcahy J, Fitzgerald K. PROMIS® general life satisfaction scale: construct validity in musculoskeletal pain patients. Chiropr Man Therap. 2020;28(1):27. [FREE Full text] [CrossRef] [Medline]
    71. Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, et al. Development of short forms from the PROMIS™ sleep disturbance and sleep-related impairment item banks. Behav Sleep Med. 2011;10(1):6-24. [FREE Full text] [CrossRef] [Medline]
    72. Bolstad CJ, Szkody E, Nadorff MR. Factor analysis and validation of the disturbing dreams and nightmare severity index. Dreaming. 2021;31(4):329-341. [CrossRef]
    73. Cameron EE, Joyce KM, Delaquis CP, Reynolds K, Protudjer JLP, Roos LE. Maternal psychological distress and mental health service use during the COVID-19 pandemic. J Affect Disord. 2020;276:765-774. [FREE Full text] [CrossRef] [Medline]
    74. Condon JT. The assessment of antenatal emotional attachment: development of a questionnaire instrument. Br J Med Psychol. 1993;66((Pt 2)):167-183. [CrossRef] [Medline]
    75. Condon JT, Corkindale CJ. The assessment of parent-to-infant attachment: development of a self-report questionnaire instrument. J Reprod Infant Psychol. 1998;16(1):57-76. [CrossRef]
    76. Medoff-Cooper B, Carey WB, McDevitt SC. The early infancy temperament questionnaire. J Dev Behav Pediatr. 1993;14(4):230-235. [Medline]
    77. Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR, Olino TM. Tutorial: power analyses for interaction effects in cross-sectional regressions. PsyArxiv. Preprint posted online on August 04 2022. [FREE Full text] [CrossRef]
    78. Rioux C, Castellanos-Ryan N, Parent S, Vitaro F, Tremblay RE, Séguin JR. Differential susceptibility to environmental influences: interactions between child temperament and parenting in adolescent alcohol use. Dev Psychopathol. 2016;28(1):265-275. [FREE Full text] [CrossRef] [Medline]
    79. Rioux C, Murray J, Castellanos-Ryan N, Séguin JR, Tremblay RE, Parent S. Moderation of parenting by inhibitory control in the prediction of the common and unique variance of hyperactivity-impulsivity and inattention. Dev Psychopathol. 2020;32(3):909-921. [CrossRef] [Medline]
    80. Rioux C, Castellanos-Ryan N, Parent S, Vitaro F, Séguin JR. The interactive effects of parental knowledge with impulsivity and sensation seeking in adolescent substance use. Child Psychiatry Hum Dev. 2019;50(1):95-107. [CrossRef] [Medline]
    81. Tomfohr-Madsen L, Rioux C, MacKinnon A, Silang K, Roos L, Lebel C, et al. Sleep and mental health in pregnancy during COVID-19: a parallel process growth model. Sleep Health. 2022;8(5):484-490. [FREE Full text] [CrossRef] [Medline]
    82. Del Giudice M. Statistical tests of differential susceptibility: performance, limitations, and improvements. Dev Psychopathol. 2017;29(4):1267-1278. [FREE Full text] [CrossRef] [Medline]
    83. Lawrence PR, Osborne MC, Sharma D, Spratling R, Calamaro CJ. Methodological challenge: addressing bots in online research. J Pediatr Health Care. 2023;37(3):328-332. [CrossRef] [Medline]
    84. Godinho A, Schell C, Cunningham JA. Out damn bot, out: recruiting real people into substance use studies on the internet. Subst Abus. 2020;41(1):3-5. [CrossRef] [Medline]
    85. Storozuk A, Ashley M, Delage V, Maloney EA. Got bots? practical recommendations to protect online survey data from bot attacks. TQMP. 2020;16(5):472-481. [FREE Full text] [CrossRef]
    86. Langkamp DL, Lehman A, Lemeshow S. Techniques for handling missing data in secondary analyses of large surveys. Acad Pediatr. 2010;10(3):205-210. [FREE Full text] [CrossRef] [Medline]
    87. Lang KM, Little TD. Principled missing data treatments. Prev Sci. 2018;19(3):284-294. [CrossRef] [Medline]
    88. Bergeron J, Doiron C, Marcon Y, Ferretti V, Fortier I. Fostering population-based cohort data discovery: the Maelstrom Research cataloguing toolkit. PLoS One. 2018;13(7):e0200926. [FREE Full text] [CrossRef] [Medline]
    89. Roisman GI, Newman DA, Fraley RC, Haltigan JD, Groh AM, Haydon KC. Distinguishing differential susceptibility from diathesis-stress: recommendations for evaluating interaction effects. Dev Psychopathol. 2012;24(2):389-409. [CrossRef] [Medline]
    90. Lawrence CE, Dunkel L, McEver M, Israel T, Taylor R, Chiriboga G, et al. A REDCap-based model for electronic consent (eConsent): moving toward a more personalized consent. J Clin Transl Sci. 2020;4(4):345-353. [FREE Full text] [CrossRef] [Medline]
    91. Pozzar R, Hammer MJ, Underhill-Blazey M, Wright AA, Tulsky JA, Hong F, et al. Threats of bots and other bad actors to data quality following research participant recruitment through social media: cross-sectional questionnaire. J Med Internet Res. 2020;22(10):e23021. [FREE Full text] [CrossRef] [Medline]
