• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Heart Lung. Author manuscript; available in PMC May 1, 2012.
Published in final edited form as:
PMCID: PMC2972356
NIHMSID: NIHMS211265

Social Network and Health Outcomes among African American Cardiac Rehabilitation Patients

Abstract

Objective

To test the hypotheses that the number of close social network members and health-related support provided by social network members is predictive of coping efficacy and health behaviors.

Methods

Cross-sectional data were collected from 115 African Americans enrolled in cardiac rehabilitation. Measures included Social Convoy Model, SF-36, Social Interaction Questionnaire, the Patient Self-Efficacy Questionnaire and an investigator developed assessment of health behaviors.

Results

Bivariate relationships were found between the number of inner network members and coping efficacy (r=.19, p<.05) health behaviors (r=.18, p<.06) and between health related support and coping efficacy (r=.22, p.05) and health behaviors (r=.28, p<.001). Regression analyses support the hypotheses that close network members predicted better coping efficacy (β=.18, p<.05) and health behaviors (β=.19, p<.05). Health-related support also predicted coping efficacy (β=.23, p<.05) and health behaviors (β=.30, p<.01).

Conclusion

African Americans with larger inner networks have more health support, better health behaviors and higher coping efficacy. The number of close social network members and related health support promote health through health behaviors and coping efficacy.

Decades of research about social networks have made it abundantly clear that strong social networks are important for health and well-being via the social support that ensues. 1,2,3,4 Social networks are defined by Kahn and Antonucci 3 as a network of family and friends who provide support to an individual. The early work of Berkman and Syme 1 on social networks and its predictive value on mortality has been replicated and extended so that researchers now understand that the social value of others translates into psychological and physical health. 5,6,7,8 However, less research has focused on the pathways that connect social networks and better health. The goal of the current study was to determine whether social network size and related support provision influenced the health behaviors and coping efficacy in a sample of African American cardiac rehabilitation patients.

Social Networks and Cardiovascular Disease

The health benefits of social networks are particularly important for patients with cardiovascular disease (CVD). Social isolation and the stress that results are considered to be especially harmful for patients with CVD as they increase the likelihood for depressive symptoms and hinder health behavior modifications. 9

Conversely, supportive behaviors from social network members are helpful and promote health and well being among patients with CVD. 10, 11 For example, research by Lett et al. 10 found that the social support provided by social network members was associated with better psychological well-being for non-depressed patients with coronary heart disease. Similarly, Rutledge and colleagues 11 identified social network size as a predictor of strokes among women with CVD, women with smaller social networks were more likely to have a stroke over a median 5.9 years follow up.

There is very little research on the behavioral pathways that explains the health benefits of social networks and the health support that is provided. Recent research by Molloy, Perkins-Porras, Strike, and Steptoe 12 found that cardiac rehabilitation patients with larger social networks had better attendance at prescribed rehabilitation sessions. However, there is still much unknown about the mechanisms in which social networks promote health, particularly among patients with CVD.

Health Behaviors and Coping Efficacy

Adherence with medical recommendations is difficult for many people. 13 This is especially true for the healthy lifestyle changes that patients must make to maintain health and well-being after a cardiac event. 14 The ability to maintain recommended health behaviors is often thought to occur through an individual's coping efficacy. Coping efficacy demonstrates the self-efficacy of an individual as he or she deals with health behavior modifications. Coping efficacy specifically reflects the individual's ability to deal with the current stressful situation and adherence with the recommended lifestyle modifications. 15 Supportive behaviors from close social network members are an integral influence on efficacy and adherence. 16, 17, 18 Patient coping efficacy is vital to the health, well-being, and mortality of patients with heart disease. 18 In a study of cardiac rehabilitation patients, Woodgate, Brawley, and Shields 19 found that social support was associated with patients' self-efficacy in cardiac rehabilitation activities, thus influencing patient's compliance with proper cardiac rehabilitation maintenance.

There is also limited research on the specific role of social networks play in helping individuals lead a healthy lifestyle. However, the research on the relationship between social support and adherence with leading a healthy lifestyle provides a basis for which to understand the contributions of social networks on health behaviors. 20, 21, 22 For example, in a study of 373 men and women, Jackson 21 found that social support, depressive symptoms, and daily hassles were significant predictors of health behaviors among women. Specifically, greater levels of social support from close family and friends (e.g. close network members) positively predicted healthier diets, greater levels of exercise, less substance abuse and greater adherence with medical treatment for the women in this study. It is not clear why men's health behaviors were not affected, but the findings suggest that gender, social support, and health behaviors be examined further. Although, this study did not specifically measure social networks, its relationship between the support provided by close friends and family and health provide a strong link between the positive benefits that may ensue.

