This paper is in the following e-collection/theme issue:

Digital Health Reviews (1183) Mobile Health (mhealth) (2760) Pregnancy Information, Education and Lifestyle Interventions (284) Web-based and Mobile Health Interventions (3113) mHealth for Wellness, Behavior Change and Prevention (2739)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48853, first published .
App-Supported Lifestyle Interventions in Pregnancy to Manage Gestational Weight Gain and Prevent Gestational Diabetes: Scoping Review

App-Supported Lifestyle Interventions in Pregnancy to Manage Gestational Weight Gain and Prevent Gestational Diabetes: Scoping Review

App-Supported Lifestyle Interventions in Pregnancy to Manage Gestational Weight Gain and Prevent Gestational Diabetes: Scoping Review

Authors of this article:

Roxana Raab1 Author Orcid Image ;   Kristina Geyer1 Author Orcid Image ;   Sophia Zagar1 Author Orcid Image ;   Hans Hauner1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

    Review

    Institute of Nutritional Medicine, Else Kröner Fresenius Centre for Nutritional Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany

    Corresponding Author:

    Hans Hauner, MD

    Institute of Nutritional Medicine, Else Kröner Fresenius Centre for Nutritional Medicine

    TUM School of Medicine and Health

    Technical University of Munich

    Georg-Brauchle-Ring 62

    Munich, 80992

    Germany

    Phone: 49 892 892 4921

    Fax:49 892 892 4922

    Email: hans.hauner@tum.de


    Background: Excessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM) are common pregnancy complications that have been shown to be preventable through the use of lifestyle interventions. However, a significant gap exists between research on pregnancy lifestyle interventions and translation into clinical practice. App-supported interventions might aid in overcoming previous implementation barriers. The current status in this emerging research area is unknown.

    Objective: This scoping review aims to provide a comprehensive overview of planned, ongoing, and completed studies on eHealth and mobile health (mHealth) app–supported lifestyle interventions in pregnancy to manage GWG and prevent GDM. The review assesses the scope of the literature in the field; describes the population, intervention, control, outcomes, and study design (PICOS) characteristics of included studies as well as the findings on GWG and GDM outcomes; and examines app functionalities.

    Methods: The scoping review was conducted according to a preregistered protocol and followed established frameworks. Four electronic databases and 2 clinical trial registers were systematically searched. All randomized and quasi-randomized controlled trials (RCTs) of app-supported lifestyle interventions in pregnancy and related qualitative and quantitative research across the different study phases were considered for inclusion. Eligible studies and reports of studies were included until June 2022. Extracted data were compiled in descriptive analyses and reported in narrative, tabular, and graphical formats.

    Results: This review included 97 reports from 43 lifestyle intervention studies. The number of published reports has steadily increased in recent years; of the 97 included reports, 38 (39%) were trial register entries. Of the 39 identified RCTs, 10 efficacy or effectiveness trials and 8 pilot trials had published results on GWG (18/39, 46%); of these 18 trials, 7 (39%) trials observed significant intervention effects on GWG outcomes. Of all 39 RCTs, 5 (13%) efficacy or effectiveness trials reported GDM results, but none observed significant intervention effects on GDM. The RCTs included in the review were heterogeneous in terms of their PICOS characteristics. Most of the RCTs were conducted in high-income countries, included women with overweight or obesity and from all BMI categories, delivered multicomponent interventions, delivered interventions during pregnancy only, and focused on diet and physical activity. The apps used in the studies were mostly mHealth apps that included features for self-monitoring, feedback, goal setting, prompts, and educational content. Self-monitoring was often supported by wearable activity monitors and Bluetooth-connected weight scales.

    Conclusions: Research in this field is nascent, and the effectiveness and implementability of app-supported interventions have yet to be determined. The complexity and heterogeneity of intervention approaches pose challenges in identifying the most beneficial app features and intervention components and call for consistent and comprehensive intervention and outcome reporting.

    J Med Internet Res 2023;25:e48853

    doi:10.2196/48853

    Keywords

    mobile health; mHealth; eHealth; mobile app; lifestyle intervention; pregnancy; gestational weight gain; gestational diabetes; prevention; overweight; obesity; mobile phone 


    Background

    Pregnancy is a sensitive period for the health of both mother and offspring. Almost half of all women enter pregnancy with overweight or obesity [1,2], increasing the risk of pregnancy complications such as excessive gestational weight gain (GWG) and gestational diabetes mellitus (GDM) [3-5]. Globally, approximately half of all pregnant women gain excessive weight during pregnancy [6], and 14% develop GDM [7]. Regardless of maternal prepregnancy BMI, these complications can negatively affect the short- and long-term health of mothers and their children. Mothers are at an increased risk for adverse pregnancy outcomes, postpartum weight retention, and metabolic disorders such as type 2 diabetes [5,8-10]. Children are susceptible for macrosomia or being large-​for-gestational-age at birth and developing obesity and its sequelae later in life [6,11-13]. These health consequences at the individual level are accompanied by immense costs to health care systems and societies [14].

    Lifestyle is a modifiable determinant with the potential to improve maternal and child health. Recent systematic reviews and meta-analyses have summarized the effects of more than 3 decades of research on conventional, primarily face-to-face lifestyle intervention programs during pregnancy [15-21]. Overall, interventions moderately reduced the incidence of excessive GWG and postpartum weight retention and prevented GDM [15-17,20,21]. Interventions also reduced the risk for macrosomic births and large-for-gestational-age infants but did not influence long-term childhood weight outcomes [18,19]. Research investigating this lack of effect is ongoing [22]. Although the optimal intervention approach, including the most effective intervention components, delivery modes, and behavioral strategies, is still undetermined [15], researchers agree that lifestyle interventions should be implemented into practice to improve health outcomes and save health care costs [15,23]. However, implementing these interventions remains an unachieved goal [2,24]. While focusing on intervention efficacy and effectiveness primarily within controlled academic settings, research groups have neglected to apply implementation strategies and report on the outcomes of the implementation itself [24]. Moreover, barriers to implementation exist at the patient, provider, and system level, including lack of time, knowledge, and resources [25,26].

    Recent advances and evolving opportunities for digital solutions open the way for innovative intervention approaches that could facilitate behavior change and overcome some of the implementation barriers. The most promising and convenient technology for providing evidence-based support for lifestyle behavior modification seems to be health apps, either accessible via mobile devices such as smartphones or tablet computers (mobile health [mHealth] apps) or via web browsers (eHealth apps). Their easy accessibility, scalability, and cost-effectiveness foster implementability [27]. Moreover, smartphones and apps are widely used across different age groups as well as socioeconomic and cultural backgrounds. App use has gained in popularity among young women; health information–seeking behavior has shifted from soliciting medical advice from health care providers to using digital media to obtain health information [28,29]. The potential of apps to offer on-demand lifestyle support with low resources and high reach is coupled with the ability to easily integrate various behavior change techniques (BCTs). These can be integrated into app features, such as self-monitoring, goal setting, or feedback, and have been shown to increase the effectiveness of interventions [30,31]. Moreover, real-time tracking of health and lifestyle data allows for the early identification of abnormal values and the tailoring of interventions according to one’s individual risk profile [32]. Thus, tailored support is possible on a large scale. Despite the increasing number of accessible lifestyle-related pregnancy apps on the market, few are of high quality, and many lack evidence-based resources and present information that is inconsistent with evidence-based guidelines, showing low use of BCTs and leaving uncertainties about data security [33-37].

    The proven effectiveness of traditional lifestyle interventions on improving pregnancy outcomes and the urgent need to implement lifestyle interventions into clinical practice, together with the potential of apps to offer tailored, scalable, and evidence-based lifestyle support as well as a dearth of available high-quality apps, highlight the research and collaborative potential in this area. Indeed, studies on the development and integration of eHealth and mHealth apps to support a healthy lifestyle during pregnancy have emerged in the last years [38-42]. Previous systematic reviews and meta-analyses included studies using different technologies, such as SMS text messaging, telephone and video calls, websites, and social media. Only a few studies using apps were identified, with limited ability to draw conclusions based on the dearth of available evidence [43-47]. To the best of our knowledge, no scoping review mapping the current evidence of registered, ongoing, and completed pregnancy lifestyle intervention studies using eHealth and mHealth apps has been undertaken, which encouraged us to conduct this scoping review.

    Objectives

    This review addresses 3 major research objectives: first, to assess the scope of the available literature in the field; second, to determine the concepts and characteristics of studies and their findings on GWG and GDM outcomes; and, third, to describe the functionalities of available eHealth and mHealth apps. On the basis of the evidence available, this review is further intended to identify knowledge gaps, inform ongoing and future intervention projects, and provide a basis for systematic reviews and meta-analyses.


    Overview

    This scoping review is based on a protocol that can be accessed through the Open Science Framework [48]. The review follows the framework developed by Arksey and O’Malley [49] and its refinement by Levac et al [50] as well as the JBI manual for evidence synthesis [51]. Accordingly, this work contains the following stages:

    1. Identification of the research question
    2. Identification of relevant studies
    3. Selection of eligible studies
    4. Charting of the data
    5. Collating, summarizing, and reporting of the results

    The optional stage 6, which includes consultation with stakeholders, is not covered by this review. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guides the reporting of this review [52] (Multimedia Appendix 1 [49-52]).

    Stage 1: Identification of the Research Question

    This scoping review addresses the following research questions:

    1. What is the scope of the literature that exists in the field of app-supported pregnancy lifestyle interventions to limit GWG and prevent GDM?
    2. What are the concepts and characteristics of the available studies in terms of population, intervention, control, outcomes, and study design (PICOS), and what are the findings on GWG and GDM?
    3. Which commercially available apps are being used in this field of research, and what kind of apps have been developed by researchers?