    92. Teitcher JEF, Bockting WO, Bauermeister JA, Hoefer CJ, Miner MH, Klitzman RL. Detecting, preventing, and responding to "fraudsters" in internet research: ethics and tradeoffs. J Law Med Ethics. 2015;43(1):116-133. [FREE Full text] [CrossRef] [Medline]
    93. Kayrouz R, Dear BF, Karin E, Titov N. Facebook as an effective recruitment strategy for mental health research of hard to reach populations. Internet Interv. 2016;4:1-10. [FREE Full text] [CrossRef] [Medline]
    94. King DB, O'Rourke N, DeLongis A. Social media recruitment and online data collection: a beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations. Can Psychol. 2014;55(4):240-249. [CrossRef]
    95. Guillory J, Wiant KF, Farrelly M, Fiacco L, Alam I, Hoffman L, et al. Recruiting hard-to-reach populations for survey research: using Facebook and Instagram advertisements and in-person intercept in LGBT bars and nightclubs to recruit LGBT young adults. J Med Internet Res. 2018;20(6):e197. [FREE Full text] [CrossRef] [Medline]
    96. Martinez O, Wu E, Shultz AZ, Capote J, Rios JL, Sandfort T, et al. Still a hard-to-reach population? using social media to recruit Latino gay couples for an HIV intervention adaptation study. J Med Internet Res. 2014;16(4):e113. [FREE Full text] [CrossRef] [Medline]
    97. Abrahamson M, Hooker E, Ajami NJ, Petrosino JF, Orwoll ES. Successful collection of stool samples for microbiome analyses from a large community-based population of elderly men. Contemp Clin Trials Commun. 2017;7:158-162. [FREE Full text] [CrossRef] [Medline]
    98. Sakhi AK, Bastani NE, Ellingjord-Dale M, Gundersen TE, Blomhoff R, Ursin G. Feasibility of self-sampled dried blood spot and saliva samples sent by mail in a population-based study. BMC Cancer. 2015;15:265. [FREE Full text] [CrossRef] [Medline]
    99. Ouellet-Morin I, Laurin M, Robitaille MP, Brendgen M, Lupien SJ, Boivin M, et al. Validation of an adapted procedure to collect hair for cortisol determination in adolescents. Psychoneuroendocrinology. 2016;70:58-62. [FREE Full text] [CrossRef] [Medline]
    100. Dvořák M, Maršala R, Kubáň P. In-vial dried urine spot collection and processing for quantitative analyses. Anal Chim Acta. 2023;1254:341071. [CrossRef] [Medline]
    101. Gustafsson HC, Young AS, Stamos G, Wilken S, Brito NH, Thomason ME, et al. Innovative methods for remote assessment of neurobehavioral development. Dev Cogn Neurosci. 2021;52:101015. [FREE Full text] [CrossRef] [Medline]
    102. Pel JJM, Manders JCW, van der Steen J. Assessment of visual orienting behaviour in young children using remote eye tracking: methodology and reliability. J Neurosci Methods. 2010;189(2):252-256. [CrossRef] [Medline]
    103. Ahmed SF, Skibbe LE, McRoy K, Tatar BH, Scharphorn L. Strategies, recommendations, and validation of remote executive function tasks for use with young children. Early Child Res Q. 2022;60:336-347. [FREE Full text] [CrossRef]
    104. Harvie HMK, Jain B, Nelson BW, Knight EL, Roos LE, Giuliano RJ. Induction of acute stress through an internet-delivered trier social stress test as assessed by photoplethysmography on a smartphone. Stress. 2021;24(6):1023-1032. [FREE Full text] [CrossRef] [Medline]
    105. Gunnar MR, Reid BM, Donzella B, Miller ZR, Gardow S, Tsakonas NC, et al. Validation of an online version of the trier social stress test in a study of adolescents. Psychoneuroendocrinology. 2021;125:105111. [FREE Full text] [CrossRef] [Medline]
    106. Oliver BR, Pike A. Introducing a novel online observation of parenting behavior: reliability and validation. Parenting. 2019;21(2):168-183. [CrossRef]
    107. Boyce WT. Differential susceptibility of the developing brain to contextual adversity and stress. Neuropsychopharmacology. 2016;41(1):142-162. [FREE Full text] [CrossRef] [Medline]
    108. Wagner SM, Rau C, Lindemann E. Multiple informant methodology: a critical review and recommendations. Sociol Methods Res. 2010;38(4):582-618. [CrossRef]

    ‎
    AI: artificial intelligence
    FIML: full information maximum likelihood
    MAR: missing at random
    PA: proportion affected
    PoI: proportion of interaction
    RA: research assistant
    REDCap: Research Electronic Data Capture

    Edited by A Mavragani; submitted 23.05.23; peer-reviewed by L Walekr, D Singla; comments to author 13.10.23; revised version received 15.10.23; accepted 09.11.23; published 06.12.23.

    Copyright

    ©Charlie Rioux, Delaney C Fulp, Parker N Haley, Jenna L LaBelle, Mary E Aasted, Kasie K Lambert, Madison T Donohue, Nkatheko T Mafu. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 06.12.2023.

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