Development and Effectiveness of educational mobile application based on integrated change model for prevention of atherosclerotic cardiovascular disease risk factors: Protocol for a Randomized, Controlled Trial
Educational mobile application for prevention of atherosclerotic cardiovascular disease
-
Afsaneh Aein
,
Mahnaz solhi
,
Ali vashegani farahani
,
Mohammad alja Sem
,
Farshid Alaeddini
,
Leila Janani
,
Negar Omidi
,
Mohammad Hossein Tagdisi
Abstract
Background and Objective: One way for preventing cardiovascular problems is the use of mobile health applications through creating an improvement in flexible risk factors. The current study aimed to find out the progress and efficiency of a pedagogic mobile app using an integrated change model to avoid risk factors of atherosclerotic cardiovascular disease (ASCVD) from average to high scores among individuals between 20-69 years old.
Methods: This study is a parallel, randomized, and single-blind medical trial conducted on 430 individuals employing the randomized blocks technique. The participants were divided into two groups: a control group (normal clinical activity) and an intervention group (normal clinical activity along with app-based education) for about 6 months.
Discussion: This application was designed to enhance motivation, consciousness, and obtrusive habits in diminishing risk factors in individuals with an increased threat of ASCVD. Hence, the obtained results can enhance cardiovascular health knowledge, control biological risk factors, and change cardiac behaviors according to phone applications. In the case that its efficacy is verified, this app can be applied in the controlling plans of the National CVD.
1. Haldane V, Tan YG, Teo KWQ, Koh JJK, Srivastava A, Cheng RX, et al. Perspectives on acceptance and use of a mobile health intervention for the prevention of atherosclerotic cardiovascular disease in Singapore: mixed-methods study. JMIR Mhealth Uhealth. 2019;7(3):e11108.
2. Tekkesin İ, Çinier G, Hayıroğlu M, Soluk Özdemir Y, Yıldırımtürk Ö, İnan D, et al. Rationale and design of lifestyle intervention using mobile technology in patients with high cardiovascular risk: a pragmatic randomized clinical trial. Turk Kardiyol Dern Ars. 2020;48(2):149-57.
3. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646.
4. Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). Eur J Prev Cardiol. 2022;29(1):5-115.
5. Dall TM, Gallo PD, Chakrabarti R, West T, Semilla AP, Storm MV. An aging population and growing disease burden will require alarge and specialized health care workforce by 2025. Health Aff. 2013;32(11):2013-20.
6. Tundjungsari V, Sofro ASM, Yugaswara H, Putra ATD. Development of mobile health application for cardiovascular disease prevention. Int J Adv Comput Sci Appl. 2018;9(11).
7. Wang W, Zhang H, Lopez V, Wu VX, Poo DCC, Kowitlawakul Y. Improving awareness, knowledge and heart‐related lifestyle of coronary heart disease among working population through a mHealth programme: study protocol. J Adv Nurs. 2015;71(9):2200-7.
8. Ullrich G, Dönmez A, Mahabadi AA, Bäuerle A, Knuschke R, Paldán K, et al. Effect of visual presentation of atherosclerotic carotid plaque on adherence to secondary preventive therapy using mHealth technologies (PreventiPlaque app): Study protocol for a randomized controlled trial. Heliyon. 2023;9(3).
9. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439-54.
10. Poorhosseini H, Abbasi SH. The Tehran heart center. OUP; 2018.
11. Heshmati A, Moftian N, Rezaei-Hachesu P, Samad-Soltani T. Simple and efficient measurement of the user experience in health information systems: A persian version. Iran J Public Health. 2021.
12. Heshmati A MN, Rezaei-Hachesu P, Samad-Soltani T. User Experience Questionnaire (UEQ). updated 2018. Available from: https://www.ueq-online.org/.
13. Organization WH. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance. WHO, 2005 9241593830.
14. Vasheghani-Farahani A, Tahmasbi M, Asheri H, Ashraf H, Nedjat S, Kordi R. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian J Sports Med. 2011;2(2):106.