    Stage 2: Identification of Relevant Studies

    The eligibility of the studies was assessed based on the population, concept, and context (PCC) framework suggested by JBI [51]. Table 1 provides an overview of the PCC criteria and the types of evidence considered in this scoping review. The population of interest was pregnant women and women who intend to become pregnant of any age and BMI category. The considered concept was the use of eHealth and mHealth apps within lifestyle intervention studies to manage GWG and prevent GDM in the context of pregnancy care and, at times combined with preconception care, postpartum care, or both in any country and any setting. Apps that were accessible through a mobile device, such as a smartphone or a tablet computer, were classified as mHealth apps, whereas those accessible through a web browser were classified as eHealth apps. Eligible reports were trial register entries (TREs), conference abstracts and further gray literature (eg, dissertations), study protocols, and full articles. Conference abstracts were excluded if a corresponding full text was available. Reviews, meta-analyses, and cohort studies without any references to pregnancy lifestyle interventions were not considered for inclusion in this review.

    Table 1. Inclusion and exclusion criteria of this scoping review based on population, concept, context, and types of evidence.

    		Inclusion criteriaExclusion criteria
    PopulationPregnant women and women who intend to become pregnant of any age and BMI categoryWomen with certain conditions or chronic diseases, such as type 1 or 2 diabetes mellitus and cancer
    ConceptApp-supported lifestyle intervention studies to limit GWGa and prevent GDMbeHealth studies involving SMS text messages, telephone or video calls, websites, and social media only
    ContextPregnancy care (+ preconception care + postpartum care), any setting, and any country (high-, middle-, and low-income countries)N/Ac
    Types of evidenceFull articles, trial register entries, study protocols, gray literature (eg, conference abstracts), RCTsd and quasi-RCTs, and related qualitative and quantitative investigations (eg, surveys, focus groups, and interviews)Reviews and meta-analyses, editorial articles, and cohort studies

    aGWG: gestational weight gain.

    bGDM: gestational diabetes mellitus.

    cN/A: not applicable.

    dRCT: randomized controlled trial.

    The PCC criteria guided the conduct of the search strategy. Preliminary searches in PubMed and Embase were performed to identify relevant keywords and controlled vocabulary to refine the search strategy. The final search strategy was adapted to fit the individual search criteria of each included database. No restrictions on publication status or language were applied. Hedges to filter for human studies were applied where appropriate. PubMed, Embase, Web of Science, and the Cochrane Library were searched on May 17, 2021 (Multimedia Appendix 2). The search was updated on November 11, 2021. The updated search only considered studies published after the initial search date or 2021, depending on the applicable custom range of each database. The International Clinical Trials Registry Platform and ClinicalTrials.gov were searched on May 30, 2022 (Multimedia Appendix 2). Complementarily, Google, Google Scholar, and PubMed were continually searched for relevant literature until June 2022. This involved searching for new studies as well as for reports of already identified studies. The reference lists of included articles and identified relevant reviews were additionally screened.

    Stage 3: Selection of Eligible Studies

    All search hits were exported to the reference management software EndNote X9.3.3 (Clarivate). Duplicates were removed by 1 reviewer. The screening of titles, abstracts, and full texts was based on the PCC criteria and performed independently by 2 reviewers. Discrepancies were resolved through discussion or by consulting a third reviewer. For full texts without access, authors were contacted to request the file to be made available. All randomized controlled trials (RCTs) and quasi-RCTs of app-supported pregnancy lifestyle interventions and related qualitative and quantitative research across the different study phases were considered for inclusion. GWG or GDM had to be a primary or secondary outcome in the studies to be included. Studies that began in the preconception period were eligible for inclusion if the intervention continued during pregnancy and if women were followed up throughout pregnancy to assess GWG and GDM outcomes.

    Stage 4: Charting of the Data

    A template for data charting was developed by the review team and pretested. Data charting was conducted by 1 reviewer and verified by 2 other reviewers. Discrepancies were discussed, and a final consensus document was produced. Study characteristics, such as author, year, recruitment status, and country, as well as PICOS characteristics and findings on GWG and GDM were extracted from the RCTs. Information on the types of evidence and app characteristics, functionalities, and tracking devices was gathered for all studies, including those with published reports of app development but without available information on the planned RCT.

    Stage 5: Collating, Summarizing, and Reporting of the Results

    The study selection process is described narratively and presented in a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram [52]. Extracted data from the included studies were categorized in the most meaningful ways, coded, synthesized by basic descriptive analysis, and presented in tabular and graphical formats. All findings are also summarized narratively.


    Study Selection and Sources of Evidence

    Figure 1 depicts the flow of identified and included studies in this review. A total of 7301 records were identified from the initial and updated database searches. Trial register searches yielded an additional 150 records. After removing duplicates, 68.03% (5069/7451) of the records were screened by title. Of these 5069 records, 4659 (91.91%) were discarded, resulting in 410 (8.09%) remaining reports that were sought for retrieval by screening the abstract according to the PCC criteria. Of these 410 reports, although 269 (65.6%) were not retrieved, 141 (34.4%) were assessed in full for eligibility. On the basis of the PICOS and PCC criteria, of these 141 reports, 74 (52.5%) were excluded at this stage. Finally, 67 (69%) reports from the database and trial register searches together with 15 (15%) eligible reports identified via internet search and 15 (15%) eligible reports identified by citation searching, amounting to a total of 97 reports from 43 studies, were included in this scoping review. Of the 97 included reports, 38 (39%) were TREs [53-90], 49 (50%) were full peer-reviewed articles [25,26,38-42,91-132], 5 (5%) were conference abstracts [133-137], 2 (2%) were preprints [138,139], 1 (1%) was a master’s thesis [140], 1 (1%) was a dissertation [141], and 1 (1%) was a grant report [142].

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram showing the identification and selection of studies.

    Scope of the Literature

    Figure 2 presents the scope of the available literature up to June 2022 in absolute numbers of published reports. All 97 reports were included in this overview. In 2011, the first eHealth app–supported RCT was registered [62]. The first mHealth app–supported RCTs were registered in 2012 [78,80,87]. The first article describing the development of an eHealth app–supported lifestyle intervention was published in 2014 [100]. One year later, the first publication on an intervention involving the development of an mHealth app followed [40]. In 2016, study protocols of effectiveness RCTs with app support were published for the first time [117,123]. The results of pilot RCTs with app support and a report evaluating an app-supported intervention were first available in 2017 [98,127,140]. The results of efficacy or effectiveness RCTs were first reported in 2018 [39,99,118,141]. Overall, the number of newly published reports steadily increased from 2015 onward: of the 97 included reports, 1 (1%) was published in 2015, up to 19 (20%) were published in 2020, and 18 (19%) were published in 2021. Overall, TREs accounted for the largest proportion of published reports (38/97, 39%). Multimedia Appendix 3 [25,26,38-42,53-142] lists the reports by study and intervention phase.

    ‎
    Figure 2. Number and type of identified reports per year up to June 2022 (a total of 97 reports were included). RCT: randomized controlled trial.

    PICOS Characteristics of Included RCTs and Findings on GWG and GDM Outcomes

    Overview

    Table 2 provides an overview of the PICOS characteristics of all identified RCTs in this review (39/43 studies, 91%). Details on study and intervention characteristics are presented in Multimedia Appendix 4 [38,39,41,42,53-91,93-95,97,99-111,​113-115,117,118,120-127,129,130,134-136,138-141]. Descriptive analyses are based on the coded data that are presented in Multimedia Appendix 5 [25,26,38-42,53-142]. Studies planning to conduct an RCT in the future (4/43, 9%) were not considered in this overview because no information on study characteristics was available [116,119,128,131].

    Table 2. Population, intervention, control, outcomes, and study design (PICOS) characteristics of the included randomized controlled trials (RCTs) with available information (n=39).
    Characteristic and categoryRCTs, n (%)
    Study design

    		Pilot13 (33)

    		Efficacy or effectiveness26 (67)
    Study arms

    		234 (87)

    		35 (13)
    Sample size

    		≤10014 (36)

    		>100-100017 (44)

    		>10008 (21)
    Recruitment status

    		Recruiting14 (36)

    		Completed17 (44)

    		Active, not recruiting2 (5)

    		Unknown4 (10)

    		Withdrawn or terminated2 (5)
    Recruitment timing

    		Preconception3 (8)

    		First trimester9 (23)

    		Until or within second trimester22 (56)

    		Until or within third trimester3 (8)

    		Unknown2 (5)
    Country or continent

    		United States16 (41)

    		Europe9 (23)

    		Asia7 (18)

    		Australia4 (10)

    		Canada2 (5)

    		Multiple1 (3)
    Population

    		All BMI categories10 (26)

    		Normal weight + overweight + obesity6 (15)

    		Overweight + obesity16 (41)

    		Obesity3 (8)

    		High risk for GDMa4 (10)
    Behavior change technique or framework

    		Any26 (67)

    		None13 (33)
    Implementation science framework

    		Any4 (10)

    		None35 (90)
    Intervention duration

    		Preconception (+ pregnancy)3 (8)

    		Pregnancy only23 (59)

    		Pregnancy + post partum10 (26)

    		Pregnancy only (intervention 1) and pregnancy + post partum (intervention 2)1 (3)

    		Uncertain2 (5)
    Intervention mode of delivery

    		App only8 (21)

    		App + technology15 (39)

    		App + nontechnology6 (15)

    		App + technology + nontechnology10 (26)
    Lifestyle content of intervention

    		Diet only5 (13)

    		PAb only6 (15)

    		Only well-being or sleep or both1 (3)

    		Diet + PA15 (39)

    		Diet + PA + well-being or sleep or both11 (28)

    		Uncertain1 (3)
    Intervention tailoringc

    		Feedback based on tracked lifestyle data and goals33 (85)

    		Individual coaching sessions21 (54)

    		Adaptive according to risk9 (23)
    Control group

    		Standard care19 (49)

    		Oral or written information8 (21)

    		App or website alternative8 (21)

    		Tracking device4 (10)
    Results on GWGd and GDM outcomes

    		Any results reported18 (46)

    		No results reported21 (54)

    aGDM: gestational diabetes mellitus.

    bPA: physical activity.

    cIndependent of the mode of delivery.

    dGWG: gestational weight gain.

