Abstract
Background:
Although studies have examined if the internet and mobile technology (IMT) can support cardiovascular health (CVH) self-management and health information-seeking efforts, limited studies have targeted African American communities. This study analyzes a possible association between CVH and IMT use and if socioeconomic status is linked to this relationship among older, African Americans in the Jackson Heart Study (JHS).
Methods:
This analysis uses JHS data from three time points: Examination 1 (2000–2004), Examination 3 (2009–2013), and the Digital Connectedness Survey (2017–2019). Participants completed measures of CVH (the American Heart Association's Life Simple 7 [LS7]), IMT use, and demographic characteristics via telephone interview. Both multivariable logistic and linear regression analyses were conducted to analyze the relationship between the LS7 composite and component scores (representing CVH) and IMT use.
Results:
Fifty eight percent of participants were 60 or older; 64% were women. Overall, 2,255 (88%) of participants were IMT users. Generally, no association was found when analyzing LS7 composite scores and IMT use except for the association between LS7 composite scores and use of other smart devices (p = 0.01). However, having ideal blood pressure, body mass index, and cholesterol had positive associations with using technology to track health (p = 0.003, p = 0.004, p = 0.052, respectively), and having ideal physical activity was positively associated with using smart devices (p = 0.012).
Conclusions:
No association was found between LS7 composite scores and IMT use. However, there were associations between individual LS7 metrics, IMT use, and IMT use characteristics. More research should be done to continue assessing the feasibility of using IMT for CVH self-management among older African Americans.
Keywords: cardiovascular, JHS, African American, heart, disease, internet, mobile, technology, LS7, telemedicine, telehealth
Introduction
More than 80 million Americans live with cardiovascular disease (CVD).1 It especially affects older adults, as CVD prevalence increases with age, and African Americans are more affected by CVD than other racial groups.2,3 This disparity for African Americans exists partly due to social conditions, barriers to acquiring CVD-related knowledge, and a shortage of culturally specific health messaging, all of which disproportionately affect them and their health.4–7
To help people improve their heart health, the American Heart Association (AHA) created Life's Simple 7 (LS7), which includes seven behavior and health factors (cigarette smoking, physical activity, diet, blood pressure, cholesterol, body mass index [BMI], and glucose) that people can target to achieve ideal cardiovascular health (CVH).8 However, self-managing these factors can be difficult and requires understanding relevant health information and communicating with health care providers. Theories support the possibility that those with chronic disease may seek health information to meet their needs and may be more likely to use technology as a tool to do so.9,10 Therefore, researchers have begun exploring how mobile devices and the internet can be used by people with or at risk for CVD to enhance their capacity to self-manage.11 In these interventions, participants were asked to use technology to monitor CVD risk factors, engage in healthier behaviors, and adhere to prescribed medication.12–14
While these interventions have been utilized to lower cardiovascular risk in individuals, they have not been adequately tested among populations that were primarily African American or older despite the increase in technology use among both groups.15,16 These interventions also rarely account for socioeconomic status (SES), which may influence both CVH and technology access.17,18 Individuals with lower SES are more likely to have higher CVD risk and are reportedly not as digitally connected as those with higher incomes.17,18
Therefore, this study examines the possible association of CVH with internet and mobile technology (IMT) use among African American men and women in the Jackson Heart Study (JHS) and whether SES modifies this relationship. We hypothesize that lower LS7 composite and component scores are associated with greater IMT use and SES modifies this association. In addition, among IMT users, those with lower composite LS7 scores are more likely to spend more time on the internet, use technology to track or manage their health, and use smart devices than those with higher scores.
Methods
RESEARCH DESIGN AND TARGET POPULATION
This research design was a descriptive, secondary analysis using data collected from the JHS. The JHS is a prospective cohort study designed to analyze the development and advancement of CVD in African American adults.19–21 The JHS consisted of 5,306 African Americans living in the Jackson, Mississippi metropolitan statistical area. JHS participants were selected using random sampling and recruiting volunteers, family members of existing participants, and participants from the Atherosclerosis Risk in Communities Study. Written informed consent was obtained from all the study participants and was approved by the Institutional Review Boards of the University of Mississippi Medical Center, Jackson State University, and Tougaloo College. The data used for this study were collected at Examination 1 (2000–2004), Examination 3 (2009–2013), and during annual follow-up (2017–2019) as shown in Table 1. The Emory University IRB deemed this secondary data analysis, not human subjects' research, and therefore, it was exempt from review.
