Effectiveness of the Internet of Things for Improving Pregnancy and Postpartum Women’s Health in High-Income Countries: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Abstract
1. Introduction
2. Materials and Methods
2.1. Study Design
2.2. Inclusion and Exclusion Criteria
2.2.1. Participants
2.2.2. Intervention
2.2.3. Comparators
2.2.4. Outcomes
- Primary maternal health outcomes included health status, such as the number of cases diagnosed or treated for high-risk pregnancies (e.g., hypertensive disorders of pregnancy, gestational diabetes, and preterm delivery). Neonatal health outcomes included low birth weight, defined as birth weight <2.5 kg, and perinatal death.
- Secondary outcomes included lifestyle and behavioral changes, maintenance of a healthy weight, and other indicators such as body mass index (BMI), body composition, waist circumference, and increased physical activity.
2.2.5. Study Designs
2.3. Search Methods for Study Identification
2.4. Study Selection
2.5. Data Extraction
2.6. Risk of Bias
2.7. Data Synthesis and Analytical Approach
2.8. Assessment of the Certainty of the Evidence
3. Results
3.1. Search Results
3.2. Characteristics of the Included Studies
3.3. Overall Risk of Bias Assessment of the Included Studies
3.4. Effects of IoT Interventions on Improving Pregnant and Postpartum Women’s Health Outcomes
3.4.1. Maternal Health Outcomes
3.4.2. Neonatal Health Outcomes
3.4.3. Maintaining a Healthy Weight or Other Indicators
3.4.4. Physical Activity
4. Discussion
4.1. Principal Findings
4.2. Role of IoT Interventions in Behavior Change Strategies
4.3. Future Research Utilizing IoT Technology
4.4. Strengths and Limitations of This Review
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
IoT | Internet of Things |
SCT | Social Cognitive Theory |
BMI | body mass index |
GA | gestational age |
GDM | gestational diabetes mellitus |
GWG | gestational weight gain |
HbA1C | glycated hemoglobin |
HOMA-IR | Homeostatic Model Assessment for Insulin Resistance |
OGTT | oral glucose tolerance test |
References
- NCD Risk Factor Collaboration (NCD-RisC). Worldwide Trends in Body-Mass Index, Underweight, Overweight, and Obesity from 1975 to 2016: A Pooled Analysis of 2416 Population-Based Measurement Studies in 128.9 Million Children, Adolescents, and Adults. Lancet 2017, 390, 2627–2642. [Google Scholar] [CrossRef] [PubMed]
- Song, Z.; Cheng, Y.; Li, T.; Fan, Y.; Zhang, Q.; Cheng, H. Prediction of Gestational Diabetes Mellitus by Different Obesity Indices. BMC Pregnancy Childbirth 2022, 22, 288. [Google Scholar] [CrossRef] [PubMed]
- Yogev, Y.; Ben-Haroush, A.; Chen, R.; Rosenn, B.; Hod, M.; Langer, O. Diurnal Glycemic Profile in Obese and Normal Weight Nondiabetic Pregnant Women. Am. J. Obstet. Gynecol. 2004, 191, 949–953. [Google Scholar] [CrossRef]
- Black, R.E.; Victora, C.G.; Walker, S.P.; Bhutta, Z.A.; Christian, P.; de Onis, M.; Ezzati, M.; Grantham-McGregor, S.; Katz, J.; Martorell, R.; et al. Maternal and Child Undernutrition and Overweight in Low-Income and Middle-Income Countries. Lancet 2013, 382, 427–451. [Google Scholar] [CrossRef]
- Han, Z.; Mulla, S.; Beyene, J.; Liao, G.; McDonald, S.D. Maternal Underweight and the Risk of Preterm Birth and Low Birth Weight: A Systematic Review and Meta-Analyses. Int. J. Epidemiol. 2011, 40, 65–101. [Google Scholar] [CrossRef]
- American Diabetes Association. Standards of Care in Diabetes—2023 Abridged for Primary Care Providers. Clin. Diabetes 2022, 41, 4–31. [Google Scholar] [CrossRef]
