References
Abd-Alrazaq, A., Abuelezz, I., Hassan, A., AlSammarraie, A., Alhuwail, D., Irshaidat, S., Serhan, H. A., Ahmed, A., Alrazak, S. A., & Househ, M. (2022). Artificial intelligence-driven serious games in health care: Scoping review. JMIR Serious Games, 10(4), e39840. https://doi.org/10.2196/39840
Adefemi, A., Ukpoju, E. A., Adekoya, O., Abatan, A., & Adegbite, A. O. (2023). Artificial intelligence in environmental health and public safety: A comprehensive review of USA strategies. World Journal of Advanced Research and Reviews, 20(3), 1420-1434. https://doi.org/10.30574/wjarr.2023.20.3.2591
Alageel, S., Gulliford, M. C., McDermott, L., & Wright, A. J. (2017). Multiple health behaviour change interventions for primary prevention of cardiovascular disease in primary care: Systematic review and meta-analysis. BMJ Open, 7(6), e015375. https://doi.org/10.1136/bmjopen-2016-015375
Althoff, R. R., Hudziak, J. J., Willemsen, G., Hudziak, V., Bartels, M., & Boomsma, D. I. (2012). Genetic and environmental contributions to self-reported thoughts of self-harm and suicide. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 159(1), 120-127. https://doi.org/10.1002/ajmg.b.32010
Austin, P. C., & Reeves, M. J. (2013). The relationship between the C-statistic of a risk-adjustment model and the accuracy of hospital report cards: A Monte Carlo study. Medical Care, 51(3), 275-284. https://doi.org/10.1097/mlr.0b013e31827ff0dc
Bell, J. A., Kivimäki, M., & Hamer, M. (2014). Metabolically healthy obesity and risk of incident type 2 diabetes: A meta-analysis of prospective cohort studies. Obesity Reviews, 15(6), 504-515. https://doi.org/10.1111/obr.12157
Brucker, N., do Nascimento, S. N., Bernardini, L., Charão, M. F., & Garcia, S. C. (2020). Biomarkers of exposure, effect, and susceptibility in occupational exposure to traffic-related air pollution: A review. Journal of Applied Toxicology, 40(6), 722-736. https://doi.org/10.1002/jat.3940
Caleyachetty, R., Echouffo-Tcheugui, J. B., Muennig, P., Zhu, W., Muntner, P., & Shimbo, D. (2015). Association between cumulative social risk and ideal cardiovascular health in US adults: NHANES 1999-2006. International Journal of Cardiology, 191, 296-300. https://doi.org/10.1016/j.ijcard.2015.05.007
Chen, S. L., Braaten, T., Borch, K. B., Ferrari, P., Sandanger, T. M., & Nøst, T. H. (2021). Combined lifestyle behaviors and the incidence of common cancer types in the Norwegian Women and Cancer Study (NOWAC). Clinical Epidemiology, 721-734. https://doi.org/10.2147/clep.s312864
Daack-Hirsch, S., Shah, L. L., & Cady, A. D. (2018). Mental models of cause and inheritance for type 2 diabetes among unaffected individuals who have a positive family history. Qualitative Health Research, 28(4), 534-547. https://doi.org/10.1177/1049732317745052
Dale, H., Brassington, L., & King, K. (2014). The impact of healthy lifestyle interventions on mental health and wellbeing: A systematic review. Mental Health Review Journal, 19(1), 1-26. https://doi.org/10.1108/mhrj-05-2013-0016
de Gage, S. B., Bégaud, B., Bazin, F., Verdoux, H., Dartigues, J. F., Pérès, K., Kurth, T., & Pariente, A. (2012). Benzodiazepine use and risk of dementia: Prospective population based study. BMJ, 345, e6231. https://doi.org/10.1136/bmj.e6231
De Vito, K. M., Baer, H. J., Dart, H., Chiuve, S. E., Rimm, E. B., & Colditz, G. A. (2015). Validation of a risk prediction tool for coronary heart disease in middle-aged women. BMC Women's Health, 15, 1-9. https://doi.org/10.1186/s12905-015-0250-x
