Citation: Carlos Menéndez Villalva, Xose Luis Muiño López-Alvarez, Martín Menéndez Rodríguez, María José Modroño Freire, Olalla Quintairos Veloso, Lea Conde Guede, Sandra Vilchez Dosantos, Manuel Blanco Ramos. Blood Pressure Monitoring in Cardiovascular Disease[J]. AIMS Medical Science, 2017, 4(2): 164-191. doi: 10.3934/medsci.2017.2.164
[1] | MaríaVictorinaAguilarVilas, GabrielaRubalcava, AntonioBecerra, MaríaCarmenMartínezPara . Nutritional Status and Obesity Prevalence in People with Gender Dysphoria. AIMS Public Health, 2014, 1(3): 137-146. doi: 10.3934/publichealth.2014.3.137 |
[2] |
Dorota Zarnowiecki, Meaghan S Christian, James Dollman, Natalie Parletta, Charlotte E.L Evans, Janet E Cade .
Comparison of school day eating behaviours of 8–11 year old children from Adelaide, South Australia, and London, England . AIMS Public Health, 2018, 5(4): 394-410. doi: 10.3934/publichealth.2018.4.394 |
[3] | Clemens Drenowatz, Madison M. DeMello, Robin P. Shook, Gregory A. Hand, Stephanie Burgess, Steven N. Blair . The association between sedentary behaviors during weekdays and weekend with change in body composition in young adults. AIMS Public Health, 2016, 3(2): 375-388. doi: 10.3934/publichealth.2016.2.375 |
[4] | Jennifer L Lemacks, Laurie S Abbott, Cali Navarro, Stephanie McCoy, Tammy Greer, Sermin Aras, Michael B Madson, Jacqueline Reese-Smith, Chelsey Lawrick, June Gipson, Byron K Buck, Marcus Johnson . Passive recruitment reach of a lifestyle management program to address obesity in the deep south during the COVID-19 pandemic. AIMS Public Health, 2023, 10(1): 116-128. doi: 10.3934/publichealth.2023010 |
[5] | Carol E. O'Neil, Theresa A. Nicklas, Victor L. Fulgoni III . Nutrient Intake, Diet Quality, and Weight Measures in Breakfast Patterns Consumed by Children Compared with Breakfast Skippers: NHANES 2001-2008. AIMS Public Health, 2015, 2(3): 441-468. doi: 10.3934/publichealth.2015.3.441 |
[6] | Anne W Taylor, Zumin Shi, Eleonora Dal Grande, Creina Stockley . The Relationship between Alcohol Consumption and other Risk Factors Assessed Using An Ongoing Population-based Surveillance System. AIMS Public Health, 2016, 3(4): 985-1002. doi: 10.3934/publichealth.2016.4.985 |
[7] | Walid El Ansari, Khalid A Khalil, Derrick Ssewanyana, Christiane Stock . Behavioral risk factor clusters among university students at nine universities in Libya. AIMS Public Health, 2018, 5(3): 296-311. doi: 10.3934/publichealth.2018.3.296 |
[8] | Christopher A Birt . Food and Agriculture Policy in Europe. AIMS Public Health, 2016, 3(1): 131-140. doi: 10.3934/publichealth.2016.1.131 |
[9] | Shannon M Farley, Rachel Sacks, Rachel Dannefer, Michael Johns, Margaret Leggat, Sungwoo Lim, Kevin Konty, Cathy Nonas . Evaluation of the New York City Green Carts program. AIMS Public Health, 2015, 2(4): 906-918. doi: 10.3934/publichealth.2015.4.906 |
[10] | Amy M. Gayman, Jessica Fraser-Thomas, Jamie E. L. Spinney, Rachael C. Stone, Joseph Baker . Leisure-time Physical Activity and Sedentary Behaviour in Older People: The Influence of Sport Involvement on Behaviour Patterns in Later Life. AIMS Public Health, 2017, 4(2): 171-188. doi: 10.3934/publichealth.2017.2.171 |
According to the Center for Disease Control and Prevention (CDC) [1], approximately 34.6% of the adult population in Mississippi (MS) is considered obese. MS has the lowest rate of Fruit and Vegetable (FV) consumption and Physical Activity (PA) participation compared to other states [2]. Furthermore, several areas within Mississippi are considered as Health Professional Shortage Areas (HPSAs) and vulnerable populations areas [3,4]. Studies indicate that there are significant increases in the risk of chronic diseases, incidence of cancers, and mortality among medically underserved and ethnic minority populations [3,5].
Developing health and nutrition programs that target the HPSAs in MS could be helpful in identifying the challenges and barriers to improve the health and nutrition status of this population and may lead to improvement of the overall obesity and chronic disease management in MS. Understanding of dietary intake and physical activity behaviors, as well as the health needs of the vulnerable population from various ethnic groups, is necessary in order to provide effective nutrition and disease management in healthcare [6]. In this study, the researchers examined FV consumption, fat intake, and PA participation among a vulnerable population (the Medically Underserved Adults [MUA] in south Mississippi) as well as described the relationship between these variables. The researchers also described the association between FV consumption, fat intake, and PA participation with socio-demographic factors among this population.
The study was approved by the Institutional Review Board for Human Subjects of The University of Southern Mississippi. The study is part of a larger project currently being conducted at the Department of Nutrition and Food Systems that is aiming to improve obesity awareness and management practices in the primary care setting serving rural and economically disadvantaged populations in south Mississippi.
The researchers approached 182 adults at five federally funded healthcare centers (providing general and family practice health care) in south Mississippi with a mission to serve predominantly rural and underserved populations. The researcher collected information from the participants using the droid SURVEY tablet application (a survey software designed for data collection on tablet devices) [7]. The inclusion criteria were: a) at least 21 years of age and b) not pregnant or six months postpartum. The survey took between seven and ten minutes for completion. Full disclosure was provided to participants and none of the participants received any kind of compensation for their participation in the study. The researcher conducted an extensive pilot study to assess the reliability and acceptability of the measures, and modify the questions to elicit more relevant responses.
Initial questions (serving as screening questions) collected information regarding age, gender, height, weight, and gestation status. Based on responses to the initial questions, participants identified as meeting any of the exclusion criteria were immediately redirected to the end of the survey and no data were gathered from these individuals. Eligible participants completed the full survey.
The survey questionnaire included three sections in addition to the screener questions. The first section of the questionnaire measured daily FV consumption, fat intake, and PA participation. The National Cancer Institute (NCI) approach was used to assess the daily intake of FV and fat [8]. This method is specifically developed by NCI to measure dietary intake in a population, and it allows for the combined estimation of both the likelihood and amount of dietary intake in an easy and short format [8]. The approach includes six questions to assess estimated total FV consumption, which asks participants how many 100% juices they drank and number of fruits and vegetables consumed as well as how many servings they usually consumed. Respondents could report their consumption per day, per week, and per month. Responses were rescaled to estimate the frequency of daily fruit and vegetable intake. The NCI method also includes a single question “When you think about the foods you ate over the past 12 months, would you say your diet was high, medium, or low in fat?” to measure fat intake; responses included low, medium, or high.
PA participation was measured with the question: in the past week, how many days have you done a total of 30 minutes or more of physical activity that raised your breathing rate; answers ranged from zero to seven days. This question was developed based on the Dietary Guidelines for Americans [9]; these guidelines recommend that individuals limit leisure time activity and increase physical activity for health benefits. The general recommendation is five or more days per week of moderate-intensity PA at least 30 minutes per day [9].