- Anothaisintawee, T.; Reutrakul, S.; Van Cauter, E.; Thakkinstian, A. Sleep Disturbances Compared to Traditional Risk Factors for Diabetes Development: Systematic Review and Meta-Analysis. Sleep Med. Rev. 2016, 30, 11–24. [Google Scholar] [CrossRef]
- Li, N.; Yang, Y.; Cui, D.; Li, C.; Ma, R.C.W.; Li, J.; Yang, X. Effects of Lifestyle Intervention on Long-Term Risk of Diabetes in Women with Prior Gestational Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Obes. Rev. 2021, 22, e13122. [Google Scholar] [CrossRef]
- Taousani, E.; Papaioannou, K.-G.; Mintziori, G.; Grammatikopoulou, M.G.; Antonakou, A.; Tzitiridou-Chatzopoulou, M.; Veneti, S.; Goulis, D.G. Lifestyle Behaviors and Gestational Diabetes Mellitus: A Narrative Review. Endocrines 2025, 6, 6. [Google Scholar] [CrossRef]
- Kawasaki, M.; Mito, A.; Waguri, M.; Sato, Y.; Abe, E.; Shimada, M.; Fukuda, S.; Sasaki, Y.; Fujikawa, K.; Sugiyama, T.; 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 2021, 21, 582. [Google Scholar] [CrossRef]
- Luo, J.; Mao, A.; Zeng, Z. Sensor-Based Smart Clothing for Women’s Menopause Transition Monitoring. Sensors 2020, 20, 1093. [Google Scholar] [CrossRef] [PubMed]
- Sarhaddi, F.; Azimi, I.; Labbaf, S.; Niela-Vilén, H.; Dutt, N.; Axelin, A.; Liljeberg, P.; Rahmani, A.M. Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation. Sensors 2021, 21, 2281. [Google Scholar] [CrossRef]
- Tsirmpas, C.; Kouris, I.; Anastasiou, A.; Giokas, K.; Iliopoulou, D.; Koutsouris, D. An Internet of Things Platform Architecture for Supporting Ambient Assisted Living Environments. Technol. Health Care 2017, 25, 391–401. [Google Scholar] [CrossRef]
- Bonato, P. Wearable Sensors and Systems. IEEE Eng. Med. Biol. Mag. 2010, 29, 25–36. [Google Scholar] [CrossRef]
- Lim, K.; Chan, S.Y.; Lim, S.L.; Tai, B.C.; Tsai, C.; Wong, S.R.; Ang, S.M.; Yew, T.W.; Tai, E.S.; Yong, E.L. A Smartphone App to Restore Optimal Weight (SPAROW) in Women with Recent Gestational Diabetes Mellitus: Randomized Controlled Trial. JMIR Mhealth Uhealth 2021, 9, e22147. [Google Scholar] [CrossRef]
- Minschart, C.; Maes, T.; De Block, C.; Van Pottelbergh, I.; Myngheer, N.; Abrams, P.; Vinck, W.; Leuridan, L.; Mathieu, C.; Billen, J.; et al. Mobile-Based Lifestyle Intervention in Women with Glucose Intolerance after Gestational Diabetes Mellitus (MELINDA), A Multicenter Randomized Controlled Trial: Methodology and Design. J. Clin. Med. 2020, 9, 2635. [Google Scholar] [CrossRef] [PubMed]
- Grym, K.; Niela-Vilén, H.; Ekholm, E.; Hamari, L.; Azimi, I.; Rahmani, A.; Liljeberg, P.; Löyttyniemi, E.; Axelin, A. Feasibility of Smart Wristbands for Continuous Monitoring during Pregnancy and One Month after Birth. BMC Pregnancy Childbirth 2019, 19, 34. [Google Scholar] [CrossRef] [PubMed]
- Saarikko, J.; Niela-Vilen, H.; Ekholm, E.; Hamari, L.; Azimi, I.; Liljeberg, P.; Rahmani, A.M.; Löyttyniemi, E.; Axelin, A. Continuous 7-Month Internet of Things-Based Monitoring of Health Parameters of Pregnant and Postpartum Women: Prospective Observational Feasibility Study. JMIR Form. Res. 2020, 4, e12417. [Google Scholar] [CrossRef]
- Alim, A.; Imtiaz, M.H. Wearable Sensors for the Monitoring of Maternal Health—A Systematic Review. Sensors 2023, 23, 2411. [Google Scholar] [CrossRef]
- Hossain, M.M.; Kashem, M.A.; Islam, M.M.; Sahidullah, M.; Mumu, S.H.; Uddin, J.; Aray, D.G.; de la Torre Diez, I.; Ashraf, I.; Samad, M.A. Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review. Sensors 2023, 23, 9367. [Google Scholar] [CrossRef]