Den Braver, N. R., de Vet, E. W. M. L., Duijzer, G., Ter Beek, J., Jansen, S. C., Hiddink, G. J., Feskens, E. J. M., & Haveman-Nies, A. (2017). Determinants of lifestyle behavior change to prevent type 2 diabetes in high-risk individuals. International Journal of Behavioral Nutrition and Physical Activity, 14, 1-11. https://doi.org/10.1186/s12966-017-0532-9
Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011). A systematic review of the impact of adherence on the effectiveness of e-therapies. Journal of Medical Internet Research, 13(3), e1772. https://doi.org/10.2196/jmir.1772
Duan, Y., Shang, B., Liang, W., Du, G., Yang, M., & Rhodes, R. E. (2021). Effects of eHealth-based multiple health behavior change interventions on physical activity, healthy diet, and weight in people with noncommunicable diseases: Systematic review and meta-analysis. Journal of Medical Internet Research, 23(2), e23786. https://doi.org/10.2196/23786
Duncan, M., Vandelanotte, C., Kolt, G. S., Rosenkranz, R. R., Caperchione, C. M., George, E. S., Ding, H., Hooker, C., Karunanithi, M., Maeder, A. J., et al. (2014). Effectiveness of a web-and mobile phone-based intervention to promote physical activity and healthy eating in middle-aged males: Randomized controlled trial of the ManUp study. Journal of Medical Internet Research, 16(6), e136. https://doi.org/10.2196/jmir.3107
European Food Safety Authority (EFSA), Craig, P. S., Dujardin, B., Hart, A., Hernandez‐Jerez, A. F., Hougaard Bennekou, S., Kneuer, C., Ossendorp, B., Pedersen, R., Wolterink, G., & Mohimont, L. (2020). Cumulative dietary risk characterisation of pesticides that have chronic effects on the thyroid. EFSA Journal, 18(4), e06088. https://doi.org/10.2903/j.efsa.2020.6088
Fatima, I., Grover, V., Khan, I. R., Ahmad, N., & Yadav, A. (2023). Artificial intelligence in the medical field. EAI Endorsed Transactions on Pervasive Health and Technology, 9, e4713. https://doi.org/10.4108/eetpht.9.4713
Figueroa, J. F., Frakt, A. B., & Jha, A. K. (2020). Addressing social determinants of health: Time for a polysocial risk score. JAMA, 323(16), 1553-1554. https://doi.org/10.1001/jama.2020.2436
Foster, H. M., Celis-Morales, C. A., Nicholl, B. I., Petermann-Rocha, F., Pell, J. P., Gill, J. M., O'Donnell, C. A., & Mair, F. S. (2018). The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: A prospective analysis of the UK Biobank cohort. The Lancet Public Health, 3(12), e576-e585. https://doi.org/10.1016/S2468-2667(18)30200-7
Gesell, S. B., Barkin, S. L., & Valente, T. W. (2013). Social network diagnostics: A tool for monitoring group interventions. Implementation Science, 8(1), 116. https://doi.org/10.1186/1748-5908-8-116
Ghafari, J., Moghadasi, N., & Shekaftik, S. O. (2020). Oxidative stress induced by occupational exposure to nanomaterials: A systematic review. Industrial Health, 58(6), 492-502. https://doi.org/10.2486/indhealth.2020-0073
Goevaerts, W. F., Tenbült-van Limpt, N. C. C. W., Kop, W. J., Birk, M. V., Liu, Y., Brouwers, R. W. M., Lu, Y., & Kemps, H. M. C. (2023). Adherence to a lifestyle monitoring system in patients with heart disease: Protocol for the care-on prospective observational trial. BMC Cardiovascular Disorders, 23(1), 196. https://doi.org/10.21203/rs.3.rs-2622267/v1
Hagger, M. S., Moyers, S., McAnally, K., & McKinley, L. E. (2020). Known knowns and known unknowns on behavior change interventions and mechanisms of action. Health Psychology Review, 14(1), 199-212. https://doi.org/10.1080/17437199.2020.1719184