The second section of the survey measured BMI perception and Stage of Change for weight loss [10]; as well as attitudes, perceived norms, and perceived behavioral control based on the Theory of Planned Behavior principles [11]. The analysis and findings of the second section are presented in another manuscript in preparation [12]. The third section requested demographic information regarding race/ethnicity, marital status, education level, and income level.
Statistical analysis was performed using SPSS (22.0) software. The socio-demographic characteristics of the participants were examined with descriptive statistics. Correlation analyses were used to examine the relationship between FV intake, PA participation, and fat intake. Bivariate correlations were used to assess the relationship between several socio-demographic variables and the FV consumption, fat intakes, and PA participation. The socio-demographic characteristics used in the analysis were: age, gender (female/ male), race (White/ Black/Hispanic), marital status (married/ cohabitating/ divorced/ separated/ single), education levels (“less than high school degree” through “four year college degree or higher”), and income levels (70, 000 or above). Statistically significant variables were those withp-values less than 0.05, or 95% confidence intervals.
One hundred and sixty one participants met eligibility criteria (48 men and 113 women). Twenty-one were excluded due to age or pre- or post-partum status. The sample population was quite homogenous, with the majority being female (70.2%), equally Non-Hispanic White (54.7%) and African American (42.2%), and less than 50 years of age (63.9%). Other characteristics of the sample are shown in (Table 1).
Characteristic | n | (%) | |
Age | 21–29 | 20 | 12.4 |
30–39 | 52 | 32.3 | |
40–49 | 31 | 19.3 | |
50–59 | 38 | 23.6 | |
60 or Over | 20 | 12.4 | |
Gender | Male | 48 | 29.8 |
Female | 113 | 70.2 | |
Race | White | 88 | 54.7 |
Black | 68 | 42.2 | |
Hispanic or Latino | 2 | 1.2 | |
I refuse to answer | 3 | 1.9 | |
Marital Status | Married | 82 | 50.9 |
Cohabitating | 5 | 3.1 | |
Divorced | 23 | 14.3 | |
Separated | 7 | 4.3 | |
Single | 39 | 24.2 | |
Education Level | Less than high school degree | 17 | 10.9 |
A high school degree | 52 | 32.3 | |
Some college, but not a college degree | 40 | 14.8 | |
A 2 year or vocational degree | 14 | 8.7 | |
A 4 year college degree or higher | 35 | 21.7 | |
Income Level | 19,999 | 65 | 40.4 |
29,999 | 38 | 23.6 | |
39,999 | 13 | 8.1 | |
49,999 | 9 | 5.6 | |
59,000 | 7 | 4.3 | |
69,00 | 1 | 0.6 | |
$70,000 to above | 4 | 2.4 | |
Currently unemployed | 9 | 5.6 |
Mean FV intake, fat intake, and PA participation are presented in Table 2. A majority of the participants (81.9%) reported consuming less than five servings of FV per day and only 14% of participants reported eating a low fat diet. Regarding the PA participation, about half of the participants (54%) reported exercising less than three times a week.
Variables | N | min | Max | mean | SD |
FV intake (servings/day) | 160 | 0 | 23.25 | 2.89 | 3.20 |
Fat intakea | 161 | 1 | 3 | 2.04 | 0.60 |
PA (times/week b) | 161 | 0 | 7 | 2.37 | 1.98 |
aFat intake measurement: 1 = low, 2 = medium, 3 = high. bPA refers to how many times participants engaged in at least 30 minutes of moderate intensity PA per week. |
Correlations, means, and standard deviations of all variables were calculated to explore associations among different variables. A correlation analysis between PA participation rates with FV and fat intakes, showed a significant positive relationship between PA rate and FV intake (r = 0.16, p = 0.05); and significant negative relationship between PA rate and fat intake (r = -0.21, p = 0.01). The correlation analysis showed no significant relationship between FV consumption and fat intake. The correlation between the socio-demographic variables, and the PA participation rates, FV consumption, and fat intakes indicated a significant positive association between PA participation rate and income level (r = 0.21 p = 0.01). The results also showed that PA participation was significantly higher among men (r = -0.16, p = 0.05), and there were no significant associations between PA participation rate and ethnicity, education, or marital status. There were also no significant associations between FV and fat intakes with gender, ethnicity, income, marital status, or education. The correlations are shown in Table 3.
Fat intake | PA | Gender | Income | |
r-value (p-value) | ||||
n | ||||
FV | 0.02 (0.81) | 0.16 (0.05) | 0.04 (0.67) | 0.01 (0.94) |
160 | 160 | 157 | 136 | |
Fat intake | -- | -0.21 (0.01) | -0.68 (0.39) | 0.01 (0.96) |
161 | 161 | 137 | ||
PA | -- | -- | -0.16 (0.04) | 0.22 (0.01) |
161 | 137 | |||
Note. Statistical significance p < 0.05. |
The present study examined a MUAs’ dietary intake and PA participation rates in cognition with socio-demographic characteristics. Our results indicated that FV intake and PA were relatively low and fat intake was high among underserved adults in south Mississippi. Only twenty percent of the participants reported consuming five servings of FV per day, and only five percent of the participants indicated exercising five times a week. The Dietary Guidelines for American recommends consuming five to six servings per day of fruits and vegetables, as well as 30 minutes of moderate to vigorous activity at least five times a week [9].
It is well documented that consumption of fruits and vegetables and regular physical activity prevent cardiovascular disease, diabetes, several cancers, depression, and obesity [13,14,15]. Furthermore, studies indicated that fruits and vegetables consumption and physical activity are two of the most important factors in disease prevention and health promotion [16,17]. According to our study, most of our population did not engage in these necessary and essential protective behaviors.
Our results also showed that self-reported PA is higher among men and increased with greater income levels. This finding is similar to other studies conducted [18,19]; however, these studies showed a positive association between PA and education [18] and negative association between PA and age [19]. Our study did not indicate any significant association among the various levels of education and age with PA. Our sample was fairly distributed among education and age levels across the population. The gender differences in the PA participation can be due to the biological and the psychological variances between men and woman; men have a more positive attitude and interest toward exercise than women, particularly, moderate and vigorous intensity physical activities [17,18,19].
According to our study, higher PA rates were associated with increased FV and decreased fat intakes among all groups, improving modifiable health practices has the potential to lead to improvements in other health practices and could lead to an overall healthy lifestyle among MUA [20]. Previous studies indicated similar associations between individuals’ dietary intakes and PA rates where a low intake of FV was associated with low rates of PA participation [20,21]. This association may be holistic in nature in that active individuals may also have healthy eating behaviors [21] or individuals with sedentary life styles may consume more fast food items and exhibit more unhealthy behaviors [13]. There are many physiological and biological effects related to PA participation, in addition to the appetite regulation, weight control, and reduced risk of cardiovascular diseases; some studies showed that exercise may be a facilitator for other behavior changes [22], such as healthy eating habits and an increase in FV consumption. This influence may be due to the positive regulation of exercise motivation, commitment, efficacy, and confidence among individuals. Some motivational models such as the hierarchical model of motivation (the situational, contextual, and global level) and achievement goal theory have been used in previous studies to explain the influence of exercise on other individual health behaviors [23]. These models suggest that if an individual is self-determined toward PA, this individual will likely be engaged in other behaviors, such as healthy eating habits, which are relevant to his/her health goals [24].
The study suggests that PA participation does not occur in isolation and is correlated with greater FV consumption among this population. Other studies have shown a positive cross-behaviors association between diet intake and PA participation; these studies suggested developing an intervention strategy in any one behavior to facilitate multiple behavior change among individuals [25,26]. Lippke, Nigg, and Maddock (2012) examined the relationship between nutrition, PA, and smoking behaviors among 3, 519 individuals [26]. Findings from this study showed a significant relationship between healthy nutrition behaviors and PA participation. The study concluded that an improvement in one behavior in an individual can help to improve the other behavior [26].