Table 1.
Demographic Characteristics of Jackson Heart Study Participants by Internet and Mobile Technology Use (N = 2,557)
| DEMOGRAPHIC AND IMT USE CHARACTERISTICS | OVERALL, n = 2,257 (100%) | IMT USERS, n = 2,255 (88.19%) | NON-IMT USERS, n = 302 (11.81%) | p |
|---|---|---|---|---|
| Age | ||||
| 20–39 | 94 (3.68%) | 93 (4.12%) | 1 (0.33%) | <0.0001 |
| 40–59 | 980 (38.33%) | 951 (42.17%) | 29 (9.60%) | |
| 60+ | 1483 (58%) | 1,211 (53.70%) | 272 (90.07%) | |
| Sex | ||||
| Male | 916 (35.82%) | 834 (36.98%) | 82 (27.15%) | 0.0008 |
| Female | 1,641 (64.18%) | 1,421 (63.02%) | 220 (72.85%) | |
| Educational attainment | ||||
| Less than high school | 360 (14.11%) | 231 (10.27%) | 129 (42.72%) | <0.0001 |
| High school diploma or GED | 476 (18.65%) | 388 (17.24%) | 88 (29.14%) | |
| Attended vocational school, trade school, or college | 1,716 (67.24%) | 1,631 (72.49%) | 85 (28.15%) | |
| Income | ||||
| Poor | 231 (10.55%) | 176 (9.09%) | 55 (21.65%) | <0.0001 |
| Lower-middle | 451 (20.59%) | 355 (18.34%) | 96 (37.80%) | |
| Upper-middle | 696 (31.78%) | 632 (32.64%) | 64 (25.20%) | |
| Affluent | 812 (37.08%) | 773 (39.93%) | 39 (15.35%) | |
| Life's Simple 7 Componentsa | ||||
| Smoking | ||||
| Poor | 266 (10.56%) | 235 (10.56%) | 31 (10.58%) | 0.9612 |
| Intermediate | 30 (1.19%) | 27 (1.21%) | 3 (0.12%) | |
| Ideal | 2,222 (88.24%) | 1,963 (88.22%) | 259 (88.40%) | |
| Physical activity | ||||
| Poor | 989 (42.85%) | 832 (40.53%) | 157 (61.57%) | <0.0001 |
| Intermediate | 709 (30.72%) | 650 (31.66%) | 59 (23.14%) | |
| Ideal | 610 (26.43%) | 571 (27.81%) | 39 (15.29%) | |
| Nutrition | ||||
| Poor | 1,611 (68.41%) | 1,431 (68.90%) | 180 (64.75%) | 0.1251 |
| Intermediate | 717 (30.45%) | 625 (30.09%) | 92 (33.09%) | |
| Ideal | 27 (1.15%) | 21 (1.01%) | 6 (2.16%) | |
| Body mass index | ||||
| Poor | 1,292 (56.25%) | 1,164 (56.75%) | 128 (52.03%) | 0.0657 |
| Intermediate | 749 (32.61%) | 669 (32.62%) | 80 (32.52%) | |
| Ideal | 256 (11.14%) | 218 (10.63%) | 38 (15.45%) | |
| Cholesterol | ||||
| Poor | 283 (13.36%) | 250 (13.24%) | 33 (14.35%) | 0.0017 |
| Intermediate | 1,248 (58.92%) | 1,092 (57.84%) | 156 (67.83%) | |
| Ideal | 587 (27.71%) | 546 (93.02%) | 41 (17.83%) | |
| Blood pressure | ||||
| Poor | 539 (23.57%) | 459 (22.61%) | 80 (31.13%) | 0.0006 |
| Intermediate | 1,515 (66.24%) | 1,351 (66.55%) | 164 (63.81%) | |
| Ideal | 233 (10.19%) | 220 (10.84%) | 13 (5.06%) | |
| Glucose | ||||
| Poor | 530 (23.67%) | 443 (22.25%) | 87 (35.08%) | <0.0001 |
| Intermediate | 1,236 (55.20%) | 1,116 (56.05%) | 120 (48.39%) | |
| Ideal | 473 (21.13%) | 432 (21.70%) | 41 (16.53%) | |
| IMT USE CHARACTERISTICS | OVERALL (IMT USERS ONLY), n = 2,255 (100%) | CHARACTERISTIC USERS | CHARACTERISTIC NONUSERS | p |
|---|---|---|---|---|
| Use of technology to track health | ||||
| Use of apps to track health |
|
519 (31.92) |
1,107 (68.08) |
0.8311 |
| Use of digital health technology |
|
1,553 (69.18) |
692 (30.82) |
|
| Use of smart devices | ||||
| Use of smartphones |
|
1,586 (72.82) |
592 (27.18) |
<0.0001 |
| Use of other smart devices | 1,366 (60.85) | 879 (39.15) | ||
Poor = 0–4 points; Intermediate = 5–9 points; Ideal = 10–14 points.