- Bertini, A.; Gárate, B.; Pardo, F.; Pelicand, J.; Sobrevia, L.; Torres, R.; Chabert, S.; Salas, R. Impact of Remote Monitoring Technologies for Assisting Patients with Gestational Diabetes Mellitus: A Systematic Review. Front. Bioeng. Biotechnol. 2022, 10, 819697. [Google Scholar] [CrossRef]
- Huhn, S.; Axt, M.; Gunga, H.C.; Maggioni, M.A.; Munga, S.; Obor, D.; Sié, A.; Boudo, V.; Bunker, A.; Sauerborn, R.; et al. The Impact of Wearable Technologies in Health Research: Scoping Review. JMIR Mhealth Uhealth 2022, 10, e34384. [Google Scholar] [CrossRef]
- Yamaji, N.; Nitamizu, A.; Nishimura, E.; Suzuki, D.; Sasayama, K.; Rahman, M.O.; Saito, E.; Yoneoka, D.; Ota, E. Effectiveness of the Internet of Things for Improving Working-Aged Women’s Health in High-Income Countries: Protocol for a Systematic Review and Network Meta-Analysis. JMIR Res. Protoc. 2023, 12, e45178. [Google Scholar] [CrossRef] [PubMed]
- Higgins, J.P.T.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. Cochrane Handbook for Systematic Reviews of Interventions, 2nd ed.; John Wiley & Sons: Chichester, UK, 2019. [Google Scholar]
- Page, M.J.; Moher, D.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews. BMJ 2021, 372, n160. [Google Scholar] [CrossRef]
- World Bank. World Bank Country and Lending Groups. 2023. Available online: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (accessed on 14 February 2023).
- WHO Global Observatory for eHealth. mHealth: New Horizons for Health through Mobile Technologies: Second Global Survey on eHealth; World Health Organization: Geneva, Switzerland, 2011; Available online: https://iris.who.int/handle/10665/44607 (accessed on 4 August 2025).
- Higgins, J.P.; Lasserson, T.; Chandler, J.; Tovey, D.; Churchill, R. Methodological Expectations of Cochrane Intervention Reviews; Cochrane: London, UK, 2016. [Google Scholar]
- Rethlefsen, M.L.; Kirtley, S.; Waffenschmidt, S.; Ayala, A.P.; Moher, D.; Page, M.J.; Koffel, J.B. PRISMA-S: An Extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews. Syst. Rev. 2021, 10, 39. [Google Scholar] [CrossRef]
- McGowan, J.; Sampson, M.; Salzwedel, D.M.; Cogo, E.; Foerster, V.; Lefebvre, C. PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J. Clin. Epidemiol. 2016, 75, 40–46. [Google Scholar] [CrossRef]
- Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A Web and Mobile App for Systematic Reviews. Syst. Rev. 2016, 5, 210. [Google Scholar] [CrossRef] [PubMed]
- Higgins, J.P.T.; Savović, J.; Page, M.J.; Elbers, R.G.; Sterne, J.A.C. Chapter 8: Assessing Risk of Bias in a Randomized Trial. In Cochrane Handbook for Systematic Reviews of Interventions; The Cochrane Collaboration: London, UK, 2022. [Google Scholar]
- Guyatt, G.; Oxman, A.D.; Akl, E.A.; Kunz, R.; Vist, G.; Brozek, J.; Norris, S.; Falck-Ytter, Y.; Glasziou, P.; deBeer, H.; et al. GRADE Guidelines: 1. Introduction—GRADE Evidence Profiles and Summary of Findings Tables. J. Clin. Epidemiol. 2011, 64, 383–394. [Google Scholar] [CrossRef] [PubMed]
- Balshem, H.; Helfand, M.; Schünemann, H.J.; Oxman, A.D.; Kunz, R.; Brozek, J.; Vist, G.E.; Falck-Ytter, Y.; Meerpohl, J.; Norris, S.; et al. GRADE Guidelines: 3. Rating the Quality of Evidence. J. Clin. Epidemiol. 2011, 64, 401–406. [Google Scholar] [CrossRef]
- GRADEpro GDT. GRADEpro Guideline Development Tool; McMaster University and Evidence Prime: Hamilton, ON, Canada, 2025. [Google Scholar]