Thus, an intervention could initially target any of the three behaviors (FV consumption, fat intake, or PA) and create a synergetic effect to reduce the risk of chronic disease and obesity among the MUA. Furthermore, our results showed that there were no significant differences in PA rates and fat or FV intake between African Americans or Caucasians who predominantly consisted of individuals with lower income levels. Therefore, improving diet and PA habits in underserved populations may require an intervention specific to individuals with limited resources but can be broad enough to address multiple populations in the same geographic region.
Our study has some limitations. The participants were recruited from a healthcare setting, and some of the participants may suffer from chronic diseases; however, we did not adjust for these conditions since this information was not pertinent to the goals of this study. In addition, the study sample was not a random sample from all the population of south MS, but a real life sample of participants who utilized healthcare services; therefore, the results cannot be generalized to other populations. The relatively low number of men in our population may also be considered another limitation. Our method for exploring physical activity may need to be modified to determine the proportion of other types of physical activity such as aerobic and strength activities [27]. Additionally, a longer procedure might be more valid than the method used in our study to accurately assess individual’s fat intake [28] but may be inhibitive in a primary care setting.
This study focused on dietary and physical activity habits of MUAs in south MS. The results indicated that the individuals residing in MUAs have undesirable health habits with low FV consumptions, high fat intake, and low PA participation rate. A promotion of healthier living habits are essential to modify the eating habits and PA behaviors in these individuals. Research among this population should explore the nutrition management services available in the primary care setting serving these individuals and identify the willingness of and availability of resources to primary care providers to address these issues. Strategies may need to be identified to increase provider readiness for lifestyle management in and access to qualified health professionals, such as Registered Dietitians, to provide patient centered care.
It is important to examine the health and nutrition issues and recognize areas in need of health intervention among the vulnerable community. This study demonstrates that despite efforts to improve lifestyle habits across the U.S., dietary intake and PA participation are still poor among medically underserved communities in south MS. The results suggest that there is a need to improve the health and nutrition services among adults in these areas. An obesity and chronic disease management intervention may be developed to address the needs of underserved populations in south Mississippi despite racial, socioeconomic or gender variability of the population targeted. Further research is needed to identify strategies to improve health habits of underserved populations. Additionally, there is a need to engage underserved males in preventive care and chronic disease management.
All authors declare no conflicts of interest in this paper.
[1] |
Bonafini S, Fava C (2015) Home blood pressure measurements: Advantages and disadvantages compared to office and ambulatory monitoring. Blood Press 24: 325–332. doi: 10.3109/08037051.2015.1070599
![]() |
[2] |
Wolak T, Wilk L, Paran E, et al. (2013) Is it possible to shorten ambulatory blood pressure monitoring? J Clin Hypertens (Greenwich) 15: 570–574. doi: 10.1111/jch.12123
![]() |
[3] |
O'Brien E, Parati G, Stergiou G, et al. (2013) European Society of Hypertension position paper on ambulatory blood pressure monitoring. J Hypertens 31: 1731–1768. doi: 10.1097/HJH.0b013e328363e964
![]() |
[4] | Norma M Kaplan, George Tomas, Marc Pohl, et al. (2016) Blood pressure measurement in the diagnosis and management of hypertension in adults. |
[5] | Krause T, Lovibond K, Caulfield M, et al. (2011) Management of hypertension: summary of NICE guidance. BMJ (Clinical Res) 343: 1–6. |
[6] | Siu AL, U.S. Preventive Services Task Force (2015) Screening for high blood pressure in adults: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 163: 778–786. |
[7] |
Chobanian AV, Bakris GL, Black HR, et al. (2003) Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 42: 1206–1252. doi: 10.1161/01.HYP.0000107251.49515.c2
![]() |
[8] | Kaplan NM, Townsend RR (2015) Ambulatory and home blood pressure monitoring and white coat hypertension in adults. |
[9] | Hermida RC, Ayala DE, Portaluppi F (2007) Circadian variation of blood pressure: The basis for the chronotherapy of hypertension. Advance Drug Delivery Rev 9: 904–922. |
[10] |
Andersen MJ, Khawandi W, Agarwal R (2005) Home blood pressure monitoring in CKD. Am J Kidney Dis 45: 994–1001. doi: 10.1053/j.ajkd.2005.02.015
![]() |
[11] |
Pickering TG, Miller NH, Ogedegbe G, et al. (2008) Call to action on use and reimbursement for home blood pressure monitoring: a joint scientific statement from the American Heart Association, American Society Of Hypertension, and Preventive Cardiovascular Nurses Association. Hypertension 52: 10–29. doi: 10.1161/HYPERTENSIONAHA.107.189010
![]() |
[12] |
Parati G, Pickering TG (2009) Home blood-pressure monitoring: US and European consensus. Lancet 373: 876–878. doi: 10.1016/S0140-6736(09)60526-2
![]() |
[13] |
Niiranen TJ, Hänninen MR, Johansson J, et al. (2010) Home-measured blood pressure is a stronger predictor of cardiovascular risk than office blood pressure: The finn-home study. Hypertension 55: 1346–1351. doi: 10.1161/HYPERTENSIONAHA.109.149336
![]() |
[14] |
Verberk WJ, Kroon AA, Kessels AGH, et al. (2005) Home blood pressure measurement: A systematic review. J Am College Cardiology 46: 743–751. doi: 10.1016/j.jacc.2005.05.058
![]() |
[15] |
Myers MG (2010) A proposed algorithm for diagnosing hypertension using automated office blood pressure measurement. J Hypertension 28: 703–708. doi: 10.1097/HJH.0b013e328335d091
![]() |
[16] |
Powers BJ, Olsen MK, Smith VA, et al. (2011) Measuring blood pressure for decision making and quality reporting: Where and how many measures? Ann Intern Med 154: 781–788. doi: 10.7326/0003-4819-154-12-201106210-00005
![]() |
[17] |
