E-cohort study: a new type of study design driven by internet technology_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Cheng ¹ , ZHANG Haoran ² , ZHUANG Rong ¹ , TIAN Ruihua ¹ , ZHANG Luyao ¹ , LI Shuangjing ¹ ,  YAN Shiyan ^1,3

1. School of Acupuncture-Moxibustion and Tuina, Beijing University of CM, Beijing 100029, P. R. China;
2. Beijing Youth Politics College, Beijing 100102, P. R. China;
3. International Acupuncture and Moxibustion Innovation Institute, Beijing University of CM, Beijing 100029, P. R. China;

Corresponding author：

YAN Shiyan, Email: yanshiyan@bucm.edu.cn

Keywords：

E-cohort; Cohort study; Internet technology; Patient involvement

DOI：

10.7507/1672-2531.202412176

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Cohort study is the observational study with the highest strength of causality demonstration, which is often used to test the etiological hypothesis and determine the risk factors of diseases. However, it often takes a lot of manpower, material and financial resources to carry out a cohort study, and it is time-consuming. At the same time, due to the long follow-up time, it is difficult to maintain the compliance of the study population, which is prone to loss to follow-up bias. In recent years, driven by network technology, a new type of cohort study design, e-cohort study, has gradually emerged, which is based on the network to recruit participants, follow up and collect data. Taking advantage of the wide coverage and high flexibility of the network, it provides a new strategy for improving the recruitment speed, participant participation and compliance of cohort study. This article summarizes the development history, current status, key points of design and implementation, advantages and challenges of e-cohort study, so as to help researchers to fully understand and apply this study design to solve practical clinical problems.