Hartman, S. J., & Rosen, R. K. (2017). Breast cancer relatives' physical activity intervention needs and preferences: Qualitative results. BMC Women's Health, 17(1), 39. https://doi.org/10.1186/s12905-017-0392-0
Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2023a). Artificial intelligence and machine learning in prediction of surgical complications: Current state, applications, and implications. The American Surgeon, 89(1), 25-30. https://doi.org/10.1177/00031348221101488
Hassan, A. M., Rajesh, A., Asaad, M., Nelson, J. A., Coert, J. H., Mehrara, B. J., & Butler, C. E. (2023b). A surgeon's guide to artificial intelligence-driven predictive models. The American Surgeon, 89(1), 11-19. https://doi.org/10.1177/00031348221103648
Hinnouho, G. M., Czernichow, S., Dugravot, A., Nabi, H., Brunner, E. J., Kivimaki, M., & Singh-Manoux, A. (2015). Metabolically healthy obesity and the risk of cardiovascular disease and type 2 diabetes: The Whitehall II cohort study. European Heart Journal, 36(9), 551-559. https://doi.org/10.1093/eurheartj/ehu123
Hu, G., Root, M., & Duncan, A. W. (2014). Adding multiple risk factors improves Framingham coronary heart disease risk scores. Vascular Health and Risk Management, 10, 557-562. https://doi.org/10.2147/VHRM.S69672
Hunter, R. F., McAneney, H., Davis, M., Tully, M. A., Valente, T. W., & Kee, F. (2015). "Hidden" social networks in behavior change interventions. American Journal of Public Health, 105(3), 513-516. https://doi.org/10.2105/AJPH.2014.302399
Hutchinson, L. C., Forshaw, M. J., McIlroy, D., & Poole, H. (2021). The role of lifestyle on NHS ambulance workers' wellbeing. Journal of Workplace Behavioral Health, 36(2), 159-171. https://doi.org/10.1080/15555240.2021.1922286
Izah, S. C., Richard, G., Stanley, H. O., Sawyer, W. E., Ogwu, M. C., & Uwaeme, O. R. (2024a). Potential applications of linear regression models in studying the relationship between fish and contaminants in their environment: One health perspective. Juniper Online Journal of Public Health, 8(4), 555743. https://doi.org/10.19080/JOJPH.2024.08.555743
Izah, S. C., Richard, G., Stanley, H. O., Sawyer, W. E., Ogwu, M. C., & Uwaeme, O. R. (2024b). Prospects and application of multivariate and reliability analyses to one health risk assessments of toxic elements. Toxicology and Environmental Health Sciences, 16(2), 127-134.
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., & Uwaeme, O. R. (2024c). Environmental health risks of trace elements in sediment using multivariate approaches and contamination indices. International Journal of Environmental Science and Technology. https://doi.org/10.1007/s13762-024-05974-1
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., & Uwaeme, O. R. (2024d). Surface water quality: A statistical perspective on the efficacy of environmental and human health assessment tools. Water, Air, & Soil Pollution, 235(3). https://doi.org/10.1007/s11270-024-06965-1
Izah, S. C., Stanley, H. O., Richard, G., Sawyer, W. E., Uwaeme, O. R., & Sylva, L. (2024e). Source and health risks of trace metals in Clarias batrachus and Chrysichthys nigrodigitatus from surface waters in Bayelsa State, Nigeria: A probabilistic model. Frontiers in Sustainable Food Systems, 8, 1419143. https://doi.org/10.3389/fsufs.2024.1419143
Izah, S. C., Uzoekwe, S. A., & Aigberua, A. O. (2021a). Source, geochemical spreading, and risks of trace metals in particulate matter 2.5 within a gas flaring area in Bayelsa State, Nigeria. Advances in Environmental Technology, 7(2), 101-118.