Mesas A E, Leon-muñoz L, Rodriguez-artalejo F, et al. (2011) The effect of coffee on blood pressure and cardiovascular disease among hypertensive individuals: Meta-analysis. J Clinical Hypertension 13: A42. doi: 10.1111/j.1751-7176.2010.00379.x
![]() |
[18] |
Other U (2001) Blood pressure measurement. BMJ 322: 1043–1047. doi: 10.1136/bmj.322.7293.1043
![]() |
[19] | Pickering TG, Hall JE, Appel LJ, et al.(2005) Recommendations for blood pressure measurement in humans: an AHA scientific statement from the Council on high blood pressure research professional and public education subcommittee.J Cinical Hypertens 7: 102–109. |
[20] |
Mancia G, Fagard R, Narkiewicz K, et al. (2013) ESH/ESC Guidelines for the management of arterial hypertension. J Hypertens 31: 1281–1357. doi: 10.1097/01.hjh.0000431740.32696.cc
![]() |
[21] |
Mancia G, De Backer G, Dominiczak A, et al. (2007) ESH-ESC Practice Guidelines for the Management of Arterial Hypertension: ESH-ESC Task Force on the Management of Arterial Hypertension. J Hypertens 25: 1751–1762. doi: 10.1097/HJH.0b013e3282f0580f
![]() |
[22] |
O'Brien (2005) Practice guidelines of the European Society of Hypertension for clinic, ambulatory and self blood pressure measurement. J Hypertens 23: 697–701. doi: 10.1097/01.hjh.0000163132.84890.c4
![]() |
[23] | U.S. Preventive Services Task Force (2007) Screening for high blood pressure: U.S. Preventive Services Task Force reaffirmation recommendation statement. Ann Intern Med 147(11):783–786. |
[24] |
Franklin SS, Thijs L, Hansen TW, et al. (2013) White-coat hypertension new insights from recent studies. Hypertension 62: 982–987. doi: 10.1161/HYPERTENSIONAHA.113.01275
![]() |
[25] | NICE (2011) Hypertension in adults: diagnosis and management. NICE Guidel :1–38. |
[26] | James PA, Oparil S, Carter BL, et al. (2013) Evidence-Based Guideline for the Management of High Blood Pressure in Adults. Jama 1097: 1–14. |
[27] | Coca A, Bertomeu V, Dalfó A, et al. (2007)Blood pressure self measurement: Spanish consensus document. Nefrol Publicación La Soc Española Nefrol 27: 139–153 |
[28] |
Bangalore S, Qin J, Sloan S, et al. (2010) What is the optimal blood pressure in patients after acute coronary syndromes? Circulation 122: 2142–2151. doi: 10.1161/CIRCULATIONAHA.109.905687
![]() |
[29] |
Vokó Z, Bots ML, Hofman A, et al. (1999) shaped relation between blood pressure and stroke in treated hypertensives. Hypertension 34: 1181–1185. doi: 10.1161/01.HYP.34.6.1181
![]() |
[30] |
Pahor M, Shorr RI, Cushman WC, et al. (1999) The role of diastolic blood pressure when treating isolated systolic hypertension. Arch Intern Med 159: 2004–2009. doi: 10.1001/archinte.159.17.2004
![]() |
[31] |
Pickering TG (1988) The influence of daily activity on ambulatory blood pressure. Am Hear Jan 116: 1141–1146. doi: 10.1016/0002-8703(88)90178-0
![]() |
[32] |
Agarwal R, Andersen M (2006) Prognostic importance of ambulatory blood pressure recordings in patients with chronic kidney disease. Kidney Int 69: 1175–1180. doi: 10.1038/sj.ki.5000247
![]() |
[33] |
Asayama K, Ohkubo T, Kikuya M, et al (2004) Prediction of stroke by self-measurement of blood pressure at home versus casual screening blood pressure measurement in relation to the Joint National Committee 7 classification: The Ohasama study. Stroke 35: 2356–2361. doi: 10.1161/01.STR.0000141679.42349.9f
![]() |
[34] |
Agarwal R, Bills JE, Hecht TJW, et al. (2011) Role of home blood pressure monitoring in overcoming therapeutic inertia and improving hypertension control. Hypertension 57: 29–38. doi: 10.1161/HYPERTENSIONAHA.110.160911
![]() |
[35] | Uhlig K, Patel K, Ip S, et al. (2013) Self-Measured Blood Pressure Monitoring in the Management of Hypertension. A systematic review and meta-analysis. Improve Patient Care 159. |
[36] |
Cappuccio FP, Kerry SM, Forbes L, et al. (2004) Blood pressure control by home monitoring: meta-analysis of randomised trials. Br Med J 329: 145. doi: 10.1136/bmj.38121.684410.AE
![]() |
[37] |
Powers BJ, Adams MB, Svetkey LP, et al. (2009) Two Self-management Interventions to Improve Hypertension Control. Ann Intern Med 151: 687–696. doi: 10.7326/0000605-200911170-00148
![]() |
[38] | McManus RJ, Mant J, Haque MS, et al.(2014) Effect of Self-monitoring and Medication Self-titration on Systolic Blood Pressure in Hypertensive Patients at High Risk of Cardiovascular Disease. Jama 312: 799. |
[39] |
McManus RJ, Mant J, Bray EP, et al (2010) Telemonitoring and self-management in the control of hypertension (TASMINH2): A randomised controlled trial. Lancet 376: 163–172. doi: 10.1016/S0140-6736(10)60964-6
![]() |
[40] | Yi SS, Tabaei BP, Angell SY, et al. (2015) Self-blood pressure monitoring in an urban, ethnically diverse population: a randomized clinical trial utilizing the electronic health record. Circulation 138–145. |
[41] |
Parati G, Stergiou GS, Asmar R, et al. (2010) European Society of Hypertension practice guidelines for home blood pressure monitoring. J Hum Hypertens 24: 779–785. doi: 10.1038/jhh.2010.54
![]() |
[42] | Dasgupta K, Quinn RR, Zarnke KB, et al.(2014) The 2014 Canadian hypertension education program recommendations for blood pressure measurement, diagnosis, assessment of risk, prevention, and treatment of hypertension. Can J Cardiol 30: 485–501. |
[43] |
Daskalopoulou SS, Rabi DM, Zarnke KB, et al. (2015) The 2015 Canadian Hypertension Education Program Recommendations for Blood Pressure Measurement, Diagnosis, Assessment of Risk, Prevention, and Treatment of Hypertension. Can J Cardiol 31: 549–568. doi: 10.1016/j.cjca.2015.02.016
![]() |
[44] | Avenue G (2011) Optimal Schedule for Home Blood Pressure Measurement. Hypertension 1081–1086. |
[45] | Lauer RM, Clarke WR (1989) Childhood risk factors for high adult blood pressure: the Muscatine Study. Pediatrics 84: 633–641. |
[46] |
Sun SS, Grave GD, Siervogel RM, et al. (2007) Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life. Pediatrics 119: 237–246. doi: 10.1542/peds.2006-2543
![]() |
[47] | Blumenthal S, Epps R, Heavenrich R (1987) Report of the Second Task Force on Blood Pressure Control in Children. Pediatrics 79: 797–820. |
[48] |
The Fourth Report on the Diagnosis, Evaluation and T of HBP in C and A (2004) National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. Pediatrics 114: 555–576. doi: 10.1542/peds.114.2.S2.555
![]() |
[49] | Rosner B, Prineas RJ, Loggie JMH, et al. (1993) Blood pressure nomograms for children and adolescents, by height, sex, and age, in the United States. J Pediatr 123(6): 871–886. |