The Social Networks of African Americans

The social networks of African Americans are considered to be smaller than those of Caucasians. 23, 24 However, some research has found these networks to be highly supportive.23, 25 For example, Ajrouch et al. 23 found that although African Americans had smaller networks than Caucasians, they had more frequent contact with their close network members than Caucasians did. Therefore, the influence of these network members should have a vital impact on one's health and well-being.

Theoretical Framework

The theoretical framework for this study is the social convoy model 3 which stems from a lifespan perspective of attachment theory. 26 According the convoy model, individuals move through life surrounded by people to whom they feel emotionally close. 27 Although some people may stay within an individual's convoy throughout the life course, others may disappear or reenter the convoy at later phases, depending, on an individual's needs and goals. The convoy model provides a visual representation of how close the individual's' network members are and what they provide in terms of social support. Social networks are characterized as having inner, middle and outer circles. The inner circle is made up of individuals to whom the person feels close, so close it would be hard to imagine life without them. The middle circle contains persons to whom the individual feels close to but not quite as close as those in the inner circle. The outer circle contains person to whom the individual feels less close to, but still are important. Kahn and Antonucci 3 posited that the inner network members provide social support to an individual. Inner network members are therefore seen as attachment figures in one's life. Therefore, the support that one receives from their close network members provides the psychological and physiological health benefits similar to those of secure attachment behaviors. 28, 26

Aims and Hypotheses

The goal of the current study is to determine if social network support and size of the inner network members will influence health behaviors such as weight management, diet, smoking and exercise, and coping efficacy in sample of patients enrolled in cardiac rehabilitation. Social networks encompass many individuals within a person's social structure. However, more connected individuals are considered to have more health benefits. 8 Closer network members are thought to be close attachment figures. 3, 29 Therefore, we hypothesized that the number of close (ie. inner circle) network members will positively predict health behaviors and coping efficacy. Additionally, health related support from the closest social network members will positively predict health behaviors and coping efficacy.

Method

Design, Setting and Sample

The current study is a secondary analysis using baseline data from a larger randomized intervention trial designed to evaluate the effectiveness of a social support intervention on health outcome for African Americans enrolled in phase II cardiac rehabilitation. Participants were recruited from 5 local cardiac rehabilitation programs.

Procedure

Trained research assistants collected data by structured interviews in a private location convenient for participants. All participants were compensated $15 for the interview which lasted about an hour. Data were collected at baseline (about a month after beginning cardiac rehabilitation) except the social network convoy data, which were collected at the 3-month follow up. The current study was approved by the Institute Review Board at Wayne State University.

Measures

Social network and social network health support

Social network data were collected using the Social Convoy Model questionnaire. 3 Participants were provided with three concentric circles and asked to place the initials of their social network members in inner, middle or outer circles depending on how close they feel to that individual. Closest members of the social network are placed in the inner-most circle of the convoy. The focus of this study was the relationship between health behaviors and coping efficacy and the size and support generated from inner network members. Inner circle members were summed to create the variable of “close social network support”.

After naming the social network members, participants were asked a series of questions about 10 of their closest members. These questions included age, sex, proximity and health-related support. This study did not include questions relating to relationship to the network members or frequency of contact with the network members. Participants were asked if they talk with this person about their health (“yes” or “no”). All “yes” responses were added to create a sum of “health-related support” from close social network members. Responses could range then from 0-10 with high numbers indicating more health-related support.