1.	Capili B, Anastasi JK. Cohort studies. Am J Nurs, 2021, 121(12): 45-48.
2.	Song T, Deng N, Cui T, et al. Measuring success of patients' continuous use of mobile health services for self-management of chronic conditions: model development and validation. J Med Internet Res, 2021, 23(7): e26670.
3.	Nkyekyer J, Clifford SA, Mensah FK, et al. Maximizing participant engagement, participation, and retention in cohort studies using digital methods: rapid review to inform the next generation of very large birth cohorts. J Med Internet Res, 2021, 23(5): e23499.
4.	Nittas V, Puhan MA, von Wyl V. Toward a working definition of ecohort studies in health research: narrative literature review. JMIR Public Health Surveill, 2021, 7(1): e24588.
5.	张晶. 沈阳市VCT门诊求询MSM人群电子队列构建基础上的HIV新发感染率监测及预防效果研究. 沈阳: 中国医科大学, 2018.
6.	Richiardi L, Baussano I, Vizzini L, et al. Feasibility of recruiting a birth cohort through the internet: the experience of the NINFEA cohort. Eur J Epidemiol, 2007, 22(12): 831-837.
7.	Turner C, Bain C, Schluter PJ, et al. Cohort profile: the nurses and midwives e-cohort study-a novel electronic longitudinal study. Int J Epidemiol, 2009, 38(1): 53-60.
8.	Mikkelsen EM, Hatch EE, Wise LA, et al. Cohort profile: the Danish web-based pregnancy planning study-'snart-gravid'. Int J Epidemiol, 2009, 38(4): 938-943.
9.	Firestone R, Cheng S, Pearce N, et al. Internet-based birth-cohort studies: is this the future for epidemiology. JMIR Res Protoc, 2015, 4(2): e71.
10.	Tran VT, Ravaud P. COllaborative open platform E-cohorts for research acceleration in trials and epidemiology. J Clin Epidemiol, 2020, 124: 139-148.
11.	Wang C, Wu X, Qi H. A comprehensive analysis of e-health literacy research focuses and trends. Healthcare (Basel), 2021, 10(1): 66.
12.	Langley K, Del Pozo-Banos M, Daalsgard S, et al. Can a nation-wide e-cohort of ADHD and ASD in childhood be established using Welsh routinely available datasets. BMJ Open, 2023, 13(8): e071851.
13.	Cannon MJ, Ng BP, Lloyd K, et al. Delivering the national diabetes prevention program: assessment of enrollment in in-person and virtual organizations. J Diabetes Res, 2022, 2022: 2942918.
14.	Hercberg S, Castetbon K, Czernichow S, et al. The nutrinet-santé study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health, 2010, 10: 242.
15.	Puhan MA, Steinemann N, Kamm CP, et al. A digitally facilitated citizen-science driven approach accelerates participant recruitment and increases study population diversity. Swiss Med Wkly, 2018, 148: w14623.
16.	NIH. All of us. 2021.
17.	Zhang Z, Shafer A, Johnson-Camacho K, et al. Novel recruitment approaches and operational results for a statewide population Cohort for cancer research: the healthy Oregon project. J Clin Transl Sci, 2024, 8(1): e32.
18.	Faulkenberry JG, Luberti A, Craig S. Electronic health records, mobile health, and the challenge of improving global health. Curr Probl Pediatr Adolesc Health Care, 2022, 52(1): 101111.
19.	Mehta CC, Hagen KS, Collins LF, et al. Nested and multipart prospective observational studies, flaming fiasco or efficiently economical. The brain, bone, heart case study. BMC Med Res Methodol, 2022, 22(1): 203.
20.	Correia RB, Li L, Rocha LM. Monitoring potential drug interactions and reactions via network analysis of instagram user timelines. Pac Symp Biocomput, 2016, 21: 492-503.
21.	Correia RB, Wood IB, Bollen J, et al. Mining social media data for biomedical signals and health-related behavior. Annu Rev Biomed Data Sci, 2020, 3: 433-458.
22.	Wood IB, Brattig Correia R, Miller WR, et al. Small cohort of patients with epilepsy showed increased activity on Facebook before sudden unexpected death. Epilepsy Behav, 2022, 128: 108580.
23.	Aguayo GA, Goetzinger C, Scibilia R, et al. Methods to generate innovative research ideas and improve patient and public involvement in modern epidemiological research: review, patient viewpoint, and guidelines for implementation of a digital cohort study. J Med Internet Res, 2021, 23(12): e25743.
24.	Biddle MSY, Gibson A, Evans D. Attitudes and approaches to patient and public involvement across Europe: a systematic review. Health Soc Care Community, 2021, 29(1): 18-27.
25.	Newman A, Bavik YL, Mount M, et al. Data collection via online platforms: challenges and recommendations for future research. Applied Psychology, 2021, 70(3): 1380-1402.
26.	Zhang Y, Pratap A, Folarin AA, et al. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med, 2023, 6(1): 25.
27.	《远程智能临床试验专家共识》编写专家组, 上海市药学会药物临床研究专业委员会, 药物信息协会中国数字健康社区. 远程智能临床试验专家共识. 中国新药与临床杂志, 2022, 41(7): 385-392.
28.	李高扬, 何金杰, 谢萍, 等. 远程智能临床试验的实施规划和合规建议. 中国食品药品监管, 2023, (12): 180-187.