IMT, internet and mobile technology.
p-Values ≤0.05 were considered statistically significant.
MEASURES
The primary outcome was IMT use. This variable measured if participants used the internet, cell phones, or both. The data for this variable were collected using the Digital Connectedness Survey, which was completed by JHS participants from 2017 to 2019 during the annual follow-up (N = 2,564). Participants were categorized as IMT users if they responded yes to using the internet, cell phones, or both.
Three IMT use characteristic variables were also outcomes in this study. The first was the average number of hours spent on the internet, a continuous variable representing an estimate of hours participants spent on the internet daily. For technology to track health, participants were classified as users if they responded yes to using software applications or digital technology to track and manage their health. For use of smart devices, participants were categorized as users if they responded yes to using smartphones or other smart devices such as tablet computers and smartwatches.
The primary exposure was LS7 composite scores, which were used to represent the overall CVH. The LS7 composite scores comprised seven metrics (cigarette smoking status, physical activity, nutrition, BMI, cholesterol, blood pressure, and glucose) as outlined by the AHA. Data for each LS7 measure were collected at Examination 1 or 3 depending on the variable, which took place from 2000–2004 and 2009–2013, respectively. Each variable has three categories: poor, intermediate, and ideal as defined by the AHA guidelines. For each factor, participants were assigned 2 points for each ideal categorization, 1 point for each intermediate categorization, and 0 points for each poor categorization. All component points were added to form the total composite score ranging from 0 to 14 such that lower scores indicate greater CVD risk.
Individual component scores for each LS7 metric were also utilized to assess how each independently relates to IMT use. Each metric has three categories: poor, intermediate, and ideal based on how much they engage in the health behavior. Lastly, dichotomous versions (ideal vs. not ideal) of each metric were also tested. Criteria for each category were defined using the AHA guidelines.8
Income, age, sex, and educational attainment were included in all models as covariates. Data for income and educational attainment were collected at Examination 1, and data for age and sex were collected at Examination 3. Income had four categories: poor, lower-middle, upper-middle, and affluent. Educational attainment included three categories: less than high school, high school diploma or GED, and attended vocational school, trade school, or college. Age was a continuous variable measured in years. Sex was a dichotomous variable representing whether the participant was male or female. Educational attainment, income, and occupational status were also used to represent SES and interchanged to serve as a moderator in the models.
DATA ANALYSIS PROCEDURES
Statistical analyses were performed using the Statistical Analysis Software (version 9.4; SAS Institute). Descriptive statistics were performed to summarize all the study variables and detect outliers and missing values. First, a multivariable logistic regression was conducted with LS7 composite scores as the exposure and IMT use as the outcome. In addition, multivariable logistic regressions were conducted for exploratory analysis using the individual component scores and dichotomous LS7 variables as the exposures and IMT use as the outcome. Covariates were included simultaneously to make adjusted models.
To analyze how IMT users utilized technology, the data were restricted to the people who indicated being IMT users. Logistic and linear regression analyses were conducted to examine relationships between LS7 composite and component scores, dichotomous control variables, and characteristics of IMT use (i.e., average number of hours spent on the internet, usage of technology to track health, and usage of smart devices). The covariates age, sex, income, and education were included in all models. For each model, analyses were also conducted to include interaction terms with the LS7 variable as the exposure, and income, occupation, and education being tested as the effect modifiers. The level of significance for all tests was p = 0.05.