The social convoy questionnaire is considered to be a valid measure. 5, 30 Previous research examining the social networks of African American participants has used the social convoy measure and has demonstrated its use as well. 23, 25

Health behaviors

The health behaviors of interest in this study included healthy eating, weight management, smoking and physical activity. Items from two sets of questions (Table 2) were used to create a health behavior score. All questions were grounded in the transtheoretical model of behavior change and addressed participant's stage of readiness for change. Participants were asked to respond yes or no to 4 questions about weight management. “Yes” items were scored as 1 and “no” items were scored as 0. For the remaining 4 health behavior scale items participants answered questions about frequency of smoking, healthy eating, physical activity and regular exercise. Participants who responded; yes, more than six months or yes, less than six months were categorized as “active”. Participants who responded; no, but intend to in 30 days, no, but intend to in six months, and no, do not intend to in six months were categorized as “inactive”. Participants who were non-smokers were placed in the “active” group for that item. “Active” responses were scored as 1 and “inactive” responses were scored as 0. Responses from all questions were summed to create an overall health behavior score, with possible scores ranging from 0-8; higher scores indicated healthier behaviors. Other investigators evaluating participant's readiness to engage in health behaviors have used similar coding and scoring strategies. 31

Table 2
Frequency of Health Behaviors

Patient coping efficacy

Patient coping efficacy was assessed with the 10-item Coyne and Smith 16 Patient Self-Efficacy questionnaire. This measure was specifically designed to evaluate coping efficacy among patients recovering from a cardiac event. Participants were asked about their ability to cope with 10 items related to their heart event-related recovery including making healthy lifestyle changes, taking care of his or her health and stress on a scale of 1 (not at all) to 7 (very much so). Although the original instrument contained 10-items, we eliminated the item related to smoking as the baseline rate of smoking was very low in our sample (about 10%). Smoking was not ignored in this study as it was calculated in the health behavior variable. The 9 items were then summed to create a total score of coping efficacy ranging from 9 to 63 with higher numbers indicating better coping efficacy. Cronbach's alpha for this measure was .84. Previous research examining coping efficacy in cardiac patients using this instrument have included African American participants demonstrating its use in this population. 18

Demographic variables

An investigator-developed questionnaire was used to collect data about age, gender, education and income. 32

Mental and physical well-being

Mental and physical well-being was assessed by the SF-36 mental and physical subscales. 33 The SF-36 is a generic health survey that contains 36 questions. The survey contains 8 dimension including physical functioning (PF); role physical (RP); bodily pain (BP); general health (GH); vitality (VT); social functioning (SF); role emotional (RE); and mental health (MH). The eight scales form two distinct higher-ordered clusters: physical and mental health. Participants are asked to respond about how they have been feeling over the last 4 weeks relative to each survey item. Items scores are summed to composite scores ranging from 0-100, higher numbers indicate health and well being. Cronbach's alpha for patient mental subscale was .90 and for patient physical subscale was .88.

Data Analysis

Data analyses were conducted using SPSS version 17.0. The significance level was set at .05. Descriptive statistics were used to assess all study variables. Pearson's correlation coefficients were used to examine the bivariate relationships between the independent and dependent variables. Stepwise hierarchal linear regressions were used to evaluate the hypotheses of the number of inner network members predicting healthier behaviors and coping efficacy and health related support predicting healthier behaviors and coping efficacy.

Results

Description of Sample

There were 115 patients in the sample; demographic characteristics are provided in Table 1. There was no age difference between those that completed the social network data and those that did not (p =.19). Participants reported a mean of 14.2 members in their combined inner, middle and outer circles, with a range of 1-63. Most participants had networks ranging from 1 to 30. As reported in Table 1, the number of inner circle network members ranged between 1-20, the number of middle circle ranged from 0-21 and the number of outer circle ranged from 0-26. No significant relationships were found between the middle and outer circles and the variables of interest in this study. Since we were interested in the presence of close supportive relationships (as described by Kahn & Antonucci 3), only the inner circle network members were used for all regression analyses.

Table 1
Demographic variables and means of study variables (N=115)

There were numerous cardiovascular disease diagnoses represented in the sample. For the purpose of reporting, diagnoses were coded into coded into six categories, congestive heart failure (n=16, 14%), myocardial infarction (n=34, 30%), stent placement (n=23, 20%), CABG surgery (n=22, 19%), angina (n=14, 12%) and other (n=4, 4%). The primary diagnosis for one participant was missing. Analysis of variance was run with these six group on the two dependent variables. No significant mean level differences were found between the six diagnosis groups on health behaviors and patient coping efficacy.

Mean scores for the major study variables are provided in Table 1. The mean score for the SF-36 physical health scale was 33.81 and 51.88 for the mental health score. These mean SF-36 scores are lower than SF-36 norms established in a sample of outpatients who had recent myocardial infarction (mean age=58 years), i.e., 63.26 for the combined physical health subscales and 72.90 for the combined mental health subscales. 33 In addition, as can be seen in Table 1, coping efficacy appeared high in this sample with a mean score of 54.10. Frequencies for each health behavior item can be seen in Table 2.