[50] | Joseph T Flynn (2017) Ambulatory blood pressure monitoring in children. |
[51] | Williams CL, Daniels SR, Robinson TN, et al. (2002) Cardiovascular health in childhood. A statement for health professionals from the committee on atherosclerosis, hypertension, and obesity in the young of the council on cardiovascular disease in the young, Americam Heart Association. Circulation 106: 143–160. |
[52] |
Flynn JT (2011) Ambulatory blood pressure monitoring in children: imperfect yet essential. Pediatr Nephrol 26: 2089–2094. doi: 10.1007/s00467-011-1984-9
![]() |
[53] |
Sorof JM, Poffenbarger T, Franco K, et al. (2001) Evaluation of white coat hypertension in children: Importance of the definitions of normal ambulatory blood pressure and the severity of casual hypertension. Am J Hypertens 14: 855–860. doi: 10.1016/S0895-7061(01)02180-X
![]() |
[54] | Lande MB, Meagher CC, Fisher SG, et al. (2008) Left ventricular mass index in children with white coat hypertension. J Pediatr153: 50–54. |
[55] | Seeman T, Palyzová D, Dušek J, et al. (2017) Reduced nocturnal blood pressure dip and sustained nighttime hypertension are specific markers of secondary hypertension. J Pediatr 147: 366–371. |
[56] | Flynn J, Daniels S, Hayman L, et al.(2014) Update: Ambulatory blood pressure monitoring in children and adolescents: A scientific statement from the American Heart Association. Hypertension 63: 1116–1135. |
[57] |
Urbina E, Alpert B, Flynn J, et al. (2008) Ambulatory Blood Pressure Monitoring in Children and Adolescents: Recommendations for Standard Assessment: A Scientific Statement From the American Heart Association Atherosclerosis, Hypertension, and Obesity in Youth Committee of the Council on Cardiovas. Hypertension 52: 433–451. doi: 10.1161/HYPERTENSIONAHA.108.190329
![]() |
[58] |
Aronow WS, Fleg JL, Pepine CJ, et al. (2011) Expert Consensus Document ACCF/AHA 2011 Expert Consensus Document on Hypertension in the Elderly. J Am College Cardiology 57: 2037–2114. doi: 10.1016/j.jacc.2011.01.008
![]() |
[59] |
Ishikawa J, Ishikawa Y, Edmondson D, et al. (2011) Age and the difference between awake ambulatory blood pressure and office blood pressure: a meta-analysis. Blood Press Monit 16: 159–167. doi: 10.1097/MBP.0b013e328346d603
![]() |
[60] | Stergiou GS, Ntineri A, Kollias A (2017) Changing relationship among office, ambulatory, and home blood pressure with increasing age: A neglected issue. Hypertension 64: 931–932. |
[61] | US Preventive Services Task Force (2017) Final Recommendation Statement: High Blood Pressure in Adults. |
[62] |
Weber MA, Schiffrin EL, White WB, et al. (2014) Clinical Practice Guidelines for the Management of Hypertension in the Community. J Clin Hypertens 16: 14–26. doi: 10.1111/jch.12237
![]() |
[63] |
Bangalore S, Messerli FH, Wun CC, et al. (2010) J-curve revisited: An analysis of blood pressure and cardiovascular events in the Treating to New Targets (TNT) Trial. Eur Heart J 31: 2897–2908. doi: 10.1093/eurheartj/ehq328
![]() |
[64] |
Maselli M, Giantin V, Franchin A, et al. (2014) Detection of blood pressure increments in active elderly individuals: the role of ambulatory blood pressure monitoring. Nutr Metab Cardiovasc Dis 24: 914–920. doi: 10.1016/j.numecd.2014.01.003
![]() |
[65] |
Angeli F, Reboldi G, Verdecchia P (2010) Masked hypertension: Evaluation, prognosis, and treatment. Am J Hypertens 23: 941–948. doi: 10.1038/ajh.2010.112
![]() |
[66] |
Cacciolati C, Hanon O, Alpérovitch A, et al. (2011) Masked hypertension in the elderly: cross-sectional analysis of a population-based sample. Am J Hypertens 24: 674–680. doi: 10.1038/ajh.2011.23
![]() |
[67] |
Verberk WWJ, Omboni S, Kollias A, et al. (2016) Screening for atrial fibrillation with automated blood pressure measurement: Research evidence and practice recommendations. Int J Cardiol 203: 465–473. doi: 10.1016/j.ijcard.2015.10.182
![]() |
[68] | Calhoun D A, Jones D, Textor S, et al. (2008) Resistant hypertension: diagnosis, evaluation, and treatment: a scientific statement from the American Heart Association Professional Education Committee of the Council for High Blood Pressure Research. Hypertension 117: 1403–1419. |
[69] | De la Sierra A, Segura J, Banegas JR, et al.(2011) Clinical features of 8295 patients with resistant hypertension classified on the basis of ambulatory blood pressure monitoring. Hypertension 57: 898–902. |
[70] |
Jiménez Navarro MF (2016) Comentarios a la guía ESC 2016 sobre prevención de la enfermedad cardiovascular en la práctica clínica. Rev Española Cardiol 69: 894–899. doi: 10.1016/j.recesp.2016.08.009
![]() |
[71] | Pickering TG (1988) Blood pressure monitoring outside the office for the evaluation of patients with resistant hypertension. Hypertension 11: II96-100. |
[72] | Lazaridis AA, Sarafidis PA, Ruilope LM (2015) Ambulatory Blood Pressure Monitoring in the Diagnosis, Prognosis, and Management of Resistant Hypertension: Still a Matter of our Resistance? Curr Hypertens Rep 17. |
[73] |
Brown MA, Buddle ML, Martin A (2001) Is resistant hypertension really resistant? Am J Hypertens 14: 1263–1269. doi: 10.1016/S0895-7061(01)02193-8
![]() |
[74] | Ríos M, Domínguez-Sardiña M, Ayala D, et al. (2013) Prevalence and clinical characteristics of isolated-office and true resistant hypertension determined by ambulatory blood pressure monitoring. Chronobiol Int 30. |
[75] |
Cardoso CRL, Salles GF (2016) Prognostic Importance of Ambulatory Blood Pressure Monitoring in Resistant Hypertension: Is It All that Matters? Curr Hypertens Rep 18: 85. doi: 10.1007/s11906-016-0693-y
![]() |
[76] |
Salles GF, Cardoso CL, Muxfeldt ES (2008) Prognostic influence of office and ambulatory blood pressures in resistant hypertension. Arch Intern Med 168: 2340–2346. doi: 10.1001/archinte.168.21.2340
![]() |
[77] | Ayala DE, Hermida RC, Mojón A, et al. (2012) Cardiovascular Risk of Resistant Hypertension: Dependence on Treatment-Time Regimen of Blood Pressure–Lowering Medications. Chronobiol Int 528: 1–13. |
[78] | Calhoun DA, Raymond MD, Townsens MD (2016) Treatment of resistant hypertension. |
[79] |
Doroszko A, Janus A, Szahidewicz-Krupska E, et al. (2016) Resistant hypertension. Adv Clin Exp Med 25: 173–183. doi: 10.17219/acem/58998
![]() |
[80] | Muxfeldt E, Bloch K, Nogueira A, et al. (2003) Twenty-four hour ambulatory blood pressure monitoring pattern of resistant hypertension. Blood Press Monit 8: 181–185. |
[81] |
Muxfeldt ES, Salles GF (2013) How to use ambulatory blood pressure monitoring in resistant hypertension. Hypertens Res 36: 385–389. doi: 10.1038/hr.2013.17