Izah, S. C., Richard, G., Aigberua, A. O., & Ekakitie, O. (2021). Variations in reference values utilized for the evaluation of complex pollution indices of potentially toxic elements: A critical review. Environmental Challenges, 5, 100322. https://doi.org/10.1016/j.envc.2021.100322
Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011). Risk prediction models for hospital readmission: A systematic review. JAMA, 306(15), 1688-1698. https://doi.org/10.1001/jama.2011.1515
Kennedy, A., Brame, J., Rycroft, T., Wood, M., Zemba, V., Weiss Jr, C., Hull, M., Hill, C., Geraci, C., & Linkov, I. (2019). A definition and categorization system for advanced materials: The foundation for risk‐informed environmental health and safety testing. Risk Analysis, 39(8), 1783-1795. https://doi.org/10.1111/risa.13304
Kerr, S., Greenland, S., Jeffrey, K., Millington, T., Bedston, S., Ritchie, L., Simpson, C. R., Fagbamigbe, A. F., Kurdi, A., Robertson, C., et al. (2023). Understanding and reporting odds ratios as rate-ratio estimates in case-control studies. Journal of Global Health, 13. https://doi.org/10.7189/jogh.13.04101
Kim, M., Jee, S. C., Kim, S., Hwang, K. H., & Sung, J. S. (2021). Identification and characterization of mRNA biomarkers for sodium cyanide exposure. Toxics, 9(11), 288. https://doi.org/10.3390/toxics9110288
Knol, M. J., Le Cessie, S., Algra, A., Vandenbroucke, J. P., & Groenwold, R. H. (2012). Overestimation of risk ratios by odds ratios in trials and cohort studies: Alternatives to logistic regression. CMAJ, 184(8), 895-899. https://doi.org/10.1503/cmaj.101715
Krukowski, R. A., Tilford, J. M., Harvey‐Berino, J., & West, D. S. (2011). Comparing behavioral weight loss modalities: Incremental cost‐effectiveness of an internet‐based versus an in‐person condition. Obesity, 19(8), 1629-1635. https://doi.org/10.1038/oby.2010.341
Langholz, B. (2010). Case-control studies = Odds ratios: Blame the retrospective model. Epidemiology, 21(1), 10-12. https://doi.org/10.1097/ede.0b013e3181c308f5
Lentz, T. J., Dotson, G. S., Williams, P. R. D., Maier, A., Gadagbui, B., Pandalai, S. P., Lamba, A., Hearl, F., & Mumtaz, M. (2015). Aggregate exposure and cumulative risk assessment-Integrating occupational and non-occupational risk factors. Journal of Occupational and Environmental Hygiene, 12(Sup1), S112-S126. https://doi.org/10.1080/15459624.2015.1060326
Li, Y., Schoufour, J., Wang, D. D., Dhana, K., Pan, A., Liu, X., Song, M., Liu, G., Shin, H. J., Sun, Q., & Al-Shaar, L. (2020). Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: Prospective cohort study. BMJ, 368. https://doi.org/10.1136/bmj.l6669
Linder, S. H., & Sexton, K. (2011). Conceptual models for cumulative risk assessment. American Journal of Public Health, 101(S1), S74-S81. https://doi.org/10.2105/ajph.2011.300318
Linkov, I., Trump, B. D., Anklam, E., Berube, D., Boisseau, P., Cummings, C., Ferson, S., Florin, M. V., Goldstein, B., Hristozov, D., et al. (2018). Comparative, collaborative, and integrative risk governance for emerging technologies. Environment Systems and Decisions, 38, 170-176. https://doi.org/10.1007/s10669-018-9686-5
Lippke, S., Corbet, J. M., Lange, D., Parschau, L., & Schwarzer, R. (2016). Intervention engagement moderates the dose-response relationships in a dietary intervention. Dose-Response, 14(1). https://doi.org/10.1177/1559325816637515
Lotta, L. A., Abbasi, A., Sharp, S. J., Sahlqvist, A. S., Waterworth, D., Brosnan, J. M., Scott, R. A., Langenberg, C., & Wareham, N. J. (2015). Definitions of metabolic health and risk of future type 2 diabetes in BMI categories: A systematic review and network meta-analysis. Diabetes Care, 38(11), 2177-2187. https://doi.org/10.2337/dc15-1218
Lyu, J., Lee, S. H., & Kim, H. Y. (2016). Associations between healthy lifestyles and health outcomes among older Koreans. Geriatrics & Gerontology International, 16(6), 663-669. https://doi.org/10.1111/ggi.12531