- Cheung, N.W.; Blumenthal, C.; Smith, B.J.; Hogan, R.; Thiagalingam, A.; Redfern, J.; Barry, T.; Cinnadaio, N.; Chow, C.K. A Pilot Randomised Controlled Trial of a Text Messaging Intervention with Customisation Using Linked Data from Wireless Wearable Activity Monitors to Improve Risk Factors Following Gestational Diabetes. Nutrients 2019, 11, 996. [Google Scholar] [CrossRef] [PubMed]
- Sung, J.H.; Lee, D.Y.; Min, K.P.; Park, C.Y. Peripartum Management of Gestational Diabetes Using a Digital Health Care Service: A Pilot, Randomized Controlled Study. Clin. Ther. 2019, 41, 2426–2434. [Google Scholar] [CrossRef]
- Chen, H.H.; Lee, C.F.; Huang, J.P.; Hsiung, Y.; Chi, L.K. Effectiveness of a Nurse-Led mHealth App to Prevent Excessive Gestational Weight Gain among Overweight and Obese Women: A Randomized Controlled Trial. J. Nurs. Scholarsh. 2023, 55, 304–318. [Google Scholar] [CrossRef] [PubMed]
- Gilmore, L.A.; Klempel, M.C.; Martin, C.K.; Myers, C.A.; Burton, J.H.; Sutton, E.F.; Redman, L.M. Personalized Mobile Health Intervention for Health and Weight Loss in Postpartum Women Receiving Women, Infants, and Children Benefit: A Randomized Controlled Pilot Study. J. Womens Health 2017, 26, 719–727. [Google Scholar] [CrossRef] [PubMed]
- Gonzalez-Plaza, E.; Bellart, J.; Arranz, A.; Lujan-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 2022, 10, e28886. [Google Scholar] [CrossRef]
- Van Uytsel, H.; Bijlholt, M.; Devlieger, R.; Ameye, L.; Jochems, L.; van Holsbeke, C.; Schreurs, A.; Catry, V.; Bogaerts, A. Effect of the E-Health Supported INTER-ACT Lifestyle Intervention on Postpartum Weight Retention and Body Composition, and Associations with Lifestyle Behavior: A Randomized Controlled Trial. Prev. Med. 2022, 164, 107321. [Google Scholar] [CrossRef]
- Rasmussen, K.M.; Yaktine, A.L.; Institute of Medicine (US) and National Research Council (US) Committee to Reexamine IOM Pregnancy Weight Guidelines (Eds.) Weight Gain during Pregnancy: Reexamining the Guidelines; The National Academies Press: Washington, DC, USA, 2009. [Google Scholar] [CrossRef]
- Choi, H.; Lim, J.Y.; Lim, N.K.; Ryu, H.M.; Kwak, D.W.; Chung, J.H.; Park, H.J.; Park, H.Y. Impact of Pre-Pregnancy Body Mass Index and Gestational Weight Gain on the Risk of Maternal and Infant Pregnancy Complications in Korean Women. Int. J. Obes. 2022, 46, 59–67. [Google Scholar] [CrossRef]
- Santos, S.; Voerman, E.; Amiano, P.; Barros, H.; Beilin, L.J.; Bergström, A.; Charles, M.A.; Chatzi, L.; Chevrier, C.; Chrousos, G.P.; et al. Impact of Maternal Body Mass Index and Gestational Weight Gain on Pregnancy Complications: An Individual Participant Data Meta-Analysis of European, North American and Australian Cohorts. BJOG 2019, 126, 984–995. [Google Scholar] [CrossRef]
- Goldstein, R.F.; Abell, S.K.; Ranasinha, S.; Misso, M.; Boyle, J.A.; Black, M.H.; Li, N.; Hu, G.; Corrado, F.; Rode, L.; et al. Association of Gestational Weight Gain with Maternal and Infant Outcomes: A Systematic Review and Meta-Analysis. JAMA 2017, 317, 2207–2225. [Google Scholar] [CrossRef]
- Mustafa, H.J.; Seif, K.; Javinani, A.; Aghajani, F.; Orlinsky, R.; Alvarez, M.V.; Ryan, A.; Crimmins, S. Gestational Weight Gain below instead of within the Guidelines per Class of Maternal Obesity: A Systematic Review and Meta-Analysis of Obstetrical and Neonatal Outcomes. Am. J. Obstet. Gynecol. MFM 2022, 4, 100682. [Google Scholar] [CrossRef] [PubMed]
- Bandura, A. Health Promotion from the Perspective of Social Cognitive Theory. Psychol. Health 1998, 13, 623–649. [Google Scholar] [CrossRef]
- Bandura, A. Health Promotion by Social Cognitive Means. Health Educ. Behav. 2004, 31, 143–164. [Google Scholar] [CrossRef]