![]() |
[82] |
Williams B, Macdonald TM, Morant S, et al. (2015) Spironolactone versus placebo, bisoprolol, and doxazosin to determine the optimal treatment for drug-resistant hypertension (Pathway-2): A randomised, double-blind, crossover trial. Lancet 386: 2059–2068. doi: 10.1016/S0140-6736(15)00257-3
![]() |
[83] |
Dudenbostel T, Siddiqui M, Oparil S, et al. (2016) Refractory hypertension: A novel phenotype of antihypertensive treatment failure. Hypertension 67: 1085–1092. doi: 10.1161/HYPERTENSIONAHA.116.06587
![]() |
[84] |
Hermida RC, Smolensky MH, Ayala DE, et al. (2013) Recomendaciones 2013 para el uso de la monitorización ambulatoria de la presión arterial para el diagnóstico de hipertensión en adultos, valoración de riesgo cardiovascular y obtención de objetivos terapéuticos (resumen). Clínica e Investig en Arterioscler 25: 74–82. doi: 10.1016/j.arteri.2013.03.002
![]() |
[85] |
Sheikh S, Sinha A, Agarwal R (2011) Home Blood Pressure Monitoring: How Good a Predictor of Long-Term Risk? Curr Hypertens Rep 13: 192–199. doi: 10.1007/s11906-011-0193-z
![]() |
[86] | Hermida RC, Moyá A, Ayala DE (2015) Monitorización ambulatoria de la presión arterial en diabetes para valoraci??n y control de riesgo vascular. Endocrinologiay Nutricion 62: 400–410. |
[87] |
Mancia G, Verdecchia P (2015) Clinical Value of Ambulatory Blood Pressure: Evidence and Limits. Circ Res 116: 1034–1045. doi: 10.1161/CIRCRESAHA.116.303755
![]() |
[88] | Leitão CB, Canani LH, Silveiro SP, et al. (2007) Ambulatory blood pressure monitoring and type 2 diabetes mellitus. Arq Bras Cardiol 89: 315–321, 347–354 |
[89] |
Care D (2016) Standards of Medical Care in Diabetes : Summary of Revisions. Diabetes Care 39: S4–5. doi: 10.2337/dc16-S003
![]() |
[90] |
Coca A, Camafort M, Doménech M, et al. (2013) Ambulatory blood pressure in stroke and cognitive dysfunction. Curr Hypertens Rep 15: 150–159. doi: 10.1007/s11906-013-0346-3
![]() |
[91] |
Castilla-Guerra L, Fernández-Moreno M del C, Espino-Montoro A, et al. (2009) Ambulatory blood pressure monitoring in stroke survivors: Do we really control our patients? Eur J Intern Med 20: 760–763. doi: 10.1016/j.ejim.2009.09.004
![]() |
[92] |
Castilla-Guerra L, Fernandez-Moreno (2016) Chronic Management of Hypertension after Stroke: The Role of Ambulatory Blood Pressure Monitoring. J stroke 18: 31–37. doi: 10.5853/jos.2015.01102
![]() |
[93] |
Agarwal R (2009) Home and ambulatory blood pressure monitoring in chronic kidney disease. Curr Opin Nephrol Hypertens 18: 507–512. doi: 10.1097/MNH.0b013e3283319b9d
![]() |
[94] |
Agarwal R, Peixoto AJ, Santos SFF, et al. (2009) Out-of-office blood pressure monitoring in chronic kidney disease. Blood Press Monit 14: 2–11. doi: 10.1097/MBP.0b013e3283262f58
![]() |
[95] | Parati G, Ochoa JE, Bilo G, et al.(2016) Hypertension in chronic kidney disease part 1: Out-of-office blood pressure monitoring: Methods, thresholds, and patterns. Hypertension 67: 1093–1101. |
[96] |
Mehta R, Drawz PE (2011) Is nocturnal blood pressure reduction the secret to reducing the rate of progression of hypertensive chronic kidney disease? Curr Hypertens Rep 13: 378–385. doi: 10.1007/s11906-011-0217-8
![]() |
[97] |
Verdecchia P (2000) Prognostic value of ambulatory blood pressure : current evidence and clinical implications. Hypertension 35: 844–851. doi: 10.1161/01.HYP.35.3.844
![]() |
[98] | O'Brien E, Sheridan J, O'Malley K (1988) Dippers and Non-dippers. Lancet 332: 397. |
[99] |
Kario K, Pickering TG, Umeda Y, et al. (2003) Morning surge in blood pressure as a predictor of silent and clinical cerebrovascular disease in elderly hypertensives: A prospective study. Circulation 107: 1401–1406. doi: 10.1161/01.CIR.0000056521.67546.AA
![]() |
[100] | Muller JE, Abela GS, Nesto RW, et al. (1994)Triggers, acute risk factors and vulnerable plaques: The lexicon of a new frontier. J Am College Cardiology 23: 809–813. |
[101] |
Li Y, Thijs L, Hansen TW, et al. (2010) Prognostic value of the morning blood pressure surge in 5645 subjects from 8 populations. Hypertension 55: 1040–1048. doi: 10.1161/HYPERTENSIONAHA.109.137273
![]() |
[102] |
Neutel JM, Schnaper H, Cheung DG, et al. (1990) Antihypertensive effects of β-blockers administered once daily: 24-hour measurements. Am Heart J 120: 166–171. doi: 10.1016/0002-8703(90)90174-V
![]() |
[103] |
Meredith PA, Donnelly R, Elliott HL, et al. (1990) Prediction of the antihypertensive response to enalapril. J Hypertens 8: 1085–1090. doi: 10.1097/00004872-199012000-00003
![]() |
[104] |
Hermida RC, Calvo C, Ayala DE, et al. (2005) Treatment of non-dipper hypertension with bedtime administration of valsartan. J Hypertens 23: 1913–1922. doi: 10.1097/01.hjh.0000182522.21569.c5
![]() |
[105] |
Kikuya M, Ohkubo T, Asayama K, et al. (2005) Ambulatory blood pressure and 10-year risk of cardiovascular and noncardiovascular mortality: The Ohasama study. Hypertension 45: 240–245. doi: 10.1161/01.HYP.0000152079.04553.2c
![]() |
[106] |
Ben-Dov IZ, Kark JD, Ben-Ishay D, et al. (2007) Predictors of All-Cause Mortality in Clinical Ambulatory Monitoring. Hypertension 49: 1235–1241. doi: 10.1161/HYPERTENSIONAHA.107.087262
![]() |
[107] | Boggia J, Li Y, Thijs L, et al.(2007) Prognostic accuracy of day versus night ambulatory blood pressure: a cohort study. Lancet 370: 1219–1229. |
[108] |
Fagard RH, Celis H, Thijs L, et al. (2008) Daytime and nighttime blood pressure as predictors of death and cause-specific cardiovascular events in hypertension. Hypertension 51: 55–61. doi: 10.1161/HYPERTENSIONAHA.107.100727
![]() |
[109] |
Fan H-Q, Li Y, Thijs L, et al. (2010) Prognostic value of isolated nocturnal hypertension on ambulatory measurement in 8711 individuals from 10 populations. J Hypertens 28: 2036–2045. doi: 10.1097/HJH.0b013e32833b49fe
![]() |
[110] |
Hermida RC, Ayala DE, Mojón A, et al. (2011) Decreasing sleep-time blood pressure determined by ambulatory monitoring reduces cardiovascular risk. J Am Coll Cardiol 58: 1165–1173. doi: 10.1016/j.jacc.2011.04.043
![]() |
[111] | Hermida RC, Ayala DE, Mojón A, et al. (2010) Influence of circadian time of hypertension treatment on cardiovascular risk:results of the MAPEC study. Chronob 278: 1629–1651. |
[112] |
Hermida RC, Ayala DE, Mojón A, et al. (2011) Influence of time of day of blood pressure-lowering treatment on cardiovascular risk in hypertensive patients with type 2 diabetes. Diabetes Care 34: 1270–1276. doi: 10.2337/dc11-0297
![]() |
[113] |