    Study Characteristics

    Of the included RCTs, 44% (17/39) had been completed, and 36% (14/39) were still recruiting. Whereas 67% (26/39) of the RCTs investigated efficacy or effectiveness, 33% (13/39) were pilot studies investigating the feasibility of interventions. Most of the studies (34/39, 87%) were 2-armed, whereas 5 (13%) studies were 3-armed, comparing 2 intervention groups and 1 control group. Of these 5 studies, 2 investigated 2 different physical activity programs [55,102], whereas 1 compared 2 different intervention lengths (pregnancy vs pregnancy and post partum) [99], 1 compared an app group with an app + coaching group [41], and 1 compared 2 different delivery modes (in person vs app) [127]. The sample size of all RCTs ranged from 26 [42] to 2039 [77] participants, with 44% (17/39) of the studies recruiting between 100 and 1000 participants (Table 2). More than half of the studies (22/39, 56%) recruited women until or within the second trimester. Of the 39 studies, 9 (23%) limited the recruitment of women to the first trimester and 3 (8%) to the preconception period, whereas 3 (8%) included women until or within the third trimester. Studies were conducted in the United States (16/39, 41%), Europe (9/39, 23%), Asia (7/39, 18%), Australia (4/39, 10%), and Canada (2/39, 5%), whereas 1 (3%) of the 39 studies was conducted across multiple countries [95].

    Population

    Most of the studies included women with overweight and obesity (16/39, 41%) and from all BMI categories (10/39, 26%). Fewer studies excluded women with underweight (6/39, 15%), focused on women with a high risk for GDM (4/39, 10%), or only included those with obesity (3/39, 8%).

    Intervention

    In 59% (23/39) of the RCTs, the intervention was delivered in pregnancy only, whereas 26% (10/39) continued the intervention post partum, and 3% (1/39) assessed 2 time frames. Of the 39 RCTs, 3 (8%) started the intervention before conception, whereas in 2 (5%), the time frame was uncertain. The intervention involved an app combined with further technological components, including tracking devices or telephone calls, in 39% (15/39) of the RCTs. In 8 (21%) of the 39 studies, an app was the only intervention component used. An app and nontechnological intervention components, such as face-to-face visits or leaflets, were applied in 6 (15%) of the 39 studies, whereas in 10 (26%), the intervention was complemented by both technological and nontechnological components. More than one-third of the studies (15/39, 39%) addressed diet and physical activity within their interventions, and 11 (28%) of the 39 studies additionally involved well-being or sleep or both. Fewer studies focused only on diet (5/39, 13%) or physical activity (6/39, 15%). Of the 39 studies, 1 (3%) focused only on well-being and sleep. Independent of the mode of delivery, the interventions of included studies were tailored to a certain degree. Most of the studies (33/39, 85%) provided individual feedback based on tracked lifestyle data and goals. Of the 39 studies, 21 (54%) offered individual coaching sessions to participants; in 9 (23%), the intervention was adaptive in response to the individual risk profile. Of the 39 RCTs, 26 (67%) based the intervention on behavior change frameworks or techniques, such as motivational interviewing (n=8); specific, measurable, achievable, reasonable, and time-bound (SMART) goals (n=8); or social cognitive theory (n=7). Of the 39 RCTs, 4 (10%) applied implementation science frameworks, including intervention mapping; the exploration, preparation, implementation, and sustainment (EPIS) framework; and the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework [94,95,102,107].

    Control

    Control groups received standard care only in nearly half of the studies (19/39, 49%). In some of the studies, the control group had access to an alternative app or website (8/39, 21%) or tracking device (4/39, 10%). Additional oral or written information was delivered to control groups in 8 (21%) of the 39 studies.

    Outcomes

    Of the 39 included RCTs, 37 (95%) assessed GWG, and 24 (62%) assessed GDM, as either primary or secondary outcomes. Of the 39 RCTs, 18 (46%) had published results on GWG outcomes. Of these 18 RCTs, 8 (44%) were pilot RCTs [41,42,105,109,125,127,139,140], and 10 (56%) were efficacy or effectiveness RCTs [39,99,103,111,115,118,120,​134,138,141]. Significant intervention effects on any GWG outcome were found in 7 (39%) of these 18 studies [39,103,115,118,120,127,134], whereas 11 (61%) observed no effect. Of the 39 RCTs, 5 (13%) reported GDM results, but none observed a significant intervention effect [39,103,115,118,120].

    App Characteristics

    All 43 included studies provided some information on the apps they used. Multimedia Appendix 6 [25,26,38-42,53-142] provides a detailed overview of app characteristics and functionalities as well as tracking devices used in the studies. Descriptive analyses were based on coded data that are presented in Multimedia Appendix 5.

    Most of the studies (30/43, 70%) used an mHealth app, 16% (7/43) used an eHealth app, and 9% (4/43) used a hybrid format or applied both an eHealth and an mHealth app. In 2 (5%) of the 43 studies, the type of app was uncertain. In more than half of the studies (24/43, 56%), research teams either developed new apps or adjusted the content of existing systems, 30% (13/43) used commercially available apps, and 5% (2/43) used both. For 9% (4/43) of the studies, no information on app origin was available.

    The tracking and self-monitoring of health and lifestyle data were possible in the apps in 88% (38/43) of the studies; most often, it was possible to track weight, physical activity, diet, or a combination of these. Most of the studies (31/43, 72%) provided educational material via the app using different media types, such as text, video, and audio. Goal setting was possible via the app in 19 (44%) of the 43 studies. Feedback to participants via the app was provided in 58% (25/43) of the studies, whereas 51% (22/43) used prompts within the apps as reminders, feedback, motivation, or to inform about health and lifestyle data being outside of normal ranges. Communication with peers (eg, through social networks) via the app was possible in 7 (16%) of the 43 studies. Gamification elements, including quizzes and rewards, were integrated into the app in 6 (14%) of the 43 studies. Health coaching via the app was delivered in 5 (12%) of the 43 studies. Health and lifestyle data were tracked using wearable or Bluetooth-connected devices in approximately half of the studies (21/43, 49%). Most often, an activity tracker (15/43, 35%) or a weight scale (12/43, 28%) was used. Of the 43 studies, 1 (2%) used a blood pressure cuff.


    Principal Findings

    After more than 3 decades of research on conventional, primarily face-to-face lifestyle interventions in pregnancy, a new era of digital interventions is currently emerging with apps showing promising potential to overcome previous barriers to implementation and dissemination. This scoping review identified 97 reports of 43 planned, ongoing, and completed studies on app-supported lifestyle interventions during pregnancy to manage GWG and prevent GDM. The steady increase in published reports from 2015 onward, most of which were TREs (38/97, 39%), and approximately one-third of the identified RCTs were still recruiting (14/39, 36%), indicates that this nascent field is developing. The studies were found to have published reports across different study phases involving exploration, intervention planning, app development and testing, and implementation and evaluation covering qualitative and quantitative research. The studies differed in their characteristics and intervention approaches, posing challenges in identifying the most effective components and implementable approaches and underlining the importance of standardized, comprehensive intervention and outcome reporting.

    Previous systematic reviews and meta-analyses on technology-supported lifestyle interventions identified only a small number of app-based studies and observed mixed findings while being unable to draw firm conclusions owing to the limited available evidence from mostly pilot RCTs [43-47].

    In this review, we included 39 app-supported RCTs, of which 8 pilot and 10 efficacy or effectiveness trials (18/39, 46%) provided results on GWG outcomes. In 7 of these 18 (39%) trials, the rates of excessive GWG or total GWG could be reduced by the intervention. Of the 39 RCTs, 5 (13%) efficacy or effectiveness trials reported results on GDM and observed no effect, which is in line with previous concerns on the overall effectiveness of lifestyle interventions on clinical outcomes and requires further elucidation [143]. On the basis of the limited number of published results on GWG and GDM outcomes (18/39, 46% of RCTs), it is currently unclear as to whether and to what extent app-supported pregnancy lifestyle interventions are effective and suitable for broad application at the population level. Moreover, the complexity and heterogeneity of intervention approaches complicate the differentiation between more or less effective components to ascertain which are the most effective. A planned systematic review and meta-analysis with a rigorous risk-of-bias assessment will provide a more comprehensive and profound interpretation of findings.

    The included studies varied in their design, population, intervention, and app characteristics. The studies were mostly conducted in high-income countries and focused on high BMI categories as an inclusion criterion. Populations considered socioeconomically disadvantaged as well as minority populations should be given more consideration in future studies because they are often the most vulnerable to adverse health outcomes in pregnancy [144,145]. Furthermore, a greater diversity of women at risk could be reached in interventions if risk factors other than high BMI would be considered in studies (eg, by using validated screening tools). One of the included studies applied the validated Monash GDM screening tool to identify women at high risk for GDM [95]. A validated screening tool to identify women at high risk for excessive GWG has recently been developed by our group [146]. Such scores could be integrated into apps and applied in clinical health care settings.

    Intervention timing and duration differed among the studies. A large proportion of the studies delivered the intervention during pregnancy, with fewer studies continuing the intervention after pregnancy and only 3 studies starting the intervention before conception. The best timing and duration for intervening are still uncertain. However, the importance of intervening early and the need for more holistic approaches spanning from preconception to the postpartum period have recently been emphasized [2,14,147,148].

    Intervention content that was delivered in the included studies primarily focused on healthy nutrition and physical activity, whereas fewer studies addressed mental health and well-being aspects in their programs. Perinatal anxiety and depression are common and have been linked to adverse behavioral and health outcomes in women [149,150]. Thus, mental health and well-being should be given more attention in interventions, as also suggested by the Health in Preconception, Pregnancy and Postpartum Global Alliance [2]. Within apps, provided content should be presented in a concise, easy way, with focus on visualization and the integration of various delivery forms, including text, audio, and video formats [26]. Moreover, women appreciate content that is tailored to their individual preferences, culture, and lifestyles as well as the consideration of social surroundings, pregnancy symptoms, and previous knowledge and risks [26,113,116].