- Islam, K.F.; Awal, A.; Mazumder, H.; Munni, U.R.; Majumder, K.; Afroz, K.; Tabassum, M.N.; Hossain, M.M. Social Cognitive Theory-Based Health Promotion in Primary Care Practice: A Scoping Review. Heliyon 2023, 9, e14889. [Google Scholar] [CrossRef]
- Muktabhant, B.; Lawrie, T.A.; Lumbiganon, P.; Laopaiboon, M. Diet or Exercise, or Both, for Preventing Excessive Weight Gain in Pregnancy. Cochrane Database Syst. Rev. 2015, 6, CD007145. [Google Scholar] [CrossRef]
- Darvall, J.N.; Wang, A.; Nazeem, M.N.; Harrison, C.L.; Clarke, L.; Mendoza, C.; Parker, A.; Harrap, B.; Teale, G.; Story, D.; et al. A Pedometer-Guided Physical Activity Intervention for Obese Pregnant Women (the Fit MUM Study): Randomized Feasibility Study. JMIR Mhealth Uhealth 2020, 8, e15112. [Google Scholar] [CrossRef]
- Amorim Adegboye, A.R.; Linne, Y.M. Diet or Exercise, or Both, for Weight Reduction in Women after Childbirth. Cochrane Database Syst. Rev. 2013, 7, CD005627. [Google Scholar] [CrossRef] [PubMed]
- Dalrymple, K.V.; Flynn, A.C.; Relph, S.A.; O’Keeffe, M.; Poston, L. Lifestyle Interventions in Overweight and Obese Pregnant or Postpartum Women for Postpartum Weight Management: A Systematic Review of the Literature. Nutrients 2018, 10, 1704. [Google Scholar] [CrossRef] [PubMed]
- Borga, M.; West, J.; Bell, J.D.; Harvey, N.C.; Romu, T.; Heymsfield, S.B.; Dahlqvist Leinhard, O. Advanced Body Composition Assessment: From Body Mass Index to Body Composition Profiling. J. Investig. Med. 2018, 66, 887–895. [Google Scholar] [CrossRef]
- Madden, A.M.; Smith, S. Body Composition and Morphological Assessment of Nutritional Status in Adults: A Review of Anthropometric Variables. J. Hum. Nutr. Diet. 2016, 29, 7–25. [Google Scholar] [CrossRef]
- Jacobs, E.J.; Newton, C.C.; Wang, Y.; Patel, A.V.; McCullough, M.L.; Campbell, P.T.; Thun, M.J.; Gapstur, S.M. Waist Circumference and All-Cause Mortality in a Large US Cohort. Arch. Intern. Med. 2010, 170, 1293–1301. [Google Scholar] [CrossRef]
- Ross, R.; Neeland, I.J.; Yamashita, S.; Shai, I.; Seidell, J.; Magni, P.; Santos, R.D.; Arsenault, B.; Cuevas, A.; Hu, F.B.; et al. Waist Circumference as a Vital Sign in Clinical Practice: A Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol. 2020, 16, 177–189. [Google Scholar] [CrossRef] [PubMed]
- Nicklas, J.M.; Rosner, B.A.; Zera, C.A.; Seely, E.W. Association between Changes in Postpartum Weight and Waist Circumference and Changes in Cardiometabolic Risk Factors among Women with Recent Gestational Diabetes. Prev. Chronic Dis. 2019, 16, E47. [Google Scholar] [CrossRef]
- Dombrowski, S.U.; Knittle, K.; Avenell, A.; Araújo-Soares, V.; Sniehotta, F.F. Long-Term Maintenance of Weight Loss with Non-Surgical Interventions in Obese Adults: Systematic Review and Meta-Analyses of Randomised Controlled Trials. BMJ 2014, 348, g2646. [Google Scholar] [CrossRef] [PubMed]
- Michie, S.; Abraham, C.; Whittington, C.; McAteer, J.; Gupta, S. Effective Techniques in Healthy Eating and Physical Activity Interventions: A Meta-Regression. Health Psychol. 2009, 28, 690–701. [Google Scholar] [CrossRef]
- Lemstra, M.; Bird, Y.; Nwankwo, C.; Rogers, M.; Moraros, J. Weight Loss Intervention Adherence and Factors Promoting Adherence: A Meta-Analysis. Patient Prefer. Adherence 2016, 10, 1547–1559. [Google Scholar] [CrossRef]
- Renbarger, K.M.; Place, J.M.; Schreiner, M. The Influence of Four Constructs of Social Support on Pregnancy Experiences in Group Prenatal Care. Womens Health Rep. (New Rochelle) 2021, 2, 154–162. [Google Scholar] [CrossRef]