Bivariate Relationships Among Major Study Variables

As can be seen in Table 3, a positive bivariate relationship was found between the number of inner network members and coping efficacy (p < .05) and a near significant positive relationship was found between the number of inner network members and health behaviors (p<.06). Similarly, health support from the inner network members was positively and significantly related to coping efficacy (p <.01) and health behaviors (p <.05). There was a strong relationship between the number of inner network members and health support from the inner network members (r = .79, p<.001), therefore two regressions were computed, one with the number of inner network members as an independent variable and the other with health support as an independent variable (Tables 4 and and55).

Table 3
Correlations of Study Variables
Table 4
Regression analyses of the number of close social network members predicting healthier behaviors and patient coping efficacy.
Table 5
Regression analyses of the number of close social network health support predicting healthier behaviors and patient coping efficacy.

Regression Analyses Evaluating Hypotheses

The first goal was to determine if the inner network size predicted health behaviors and patient coping efficacy. As can be seen in Tables 4 and and5,5, separate hierarchal linear regressions were run for each of the dependent variables. Due to missing data 3 participants were deleted from the health behavior analysis (N=112) and 6 participants were deleted from the coping efficacy analysis. As hypothesized, after controlling for age, gender, mental health, and physical health, the number of inner social network members significantly positively predicted healthy behaviors and patient coping efficacy (Table 4). Larger inner networks were positively associated with better health behaviors and better coping efficacy. Similarly, health support of network members also predicted health behaviors and patient coping efficacy (Table 5). Not surprisingly patient mental and physical health also predicted coping efficacy. Interestingly, gender and age predicted health behaviors in both sets of regression. Meaning, that females had better health behaviors than males and younger participants had better health behaviors than older participants.

Discussion

The goal of this study was to determine if the number of close network members and health support provided by those close members predicted better health behaviors and coping efficacy. Participants with larger inner circle networks had more health support, better health behaviors, were able to cope better with recovery and had better mental health.

The data demonstrated that social networks and the health support that they provide promote health via health behaviors and coping efficacy. On the bivariate level, the relationship between the number of inner network members and health behaviors was marginal. In addition, the relationships of gender with health behaviors and coping efficacy were also marginal and the relationship of age and health behaviors and coping efficacy were not significant. The lack of significance in these relationships was assumed to be caused by the sample size and age variability. With a larger sample these correlations may have been significant. Therefore, it was not surprising that when examining these relationships on the multivariate level they became significant.

Previous research has found that the relationship between social networks and health exists especially among patients with cardiovascular disease. 10, 11 The current study had similar findings. The number of inner circle members and the total number of network members were positively related to patients' mental health and the number of inner circle members was marginally positively related to patients' physical health. However, this study extended previous research by providing important behavioral and psychosocial mechanisms through which social networks may have protective effects on health. The number of inner circle members was predictive of better health behaviors and greater coping efficacy. Better health behaviors and greater coping efficacy will result in better health, especially after a cardiac event. Similarly, more health support from close network members was also positively related to better health behaviors and better coping efficacy. These finding provide a different pathway between social networks and better health than found in previous literature. Research discussed earlier had found that lower levels of social support provided by the network members were related to greater likelihood of depression and strokes. 10, 11 The findings of the present study highlight the importance of assessing both the social network size as well as the support received from the network as it may be of great importance in the physical and psychological recovery after a cardiac event. Individuals with small networks or lower support may need added assistance or referral to community resources. Although previous literature discussed the smaller networks of African Americans as being sufficient, 23, 24 neither of these studies examined these differences after a cardiac event. After a major medical event, such as the case in this study, network size is of great importance.

The multivariate analyses provided further insight into the relationships that predict health behaviors. The relationship between gender and health behaviors was an unexpected finding that was not part of the original hypothesis. Previous literature on gender differences in health behaviors had been mixed, with some researchers finding support for females reporting better health behaviors and some reporting this relationship for males. 34, 35 36 In a large sample of older adults, Gallant and Dorn 35 found that social network characteristics (e.g. social support from the social network members) influenced positive health behaviors for the women in the study. Women in this study reported less smoking and alcohol consumption than men did. The social support literature has often found gender differences in social support, with women reporting receiving and providing more support. 37, 38, 39 The current study did not examine these relationships but this may partially explain the relationship of females and health behaviors. Perhaps, in the larger context of support exchanges, women are receiving more support from their networks and therefore better health behaviors ensue.