Results
DESCRIPTIVE ANALYSIS
Of the 2,557 participants who completed the Digital Connectedness Survey, 2,255 (88%) met the criteria for being IMT users (Table 1). Of these users, 1,211 (54%) participants were older than 60 years (range = 20–100; SD = 12.1), 1,421 (63%) were female, and 1,631 (73%) attended vocational school, trade school, or college. For income, a majority of IMT users were classified as affluent (40%) and upper-middle class (33%).
For each of the LS7 components, participants were placed in the poor, intermediate, or ideal category based on their engagement in healthy behaviors as outlined by the AHA. Descriptive data for each of the seven components can be found in Table 2. For smoking, most participants were classified in the ideal category (88%). Alternatively, for physical activity, the least number of participants (26%) was categorized as ideal, while most participants (43%) were categorized into the poor category. Nutrition was similar with 68% of participants being assigned to the poor category. For BMI, most participants were also categorized in the poor category (56%). Most participants were in the intermediate categories for both cholesterol (59%) and blood pressure (66%). Lastly, more participants were also in the intermediate category for glucose (55%), followed by poor (24%) and then ideal (21%).
Table 2.
Adjusted Odds Ratios for the Association Between Each Life's Simple 7 Component and Internet and Mobile Technology Use (N = 2,557)
| OR | 95% CI | p | |
|---|---|---|---|
| Life's Simple 7 Componentsa | |||
| Smoking | |||
| Intermediate vs. poor | 0.48 | 0.12–1.94 | 0.58 |
| Ideal vs. poor | 0.87 | 0.52–1.47 | |
| Physical activity | |||
| Intermediate vs. poor | 1.30 | 0.89–1.90 | 0.16 |
| Ideal vs. poor | 1.48 | 0.94–2.35 | |
| Nutrition | |||
| Intermediate vs. poor | 1.04 | 0.74–1.45 | 0.94 |
| Ideal vs. poor | 0.84 | 0.24–2.91 | |
| Body mass index | |||
| Intermediate vs. poor | 0.90 | 0.62–1.30 | 0.84 |
| Ideal vs. poor | 0.92 | 0.55–1.53 | |
| Cholesterol | |||
| Intermediate vs. poor | 1.24 | 0.77–2.01 | 0.23 |
| Ideal vs. poor | 1.66 | 0.92–2.98 | |
| Blood pressure | |||
| Intermediate vs. poor | 0.98 | 0.69–1.40 | 0.72 |
| Ideal vs. poor | 0.74 | 0.35–1.56 | |
| Glucose | |||
| Intermediate vs. poor | 1.59 | 1.10–2.29 | 0.01 |
| Ideal vs. poor | 1.89 | 1.14–3.14 | |
Poor = 0–4 points; Intermediate = 5–9 points; Ideal = 10–14 points.
CI, confidence interval.
p-Values ≤0.05 were considered statistically significant. Each model was adjusted for income, age, sex, and educational attainment.
Of the IMT users, 519 (32%) used apps to manage their health, 1,553 (69%) used digital health technology to track their health, 1,586 (73%) specifically used smartphones, and 1,366 (61%) used other smart devices such as tablet computers and physical activity trackers. The average amount of hours spent on the internet was 2.5 h per day (SD 2.42).
LS7 COMPOSITE RESULTS
The results of the multivariable logistic regression demonstrated that the association between LS7 composite scores and IMT use was not statistically significant (p = 0.50). In models that included SES variables, LS7 composite scores, and their interaction, the interactions were not significant (p = 0.83 for income; p = 0.51 for occupation; p = 0.26 for education). Next, we examined the three IMT characteristic variables and found that the relationship between LS7 composite scores and use of other smart devices was the only statistically significant association (p = 0.01). For every 1 U increase in LS7 composite scores, the odds of using smart devices increased by 1.11 (confidence interval [95% CI] = 1.02–1.20) indicating that better CVH was associated with using smart devices such as smartphones, tablet computers, and smartwatches.
LS7 COMPONENT RESULTS
There were significant results when analyzing individual LS7 metrics in relation to overall IMT use. The bivariate results indicated that IMT users and nonusers differed on four different LS7 components: physical activity, cholesterol, blood pressure, and glucose (Table 1). Seven multivariable logistic regressions were conducted, with each of the LS7 components as the independent variable and IMT use as the dependent variable controlling for income, age, sex, and educational attainment (Table 2).
