Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
SMART goal setting is a patient-led method that can help improve execution and facilitate behavioral changes. Functions such as diet tracking, interaction, and feedback in smartphone application may help enhance patient compliance. This study aims to explore the nutrition intervention measures of SMART goal setting combined with smartphone applications for daily self-management on the effect of improving diet quality of people with high blood pressure.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention group | Experimental | The intervention group will receive nutrition education on blood pressure management. The dietitian will work with the participants to set individual SMART goals. The participants will have access to diet tracking function, weekly reports and monthly virtual meeting with dietitian. |
|
| Control group | Active Comparator | The control group will receive nutrition education on blood pressure management. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Goal setting with mobile-based self-management tool | Behavioral | In addition to the nutrition education provided to both groups, the participants of the intervention group will work with the dietitian to make behavior-change goals based on SMART goal setting strategy. They will also have access to diet tracking function and weekly report on smart phone application. The dietitian will also meet with the participants virtually to track their progress and provide suggestions. |
| Measure | Description | Time Frame |
|---|---|---|
| Diet quality | Diet quality is measured by the DASH Score that includes eight dietary components to reflect the adherence to the DASH dietary pattern. | From enrollment to the end of treatment at 12 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Blood pressure | Blood pressure will be measured at baseline visit and the follow-up visit after the 12-week intervention. Blood pressure will be measured using calibrated electronic blood pressure monitor. | From enrollment to the end of treatment at 12 weeks |
| Body Weight |
| Measure | Description | Time Frame |
|---|---|---|
| Sleep Quality | Sleep quality will be measured by the Pittsburgh Sleep Quality Index | From enrollment to the end of treatment at 12 weeks |
| Depression | Depressive status will be measured by the Patient Health Questionnaire-9 |
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xuezhu Sun | Contact | +86 10 88396188 | hlm2020fw@163.com |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fuwai Hospital, National Center for Cardiovascular Diseases | Recruiting | Beijing | Beijing Municipality | 100037 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35819830 | Background | Kassavou A, Wang M, Mirzaei V, Shpendi S, Hasan R. The Association Between Smartphone App-Based Self-monitoring of Hypertension-Related Behaviors and Reductions in High Blood Pressure: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth. 2022 Jul 12;10(7):e34767. doi: 10.2196/34767. | |
| 31353783 | Background |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
|
| Nutrition education | Other | The control group will receive nutrition education about blood pressure management by the dietitian |
|
Body weight will be measured by the bioelectrical impedance analysis. |
| From enrollment to the end of treatment at 12 weeks |
| From enrollment to the end of treatment at 12 weeks |
| Anxiety | Anxiety status will be measured by the Generalized Anxiety Disorder-7. | From enrollment to the end of treatment at 12 weeks |
| 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. 2019 Oct;20(10):1465-1484. doi: 10.1111/obr.12903. Epub 2019 Jul 28. |
| 34123579 | Background | Limketkai BN, Mauldin K, Manitius N, Jalilian L, Salonen BR. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Curr Surg Rep. 2021;9(7):20. doi: 10.1007/s40137-021-00297-3. Epub 2021 Jun 8. |
| 32477026 | Background | White ND, Bautista V, Lenz T, Cosimano A. Using the SMART-EST Goals in Lifestyle Medicine Prescription. Am J Lifestyle Med. 2020 Feb 17;14(3):271-273. doi: 10.1177/1559827620905775. eCollection 2020 May-Jun. |
| 10028217 | Background | Wing RR, Jeffery RW. Benefits of recruiting participants with friends and increasing social support for weight loss and maintenance. J Consult Clin Psychol. 1999 Feb;67(1):132-8. doi: 10.1037//0022-006x.67.1.132. |
| 10431937 | Background | Boutelle KN, Kirschenbaum DS, Baker RC, Mitchell ME. How can obese weight controllers minimize weight gain during the high risk holiday season? By self-monitoring very consistently. Health Psychol. 1999 Jul;18(4):364-8. doi: 10.1037//0278-6133.18.4.364. |
| 18268173 | Background | Mellen PB, Gao SK, Vitolins MZ, Goff DC Jr. Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004. Arch Intern Med. 2008 Feb 11;168(3):308-14. doi: 10.1001/archinternmed.2007.119. |
| 24944055 | Background | Yu D, Zhang X, Xiang YB, Yang G, Li H, Gao YT, Zheng W, Shu XO. Adherence to dietary guidelines and mortality: a report from prospective cohort studies of 134,000 Chinese adults in urban Shanghai. Am J Clin Nutr. 2014 Aug;100(2):693-700. doi: 10.3945/ajcn.113.079194. Epub 2014 Jun 18. |
| 20645853 | Background | Savica V, Bellinghieri G, Kopple JD. The effect of nutrition on blood pressure. Annu Rev Nutr. 2010 Aug 21;30:365-401. doi: 10.1146/annurev-nutr-010510-103954. |
| 35706092 | Background | Wang A, Tian X, Zuo Y, Chen S, Zhang Y, Zhang X, Deng X, Xu Q, Wang P, Wu S, Zhou Y. Control of Blood Pressure and Risk of Cardiovascular Disease and Mortality in Elderly Chinese: A Real-World Prospective Cohort Study. Hypertension. 2022 Aug;79(8):1866-1875. doi: 10.1161/HYPERTENSIONAHA.122.19587. Epub 2022 Jun 15. |
| 12493255 | Background | Lewington S, Clarke R, Qizilbash N, Peto R, Collins R; Prospective Studies Collaboration. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002 Dec 14;360(9349):1903-13. doi: 10.1016/s0140-6736(02)11911-8. |
| 31865786 | Background | Fuchs FD, Whelton PK. High Blood Pressure and Cardiovascular Disease. Hypertension. 2020 Feb;75(2):285-292. doi: 10.1161/HYPERTENSIONAHA.119.14240. Epub 2019 Dec 23. |
| ID | Term |
|---|---|
| D006973 | Hypertension |
| ID | Term |
|---|---|
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
Not provided
Not provided
| ID | Term |
|---|---|
| D015596 | Nutrition Assessment |
| ID | Term |
|---|---|
| D003625 | Data Collection |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
| D017530 | Health Care Quality, Access, and Evaluation |
| D015991 | Epidemiologic Measurements |
| D011634 | Public Health |
| D004778 | Environment and Public Health |
Not provided
Not provided