Females reporting better health behaviors were also found by Jackson; 21 the author attributes gender differences in health behaviors to a combination of societal norms and women's greater willingness to seek and comply with medical recommendations. However, the study by Jackson 21 was not among CVD patients and the health behaviors included drinking and substance abuse, not measured in this study. Therefore, the findings by Jackson, 21 based on a different study population, may not be fully applicable to the sample in the current study. In addition, Jackson 21 found the opposite relationship between age and health behaviors with younger adults reporting worse health behaviors than older adults. Again, this may reflect the population and the variables measured.

Younger patients in this study also reported better health behaviors than older adults. This finding is easier to explain as younger adults are often more adaptable to change in diet and smoking and may find it easier to achieve physical activity and regular exercise than older adults do. Previous literature has shown that physical activity and aerobic exercise capacity decrease with age. 40, 41 In a longitudinal study Fleg et al 40 found that rates of physical activity significantly decline as individuals' age. Therefore, this study's findings on younger adults reporting better health behaviors, a measure which included multiple questions on physical activity and exercise, may reflect this age difference. Further, the study by Fleg et al 40 also found that although both men and women showed this decline, men showed a larger decline.

This study has multiple implications for cardiac rehabilitation patient care. Patients with smaller support systems may be at a higher risk for a poorer recovery after a cardiac event than those with larger support systems. Therefore, it will be of great importance for nursing and rehabilitation staff to identify those who may have these smaller or weaker support systems as to better aid in the recovery of the cardiac event. The gender differences found in this study, with women reporting better health behaviors, also brings into question the likelihood that male patients may not be asking for or receiving the help that they need. Therefore, clinical staff should pay close attention to the needs of male patients. In addition, nursing and rehabilitation staff may serve as an alternative support system for cardiac rehabilitation patients and their families as patients face the long road of recovery and must maintain a healthier lifestyle.

Clinicians and researchers have understood that support from family members is important to recovery. However, the extent to which close network members actually promote health via health behaviors and efficacy had not been fully recognized. The current study underscores the importance of including both the patient and the close social network members in cardiac rehabilitation classes as these network members contribute to the patients health behaviors and psychological well-being after a cardiac event.

This study extends knowledge about the benefits of social networks among African Americans 23, 24 and emphasizes the important influence that social networks may have on health behaviors and coping efficacy among African Americans enrolled in cardiac rehabilitation. In order to eliminate the treatment and mortality disparities that exists between African Americans and Caucasians with cardiovascular disease, there is a need to further our understanding of the psychosocial factors that contribute to disease self-management and coping following a cardiac event.

Limitations

The study was limited in that it was cross-sectional data. The participants were interviewed shortly after their cardiac event therefore it is possible that as time went on changes in network size and network health support changed, as did their effects. The patients also varied as to when they started cardiac rehabilitation and so it is unknown how that may have affected the variables of interest. In addition, the sample size was small for this study as not all of the participants completed this measure. Therefore a representative picture of the network characteristics of the larger sample was not available. In addition, a larger sample may provide a clearer understanding of the contributions of social networks and health. Another limitation was that, although significant, the total variance accounted for was small. Therefore, it is likely that other variables not measured are also contributing to health behaviors and coping efficacy. The goal of this study was to demonstrate the importance of social networks in supporting health behaviors and coping efficacy, not to determine every contributing variable. Thus, this study extended the research on the relationships between social networks and health behaviors and provided further evidence that close network members are beneficial for health and well-being.

Future Directions

The current study elucidates the relationship between social networks, social support, coping efficacy and health outcomes. Future research should examine the longitudinal effects on social networks on patients with CVD as they learn to live with their disease and manage their lifestyle as it relates to their health.

Acknowledgments

The authors acknowledge funding support from the National Institute of Environmental Health Sciences (P50 ES012395)

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Rifky Tkatch, Wayne State University.

Nancy T. Artinian, Wayne State University.

Judith Abrams, Wayne State University.

Jennifer R. Mahn, Wayne State University.

Melissa M. Franks, Purdue University.

Steven J. Keteyian, Henry Ford Hospital.