The association between IMT use and glucose was the only significant relationship (p = 0.01). Compared with those with poorer glucose control, those in the intermediate category had 1.59 times the odds of using IMT (95% CI = 1.10–2.29; Table 2). Compared with those in the poor category for glucose, those in the ideal category had 1.89 times the odds for using IMT (95% CI = 1.14–3.14; Table 3). Representing SES, income and education were associated with the dichotomous glucose variable and IMT use (p = 0.03; p = 0.01).
Table 3.
Adjusted Odds Ratios for the Association Between Life's Simple 7 Composite Scores and Internet and Mobile Technology Use Characteristics (N = 2,255)
| IMT USE CHARACTERISTICS | OR | 95% CI | p |
|---|---|---|---|
| Use of technology to track health | 0.97 | 0.91–1.04 | 0.38 |
| Use of other smart devices | 1.11 | 1.02–1.20 | 0.01 |
p-Values ≤0.05 were considered statistically significant. Each model was adjusted for income, age, sex, and educational attainment.
Multivariable logistic regressions were also conducted, with each of the dichotomous LS7 component variables as exposures and IMT use characteristics as the outcomes. There were four significant results (Table 4). Compared with those who did not meet the ideal criteria for blood pressure, those who did had 0.58 times the odds for using technology to track health (p = 0.003; 95% CI = 0.41–0.83).
Table 4.
Adjusted Odds Ratios for the Association Between Each Life's Simple 7 Component Dichotomous Variables (Ideal vs. Not Ideal) and Internet and Mobile Technology Use (N = 2,255)
| OR | 95% CI | p | |
|---|---|---|---|
| Smoking | |||
| Use of technology to track health | 1.30 | 0.95–1.79 | 0.10 |
| Use of smart devices | 1.14 | 0.76–1.69 | 0.53 |
| Physical activity | |||
| Use of technology to track health | 1.27 | 0.97–1.66 | 0.08 |
| Use of smart devices | 1.58 | 1.10–2.20 | 0.01 |
| Nutrition | |||
| Use of technology to track health | 0.98 | 0.32–3.00 | 0.97 |
| Use of smart devices | 0.45 | 0.16–1.25 | 0.12 |
| Body mass index | |||
| Use of technology to track health | 0.61 | 0.44–0.87 | 0.004 |
| Use of smart devices | 1.07 | 0.70–1.65 | 0.75 |
| Cholesterol | |||
| Use of technology to track health | 0.77 | 0.60–1.00 | 0.05 |
| Use of smart devices | 1.02 | 0.73–1.41 | 0.93 |
| Blood pressure | |||
| Use of technology to track health | 0.58 | 0.41–0.83 | 0.003 |
| Use of smart devices | 1.25 | 0.69–2.23 | 0.46 |
| Glucose | |||
| Use of technology to track health | 0.87 | 0.66–1.14 | 0.31 |
| Use of smart devices | 1.02 | 0.71–1.46 | 0.92 |
p-Values ≤0.05 were considered statistically significant. Ideal = 10–14 points; Not Ideal 0–9 points. Each model was adjusted for income, age, sex, and educational attainment.
Participants who met the ideal criteria for BMI had 0.61 times the odds for using technology to track health (p = 0.004; 95% CI = 0.44–0.86). Those with ideal cholesterol levels had 0.77 times the odds of using technology to track health than those in the intermediate and poor categories (p = 0.05; 95% CI = 0.60–1.00). Lastly, participants who met the ideal criteria for physical activity had 1.58 times the odds of using smart devices than those who did not (p = 0.01; 95% CI = 1.10–2.20).
Discussion
This study explored whether LS7 measures were related to IMT use and specific IMT use characteristics. Most participants (88%) reported utilizing the internet and/or cellphone devices. Yet, the relationship between LS7 composite scores and IMT use was generally not significant, with only one exception. As LS7 composite scores increased (indicating better CVH) so did the odds of using smart devices such as smartphones, tablet computers, and physical activity trackers.