Marck, C. H., De Livera, A. M., Brown, C. R., Neate, S. L., Taylor, K. L., Weiland, T. J., Hadgkiss, E. J., & Jelinek, G. A. (2018). Health outcomes and adherence to a healthy lifestyle after a multimodal intervention in people with multiple sclerosis: Three-year follow-up. PLOS ONE, 13(5), e0197759. https://doi.org/10.1371/journal.pone.0197759
Moreno, M. A., Eickhoff, J., Zhao, Q., & Suris, J. C. (2019). College students and problematic internet use: A pilot study assessing self-appraisal and independent behavior change. Journal of Adolescent Health, 64(1), 131-133. https://doi.org/10.1016/j.jadohealth.2018.06.029
Ozieh, M. N., Garacci, E., Walker, R. J., Palatnik, A., & Egede, L. E. (2021). The cumulative impact of social determinants of health factors on mortality in adults with diabetes and chronic kidney disease. BMC Nephrology, 22, 1-10. https://doi.org/10.1186/s12882-021-02277-2
Prochaska, J. D., Nolen, A. B., Kelley, H., Sexton, K., Linder, S. H., & Sullivan, J. (2014). Social determinants of health in environmental justice communities: Examining cumulative risk in terms of environmental exposures and social determinants of health. Human and Ecological Risk Assessment: An International Journal, 20(4), 980-994. https://doi.org/10.1080/10807039.2013.805957
Rhee, E. J., Lee, M. K., Kim, J. D., Jeon, W. S., Bae, J. C., Park, S. E., Park, C. Y., Oh, K. W., Park, S. W., & Lee, W. Y. (2014). Metabolic health is a more important determinant for diabetes development than simple obesity: A 4-year retrospective longitudinal study. PLOS ONE, 9(5), e98369. https://doi.org/10.1371/journal.pone.0098369
Rodondi, N., Locatelli, I., Aujesky, D., Butler, J., Vittinghoff, E., Simonsick, E., Satterfield, S., Newman, A. B., Wilson, P. W., Pletcher, M. J., et al. (2012). Framingham risk score and alternatives for prediction of coronary heart disease in older adults. PLOS ONE, 7(3), e34287. https://doi.org/10.1371/journal.pone.0034287
Rudel, R. A., Ackerman, J. M., Attfield, K. R., & Brody, J. G. (2014). New exposure biomarkers as tools for breast cancer epidemiology, biomonitoring, and prevention: A systematic approach based on animal evidence. Environmental Health Perspectives, 122(9), 881-895. https://doi.org/10.1289/ehp.1307455
Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579-1586. https://doi.org/10.1016/S0140-6736(20)30226-9
Shea, S., Lima, J., Diez-Roux, A., Jorgensen, N. W., & McClelland, R. L. (2016). Socioeconomic status and poor health outcome at 10 years of follow-up in the multi-ethnic study of atherosclerosis. PLOS ONE, 11(11), e0165651. https://doi.org/10.1371/journal.pone.0165651
Shin, J. A., Lee, J. H., Lim, S. Y., Ha, H. S., Kwon, H. S., Park, Y. M., Lee, W. C., Kang, M. I., Yim, H. W., Yoon, K. H., & Son, H. Y. (2013). Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. Journal of Diabetes Investigation, 4(4), 334-343. https://doi.org/10.1111/jdi.12075
Silva, C. C., Presseau, J., Van Allen, Z., Dinsmore, J., Schenk, P., Moreto, M., & Marques, M. M. (2024). Components of multiple health behaviour change interventions for patients with chronic conditions: A systematic review and meta-regression of randomized trials. Health Psychology Review, 1-56. https://doi.org/10.31234/osf.io/yfmjx
Silva, C. C., Presseau, J., van Allen, Z., Schenk, P. M., Moreto, M., Dinsmore, J., & Marques, M. M. (2024). Effectiveness of interventions for changing more than one behavior at a time to manage chronic conditions: A systematic review and meta-analysis. Annals of Behavioral Medicine, 58(6), 432-444. https://doi.org/10.31234/osf.io/ch2yx
Stern, R. H. (2022). Why risk factors minimally change the ROC curve AUC. medRxiv, 2022-02. https://doi.org/10.1101/2022.02.24.22271481
Taylor, K. L., Hadgkiss, E. J., Jelinek, G. A., Weiland, T. J., Pereira, N. G., Marck, C. H., & van der Meer, D. M. (2014). Lifestyle factors, demographics, and medications associated with depression risk in an international sample of people with multiple sclerosis. BMC Psychiatry, 14, 1-12. https://doi.org/10.1186/s12888-014-0327-3
Uhernik, I. A., Skoko-Poljak, D., Dečković-Vukres, V., Jelavić, M., Mihel, S., Benjak, T., Štefančić, V., Draušnik, Ž., & Stevanović, R. (2019). Association of poor self-perceived health with demographic, socioeconomic, and lifestyle factors in the Croatian adult population. Društvena Istraživanja: Časopis Za Opća Društvena Pitanja, 28(2), 229-248. https://doi.org/10.5559/di.28.2.03