    Individualization is a key determinant for intervention success because women differ in their needs and barriers regarding lifestyle support. Previously, tailored advice was often given in individual coaching sessions, requiring time and resources. Apps and connected devices enable readily available lifestyle advice and the continuous monitoring of health and lifestyle data with individual goal setting and feedback on progress. These functionalities and BCTs have been applied in most of the included studies and could potentially help to increase motivation and engagement. However, women might get annoyed if they receive prompts at unfavorable times or too frequently [112]. In addition, although some women value the opportunity to self-monitor health behaviors and receive feedback on their progress, tracking can be perceived as time consuming, and some women might feel extra pressure when goals are not reached, prompting negative emotions [40-42,112,116]. Body weight is a particularly sensitive topic on which women would value sensitive support [97]. Thus, sensitive handling and the opportunity for women to individually adapt functionalities according to their own preferences are suggested to ensure continued app engagement. None of the studies used artificial intelligence–based tools, such as chatbots. Such internet-based assistance and self-learning tools could facilitate the timely delivery of personalized advice and thus increase intervention efficiency. The adaptation of interventions according to risk profiles based on monitored data is another promising strategy for improving precision prevention and the potential efficiency of interventions while saving time, costs, and resources [32]. In the future, maternal phenotyping could be improved by collecting data on clinical and mental health parameters, such as glucose and blood pressure levels, mood, and sleep. Moreover, newly emerging artificial intelligence–based diet assessment tools could also be used to facilitate the tracking of nutritional behavior and nutrient intake.

    Overall, lifestyle interventions are increasing in complexity, and it will be a future challenge to differentiate the effects of intervention components and identify the most effective and implementable approaches to overcome the existing translational research gap. Grounding intervention development, implementation, and evaluation on implementation science frameworks could aid in the successful implementation of interventions [151]. Among the studies included in this review, only a few made use of such frameworks. However, all studies can contribute to translating research findings into practice by reporting on contextual, implementation, and process evaluation outcomes. Moreover, facilitators and barriers at the patient, health care provider, and system levels should be examined and considered.

    Strengths and Limitations

    The review has several strengths and limitations. It is based on relevant frameworks and the searching and inclusion of reports was conducted without language and publication status restrictions. Register entries and gray literature sources were included to provide a comprehensive overview of the ongoing literature. However, by including these evidence sources, the often limited and not always up-to-date information available from these reports made the identification and classification of key characteristics challenging. In the case of an included ongoing RCT with limited information, it was uncertain whether the study accurately met the predetermined inclusion criteria because the intervention may have been conducted only in the preconceptional phase and not continued during pregnancy, but an assessment of GWG and GDM outcomes is planned [54]. Given the exploratory and comprehensive nature of this review and the importance of early intervention, we decided to include the study [54]. We considered qualitative and quantitative investigations, whereby no systematic approach to qualitative research synthesis was used. This scoping review did not consider reviews and meta-analyses for inclusion. The studies used different terminologies and often lacked clear definitions with regard to apps and digital devices, which made classification and interpretation often difficult. As this is a scoping review, quality assessment has not been conducted, which precluded a profound interpretation of the findings on GWG and GDM outcomes in the included RCTs.

    Implications for Future Research

    This review has identified research gaps and provided suggestions for future app and intervention development. The increasing number of identified studies in the field justifies the conduct of systematic reviews and meta-analyses, summarizing qualitative and quantitative data across all study phases. To increase comparability and enable useful synthesis, ongoing and future studies are encouraged to use current guidelines, frameworks, and BCT taxonomies to guide outcome reporting [152-156]. It will be exciting to see which of the apps developed in research settings make it to market and to assess their impact as well as evaluate their quality using tools such as the Mobile App Rating Scale [157].

    Conclusions

    This scoping review provides, for the first time, a comprehensive overview of ongoing research in the field of app-supported lifestyle interventions to manage GWG and prevent GDM. Although various approaches are emerging, it remains to be determined whether the interventions are effective and which app features and elements are the most beneficial. It also needs to be elucidated whether these interventions can be implemented outside of trial settings. This has been missed to be considered by most of the previous research in this area. As the entire process from app development to the implementation of an app-supported intervention into practice requires time and resources, it may be reasonable for future investigations to collaborate more intensively across disciplines and build upon previous research and available resources.

    Acknowledgments

    The authors gratefully thank Helen Sauer for her assistance in verifying the data extraction. Moreover, the authors gratefully thank Dora Meyer for proofreading the final version of the manuscript. The authors also thank the Else Kröner-Fresenius Foundation, Bad Homburg, Germany.

    Data Availability

    Data obtained or analyzed in this review are included in this document and its multimedia appendices.

    Authors' Contributions

    RR, KG, and HH developed the research question. RR designed the methodology and guided the review process. KG and HH resolved discrepancies during the review process. RR and SZ screened and selected the studies and extracted and coded the data. RR performed descriptive analyses and prepared the tables and figures for the manuscript. All authors contributed to the interpretation of the findings. RR drafted the manuscript. All authors revised the manuscript and approved the final version.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

    DOCX File , 107 KB
    Multimedia Appendix 2

    Full electronic search strategies applied to databases and clinical trial registers.

    DOCX File , 22 KB
    Multimedia Appendix 3

    Categorization of published reports by study and type of evidence.

    DOCX File , 26 KB
    Multimedia Appendix 4

    Population, intervention, control, outcomes, and study design (PICOS) characteristics of the included randomized controlled trials.

    DOCX File , 47 KB
    Multimedia Appendix 5

    Coding of extracted data.

    XLSX File (Microsoft Excel File), 18 KB
    Multimedia Appendix 6

    Characteristics of the apps and tracking devices used in the 43 included studies.