1. Capili B, Anastasi JK. Cohort studies. Am J Nurs, 2021, 121(12): 45-48.
2. Song T, Deng N, Cui T, et al. Measuring success of patients' continuous use of mobile health services for self-management of chronic conditions: model development and validation. J Med Internet Res, 2021, 23(7): e26670.
3. Nkyekyer J, Clifford SA, Mensah FK, et al. Maximizing participant engagement, participation, and retention in cohort studies using digital methods: rapid review to inform the next generation of very large birth cohorts. J Med Internet Res, 2021, 23(5): e23499.
4. Nittas V, Puhan MA, von Wyl V. Toward a working definition of ecohort studies in health research: narrative literature review. JMIR Public Health Surveill, 2021, 7(1): e24588.
5. 张晶. 沈阳市VCT门诊求询MSM人群电子队列构建基础上的HIV新发感染率监测及预防效果研究. 沈阳: 中国医科大学, 2018.
6. Richiardi L, Baussano I, Vizzini L, et al. Feasibility of recruiting a birth cohort through the internet: the experience of the NINFEA cohort. Eur J Epidemiol, 2007, 22(12): 831-837.
7. Turner C, Bain C, Schluter PJ, et al. Cohort profile: the nurses and midwives e-cohort study-a novel electronic longitudinal study. Int J Epidemiol, 2009, 38(1): 53-60.
8. Mikkelsen EM, Hatch EE, Wise LA, et al. Cohort profile: the Danish web-based pregnancy planning study-'snart-gravid'. Int J Epidemiol, 2009, 38(4): 938-943.
9. Firestone R, Cheng S, Pearce N, et al. Internet-based birth-cohort studies: is this the future for epidemiology. JMIR Res Protoc, 2015, 4(2): e71.
10. Tran VT, Ravaud P. COllaborative open platform E-cohorts for research acceleration in trials and epidemiology. J Clin Epidemiol, 2020, 124: 139-148.
11. Wang C, Wu X, Qi H. A comprehensive analysis of e-health literacy research focuses and trends. Healthcare (Basel), 2021, 10(1): 66.
12. Langley K, Del Pozo-Banos M, Daalsgard S, et al. Can a nation-wide e-cohort of ADHD and ASD in childhood be established using Welsh routinely available datasets. BMJ Open, 2023, 13(8): e071851.
13. Cannon MJ, Ng BP, Lloyd K, et al. Delivering the national diabetes prevention program: assessment of enrollment in in-person and virtual organizations. J Diabetes Res, 2022, 2022: 2942918.
14. Hercberg S, Castetbon K, Czernichow S, et al. The nutrinet-santé study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status. BMC Public Health, 2010, 10: 242.
15. Puhan MA, Steinemann N, Kamm CP, et al. A digitally facilitated citizen-science driven approach accelerates participant recruitment and increases study population diversity. Swiss Med Wkly, 2018, 148: w14623.
16. NIH. All of us. 2021.
17. Zhang Z, Shafer A, Johnson-Camacho K, et al. Novel recruitment approaches and operational results for a statewide population Cohort for cancer research: the healthy Oregon project. J Clin Transl Sci, 2024, 8(1): e32.
18. Faulkenberry JG, Luberti A, Craig S. Electronic health records, mobile health, and the challenge of improving global health. Curr Probl Pediatr Adolesc Health Care, 2022, 52(1): 101111.
19. Mehta CC, Hagen KS, Collins LF, et al. Nested and multipart prospective observational studies, flaming fiasco or efficiently economical. The brain, bone, heart case study. BMC Med Res Methodol, 2022, 22(1): 203.
20. Correia RB, Li L, Rocha LM. Monitoring potential drug interactions and reactions via network analysis of instagram user timelines. Pac Symp Biocomput, 2016, 21: 492-503.
21. Correia RB, Wood IB, Bollen J, et al. Mining social media data for biomedical signals and health-related behavior. Annu Rev Biomed Data Sci, 2020, 3: 433-458.
22. Wood IB, Brattig Correia R, Miller WR, et al. Small cohort of patients with epilepsy showed increased activity on Facebook before sudden unexpected death. Epilepsy Behav, 2022, 128: 108580.
23. Aguayo GA, Goetzinger C, Scibilia R, et al. Methods to generate innovative research ideas and improve patient and public involvement in modern epidemiological research: review, patient viewpoint, and guidelines for implementation of a digital cohort study. J Med Internet Res, 2021, 23(12): e25743.
24. Biddle MSY, Gibson A, Evans D. Attitudes and approaches to patient and public involvement across Europe: a systematic review. Health Soc Care Community, 2021, 29(1): 18-27.
25. Newman A, Bavik YL, Mount M, et al. Data collection via online platforms: challenges and recommendations for future research. Applied Psychology, 2021, 70(3): 1380-1402.
26. Zhang Y, Pratap A, Folarin AA, et al. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med, 2023, 6(1): 25.
27. 《远程智能临床试验专家共识》编写专家组, 上海市药学会药物临床研究专业委员会, 药物信息协会中国数字健康社区. 远程智能临床试验专家共识. 中国新药与临床杂志, 2022, 41(7): 385-392.
28. 李高扬, 何金杰, 谢萍, 等. 远程智能临床试验的实施规划和合规建议. 中国食品药品监管, 2023, (12): 180-187.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesE-cohort study: a new type of study design driven by internet technology

Abstract Full text Figures/Tables Video References Cited by

Format

Content