- Persell, S.D.; Peprah, Y.A.; Lipiszko, D.; Lee, J.Y.; Li, J.J.; Ciolino, J.D.; Karmali, K.N.; Sato, H. Effect of Home Blood Pressure Monitoring via a Smartphone Hypertension Coaching Application or Tracking Application on Adults with Uncontrolled Hypertension: A Randomized Clinical Trial. JAMA Netw. Open 2020, 3, e200255. [Google Scholar] [CrossRef]
- Waleed, M.; Kamal, T.; Um, T.-W.; Hafeez, A.; Habib, B.; Skouby, K.E. Unlocking Insights in IoT-Based Patient Monitoring: Methods for Encompassing Large-Data Challenges. Sensors 2023, 23, 6760. [Google Scholar] [CrossRef] [PubMed]
- Sasayama, K.; Nishimura, E.; Yamaji, N.; Ota, E.; Tachimori, H.; Igarashi, A.; Arata, N.; Yoneoka, D.; Saito, E. Current Use and Discrepancies in the Adoption of Health-Related Internet of Things and Apps among Working Women in Japan: Large-Scale, Internet-Based, Cross-Sectional Survey. JMIR Public Health Surveill. 2024, 10, e51537. [Google Scholar] [CrossRef] [PubMed]
- Elena-Bucea, A.; Cruz-Jesus, F.; Oliveira, T.; Coelho, P.S. Assessing the Role of Age, Education, Gender and Income on the Digital Divide: Evidence for the European Union. Inf. Syst. Front. 2021, 23, 1007–1021. [Google Scholar] [CrossRef]
- Venn, R.A.; Khurshid, S.; Grayson, M.; Ashburner, J.M.; Al-Alusi, M.A.; Chang, Y.; Foulkes, A.; Ellinor, P.T.; McManus, D.D.; Singer, D.E.; et al. Characteristics and Attitudes of Wearable Device Users and Nonusers in a Large Health Care System. J. Am. Heart Assoc. 2024, 13, e032126. [Google Scholar] [CrossRef]
- Scheibner, J.; Jobin, A.; Vayena, E. Ethical Issues with Using Internet of Things Devices in Citizen Science Research: A Scoping Review. Front. Environ. Sci. 2021, 9, 629649. [Google Scholar] [CrossRef]
- Bonner, C.; Fajardo, M.A.; Doust, J.; McCaffery, K.; Trevena, L. Implementing Cardiovascular Disease Prevention Guidelines to Translate Evidence-Based Medicine and Shared Decision Making into General Practice: Theory-Based Intervention Development, Qualitative Piloting and Quantitative Feasibility. Implement. Sci. 2019, 14, 86. [Google Scholar] [CrossRef] [PubMed]
- Presseau, J.; Hawthorne, G.; Sniehotta, F.F.; Steen, N.; Francis, J.J.; Johnston, M.; Mackintosh, J.; Grimshaw, J.M.; Kaner, E.; Elovainio, M.; et al. Improving Diabetes Care through Examining, Advising, and Prescribing (IDEA): Protocol for a Theory-Based Cluster Randomised Controlled Trial of a Multiple Behaviour Change Intervention Aimed at Primary Healthcare Professionals. Implement. Sci. 2014, 9, 61. [Google Scholar] [CrossRef] [PubMed]
- Say, L.; Chou, D.; Gemmill, A.; Tunçalp, Ö.; Moller, A.B.; Daniels, J.; Gülmezoglu, A.M.; Temmerman, M.; Alkema, L. Global Causes of Maternal Death: A WHO Systematic Analysis. Lancet Glob. Health 2014, 2, e323–e333. [Google Scholar] [CrossRef] [PubMed]
- Jiang, L.; Tang, K.; Magee, L.A.; von Dadelszen, P.; Ekeroma, A.; Li, X.; Zhang, E.; Bhutta, Z.A. A Global View of Hypertensive Disorders and Diabetes Mellitus during Pregnancy. Nat. Rev. Endocrinol. 2022, 18, 760–775. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
Author, Year | N (Intervention, Comparison) | Intervention | Outcomes Reported |
---|---|---|---|