    DOCX File , 33 KB
    1. Branum AM, Kirmeyer SE, Gregory EC. Prepregnancy body mass index by maternal characteristics and state: data from the birth certificate, 2014. Natl Vital Stat Rep. Aug 2016;65(6):1-11. [FREE Full text] [Medline]
    2. Hill B, Skouteris H, Boyle JA, Bailey C, Walker R, Thangaratinam S, et al. Health in preconception, pregnancy and postpartum global alliance: international network pregnancy priorities for the prevention of maternal obesity and related pregnancy and long-term complications. J Clin Med. Mar 18, 2020;9(3):822. [FREE Full text] [CrossRef] [Medline]
    3. Najafi F, Hasani J, Izadi N, Hashemi-Nazari SS, Namvar Z, Mohammadi S, et al. The effect of prepregnancy body mass index on the risk of gestational diabetes mellitus: a systematic review and dose-response meta-analysis. Obes Rev. Mar 2019;20(3):472-486. [CrossRef] [Medline]
    4. Poston L, Caleyachetty R, Cnattingius S, Corvalán C, Uauy R, Herring S, et al. Preconceptional and maternal obesity: epidemiology and health consequences. Lancet Diabetes Endocrinol. Dec 2016;4(12):1025-1036. [CrossRef] [Medline]
    5. LifeCycle Project-Maternal Obesity and Childhood Outcomes Study Group; Voerman E, Santos S, Inskip H, Amiano P, Barros H, et al. Association of gestational weight gain with adverse maternal and infant outcomes. JAMA. May 07, 2019;321(17):1702-1715. [FREE Full text] [CrossRef] [Medline]
    6. Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al. Association of gestational weight gain with maternal and infant outcomes: a systematic review and meta-analysis. JAMA. Jun 06, 2017;317(21):2207-2225. [FREE Full text] [CrossRef] [Medline]
    7. Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF Diabetes Atlas Committee Hyperglycaemia in Pregnancy Special Interest Group. IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by international association of diabetes in pregnancy study group's criteria. Diabetes Res Clin Pract. Jan 2022;183:109050. [CrossRef] [Medline]
    8. Nehring I, Schmoll S, Beyerlein A, Hauner H, von Kries R. Gestational weight gain and long-term postpartum weight retention: a meta-analysis. Am J Clin Nutr. Nov 2011;94(5):1225-1231. [FREE Full text] [CrossRef] [Medline]
    9. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL. Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ. May 13, 2020;369:m1361. [FREE Full text] [CrossRef] [Medline]
    10. Meyer D, Gjika E, Hauner H, Michel S, Raab R. OR21-07-23 gestational weight gain and its effect on short-and long-term postpartum weight retention: an updated systematic review and meta-analysis. Curr Dev Nutr. Jul 2023;7(Supplement 1):100991. [FREE Full text] [CrossRef]
    11. Voerman E, Santos S, Patro Golab B, Amiano P, Ballester F, Barros H, et al. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: an individual participant data meta-analysis. PLoS Med. Mar 2019;16(2):e1002744. [FREE Full text] [CrossRef] [Medline]
    12. Ornoy A, Becker M, Weinstein-Fudim L, Ergaz Z. Diabetes during pregnancy: a maternal disease complicating the course of pregnancy with long-term deleterious effects on the offspring. A clinical review. Int J Mol Sci. Mar 15, 2021;22(6):2965. [FREE Full text] [CrossRef] [Medline]
    13. Tam WH, Ma RC, Ozaki R, Li AM, Chan MH, Yuen LY, et al. In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in offspring. Diabetes Care. May 2017;40(5):679-686. [FREE Full text] [CrossRef] [Medline]
    14. WHO European regional obesity report 2022. World Health Organization. 2022. URL: https://www.who.int/europe/publications/i/item/9789289057738
    15. Teede HJ, Bailey C, Moran LJ, Bahri Khomami M, Enticott J, Ranasinha S, et al. Association of antenatal diet and physical activity-based interventions with gestational weight gain and pregnancy outcomes: a systematic review and meta-analysis. JAMA Intern Med. Mar 01, 2022;182(2):106-114. [FREE Full text] [CrossRef] [Medline]
    16. International Weight Management in Pregnancy (i-WIP) Collaborative Group. Effect of diet and physical activity based interventions in pregnancy on gestational weight gain and pregnancy outcomes: meta-analysis of individual participant data from randomised trials. BMJ. Jul 19, 2017;358:j3119. [FREE Full text] [CrossRef] [Medline]
    17. Cantor AG, Jungbauer RM, McDonagh M, Blazina I, Marshall NE, Weeks C, et al. Counseling and behavioral interventions for healthy weight and weight gain in pregnancy: evidence report and systematic review for the US preventive services task force. JAMA. May 25, 2021;325(20):2094-2109. [CrossRef] [Medline]
    18. Louise J, Poprzeczny AJ, Deussen AR, Vinter C, Tanvig M, Jensen DM, et al. The effects of dietary and lifestyle interventions among pregnant women with overweight or obesity on early childhood outcomes: an individual participant data meta-analysis from randomised trials. BMC Med. Jun 02, 2021;19(1):128. [FREE Full text] [CrossRef] [Medline]
    19. Raab R, Michel S, Günther J, Hoffmann J, Stecher L, Hauner H. Associations between lifestyle interventions during pregnancy and childhood weight and growth: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. Jan 07, 2021;18(1):8. [FREE Full text] [CrossRef] [Medline]
    20. Michel S, Raab R, Drabsch T, Günther J, Stecher L, Hauner H. Do lifestyle interventions during pregnancy have the potential to reduce long-term postpartum weight retention? A systematic review and meta-analysis. Obes Rev. Apr 2019;20(4):527-542. [CrossRef] [Medline]
    21. Peaceman AM, Clifton RG, Phelan S, Gallagher D, Evans M, Redman LM, et al. LIFE‐Moms Research Group. Lifestyle interventions limit gestational weight gain in women with overweight or obesity: LIFE-Moms prospective meta-analysis. Obesity (Silver Spring). Sep 2018;26(9):1396-1404. [FREE Full text] [CrossRef] [Medline]
    22. Philippe K, Perrotta C, O'Donnell A, McAuliffe FM, Phillips CM. Why do preconception and pregnancy lifestyle interventions demonstrate limited success in preventing overweight and obesity in children? A scoping review protocol. PLoS One. Nov 3, 2022;17(11):e0276491. [FREE Full text] [CrossRef] [Medline]
    23. Bailey C, Skouteris H, Harrison CL, Hill B, Thangaratinam S, Teede H, et al. A comparison of the cost-effectiveness of lifestyle interventions in pregnancy. Value Health. Mar 2022;25(2):194-202. [FREE Full text] [CrossRef] [Medline]
    24. Bahri Khomami M, Teede HJ, Enticott J, O'Reilly S, Bailey C, Harrison CL. Implementation of antenatal lifestyle interventions into routine care: secondary analysis of a systematic review. JAMA Netw Open. Oct 03, 2022;5(10):e2234870. [FREE Full text] [CrossRef] [Medline]
    25. Scott J, Oxlad M, Dodd J, Szabo C, Deussen A, Turnbull D. Creating healthy change in the preconception period for women with overweight or obesity: a qualitative study using the information-motivation-behavioural skills model. J Clin Med. Oct 19, 2020;9(10):3351. [FREE Full text] [CrossRef] [Medline]
    26. Söderström E, Müssener U, Löfgren M, Sandell L, Thomas K, Löf M. Healthcare professionals' perceptions of promoting healthy lifestyle behaviors in pregnant migrant women and the potential of a digital support tool-a qualitative study. Int J Environ Res Public Health. Feb 17, 2022;19(4):2328. [FREE Full text] [CrossRef] [Medline]
    27. Holzmann SL, Holzapfel C. A scientific overview of smartphone applications and electronic devices for weight management in adults. J Pers Med. Jun 07, 2019;9(2):31. [FREE Full text] [CrossRef] [Medline]
    28. Jaks R, Baumann I, Juvalta S, Dratva J. Parental digital health information seeking behavior in Switzerland: a cross-sectional study. BMC Public Health. Feb 21, 2019;19(1):225. [FREE Full text] [CrossRef] [Medline]
    29. Lu Y, Barrett LA, Lin RZ, Amith M, Tao C, He Z. Understanding information needs and barriers to accessing health information across all stages of pregnancy: systematic review. JMIR Pediatr Parent. Feb 21, 2022;5(1):e32235. [FREE Full text] [CrossRef] [Medline]
    30. Villinger K, Wahl DR, Boeing H, Schupp HT, Renner B. The effectiveness of app-based mobile interventions on nutrition behaviours and nutrition-related health outcomes: a systematic review and meta-analysis. Obes Rev. Oct 2019;20(10):1465-1484. [FREE Full text] [CrossRef] [Medline]
    31. Ranasinha S, Hill B, Teede HJ, Enticott J, Wang R, Harrison CL. Efficacy of behavioral interventions in managing gestational weight gain (GWG): a component network meta-analysis. Obes Rev. Apr 2022;23(4):e13406. [CrossRef] [Medline]
    32. Sparks JR, Ghildayal N, Hivert MF, Redman LM. Lifestyle interventions in pregnancy targeting GDM prevention: looking ahead to precision medicine. Diabetologia. Nov 2022;65(11):1814-1824. [CrossRef] [Medline]
    33. Alfawzan N, Christen M, Spitale G, Biller-Andorno N. Privacy, data sharing, and data security policies of women's mHealth apps: scoping review and content analysis. JMIR Mhealth Uhealth. May 06, 2022;10(5):e33735. [FREE Full text] [CrossRef] [Medline]
    34. Hayman MJ, Alfrey K, Waters K, Cannon S, Mielke GI, Keating SE, et al. Evaluating evidence-based content, features of exercise instruction, and expert involvement in physical activity apps for pregnant women: systematic search and content analysis. JMIR Mhealth Uhealth. Jan 19, 2022;10(1):e31607. [FREE Full text] [CrossRef] [Medline]
    35. Musgrave LM, Kizirian NV, Homer CS, Gordon A. Mobile phone apps in Australia for improving pregnancy outcomes: systematic search on app stores. JMIR Mhealth Uhealth. Nov 16, 2020;8(11):e22340. [FREE Full text] [CrossRef] [Medline]
    36. Faessen JP, Lucassen DA, Buso ME, Camps G, Feskens EJ, Brouwer-Brolsma EM. Eating for 2: a systematic review of dutch app stores for apps promoting a healthy diet during pregnancy. Curr Dev Nutr. Jun 2022;6(6):nzac087. [FREE Full text] [CrossRef] [Medline]
    37. Yu H, He J, Wang X, Yang W, Sun B, Szumilewicz A. A comparison of functional features of Chinese and US mobile apps for pregnancy and postnatal care: a systematic app store search and content analysis. Front Public Health. 2022;10:826896. [FREE Full text] [CrossRef] [Medline]
    38. Henriksson P, Sandborg J, Blomberg M, Alexandrou C, Maddison R, Silfvernagel K, et al. A smartphone app to promote healthy weight gain, diet, and physical activity during pregnancy (HealthyMoms): protocol for a randomized controlled trial. JMIR Res Protoc. Mar 01, 2019;8(3):e13011. [FREE Full text] [CrossRef] [Medline]