Hermida RC, Ayala DE, Mojon A, et al. (2011) Bedtime Dosing of Antihypertensive Medications Reduces Cardiovascular Risk in CKD. J Am Soc Nephrol 22: 2313–2321. doi: 10.1681/ASN.2011040361
![]() |
[114] | Pogue V, Rahman M, Lipkowitz M, et al. (2008) Disparate Estimates of Hypertension Control From Ambulatory and Clinic Blood Pressure Measurements in Hypertensive Kidney Disease. Hypertension 53. |
[115] | Hermida RC (2007)Ambulatory blood pressure monitoring in the prediction of cardiovascular events and effects of chronotherapy: rationale and design of the MAPEC study. Chronobiol Int 24: 749–775. |
[116] | Minutolo R, Gabbai FB, Borrelli S, et al.(2007) Changing the Timing of Antihypertensive Therapy to Reduce Nocturnal Blood Pressure in CKD: An 8-Week Uncontrolled Trial. Am J Kidney Dis 50: 908–917. |
[117] |
Hermida RC, Ayala DE, Fernández JR, et al. (2008) Chronotherapy improves blood pressure control and reverts the nondipper pattern in patients with resistant hypertension. Hypertension 51: 69–76. doi: 10.1161/HYPERTENSIONAHA.107.096933
![]() |
[118] |
Carter BL, Chrischilles EA, Rosenthal G, et al. (2014) Efficacy and Safety of Nighttime Dosing of Antihypertensives: Review of the Literature and Design of a Pragmatic Clinical Trial. J Clin Hypertens 16: 115–121. doi: 10.1111/jch.12238
![]() |
[119] |
Ohkubo T, Imai Y, Tsuji I, et al. (1997) Prediction of mortality by ambulatory blood pressure monitoring versus screening blood pressure measurements: a pilot study in Ohasama. J Hypertens 15: 357–364. doi: 10.1097/00004872-199715040-00006
![]() |
[120] |
Guidelines JCS (2012) Guidelines for the Clinical Use of 24 Hour Ambulatory Blood Pressure Monitoring (ABPM) (JCS 2010). Circ J 76: 508–519. doi: 10.1253/circj.CJ-88-0020
![]() |
[121] | Verdecchia P, Angeli F, Mazzotta G, et al. (2012) Day-night dip and early-morning surge in blood pressure in hypertension: Prognostic implications. Hypertension :34–42. |
[122] | Glynn LG, Murphy AW, Smith SM, et al. (2010) Interventions used to improve control of blood pressure in patients with hypertension. The Cochrane. |
[123] |
Santschi V, Chiolero A, Colosimo AL, et al. (2014) Improving Blood Pressure Control Through Pharmacist Interventions: A Meta-Analysis of Randomized Controlled Trials. J Am Heart Assoc 3: e000718. doi: 10.1161/JAHA.113.000718
![]() |
[124] |
Floras JS (2007) Ambulatory blood pressure: facilitating individualized assessment of cardiovascular risk. J Hypertens 25: 1565–1568. doi: 10.1097/HJH.0b013e32829baafe
![]() |
[125] | Home. Available from: https://medicalhomeinfo.aap.org/Pages/default.aspx |
[126] | Ahern DK, Stinson LJ, Uebelacker LA, et al. (2012) E-health blood pressure control program. J Med Pract Manag 28: 91–100. |
[127] |
Anthony CA, Polgreen LA, Chounramany J, et al. (2015) Outpatient blood pressure monitoring using bi-directional text messaging. J Am Soc Hypertens 9: 375–381. doi: 10.1016/j.jash.2015.01.008
![]() |
[128] |
Zullig LL, Dee Melnyk S, Goldstein K, et al. (2013) The role of home blood pressure telemonitoring in managing hypertensive populations. Curr Hypertens Rep 15: 346–355. doi: 10.1007/s11906-013-0351-6
![]() |
[129] |
Margolis KLK, Asche SES, Bergdall AAR, et al. (2013) Effect of Home Blood Pressure Telemonitoring and Pharmacist Management on Blood Pressure Control. Jama 310: 46. doi: 10.1001/jama.2013.6549
![]() |
[130] |
Margolis KLK, Asche SES, Bergdall ARA, et al (2015) A Successful Multifaceted Trial to Improve Hypertension Control in Primary Care: Why Did it Work? J Gen Intern Med 30: 1665–1672. doi: 10.1007/s11606-015-3355-x
![]() |
[131] |
Green B, Cook A, Ralston J, et al. (2008) Effectiveness of Home Blood Pressure Monitoring, Web Communication, and Pharmacist Care on Hypertension Control: The e-BP Randomized Controlled Trial. Jama 299: 2857–2867. doi: 10.1001/jama.299.24.2857
![]() |
[132] | Fishman PA, Cook AJ, Anderson ML, et al. (2013) Improving BP control through electronic communications: An economic evaluation. Am J Manag Care 19: 709–716. |
[133] | Polgreen LA, Han J, Carter BL, et al. (2015) Cost-Effectiveness of a Physician-Pharmacist Collaboration Intervention to Improve Blood Pressure Control. Hypertension 66: 1145–1151. |
[134] |
Robins LS, Jackson JE, Green BB, et al. (2013) Barriers and facilitators to evidence-based blood pressure control in community practice. J Am Board Fam Med 26: 539–557. doi: 10.3122/jabfm.2013.05.130060
![]() |
[135] | Magid D J, Olson K L, Billups S J, et al. (2013) A pharmacist-led, American heart association Heart360 web-enabled home blood pressure monitoring program. Circulation 6: 157–163. |
[136] |
Bosworth H B, Powers B J, Olsen M K, et al. (2011) Home blood pressure management and improved blood pressure control: Results from a randomized controlled trial. Arch Int Med 171: 1173–1180. doi: 10.1001/archinternmed.2011.276
![]() |
[137] | Omboni S, Sala E (2015) The pharmacist and the management of arterial hypertension: the role of blood pressure monitoring and telemonitoring. Expert Rev Cardiovasc Ther13: 209–221. |
[138] |
Ernst ME (2013) Ambulatory blood pressure monitoring: recent evidence and clinical pharmacy applications. Pharmacotherapy 33: 69–83. doi: 10.1002/phar.1167
![]() |
[139] |
James K, Dolan E, O'Brien E (2014). Making ambulatory blood pressure monitoring accessible in pharmacies. Blood Press Monit 19: 134–139. doi: 10.1097/MBP.0000000000000034
![]() |
[140] |
Gregoski MJ, Vertegel A, Shaporev A, et al. (2013) Tension Tamer: delivering meditation with objective heart rate acquisition for adherence monitoring using a smart phone platform. J Altern Complement Med 19: 17–19. doi: 10.1089/acm.2011.0772
![]() |
[141] |
Rifkin DE, Abdelmalek JA, Miracle CM, et al. (2013) Linking clinic and home: a randomized, controlled clinical effectiveness trial of real-time, wireless blood pressure monitoring for older patients with kidney disease and hypertension. Blood Press Monit 18: 8–15. doi: 10.1097/MBP.0b013e32835d126c
![]() |
[142] |
Kim KB, Han HR, Huh B, et al. (2014). The effect of a community-based self-help multimodal behavioral intervention in Korean American seniors with high blood pressure. Am J Hypertens 27: 1199–1208. doi: 10.1093/ajh/hpu041
![]() |
[143] |
Sieverdes JC, Treiber F, Jenkins C, et al. (2013). Improving Diabetes Management With Mobile Health Technology. Am J Med Sci 345: 289–295. doi: 10.1097/MAJ.0b013e3182896cee
![]() |
[144] | O'Reilly DJ, Bowen JM, Sebaldt RJ, et al. (2014) Evaluation of a Chronic Disease Management System for the Treatment and Management of Diabetes in Primary Health Care Practices in Ontario: An Observational Study. Ont Heal Technol Assess Ser14: 1–37. |