Gilmore et al., 2017 [39] | 40 (20 vs. 20) | A smartphone-based remote intervention using the SmartLoss® application The IoT devices included a Bluetooth-enabled scale (BodyTrace) and a Fitbit Zip for step tracking. Personalized goals were set for both body weight and step count. Educational content covering nutrition, physical activity, and behavior change was delivered through the application. When the application-linked weight data deviated from the target zone for more than three consecutive days, an automatic trigger was activated, prompting individualized feedback from a registered dietitian via phone or email. | Primary: change in weight (kg) |
Cheung et al., 2019 [36] | 60 (40, 20) | A text messaging intervention program was linked to a Fitbit Flex® active monitor that enabled tracking of activity and further customization of text messages that were sent during two 30 min lifestyle counseling sessions (a face-to-face session and a session by phone). Based on the step count data, adaptive weekly step goals and encouraging messages were automatically delivered. | (i) Attendance for the postpartum GTT within 12 weeks postpartum; (ii) Adherence to physical activity recommendations, constituting 30 min of moderate intensity physical activity at least 5 days a week as a self-reported outcome, along with achieving a daily step count of 10,000 recorded by pedometer count, assessed at the 6-month mark; (iii) Achievement of dietary macronutrient recommendations regarding fat and fiber intake, including a dietary fat intake of ≤30% of total daily caloric intake, saturated fat consumption below 10%, and the consumption of 15 g of fiber per 1000 calories, evaluated at the 6-month interval; (iv) Evaluation of the change in self-reported weight (kg) recorded at the 6-month follow-up. |
Sung et al., 2019 [37] | 21 (11, 10) | Tailored mobile health care services provided by the mobile phone application designed for the study. The IoT devices included a Bluetooth-enabled glucometer and an accelerometer for monitoring physical activity. A multidisciplinary health care team (including endocrinologists, nurses, and dietitians) reviewed the transmitted data twice weekly and provided personalized feedback and guidance through the application’s messaging system. Educational content and recommendations on diet and physical activity were also delivered regularly via the application. | Obstetrical outcomes: GA at delivery, birth weight (kg), small for GA, large for GA, cesarean section. Metabolic outcomes: maternal BMI, weight (kg), body fat (%), HOMA-IR. |
Chen et al., 2023 [38] | 92 (46, 46) | Nurse-led mobile health intervention. Multifunctional application design based on the behavior change theory (social cognitive theory). The IoT device used was a wrist-worn activity tracker (Mi Band 5). Individually tailored SMS text messages were delivered to promote behavioral changes in pregnant women, including encouragement based on their progress in weight management. The application provided automated feedback and included daily step goals. | Primary: rate of excessive weekly GWG (kg/week), rate of excessive total GWG (kg), changes and trajectories of GWG (kg) in both groups throughout pregnancy |
Gonzalez-Plaza et al., 2022 [40] | 150 (78, 72) | A complex intervention based on social cognitive theory (SCT), combining a mobile health approach using a smartband (Mi Band 2) and smartphone application (Mi Fit) with remote midwife counseling. The intervention aimed to enhance self-monitoring, self-efficacy, and outcome expectations. Through the Hangouts application, individually tailored educational messages (videos and texts) were delivered twice a week according to the gestational week. Additionally, monthly personalized feedback from a midwife and on-demand support with responses within 1 h were provided. | (i) Primary: GWG (gestation weight gain (kg)) and total physical activity. |