    39. Kennelly MA, Ainscough K, Lindsay KL, O'Sullivan E, Gibney ER, McCarthy M, et al. Pregnancy exercise and nutrition with smartphone application support: a randomized controlled trial. Obstet Gynecol. May 2018;131(5):818-826. [CrossRef] [Medline]
    40. Knight-Agarwal C, Davis DL, Williams L, Davey R, Cox R, Clarke A. Development and pilot testing of the Eating4two mobile phone app to monitor gestational weight gain. JMIR Mhealth Uhealth. Jun 05, 2015;3(2):e44. [FREE Full text] [CrossRef] [Medline]
    41. Darvall JN, Wang A, Nazeem MN, Harrison CL, Clarke L, Mendoza C, et al. A pedometer-guided physical activity intervention for obese pregnant women (the Fit MUM study): randomized feasibility study. JMIR Mhealth Uhealth. May 26, 2020;8(5):e15112. [FREE Full text] [CrossRef] [Medline]
    42. Coughlin JW, Martin LM, Henderson J, Dalcin AT, Fountain J, Wang NY, et al. Feasibility and acceptability of a remotely-delivered behavioural health coaching intervention to limit gestational weight gain. Obes Sci Pract. Oct 2020;6(5):484-493. [FREE Full text] [CrossRef] [Medline]
    43. Iyawa GE, Dansharif AR, Khan A. Mobile apps for self-management in pregnancy: a systematic review. Health Technol. Feb 04, 2021;11(2):283-249. [CrossRef]
    44. Hussain T, Smith P, Yee LM. Mobile phone-based behavioral interventions in pregnancy to promote maternal and fetal health in high-income countries: systematic review. JMIR Mhealth Uhealth. May 28, 2020;8(5):e15111. [FREE Full text] [CrossRef] [Medline]
    45. Rhodes A, Smith AD, Chadwick P, Croker H, Llewellyn CH. Exclusively digital health interventions targeting diet, physical activity, and weight gain in pregnant women: systematic review and meta-analysis. JMIR Mhealth Uhealth. Jul 10, 2020;8(7):e18255. [FREE Full text] [CrossRef] [Medline]
    46. Chan KL, Chen M. Effects of social media and mobile health apps on pregnancy care: meta-analysis. JMIR Mhealth Uhealth. Jan 30, 2019;7(1):e11836. [FREE Full text] [CrossRef] [Medline]
    47. Overdijkink SB, Velu AV, Rosman AN, van Beukering MD, Kok M, Steegers-Theunissen RP. The usability and effectiveness of mobile health technology-based lifestyle and medical intervention apps supporting health care during pregnancy: systematic review. JMIR Mhealth Uhealth. Apr 24, 2018;6(4):e109. [FREE Full text] [CrossRef] [Medline]
    48. Raab R, Geyer K, Zagar S, Sauer H, Spies M, Hauner H. App-supported lifestyle interventions in pregnancy to manage gestational weight gain and prevent gestational diabetes – a scoping review protocol. OSF Home. 2022. URL: https://doi.org/10.17605/OSF.IO/HJDC8 [accessed 2022-01-28]
    49. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. Feb 2005;8(1):19-32. [CrossRef]
    50. Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. Sep 20, 2010;5:69. [FREE Full text] [CrossRef] [Medline]
    51. Peters M, Godfrey C, McInerney P, Munn Z, Tricco A, Khalil H. Chapter 11: scoping reviews. In: Aromataris E, Munn Z, editors. Joanna Briggs Institute Reviewer's Manual. Adelaide, Australia. The Joanna Briggs Institute; Mar 2020.
    52. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. Oct 02, 2018;169(7):467-473. [FREE Full text] [CrossRef] [Medline]
    53. Mobile app for prenatal care to reduce visits and improve satisfaction study (SPROUT). National Institutes of Health U.S. National Library of Medicine. 2016. URL: https://classic.clinicaltrials.gov/ct2/show/NCT02914301?term=NCT02914301&​draw=2&rank=1 [accessed 2022-05-31]
    54. The effect of pre-pregnancy dietary advice and regular exercise to promote weight loss in overweight or obese women on pregnancy outcomes: the BEGIN BETTER randomised trial. World Health Organization International Clinical Trials Registry Platform Search Portal. URL: https://trialsearch.who.int/Trial2.aspx?TrialID=ACTRN12621000128897 [accessed 2022-06-07]
    55. The blossom project: "BlossomUP" methods to decrease sedentary time in pregnancy (BUP). National Institutes of Health National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT02909725?term=NCT02909725&​draw=2&rank=1
    56. Bump 2 Baby and Me: a study to test health coaching for healthy eating and activity during pregnancy and the first year after a baby is born. Australian New Zealand Clinical Trials Registry. Sep 22, 2020. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380020&isReview=true [accessed 2022-05-31]
    57. Pilot testing a mobile app to designed to increase physical activity among pregnant and postpartum women. National Institutes of Health National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT04480931?term=​NCT04480931&draw=1&rank=1
    58. Monitored home exercise in pregnancy. National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT03551535 [accessed 2022-05-31]
    59. A behavioral intervention to prevent gestational diabetes mellitus (DIGITAL-G). National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT03987412?term=NCT03987412&draw=2&rank=1 [accessed 2022-06-01]
    60. Efficacy of the Eating4Two smartphone application for the prevention of excessive gestational weight gain. World Health Organization International Clinical Trials Registry Platform Search Portal. URL: https://trialsearch.who.int/Trial2.aspx?TrialID=ACTRN12617000169347 [accessed 2022-05-31]
    61. E-health app and lifestyle changes during pregnancy (E-HEALTH). National Institutes of Health National Library of Medicine. URL: https://ClinicalTrials.gov/show/NCT05094479 [accessed 2022-06-01]
    62. eMOMS of Rochester (eMOMS). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT01331564 [accessed 2022-06-01]
    63. Electronic-personalized program for obesity in pregnancy to improve delivery (ePPOP-ID). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT02924636 [accessed 2022-05-31]
    64. Feasibility of a pedometer guided physical activity intervention in limiting weight gain in pregnant obese women (The Fit MUM feasibility study). Australian New Zealand Clinical Trials Registry. URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370884 [accessed 2022-05-31]
    65. FitMum: fitness for good health of mother and child (FitMum-RCT). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT03679130 [accessed 2022-05-31]
    66. GeMuKi – Gemeinsam gesund: vorsorge plus für mutter und kind. Gemeinsamer Bundesausschuss. URL: https://drks.de/search/de/trial/DRKS00013173
    67. Goals for reaching optimum wellness (GROWell). National Institutes of Health National Library of Medicine. URL: https://ClinicalTrials.gov/show/NCT02904473 [accessed 2022-05-31]
    68. Pragmatic randomized clinical trial to limit weight gain in pregnancy and prevent obesity (H42/H4U). National Institutes of Health U.S. National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT04724330 [accessed 2022-05-31]
    69. Healthy for two, healthy for you (H42/H4U). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT03551054 [accessed 2022-05-31]
    70. The effects of mHealth intervention on health empower of overweight and obese women's body weight during pregnancy. National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT04553731 [accessed 2022-05-31]
    71. Healthy for my baby- RCT of a lifestyle intervention for overweight women in preconception. National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT04242069?term=NCT04242069&​draw=2&rank=1 [accessed 2022-05-31]
    72. Healthy motivations for moms-to-be study. National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT03063528?term=NCT03063528&draw=2&rank=1 [accessed 2022-05-31]
    73. Healthy mom zone: a gestational weight gain management intervention (HMZ). National Institutes of Health National Library of Medicine. URL: https://www.clinicaltrials.gov/study/NCT03945266 [accessed 2022-05-31]
    74. The HealthyMoms trial to promote healthy gestational weight gain. National Institutes of Health National Library of Medicine. 2020. URL: https://clinicaltrials.gov/show/NCT03298555 [accessed 2022-05-31]
    75. Healthy habits in pregnancy and beyond (HHIPBe). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT04336878 [accessed 2022-05-31]
    76. INTER-pregnAncy Coaching for a healthy future. FRIS Research Portal. URL: https://clinicaltrials.gov/study/NCT02989142
    77. A mobile health intervention to achieve appropriate gestational weight gain in overweight/obese women. National Institutes of Health U.S. National Library of Medicine. 2019. URL: https://www.clinicaltrials.gov/study/NCT03880461?term=​NCT03880461&rank=1
    78. Low glycemic index (GI) diet management for pregnant woman with overweight. National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT01628835?term=NCT01628835&draw=2&rank=1 [accessed 2022-05-31]
    79. Effects of dietary and weight management on pregnancy outcomes in mobile medical platform. National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT04989634 [accessed 2022-06-01]
    80. Maternal-offspring metabolics: family intervention trial (MOMFIT). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/ct2/show/NCT01631747 [accessed 2022-05-31]
    81. An mHealth intervention for sedentary behavior in pregnant women. National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/study/NCT04903574 [accessed 2022-05-31]
    82. Perinatal effects of mindfulness phone app use in pregnancy (PaMPPr study). National Institutes of Health U.S. National Library of Medicine. URL: https://classic.clinicaltrials.gov/ct2/show/NCT03802734?term=NCT03802734&draw=2&rank=1 [accessed 2022-05-31]
    83. Efficacy of an app for monitoring physical activity and weight of obese pregnant women (Pas and Pes). National Institutes of Health U.S. National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT03706872 [accessed 2022-05-31]
    84. Pregnancy, exercise and nutrition research study with app support. International Standard Randomised Controlled Trial Number Registry. URL: https://www.isrctn.com/ISRCTN29316280 [accessed 2022-05-31]
    85. The PLAN Project: pregnancy Lifestyle Activity and Nutrition: effectiveness of maternal lifestyle intervention commenced before 10 weeks gestation, in normal, overweight and obese mothers on maternal behaviours and infant's adiposity. World Health Organization International Clinical Trials Registry Platform Search Portal. 2017. URL: https://trialsearch.who.int/Trial2.aspx?TrialID=ACTRN12617000725369 [accessed 2022-05-31]
    86. Mobile health (m-Health) coaching program during pregnancy. National Institutes of Health National Library of Medicine. URL: https://ClinicalTrials.gov/show/NCT04216446 [accessed 2022-05-31]