Uno, H., Cai, T., Pencina, M. J., D'Agostino, R. B., & Wei, L. J. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine, 30(10), 1105-1117. https://doi.org/10.1002/sim.4154
Uzoekwe, S. A., Izah, S. C., & Aigberua, A. O. (2021). Environmental and human health risk of heavy metals in atmospheric particulate matter (PM10) around gas flaring vicinity in Bayelsa State, Nigeria. Toxicology and Environmental Health Sciences, 13(4), 323-335. https://doi.org/10.1007/s13530-021-00085-7
Viallon, V., Freisling, H., Matta, K., Nannsen, A. Ø., Dahm, C. C., Tjønneland, A., Eriksen, A. K., Kaaks, R., Katzke, V. A., Schulze, M. B., et al. (2024). On the use of the healthy lifestyle index to investigate specific disease outcomes. Scientific Reports, 14(1), 16330. https://doi.org/10.21203/rs.3.rs-3485042/v1
Wang, C., Ramasamy, A., Verduzco-Gutierrez, M., Brode, W. M., & Melamed, E. (2023). Acute and post-acute sequelae of SARS-CoV-2 infection: A review of risk factors and social determinants. Virology Journal, 20(1), Article 124. https://doi.org/10.1186/s12985-023-02061-8
Wang, J., & Geng, L. (2019). Effects of socioeconomic status on physical and psychological health: Lifestyle as a mediator. International Journal of Environmental Research and Public Health, 16(2), Article 281. https://doi.org/10.3390/ijerph16020281
Webb, T., Joseph, J., Yardley, L., & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), Article e1376. https://doi.org/10.2196/jmir.1376
Williams, P. R., Dotson, G. S., & Maier, A. (2012). Cumulative risk assessment (CRA): Transforming the way we assess health risks. Environmental Science & Technology, 46(20), 10868-10874. https://doi.org/10.1021/es3025353
Wilson, K., Senay, I., Durantini, M., Sánchez, F., Hennessy, M., Spring, B., & Albarracín, D. (2015). When it comes to lifestyle recommendations, more is sometimes less: A meta-analysis of theoretical assumptions underlying the effectiveness of interventions promoting multiple behavior domain change. Psychological Bulletin, 141(2), 474-509. https://doi.org/10.1037/a0038295
Woo, J., Chan, R., Leung, J., & Wong, M. (2010). Relative contributions of geographic, socioeconomic, and lifestyle factors to quality of life, frailty, and mortality in elderly. PLoS One, 5(1), Article e8775. https://doi.org/10.1371/journal.pone.0008775
Wray, C. M., Vali, M., Walter, L. C., Christensen, L., Chapman, W., Austin, P. C., Byers, A. L., & Keyhani, S. (2021). Examining the association of social risk with heart failure readmission in the Veterans Health Administration. BMC Health Services Research, 21, Article 888. https://doi.org/10.1186/s12913-021-06888-1
Yardley, L., Miller, S., Schlotz, W., & Little, P. (2011). Evaluation of a Web-based intervention to promote hand hygiene: Exploratory randomized controlled trial. Journal of Medical Internet Research, 13(4), Article e1963. https://doi.org/10.2196/jmir.1963
Zhang, W., Ahmad, M. I., & Soliman, E. Z. (2020). The role of traditional risk factors in explaining the social disparities in cardiovascular death: The National Health and Nutrition Examination Survey III (NHANES III). American Journal of Preventive Cardiology, 4, Article 100094. https://doi.org/10.1016/j.ajpc.2020.100094
Zhang, Y., Guo, X., Zhang, N., Yan, X., Li, M., Zhou, M., He, H., Li, Y., Guo, W., Zhang, M., & Zhang, J. (2021). Effect of mobile-based lifestyle intervention on body weight, glucose, and lipid metabolism among the overweight and obese elderly population in China: A randomized controlled trial protocol. International Journal of Environmental Research and Public Health, 18(9), Article 4854. https://doi.org/10.3390/ijerph18094854
Zhou, X. H., Wang, X., Duncan, A., Hu, G., & Zheng, J. (2017). Statistical evaluation of adding multiple risk factors improves Framingham stroke risk score. BMC Medical Research Methodology, 17, Article 330. https://doi.org/10.1186/s12874-017-0330-8