15. Faul F, Erdfelder E, Lang A-G, Buchner A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-91.
16. Patnode CD, Evans CV, Senger CA, Redmond N, Lin JS. Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults without known cardiovascular disease risk factors: updated evidence report and systematic review for the US Preventive Services Task Force. Jama. 2017;318(2):175-93.
17. Siopis G, Chey T, Allman‐Farinelli M. A systematic review and meta‐analysis of interventions for weight management using text messaging. J Hum Nutr Diet. 2015;28:1-15.
18. Liu S, Dunford SD, Leung YW, Brooks D, Thomas SG, Eysenbach G, et al. Reducing blood pressure with Internet-based interventions: a meta-analysis. Can J Cardiol. 2013;29(5):613-21.
19. Uhlig K, Patel K, Ip S, Kitsios GD, Balk EM. Self-measured blood pressure monitoring in the management of hypertension: a systematic review and meta-analysis. Ann Intern Med. 2013;159(3):185-94.
20. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med. 2014;44:671-86.
21. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone‐based interventions for smoking cessation. Cochrane Database Syst Rev. 2016(4).
22. Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R, et al. Computer‐based diabetes self‐management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013(3).
23. De Vries H. An integrated approach for understanding health behavior; the I-change model as an example. Psychol Behav Sci Int J. 2017;2(2):555-85.
24. Moreau M, Gagnon M-P, Boudreau F. Development of a fully automated, web-based, tailored intervention promoting regular physical activity among insufficiently active adults with type 2 diabetes: integrating the I-change model, self-determination theory, and motivational interviewing components. JMIR Res Protoc. 2015;4(1):e4099.
25. AlBurno H, Schneider F, de Vries H, Al Mohannadi D, Mercken L. Determinants of adherence to insulin and blood glucose monitoring among adolescents and young adults with type 1 diabetes in Qatar: a qualitative study. F1000Res. 2022;11(907):907.
26. Lavoie A, Dubé V. Web-Based Interventions to Promote Healthy Lifestyles for Older Adults: Scoping Review. Interact J Med Res. 2022;11(2):e37315.
27. Mohebbi B, Sabouri M, Tol A. Application of health education and promotion theory-based interventions on patients with cardiovascular disease: A systematic review. J Educ Health Promot. 2021;10.
2. Tekkesin İ, Çinier G, Hayıroğlu M, Soluk Özdemir Y, Yıldırımtürk Ö, İnan D, et al. Rationale and design of lifestyle intervention using mobile technology in patients with high cardiovascular risk: a pragmatic randomized clinical trial. Turk Kardiyol Dern Ars. 2020;48(2):149-57.
3. Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2019;140(11):e596-e646.
4. Visseren FL, Mach F, Smulders YM, Carballo D, Koskinas KC, Bäck M, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice: Developed by the Task Force for cardiovascular disease prevention in clinical practice with representatives of the European Society of Cardiology and 12 medical societies With the special contribution of the European Association of Preventive Cardiology (EAPC). Eur J Prev Cardiol. 2022;29(1):5-115.
5. Dall TM, Gallo PD, Chakrabarti R, West T, Semilla AP, Storm MV. An aging population and growing disease burden will require alarge and specialized health care workforce by 2025. Health Aff. 2013;32(11):2013-20.
6. Tundjungsari V, Sofro ASM, Yugaswara H, Putra ATD. Development of mobile health application for cardiovascular disease prevention. Int J Adv Comput Sci Appl. 2018;9(11).
7. Wang W, Zhang H, Lopez V, Wu VX, Poo DCC, Kowitlawakul Y. Improving awareness, knowledge and heart‐related lifestyle of coronary heart disease among working population through a mHealth programme: study protocol. J Adv Nurs. 2015;71(9):2200-7.
8. Ullrich G, Dönmez A, Mahabadi AA, Bäuerle A, Knuschke R, Paldán K, et al. Effect of visual presentation of atherosclerotic carotid plaque on adherence to secondary preventive therapy using mHealth technologies (PreventiPlaque app): Study protocol for a randomized controlled trial. Heliyon. 2023;9(3).
9. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439-54.
10. Poorhosseini H, Abbasi SH. The Tehran heart center. OUP; 2018.
11. Heshmati A, Moftian N, Rezaei-Hachesu P, Samad-Soltani T. Simple and efficient measurement of the user experience in health information systems: A persian version. Iran J Public Health. 2021.
12. Heshmati A MN, Rezaei-Hachesu P, Samad-Soltani T. User Experience Questionnaire (UEQ). updated 2018. Available from: https://www.ueq-online.org/.
13. Organization WH. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance. WHO, 2005 9241593830.
14. Vasheghani-Farahani A, Tahmasbi M, Asheri H, Ashraf H, Nedjat S, Kordi R. The Persian, last 7-day, long form of the International Physical Activity Questionnaire: translation and validation study. Asian J Sports Med. 2011;2(2):106.
15. Faul F, Erdfelder E, Lang A-G, Buchner A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175-91.
16. Patnode CD, Evans CV, Senger CA, Redmond N, Lin JS. Behavioral counseling to promote a healthful diet and physical activity for cardiovascular disease prevention in adults without known cardiovascular disease risk factors: updated evidence report and systematic review for the US Preventive Services Task Force. Jama. 2017;318(2):175-93.
17. Siopis G, Chey T, Allman‐Farinelli M. A systematic review and meta‐analysis of interventions for weight management using text messaging. J Hum Nutr Diet. 2015;28:1-15.
18. Liu S, Dunford SD, Leung YW, Brooks D, Thomas SG, Eysenbach G, et al. Reducing blood pressure with Internet-based interventions: a meta-analysis. Can J Cardiol. 2013;29(5):613-21.
19. Uhlig K, Patel K, Ip S, Kitsios GD, Balk EM. Self-measured blood pressure monitoring in the management of hypertension: a systematic review and meta-analysis. Ann Intern Med. 2013;159(3):185-94.
20. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports Med. 2014;44:671-86.
21. Whittaker R, McRobbie H, Bullen C, Rodgers A, Gu Y. Mobile phone‐based interventions for smoking cessation. Cochrane Database Syst Rev. 2016(4).
22. Pal K, Eastwood SV, Michie S, Farmer AJ, Barnard ML, Peacock R, et al. Computer‐based diabetes self‐management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013(3).
23. De Vries H. An integrated approach for understanding health behavior; the I-change model as an example. Psychol Behav Sci Int J. 2017;2(2):555-85.
24. Moreau M, Gagnon M-P, Boudreau F. Development of a fully automated, web-based, tailored intervention promoting regular physical activity among insufficiently active adults with type 2 diabetes: integrating the I-change model, self-determination theory, and motivational interviewing components. JMIR Res Protoc. 2015;4(1):e4099.
25. AlBurno H, Schneider F, de Vries H, Al Mohannadi D, Mercken L. Determinants of adherence to insulin and blood glucose monitoring among adolescents and young adults with type 1 diabetes in Qatar: a qualitative study. F1000Res. 2022;11(907):907.
26. Lavoie A, Dubé V. Web-Based Interventions to Promote Healthy Lifestyles for Older Adults: Scoping Review. Interact J Med Res. 2022;11(2):e37315.
27. Mohebbi B, Sabouri M, Tol A. Application of health education and promotion theory-based interventions on patients with cardiovascular disease: A systematic review. J Educ Health Promot. 2021;10.
| Files | ||
| Issue | Articles In Press | |
| Section | Articles | |
| Keywords | ||
| Atherosclerotic, Education, Mobile app, Cardiovascular disease, Prevention, Risk factor | ||
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How to Cite
1.
Aein A, solhi M, vashegani farahani A, alja Sem M, Alaeddini F, Janani L, Omidi N, Tagdisi MH. Development and Effectiveness of educational mobile application based on integrated change model for prevention of atherosclerotic cardiovascular disease risk factors: Protocol for a Randomized, Controlled Trial. J Tehran Heart Cent. 2024;.