    87. Personalized management of body weight during pregnancy. National Institutes of Health. URL: https://clinicaltrials.gov/ct2/show/NCT01610752 [accessed 2022-05-31]
    88. A smartphone intervention for WIC mothers to improve nutrition and weight gain during pregnancy (SmartMomsinWIC). National Institutes of Health National Library of Medicine. URL: https://clinicaltrials.gov/study/NCT04028843 [accessed 2022-05-31]
    89. Intervention study to reduce postpartum weight retention by internet of things and mobile application in obesity mothers. World Health Organization International Clinical Trials Registry Platform Search Portal. URL: https://trialsearch.who.int/Trial2.aspx?TrialID=JPRN-UMIN000041460 [accessed 2022-05-31]
    90. Study of a randomized intervention designed to increase exercise in pregnancy (STRIDE). National Institutes of Health U.S. National Library of Medicine. URL: https://clinicaltrials.gov/show/NCT03936283 [accessed 2022-06-01]
    91. Marko KI, Ganju N, Krapf JM, Gaba ND, Brown JA, Benham JJ, et al. A mobile prenatal care app to reduce in-person visits: prospective controlled trial. JMIR Mhealth Uhealth. May 01, 2019;7(5):e10520. [FREE Full text] [CrossRef] [Medline]
    92. Litman EA, Kavathekar T, Amdur R, Sebastian A, Marko K. Remote gestational weight gain monitoring in a large low-risk US population. Obes Sci Pract. Apr 2022;8(2):147-152. [FREE Full text] [CrossRef] [Medline]
    93. Marko KI, Krapf JM, Meltzer AC, Oh J, Ganju N, Martinez AG, et al. Testing the feasibility of remote patient monitoring in prenatal care using a mobile app and connected devices: a prospective observational trial. JMIR Res Protoc. Nov 18, 2016;5(4):e200. [FREE Full text] [CrossRef] [Medline]
    94. Scott J, Oxlad M, Dodd J, Szabo C, Deussen A, Turnbull D. Promoting health behavior change in the preconception period: combined approach to intervention planning. JMIR Form Res. Apr 28, 2022;6(4):e35108. [FREE Full text] [CrossRef] [Medline]
    95. O'Reilly SL, Burden C, Campoy C, McAuliffe FM, Teede H, Andresen J, et al. Bump2Baby and Me: protocol for a randomised trial of mHealth coaching for healthy gestational weight gain and improved postnatal outcomes in high-risk women and their children. Trials. Dec 28, 2021;22(1):963. [FREE Full text] [CrossRef] [Medline]
    96. Davis D, Davey R, Williams LT, Foureur M, Nohr E, Knight-Agarwal C, et al. Optimizing gestational weight gain with the Eating4Two smartphone app: protocol for a randomized controlled trial. JMIR Res Protoc. May 30, 2018;7(5):e146. [FREE Full text] [CrossRef] [Medline]
    97. Fealy S, Jones D, Davis D, Hazelton M, Foureur M, Attia J, et al. Pregnancy weight gain a balancing act: the experience and perspectives of women participating in a pilot randomised controlled trial. Midwifery. Mar 2022;106:103239. [CrossRef] [Medline]
    98. Olson CM, Strawderman MS, Graham ML. Association between consistent weight gain tracking and gestational weight gain: secondary analysis of a randomized trial. Obesity (Silver Spring). Jul 2017;25(7):1217-1227. [FREE Full text] [CrossRef] [Medline]
    99. Olson CM, Groth SW, Graham ML, Reschke JE, Strawderman MS, Fernandez ID. The effectiveness of an online intervention in preventing excessive gestational weight gain: the e-moms roc randomized controlled trial. BMC Pregnancy Childbirth. May 09, 2018;18(1):148. [FREE Full text] [CrossRef] [Medline]
    100. Graham ML, Uesugi KH, Niederdeppe J, Gay GK, Olson CM. The theory, development, and implementation of an e-intervention to prevent excessive gestational weight gain: e-Moms Roc. Telemed J E Health. Dec 2014;20(12):1135-1142. [FREE Full text] [CrossRef] [Medline]
    101. Deruelle P, Lelorain S, Deghilage S, Couturier E, Guilbert E, Berveiller P, et al. Rationale and design of ePPOP-ID: a multicenter randomized controlled trial using an electronic-personalized program for obesity in pregnancy to improve delivery. BMC Pregnancy Childbirth. Oct 07, 2020;20(1):602. [FREE Full text] [CrossRef] [Medline]
    102. Roland CB, Knudsen SD, Alomairah SA, Andersen AD, Bendix J, Clausen TD, et al. Structured supervised exercise training or motivational counselling during pregnancy on physical activity level and health of mother and offspring: FitMum study protocol. BMJ Open. Mar 19, 2021;11(3):e043671. [FREE Full text] [CrossRef] [Medline]
    103. Krebs F, Lorenz L, Nawabi F, Alayli A, Stock S. Effectiveness of a brief lifestyle intervention in the prenatal care setting to prevent excessive gestational weight gain and improve maternal and infant health outcomes. Int J Environ Res Public Health. May 11, 2022;19(10):5863. [FREE Full text] [CrossRef] [Medline]
    104. Nawabi F, Alayli A, Krebs F, Lorenz L, Shukri A, Bau AM, et al. Health literacy among pregnant women in a lifestyle intervention trial: protocol for an explorative study on the role of health literacy in the perinatal health service setting. BMJ Open. Jul 01, 2021;11(7):e047377. [FREE Full text] [CrossRef] [Medline]
    105. Li LJ, Aris IM, Han WM, Tan KH. A promising food-coaching intervention program to achieve optimal gestational weight gain in overweight and obese pregnant women: pilot randomized controlled trial of a smartphone app. JMIR Form Res. Oct 24, 2019;3(4):e13013. [FREE Full text] [CrossRef] [Medline]
    106. Simmons LA, Phipps JE, Overstreet C, Smith PM, Bechard E, Liu S, et al. Goals for Reaching Optimal Wellness (GROWell): a clinical trial protocol of a digital dietary intervention for pregnant and postpartum people with prenatal overweight or obesity. Contemp Clin Trials. Feb 2022;113:106627. [FREE Full text] [CrossRef] [Medline]
    107. Bennett WL, Coughlin JW, Henderson J, Martin S, Yazdy GM, Drabo EF, et al. Healthy for two/healthy for you: design and methods for a pragmatic randomized clinical trial to limit gestational weight gain and prevent obesity in the prenatal care setting. Contemp Clin Trials. Feb 2022;113:106647. [FREE Full text] [CrossRef] [Medline]
    108. Hardy I, Lloyd A, Morisset AS, Camirand Lemyre F, Baillargeon JP, Fraser WD. Healthy for my baby research protocol- a randomized controlled trial assessing a preconception intervention to improve the lifestyle of overweight women and their partners. Front Public Health. Aug 03, 2021;9:670304. [FREE Full text] [CrossRef] [Medline]
    109. Downs DS, Savage JS, Rivera DE, Pauley AM, Leonard KS, Hohman EE, et al. Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity. J Behav Med. Oct 2021;44(5):605-621. [FREE Full text] [CrossRef] [Medline]
    110. Symons Downs D, Savage JS, Rivera DE, Smyth JM, Rolls BJ, Hohman EE, et al. Individually tailored, adaptive intervention to manage gestational weight gain: protocol for a randomized controlled trial in women with overweight and obesity. JMIR Res Protoc. Jun 08, 2018;7(6):e150. [FREE Full text] [CrossRef] [Medline]
    111. Sandborg J, Söderström E, Henriksson P, Bendtsen M, Henström M, Leppänen MH, et al. Effectiveness of a smartphone app to promote healthy weight gain, diet, and physical activity during pregnancy (HealthyMoms): randomized controlled trial. JMIR Mhealth Uhealth. Mar 11, 2021;9(3):e26091. [FREE Full text] [CrossRef] [Medline]
    112. Sandborg J, Henriksson P, Larsen E, Lindqvist AK, Rutberg S, Söderström E, et al. Participants' engagement and satisfaction with a smartphone app intended to support healthy weight gain, diet, and physical activity during pregnancy: qualitative study within the HealthyMoms trial. JMIR Mhealth Uhealth. Mar 05, 2021;9(3):e26159. [FREE Full text] [CrossRef] [Medline]
    113. Bogaerts A, Bijlholt M, Mertens L, Braeken M, Jacobs B, Vandenberghe B, et al. Development and field evaluation of the INTER-ACT app, a pregnancy and interpregnancy coaching app to reduce maternal overweight and obesity: mixed methods design. JMIR Form Res. Feb 14, 2020;4(2):e16090. [FREE Full text] [CrossRef] [Medline]
    114. Bogaerts A, Ameye L, Bijlholt M, Amuli K, Heynickx D, Devlieger R. INTER-ACT: prevention of pregnancy complications through an e-health driven interpregnancy lifestyle intervention - study protocol of a multicentre randomised controlled trial. BMC Pregnancy Childbirth. May 26, 2017;17(1):154. [FREE Full text] [CrossRef] [Medline]
    115. Zhang Y, Wang L, Yang W, Niu D, Li C, Wang L, et al. Effectiveness of low glycemic index diet consultations through a diet glycemic assessment app tool on maternal and neonatal insulin resistance: a randomized controlled trial. JMIR Mhealth Uhealth. Apr 18, 2019;7(4):e12081. [FREE Full text] [CrossRef] [Medline]
    116. Lau Y, Cheng LJ, Chi C, Tsai C, Ong KW, Ho-Lim SS, et al. Development of a healthy lifestyle mobile app for overweight pregnant women: qualitative study. JMIR Mhealth Uhealth. Apr 23, 2018;6(4):e91. [FREE Full text] [CrossRef] [Medline]
    117. Clifton RG, Evans M, Cahill AG, Franks PW, Gallagher D, Phelan S, et al. LIFE-Moms Research Group. Design of lifestyle intervention trials to prevent excessive gestational weight gain in women with overweight or obesity. Obesity (Silver Spring). Feb 2016;24(2):305-313. [FREE Full text] [CrossRef] [Medline]
    118. Van Horn L, Peaceman A, Kwasny M, Vincent E, Fought A, Josefson J, et al. Dietary approaches to stop hypertension diet and activity to limit gestational weight: maternal offspring metabolics family intervention trial, a technology enhanced randomized trial. Am J Prev Med. Nov 2018;55(5):603-614. [CrossRef] [Medline]
    119. Krishnamurti T, Davis AL, Wong-Parodi G, Fischhoff B, Sadovsky Y, Simhan HN. Development and testing of the MyHealthyPregnancy app: a behavioral decision research-based tool for assessing and communicating pregnancy risk. JMIR Mhealth Uhealth. Apr 10, 2017;5(4):e42. [FREE Full text] [CrossRef] [Medline]
    120. Gonzalez-Plaza E, Bellart J, Arranz Á, Luján-Barroso L, Crespo Mirasol E, Seguranyes G. Effectiveness of a step counter smartband and midwife counseling intervention on gestational weight gain and physical activity in pregnant women with obesity (pas and pes study): randomized controlled trial. JMIR Mhealth Uhealth. Feb 15, 2022;10(2):e28886. [FREE Full text] [CrossRef] [Medline]
    121. Greene EM, O'Brien EC, Kennelly MA, O'Brien OA, Lindsay KL, McAuliffe FM. Acceptability of the pregnancy, exercise, and nutrition research study with smartphone app support (PEARS) and the use of mobile health in a mixed lifestyle intervention by pregnant obese and overweight women: secondary analysis of a randomized controlled trial. JMIR Mhealth Uhealth. May 12, 2021;9(5):e17189. [FREE Full text] [CrossRef] [Medline]