It is probable that if healthier people are more likely to use IMT, then they would also utilize smart devices, as they provide additional means for managing health. SES did not have any modifying effects either, which may have occurred because there was not much variability in SES among study participants. Most were categorized as either affluent (40%) or upper-middle class (33%). Overall, these findings coincided with the literature already published about technological interventions for older populations where mobile interventions had limited effect on participants' health outcomes.22
While LS7 composite scores were not generally associated with IMT use, individual metrics yielded more results, which suggests that the components of LS7 may function differently than the composite measure. This differs from other studies that used LS7 composite scores to represent heart health.23,24 Individuals in the intermediate and ideal categories for glucose were more likely to be IMT users than those in the poor category, suggesting that those with better glucose control could be using technology for self-management. Income and education also modified the association between the dichotomous glucose variable and IMT use (p = 0.03; p = 0.01), meaning SES should be considered when implementing the use of technology, at least for glucose and diabetes intervention.
Lastly, participants meeting the ideal criteria for blood pressure, BMI, and cholesterol were more likely to use technology to track their health, and participants meeting the ideal criteria for physical activity had greater chances of using smart devices.
The use of smart devices and technology to track health seems to be frequently used by participants with ideal CVH metrics. These findings indicate that older, African Americans with better CVH could be more likely to use technology as a tool to seek health information for their CVH. This aligns with the present literature demonstrating increased use of tablet computers and smartphones among older populations and African Americans.15,16
Findings may not have been significant for composite scores and for all the LS7 metrics for multiple reasons. First, health information seeking on the internet may only be necessary for certain health metrics. Managing blood sugar, blood pressure, BMI, cholesterol, and physical activity requires continuous effort and vigilance on behalf of the individual, thereby necessitating more health information and a means to access it. Also, this population may have access to technology, but that does not mean they use it for health information seeking. Previous studies demonstrate that older participants think themselves less capable of using technology for health information seeking than younger individuals.25,26 Due to fears of privacy, mistrust in online information, and not knowing how to find and understand accurate health information, this population may prefer talking to a doctor rather than using technology.
This study provides further evidence that older African American men and women are using the internet and mobile phones given the high level of IMT use in the study sample. Future research should be conducted to strengthen and explore these possible implications. Qualitative studies could be conducted to analyze how older black adults are using IMT and other smart devices. More research should also be conducted to better understand specifically what smart devices and forms of digital health technology this population is using. Understanding this could determine which forms of technology are most suitable for health communication and information-seeking purposes.
Future research is also needed to better understand this population's perceptions about technology use for health information seeking and which health problems are most appropriate to search on the internet. Lastly, research also can be conducted to determine what supports they would need if they attempted to use IMT for CVD self-management. Specifically, it would be beneficial to determine what training they need, where and how they should receive this training, what types of training would be most effective, and which types of software applications would be most useful.
There were limitations with the current study. Multiple testing was not accounted for. Data for the LS7 metrics were from two different time periods. Data for smoking and nutrition were measured at Examination 1 (2000–2004), while data for physical activity, BMI, cholesterol, blood pressure, and glucose were measured at Examination 3 (2009–2013). As a result, data used for the LS7 composite scores could have been missing for certain individuals due to attrition. Also, some of the data for LS7 were gathered several years before the collection of IMT data. Therefore, there may have been substantial misclassification of LS7 for this study.
Another limitation was that there were a great number of missing data for some of the statistical tests, meaning several participants were excluded from some of the analyses thereby increasing the selectivity of the sample. Also, participants in the JHS are from three urban areas of Mississippi. These results may not be generalizable to older African Americans living in more rural areas or other parts of the country. Lastly, the data are observational, so causal relationships cannot be established, nor can residual confounding be ruled out.
Conclusions
Technology is increasingly being utilized by health care workers and the general public. Therefore, it is important to consider how the internet and smartphones can be used to help people, especially minorities such as African Americans, improve their CVH. While no association was found between LS7 composite scores and IMT use, there were positive associations between individual LS7 metrics, IMT use, and specific IMT use characteristics. These findings demonstrate a need to continue assessing the feasibility of using technology to manage heart health among older African Americans.
Acknowledgments
The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I, and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). The authors also thank the staff and participants of the JHS.
Authors' Contributions
All participated in the conceptualization of this research. M.S., K.W., and A.C. provided access to the study data. S.S. and K.J.A. conducted the statistical analyses and interpreted the data. S.S. drafted the article. All provided revisions and feedback for the draft. All approved the submission of the article.
Disclaimer
The views expressed in this article are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.
Disclosure Statement
No competing financial interests exist.
Funding Information
Research reported in this article was also supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number (HHSN268201800010I).
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