[145] |
Green BB, Anderson ML, Cook AJ, et al. (2014) E-care for heart wellness: A feasibility trial to decrease blood pressure and cardiovascular risk. Am J Prev Med 46: 368–377. doi: 10.1016/j.amepre.2013.11.009
![]() |
[146] |
Gandhi PU, Pinney S (2014) Management of chronic heart failure: biomarkers, monitors, and disease management programs. Ann Glob Heal 80: 46–54. doi: 10.1016/j.aogh.2013.12.005
![]() |
[147] |
Aberger EW, Migliozzi D, Follick MJ, et al. (2014). Enhancing Patient Engagement and Blood Pressure Management for Renal Transplant Recipients via Home Electronic Monitoring and Web-Enabled Collaborative Care. Telemed J e-Health 20: 850–854. doi: 10.1089/tmj.2013.0317
![]() |
[148] |
Neumann CL, Schulz EG (2014) Interventionelles dezentrales Telemonitoring: Mögliche Indikationen und Perspektiven einer neuen Methode in der Telemedizin. Praxis 103: 519–526. doi: 10.1024/1661-8157/a001642
![]() |
1. | Melinda Craike, Matthew Bourke, Toni A. Hilland, Glen Wiesner, Michaela C. Pascoe, Enrique Garcia Bengoechea, Alexandra G. Parker, Correlates of Physical Activity Among Disadvantaged Groups: A Systematic Review, 2019, 57, 07493797, 700, 10.1016/j.amepre.2019.06.021 | |
2. | Parvin Mirmiran, Bahar Bakhshi, Somayeh Hosseinpour-Niazi, Narges Sarbazi, Jalal Hejazi, Fereidoun Azizi, Does the association between patterns of fruit and vegetables and metabolic syndrome incidence vary according to lifestyle factors and socioeconomic status?, 2020, 30, 09394753, 1322, 10.1016/j.numecd.2020.04.008 | |
3. | Keyhan Lotfi, Gholamreza Askari, Hamed Mohammad, Abdulmannan Fadel, Fariborz Khorvash, Arman Arab, Association between dietary acid load and clinical features of migraine headaches among Iranian individuals, 2022, 12, 2045-2322, 10.1038/s41598-022-06515-x | |
4. | Joane Diomara Coleone, Ericles Andrei Bellei, Mateus Klein Roman, Vanessa Ramos Kirsten, Ana Carolina Bertoletti De Marchi, Dietary Intake and Health Status of Elderly Patients With Type 2 Diabetes Mellitus: Cross-sectional Study Using a Mobile App in Primary Care, 2021, 5, 2561-326X, e27454, 10.2196/27454 | |
5. | Samantha L. Hahn, Eydie N. Kramer-Kostecka, Vivienne M. Hazzard, Daheia J. Barr-Anderson, Nicole Larson, Dianne Neumark-Sztainer, Weight-related Self-monitoring App Use Among Emerging Adults is Cross-sectionally Associated With Amount and Type of Physical Activity and Screen Time, 2023, 60, 0046-9580, 10.1177/00469580231212086 | |
6. | Morenike Folayan, Maha El Tantawi, Decarbonization of Transport and Oral Health, 2023, 3, 2673-8430, 392, 10.3390/biomed3030032 | |
7. | Abrar Bardesi, Alaa Alabadi-Bierman, Michael Paalani, W. Lawrence Beeson, Hildemar Dos Santos, The Association Between Healthy Lifestyle Behaviors and Polypharmacy in Older Adults: The Loma Linda Longevity Study, 2024, 1559-8276, 10.1177/15598276241299383 | |
8. | David Abernethy, Jason Bennie, Toby Pavey, Henri Tilga, Temporal trends in aerobic physical activity guideline adherence among nationally representative samples of U.S adults between 2011 and 2019: Cross-sectional findings from a sample of over 2 million adults, 2025, 20, 1932-6203, e0316051, 10.1371/journal.pone.0316051 |
Characteristic | n | (%) | |
Age | 21–29 | 20 | 12.4 |
30–39 | 52 | 32.3 | |
40–49 | 31 | 19.3 | |
50–59 | 38 | 23.6 | |
60 or Over | 20 | 12.4 | |
Gender | Male | 48 | 29.8 |
Female | 113 | 70.2 | |
Race | White | 88 | 54.7 |
Black | 68 | 42.2 | |
Hispanic or Latino | 2 | 1.2 | |
I refuse to answer | 3 | 1.9 | |
Marital Status | Married | 82 | 50.9 |
Cohabitating | 5 | 3.1 | |
Divorced | 23 | 14.3 | |
Separated | 7 | 4.3 | |
Single | 39 | 24.2 | |
Education Level | Less than high school degree | 17 | 10.9 |
A high school degree | 52 | 32.3 | |
Some college, but not a college degree | 40 | 14.8 | |
A 2 year or vocational degree | 14 | 8.7 | |
A 4 year college degree or higher | 35 | 21.7 | |
Income Level | 19,999 | 65 | 40.4 |
29,999 | 38 | 23.6 | |
39,999 | 13 | 8.1 | |
49,999 | 9 | 5.6 | |
59,000 | 7 | 4.3 | |
69,00 | 1 | 0.6 | |
$70,000 to above | 4 | 2.4 | |
Currently unemployed | 9 | 5.6 |
Variables | N | min | Max | mean | SD |
FV intake (servings/day) | 160 | 0 | 23.25 | 2.89 | 3.20 |
Fat intakea | 161 | 1 | 3 | 2.04 | 0.60 |
PA (times/week b) | 161 | 0 | 7 | 2.37 | 1.98 |
aFat intake measurement: 1 = low, 2 = medium, 3 = high. bPA refers to how many times participants engaged in at least 30 minutes of moderate intensity PA per week. |
Fat intake | PA | Gender | Income | |
r-value (p-value) | ||||
n | ||||
FV | 0.02 (0.81) | 0.16 (0.05) | 0.04 (0.67) | 0.01 (0.94) |
160 | 160 | 157 | 136 | |
Fat intake | -- | -0.21 (0.01) | -0.68 (0.39) | 0.01 (0.96) |
161 | 161 | 137 | ||
PA | -- | -- | -0.16 (0.04) | 0.22 (0.01) |
161 | 137 | |||
Note. Statistical significance p < 0.05. |
Characteristic | n | (%) | |
Age | 21–29 | 20 | 12.4 |
30–39 | 52 | 32.3 | |
40–49 | 31 | 19.3 | |
50–59 | 38 | 23.6 | |
60 or Over | 20 | 12.4 | |
Gender | Male | 48 | 29.8 |
Female | 113 | 70.2 | |
Race | White | 88 | 54.7 |
Black | 68 | 42.2 | |
Hispanic or Latino | 2 | 1.2 | |
I refuse to answer | 3 | 1.9 | |
Marital Status | Married | 82 | 50.9 |
Cohabitating | 5 | 3.1 | |
Divorced | 23 | 14.3 | |
Separated | 7 | 4.3 | |
Single | 39 | 24.2 | |
Education Level | Less than high school degree | 17 | 10.9 |
A high school degree | 52 | 32.3 | |
Some college, but not a college degree | 40 | 14.8 | |
A 2 year or vocational degree | 14 | 8.7 | |
A 4 year college degree or higher | 35 | 21.7 | |
Income Level | 19,999 | 65 | 40.4 |
29,999 | 38 | 23.6 | |
39,999 | 13 | 8.1 | |
49,999 | 9 | 5.6 | |
59,000 | 7 | 4.3 | |
69,00 | 1 | 0.6 | |
$70,000 to above | 4 | 2.4 | |
Currently unemployed | 9 | 5.6 |
Variables | N | min | Max | mean | SD |
FV intake (servings/day) | 160 | 0 | 23.25 | 2.89 | 3.20 |
Fat intakea | 161 | 1 | 3 | 2.04 | 0.60 |
PA (times/week b) | 161 | 0 | 7 | 2.37 | 1.98 |
aFat intake measurement: 1 = low, 2 = medium, 3 = high. bPA refers to how many times participants engaged in at least 30 minutes of moderate intensity PA per week. |
Fat intake | PA | Gender | Income | |
r-value (p-value) | ||||
n | ||||
FV | 0.02 (0.81) | 0.16 (0.05) | 0.04 (0.67) | 0.01 (0.94) |
160 | 160 | 157 | 136 | |
Fat intake | -- | -0.21 (0.01) | -0.68 (0.39) | 0.01 (0.96) |
161 | 161 | 137 | ||
PA | -- | -- | -0.16 (0.04) | 0.22 (0.01) |
161 | 137 | |||
Note. Statistical significance p < 0.05. |