    122. O'Sullivan EJ, Rokicki S, Kennelly M, Ainscough K, McAuliffe FM. Cost-effectiveness of a mobile health-supported lifestyle intervention for pregnant women with an elevated body mass index. Int J Obes (Lond). May 2020;44(5):999-1010. [CrossRef] [Medline]
    123. Kennelly MA, Ainscough K, Lindsay K, Gibney E, Mc Carthy M, McAuliffe FM. Pregnancy, exercise and nutrition research study with smart phone app support (Pears): study protocol of a randomized controlled trial. Contemp Clin Trials. Jan 2016;46:92-99. [CrossRef] [Medline]
    124. Willcox JC, Chai D, Beilin LJ, Prescott SL, Silva D, Neppe C, et al. Evaluating engagement in a digital and dietetic intervention promoting healthy weight gain in pregnancy: mixed methods study. J Med Internet Res. Jun 26, 2020;22(6):e17845. [FREE Full text] [CrossRef] [Medline]
    125. Huang RC, Silva D, Beilin L, Neppe C, Mackie KE, Roffey E, et al. Feasibility of conducting an early pregnancy diet and lifestyle e-health intervention: the Pregnancy Lifestyle Activity Nutrition (PLAN) project. J Dev Orig Health Dis. Feb 08, 2020;11(1):58-70. [CrossRef] [Medline]
    126. Nuruddin R, Vadsaria K, Mohammed N, Sayani S. The efficacy of a personalized mHealth coaching program during pregnancy on maternal diet, supplement use, and physical activity: protocol for a parallel-group randomized controlled trial. JMIR Res Protoc. Nov 16, 2021;10(11):e31611. [FREE Full text] [CrossRef] [Medline]
    127. Redman LM, Gilmore LA, Breaux J, Thomas DM, Elkind-Hirsch K, Stewart T, et al. Effectiveness of SmartMoms, a novel eHealth intervention for management of gestational weight gain: randomized controlled pilot trial. JMIR Mhealth Uhealth. Sep 13, 2017;5(9):e133. [FREE Full text] [CrossRef] [Medline]
    128. Halili L, Liu R, Hutchinson KA, Semeniuk K, Redman LM, Adamo KB. Development and pilot evaluation of a pregnancy-specific mobile health tool: a qualitative investigation of SmartMoms Canada. BMC Med Inform Decis Mak. Nov 12, 2018;18(1):95. [FREE Full text] [CrossRef] [Medline]
    129. Flanagan EW, Altazan AD, Comardelle NR, Gilmore LA, Apolzan JW, St Romain J, et al. The design of a randomized clinical trial to evaluate a pragmatic and scalable eHealth intervention for the management of gestational weight gain in low-income women: protocol for the SmartMoms in WIC trial. JMIR Res Protoc. Sep 10, 2020;9(9):e18211. [FREE Full text] [CrossRef] [Medline]
    130. Kawasaki M, Mito A, Waguri M, Sato Y, Abe E, Shimada M, et al. Protocol for an interventional study to reduce postpartum weight retention in obese mothers using the internet of things and a mobile application: a randomized controlled trial (SpringMom). BMC Pregnancy Childbirth. Aug 23, 2021;21(1):582. [FREE Full text] [CrossRef] [Medline]
    131. Gance-Cleveland B, Leiferman J, Aldrich H, Nodine P, Anderson J, Nacht A, et al. Using the technology acceptance model to develop StartSmart: mHealth for screening, brief intervention, and referral for risk and protective factors in pregnancy. J Midwifery Womens Health. Sep 2019;64(5):630-640. [CrossRef] [Medline]
    132. Ayyala MS, Coughlin JW, Martin L, Henderson J, Ezekwe N, Clark JM, et al. Perspectives of pregnant and postpartum women and obstetric providers to promote healthy lifestyle in pregnancy and after delivery: a qualitative in-depth interview study. BMC Womens Health. Mar 04, 2020;20(1):44. [FREE Full text] [CrossRef] [Medline]
    133. Drexler K, Cheu L, Donelan E, Kominiarek M. 343 evaluating compliance with a remote self-monitoring weight program in low risk women and perinatal outcomes. Am J Obstet Gynecol. Feb 2021;224(2):S225-S226. [CrossRef]
    134. Drexler K, Cheu L, Donelan E, Kominiarek M. 415: remote self-monitoring of perinatal weight and perinatal outcomes in low-risk women. Am J Obstet Gynecol. Jan 2020;222(1):S272-S273. [CrossRef]
    135. DeNicola N, Sheth S, Leggett K, Woodland M, Ganju N, Marko K. Evaluating patient satisfaction and experience for technology-enabled prenatal care for low risk women [1L]. Obstet Gynecol. May 2018;131:129S. [CrossRef]
    136. Ainscough K, Kennelly MA, O'Sullivan EJ, Lindsay KL, Gibney ER, McCarthy M, et al. 1014: impact of a smartphone app supporting a lifestyle intervention in overweight and obese pregnancy on on maternal health and lifestyle outcomes. Am J Obstet Gynecol. Jan 2018;218(1):S598-S599. [CrossRef]
    137. McClelland J, McGowan L, Gallagher D, Moore SE, Beeken RJ, Cardwell CR, et al. A real-world example of using personal and public involvement to develop a healthy eating and physical activity intervention for pregnant women. Proc Nutr Soc. Aug 17, 2021;80(OCE3):E155. [CrossRef]
    138. Chen HH, Lee CF, Huang JP, Hsiung Y, Chi LK. Effect of an mHealth-based intervention on excessive gestational weight gain prevention among overweight and obese women: a randomized controlled trial. JMIR Preprints. Preprint posted online November 11, 2021. (1) [CrossRef]
    139. Thomas T, Xu F, Sridhar S, Sedgwick T, Nkemere L, Badon SE, et al. A web-based mHealth intervention with telephone support to increase physical activity among pregnant patients with overweight or obesity: feasibility randomized controlled trial. JMIR Preprints. Preprint posted online September 29, 2021. 2023 [FREE Full text] [CrossRef]
    140. McKinney CL. BlossomUP (BUP): a pilot randomized control trial to assess strategies to reduce sedentary time during pregnancy. Iowa State University. Iowa. Iowa State University; 2017. URL: https://dr.lib.iastate.edu/server/api/core/bitstreams/54c2b2a6-647d-4855-9749-d31cf425492e/content [accessed 2022-06-09]
    141. Dahl AA. Healthy motivations for moms-to-be (Healthy MoM2B) study: a mobile health intervention targeting gestational weight gain among U.S. Women. University of South Carolina. 2018. URL: https://scholarcommons.sc.edu/etd/4862/ [accessed 2021-06-21]
    142. Gance-Cleveland B. StartSmart: health information technology to improve adherence to prenatal guidelines. Agency for Healthcare Research and Quality. 2016. URL: https:/​/digital.​ahrq.gov/​sites/​default/​files/​docs/​citation/​r21hs024738-gance-cleveland-final-report-2019.​pdf [accessed 2022-06-04]
    143. Poston L. Interventions to prevent DOHaD effects in pregnancy. In: Poston L, Godfrey KM, Gluckman PD, Hanson MA, editors. Developmental Origins of Health and Disease. Cambridge, UK. Cambridge University Press; Dec 1, 2022.
    144. O'Brien EC, Alberdi G, McAuliffe FM. The influence of socioeconomic status on gestational weight gain: a systematic review. J Public Health (Oxf). Mar 01, 2018;40(1):41-55. [CrossRef] [Medline]
    145. Jardine J, Walker K, Gurol-Urganci I, Webster K, Muller P, Hawdon J, et al. National Maternity and Perinatal Audit Project Team. Adverse pregnancy outcomes attributable to socioeconomic and ethnic inequalities in England: a national cohort study. Lancet. Nov 20, 2021;398(10314):1905-1912. [CrossRef] [Medline]
    146. Geyer K, Raab R, Hoffmann J, Hauner H. Development and validation of a screening questionnaire for early identification of pregnant women at risk for excessive gestational weight gain. BMC Pregnancy Childbirth. Apr 13, 2023;23(1):249. [FREE Full text] [CrossRef] [Medline]
    147. Fleming TP, Watkins AJ, Velazquez MA, Mathers JC, Prentice AM, Stephenson J, et al. Origins of lifetime health around the time of conception: causes and consequences. Lancet. May 05, 2018;391(10132):1842-1852. [FREE Full text] [CrossRef] [Medline]
    148. Raab R, Geyer K, Hauner H. Adipositasprävention in den ersten 1000 Tagen. Adipositas Ursachen Folgeerkrankungen Therapie. Oct 11, 2022;16(03):141-148. [CrossRef]
    149. Johar H, Hoffmann J, Günther J, Atasoy S, Stecher L, Spies M, et al. Evaluation of antenatal risk factors for postpartum depression: a secondary cohort analysis of the cluster-randomised GeliS trial. BMC Med. Jul 24, 2020;18(1):227. [FREE Full text] [CrossRef] [Medline]
    150. Avalos LA, Flanagan T, Li DK. Preventing perinatal depression to improve maternal and child health-a health care imperative. JAMA Pediatr. Apr 01, 2019;173(4):313-314. [CrossRef] [Medline]
    151. Wolfenden L, Foy R, Presseau J, Grimshaw JM, Ivers NM, Powell BJ, et al. Designing and undertaking randomised implementation trials: guide for researchers. BMJ. Jan 18, 2021;372:m3721. [FREE Full text] [CrossRef] [Medline]
    152. Agarwal S, LeFevre AE, Lee J, L'Engle K, Mehl G, Sinha C, et al. Guidelines for reporting of health interventions using mobile phones: mobile health (mHealth) evidence reporting and assessment (mERA) checklist. BMJ. Mar 17, 2016;352:i1174. [CrossRef] [Medline]
    153. Eysenbach G, CONSORT-EHEALTH Group. CONSORT-EHEALTH: improving and standardizing evaluation reports of Web-based and mobile health interventions. J Med Internet Res. Dec 31, 2011;13(4):e126. [FREE Full text] [CrossRef] [Medline]
    154. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. Mar 07, 2014;348:g1687. [FREE Full text] [CrossRef] [Medline]
    155. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. Aug 2013;46(1):81-95. [FREE Full text] [CrossRef] [Medline]
    156. Rhon DI, Fritz JM, Kerns RD, McGeary DD, Coleman BC, Farrokhi S, et al. TIDieR-telehealth: precision in reporting of telehealth interventions used in clinical trials - unique considerations for the Template for the Intervention Description and Replication (TIDieR) checklist. BMC Med Res Methodol. Jun 02, 2022;22(1):161. [FREE Full text] [CrossRef] [Medline]
    157. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. Mar 11, 2015;3(1):e27. [FREE Full text] [CrossRef] [Medline]

    ‎
    BCT: behavior change technique
    EPIS: exploration, preparation, implementation, and sustainment
    GDM: gestational diabetes mellitus
    GWG: gestational weight gain
    mHealth: mobile health
    PCC: population, concept, and context
    PICOS: population, intervention, control, outcomes, and study design
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
    RCT: randomized controlled trial
    RE-AIM: reach, effectiveness, adoption, implementation, and maintenance
    SMART: specific, measurable, achievable, reasonable, and time-bound
    TRE: trial register entry

    Edited by A Mavragani; submitted 09.05.23; peer-reviewed by J Scott, S Groth; comments to author 20.08.23; revised version received 31.08.23; accepted 04.09.23; published 10.11.23.

    Copyright

    ©Roxana Raab, Kristina Geyer, Sophia Zagar, Hans Hauner. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.11.2023.

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