Lim et al., 2021 [15] | 200 (101, 99) | The intervention was a smartphone-based lifestyle program using the locally developed nBuddy application, designed for women with recent gestational diabetes mellitus (GDM). Calorie and activity level goals were individually tailored to achieve the target weight based on each participant’s profile. Live interaction with a research team consisting of dietitians, physiotherapists, and occupational therapists was available. | Primary: the percentage of women who regained their first trimester weight by 4 months postpartum if their first trimester BMI was ≤23 kg/m2, or achieved a weight loss of at least 5% from their first trimester weight if their first trimester BMI was >23 kg/m2. |
Van Uytsel et al., 2022 [41] | 1075 (551, 524) | Four face-to-face lifestyle coaching sessions using a smartphone application. A Bluetooth connection was set up with an activity tracker (Withings Go) and a weighing scale (Withings Body+). Four coaching sessions were conducted in person (at 6, 8, 12 weeks, and 6 months postpartum), focusing on nutrition, physical activity, and mental well-being. The sessions employed motivational interviewing techniques and behavior change strategies such as goal setting, action planning, and self-monitoring. The application provided continuous support between sessions, including self-tracking, goal visualization, and personalized motivational messages based on the collected data. | Weight retention (kg), fat percentage, waist and hip circumference (cm), energy intake, improved physical activity (an increase of 700 MET-minutes/week), and improved sedentary time (a decrease of 1 sedentary hours/day). |
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Nishimura, E.; Yamaji, N.; Sasayama, K.; Rahman, M.O.; da Silva Lopes, K.; Mamahit, C.G.; Ninohei, M.; Tun, P.P.; Shoki, R.; Suzuki, D.; et al. Effectiveness of the Internet of Things for Improving Pregnancy and Postpartum Women’s Health in High-Income Countries: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Healthcare 2025, 13, 2103. https://doi.org/10.3390/healthcare13172103
Nishimura E, Yamaji N, Sasayama K, Rahman MO, da Silva Lopes K, Mamahit CG, Ninohei M, Tun PP, Shoki R, Suzuki D, et al. Effectiveness of the Internet of Things for Improving Pregnancy and Postpartum Women’s Health in High-Income Countries: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Healthcare. 2025; 13(17):2103. https://doi.org/10.3390/healthcare13172103
Chicago/Turabian StyleNishimura, Etsuko, Noyuri Yamaji, Kiriko Sasayama, Md. Obaidur Rahman, Katharina da Silva Lopes, Citra Gabriella Mamahit, Mika Ninohei, Phyu Phyu Tun, Rina Shoki, Daichi Suzuki, and et al. 2025. "Effectiveness of the Internet of Things for Improving Pregnancy and Postpartum Women’s Health in High-Income Countries: A Systematic Review and Meta-Analysis of Randomized Controlled Trials" Healthcare 13, no. 17: 2103. https://doi.org/10.3390/healthcare13172103
APA StyleNishimura, E., Yamaji, N., Sasayama, K., Rahman, M. O., da Silva Lopes, K., Mamahit, C. G., Ninohei, M., Tun, P. P., Shoki, R., Suzuki, D., Nitamizu, A., Yoneoka, D., Saito, E., & Ota, E. (2025). Effectiveness of the Internet of Things for Improving Pregnancy and Postpartum Women’s Health in High-Income Countries: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Healthcare, 13(17), 2103. https://doi.org/10.3390/healthcare13172103