Barry Franklin, William Beaumont Hospital.

Amy Pienta, University of Michigan.

Steven Schwartz, Health Media Inc.

References

1. Berkman LF, Syme SL. Social networks, host resistance, and mortality: A nine-year follow-up study of Alameda County residents. American Journal of Epidemiology. 1979;109:186–204. [PubMed]
2. Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. Social Sciences & Medicine. 2000;51:843–857. [PubMed]
3. Kahn R, Antonucci TC. Convoys over the life course: Attachment, roles, and social support. In: Baltes PB, Brim OJ, editors. Life-span development and behavior. Vol. 3. New York: Academic Press; 1980. pp. 253–286.
4. Uchino BN. Social support and physical health: Understanding the health consequences of relationships. New Haven: Yale University Press; 2004.
5. Antonucci TC, Akiyama H. Social networks in adult life and a preliminary examination of the convoy model. Journals of Gerontology. 1987;42(5):519–527. [PubMed]
6. Fiori KL, Smith J, Antonucci TC. Social network types among older adults: A multidimensional approach. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences. 2007;62B(6):P322–P330. [PubMed]
7. Ong AD, Allaire JC. Cardiovascular intraindividual variability in later life: The influence of social connectedness and positive emotions. Psychology and Aging. 2005;20:476–48. [PubMed]
8. Uchino BN, Holt-Lunstad J, Uno D, Flinders JB. Heterogeneity in the social networks of young and older adults: Prediction of mental health and cardiovascular disease during acute stress. Journal of Behavioral Medicine. 2001;24:361–382. [PubMed]
9. Smith TW, Ruiz JM. Psychosocial influences on the development of and course of coronary artery disease: Current status and implications for research and practice. Journal of Consulting and Clinical Psychology, Special Issue: Behavioral medicine and clinical health psychology. 2002;70:548–568. [PubMed]
10. Lett HS, Blumenthal JA, Babyak MA, Catellier DJ, Carney RM, Berkman LF, et al. Social support and prognosis in patients at increased psychosocial risk recovering from myocardial infarction. Health Psychology. 2007;26:418–427. [PubMed]
11. Rutledge T, Linke SE, Olson MB, Francis J, Johnson BD, Bittner V, et al. Social networks and incident stroke among women with suspected myocardial ischemia. Psychosomatic Medicine. 2008;70:282–287. [PubMed]
12. Molloy GJ, Perkins-Porras L, Strike PC, Steptoe A. Social networks and partner stress as predictors of adherence with medication, rehabilitation attendance, and quality of life following acute coronary syndrome. Health Psychology. 2008;27:52–58. [PubMed]
13. Haynes RB, McDonald HP, Garg AX. Helping patients follow prescribed treatment: Clinical applications. JAMA. 2002;288:2880–2883. [PubMed]
14. Sniehotta FF, Schwarzer R, Scholz U, Schuz B. Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology. 2005;35:565–576.
15. Coyne JC, Smith DAF. Couples coping with myocardial infarction: A contextual perspective on wives' distress. Journal of Personality and Social Psychology. 1991;61(3):404–412. [PubMed]
16. Coyne JC, Smith DAF. Couples coping with a myocardial infarction: Contextual perspective on patient self-efficacy. Journal of Family Psychology. 1994;8:43–54.
17. Lewis MA, McBride CM, Pollak KI, Puleo E, Butterfield RM, Emmons KM. Understanding health behavior change among couples: An interdependence and communal coping approach. Social Science & Medicine. 2006;62:1369–1380. [PubMed]
18. Rohrbaugh MJ, Shoham V, Coyne J, Cranford JA, Sonnega JS, Niklas JM. Beyond the “self” in self-efficacy: Spouse confidence predicts patient survival following heart failure. Journal of Family Psychology, 18. 2004;1:184–193. [PubMed]
19. Woodgate J, Brawley LR, Shields CA. Social support in cardiac rehabilitation exercise maintenance: Associations with self-efficacy and health-related quality of life. Journal of Applied Social Psychology. 2007;37(5):1041–1059.
20. Gore-Felton C, Koopman C. Behavioral mediation of the relationship between psychosocial factors and HIV disease progression. Psychosomatic Medicine: Special Issue: Psychosocial Influences of HIV/AIDS. 2008;70(5):569–574. [PubMed]
21. Jackson T. Relationships between perceived close social support and health practices within community samples of American men and women. Journal of Psychology: Interdisciplinary and Applied. 2006;140(3):229–246. [PubMed]
22. McCauley E, Jerome GJ, Elavsky S, Maquez DX, Ramsey SN. Predicting long-term maintenance of physical activity in older adults. Preventative Medicine: An International Journal Devoted to Practice and Theory. 2003;37(2):110–118.
23. Ajourch KJ, Antonucci TC, Janevic MR. Social networks among Blacks and Whites: The interaction between race and age. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences. 2001;56B:S112–S118. [PubMed]
24. Barnes LL, de Leon CFM, Bienias JL, Evans DA. A longitudinal study of Black-White differences in social resources. Journals of Gerontology: Series B: Psychological Sciences and Social Sciences. 2004;59B:S146–S153. [PubMed]
25. Fung HH, Carstensen LL, Lang FR. Age-related patterns in social networks among European Americans and African Americans: Implications for socioemotional selectivity across the lifespan. International Journal of Aging and Human Development. 2001;52(3):185–206. [PubMed]
26. Bowlby J. Attachment and loss: Retrospect and prospect. American Journal of Orthopsychiatry. 1982;52(4):664–678. [PubMed]
27. Antonucci TC. Attachment in adulthood and aging. In: Sperling MB, Berman WH, editors. Attachment in adults: Clinical and developmental perspectives. 1994. pp. 256–272.
28. Ainsworth MDS. Attachments beyond infancy. American Psychologist. 1989;44(4):709–716. [PubMed]
29. Levitt MJ. Social relations in childhood and adolescence: The convoy model perspective. Human Development. 2005;48(1-2):28–47.
30. Brissette I, Cohen S, Seeman TE. Measuring social integration and social networks. In: Cohen S, Underwood LG, Gottleib BH, editors. Social support measurement and intervention: A guide for health and social scientists. New York: Oxford University Press; 2000. pp. 53–85.
31. Hong TB, Franks MM, Gonzalez R, Keteyian SJ, Franklin BA, Artinian NT. A dyadic investigation of exercise support between cardiac patients and their spouses. Health Psychology. 2005;24(4):430–434. [PubMed]
32. Artinian NT, Abrams J, Keteyian SJ, Franks MM, Franklin B, Pienta A, Tkatch R, Cuff L, Alexander P, Schwartz S. Correlates of depression at baseline among African American enrolled in cardiac rehabilitation. Journal of Cardiopulmonary Rehabilitation and Prevention. 2009;29:24–31. [PMC free article] [PubMed]
33. Ware JE, Kosinski M, Keller SD. SF-36 Physical and Mental Health Summary Scales: A user's manual. Boston, MA: The Health Institute; 1995.
34. Antonucci TC, Akiyama H, Adelmann PK. Health behaviors and social roles among mature men and women. Journal of Aging and Health. 1990;2(1):3–14.
35. Gallant MP, Dorn GP. Gender and race differences in the predictors of daily health practices among older adults. Health Education Research: Theory and Practice. 2001;16(1):21–31. [PubMed]
36. O'Hea EL, Wood KB, Brantley PJ. The Transtheoretical Model: Gender differences across 3 health behaviors. American Journal of Health Behavior. 2003;27(6):645–656. [PubMed]
37. Abbey A, Andrews FM, Halman LJ. Provision and receipt of social support and disregard: What is their impact on the marital life quality of infertile and fertile couples? Journal of Personality and Social Psychology. 1995;68:455–469. [PubMed]
38. Neff LA, Karney BR. Gender differences in social support: A question of skill or responsiveness? Journal of Personality and Social Psychology. 2005;88(1):79–90. [PubMed]
39. Schulz U, Schwarzer R. Long-term effects of spousal support on coping with cancer after surgery. Journal of Social and Clinical Psychology. 2004;23:716–732.
40. Fleg JL, Morrell CH, Bos AG, Brant LJ, Talbot LA, Wright JG, Lakatta EG. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation. 2005;112:674–682. [PubMed]
41. Talbot L, Metter E, Fleg J. Leisure-time physical activities and their relationship to cardiorespiratory fitness in healthy men and women 18-95 years old. Med Sci Sports Exerc. 2000;32:417–425. [PubMed]
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

    Your browsing activity is empty.

    Activity recording is turned off.

    Turn recording back on

    See more...