Annals of Behavioral Medicine

, Volume 51, Issue 4, pp 477–488 | Cite as

Psychosocial Factors in the Relationship between Socioeconomic Status and Cardiometabolic Risk: the HCHS/SOL Sociocultural Ancillary Study

  • Jessica L. McCurley
  • Frank Penedo
  • Scott C. Roesch
  • Carmen R. Isasi
  • Mercedes Carnethon
  • Daniela Sotres-Alvarez
  • Neil Schneiderman
  • Patricia Gonzalez
  • Diana A. Chirinos
  • Alvaro Camacho
  • Yanping Teng
  • Linda C. Gallo
Original Article



U.S. Hispanics/Latinos display a high prevalence of metabolic syndrome (MetSyn), a group of co-occurring cardiometabolic risk factors (abdominal obesity, impaired fasting glucose, dyslipidemia, elevated blood pressure) associated with higher cardiovascular disease and mortality risk. Low socioeconomic status (SES) is associated with higher risk for MetSyn in Hispanics/Latinos, and psychosocial factors may play a role in this relationship.


This cross-sectional study examined psychosocial factors in the association of SES and MetSyn components in 4,996 Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Sociocultural Ancillary Study.


MetSyn components were measured at the baseline examination. Participants completed interviews to determine psychosocial risks (e.g., depression) and resources (e.g., social support) within 9 months of baseline (< 4 months in 72.6% of participants). Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to identify latent constructs and examine associations.


Participant mean age was 41.7 years (SE = 0.4) and 62.7% were female. CFA identified single latent factors for SES and psychosocial indicators, and three factors for MetSyn [blood pressure, lipids, metabolic factors]. SEMs showed that lower SES was related to MetSyn factors indirectly through higher psychosocial risk/lower resources (Y-Bχ2 (df = 420) = 4412.90, p < .05, RMSEA = .042, SRMR = .051). A statistically significant effect consistent with mediation was found from lower SES to higher metabolic risk (glucose/waist circumference) via psychosocial risk/resource variables (Mackinnon’s 95% asymmetric CI = −0.13 to −0.02).


SES is related to metabolic variables indirectly through psychosocial factors in U.S. Hispanics/Latinos of diverse ancestries.


Cardiovascular Hispanic Latino Metabolic syndrome Psychosocial Socioeconomic status 



The Hispanic Community Health Study/Study of Latinos was carried out as a collaborative study supported by contracts from the National Heart, Lung, and Blood Institute (75) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, Office of Dietary Supplements. The HCHS/SOL Sociocultural Ancillary Study was supported by grant 1 RC2 HL101649 from the NIH/NHLBI (Gallo/Penedo PIs). The authors thank the staff and participants of HCHS/SOL and the HCHS/SOL Sociocultural Ancillary Study for their important contributions.

Compliance with Ethical Standards

The study was approved by the institutional review boards at all HCHS/SOL institutions, including the coordinating center and reading centers.


The Hispanic Community Health Study/Study of Latinos was supported by contracts from the National Heart, Lung, and Blood Institute (75) to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236), and San Diego State University (N01-HC65237). The following Institutes/Centers/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Institute on Minority Health and Health Disparities, National Institute on Deafness and Other Communication Disorders, National Institute of Dental and Craniofacial Research, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, Office of Dietary Supplements. The HCHS/SOL Sociocultural Ancillary Study was supported by grant 1 RC2 HL101649 from the NIH/NHLBI (Gallo/Penedo PIs). Author Jessica L. McCurley was additionally supported by an NIH T32 training grant in Cardiovascular Epidemiology from the NHLBI and UC San Diego (5T32HL079891–06) and a GloCal Health Fellowship funded by the Fogarty International Center, NHLBI, and the University of California Global Health Institute (R25 TW009343).

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.


  1. 1.
    Bureau USC. United States Census: Quick Facts 2015 2015.Google Scholar
  2. 2.
    Kavanagh A, Bentley RJ, Turrell G, Shaw J, Dunstan D, Subramanian SV. Socioeconomic position, gender, health behaviours and biomarkers of¬†cardiovascular disease and diabetes. Social science &amp; Medicine. 2010;71(6):1150–60.Google Scholar
  3. 3.
    Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and International Association for the Study of obesity. Circulation. 2009;120(16):1640–5.CrossRefPubMedGoogle Scholar
  4. 4.
    Grundy SM. The metabolic syndrome. In: Grundy SM, editor. Atlas of atherosclerosis and metabolic syndrome: Springer New York; 2011. p. 1–26-.CrossRefGoogle Scholar
  5. 5.
    Ford ES, Li C, Sattar N. Metabolic syndrome and incident diabetes: current state of the evidence. Diabetes Care. 2008;31(9):1898–904.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lakka HMDPD, Laaksonen DEMDMPH, Lakka TAM, Niskanen LKM, Kumpusalo EMDPD, Tuomilehto JMDPD, et al. The metabolic syndrome and Total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;288(21):2709–16.CrossRefPubMedGoogle Scholar
  7. 7.
    Heiss G, Snyder M, Teng Y, Schneiderman N, Llabre M, Cowie CC, et al. Prevalence of metabolic syndrome among Hispanics/Latinos of diverse background: the Hispanic community health study/study of Latinos. Diabetes Care. 2014;37(8):2391–9.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Bureau USC. 2014 National Population Projections2014.Google Scholar
  9. 9.
    Chandola T, Brunner E, Marmot M. Chronic stress at work and the metabolic syndrome: prospective study. BMJ. 2006;332(7540):521–5.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Loucks EB, Rehkopf DH, Thurston RC, Kawachi I. Socioeconomic disparities in metabolic syndrome differ by gender: evidence from NHANES III. Annals of epidemiology. 2007;17(1):19–26.CrossRefPubMedGoogle Scholar
  11. 11.
    Karlamangla AS, Merkin SS, Crimmins EM, Seeman TE. Socioeconomic and ethnic disparities in cardiovascular risk in the United States, 2001-2006. Annals of epidemiology. 2010;20(8):617–28.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Montez JK, Bromberger JT, Harlow SD, Kravitz HM, Matthews KA. Life-course socioeconomic status and metabolic syndrome among midlife women. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2016:gbw014.Google Scholar
  13. 13.
    Goldman N, Kimbro RT, Turra CM, Pebley AR. Socioeconomic gradients in health for white and Mexican-origin populations. American journal of public health. 2006;96(12):2186–93.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Gallo LC, de Los Monteros KE, Allison M, Diez Roux A, Polak JF, Watson KE, et al. Do socioeconomic gradients in subclinical atherosclerosis vary according to acculturation level? Analyses of Mexican-Americans in the multi-ethnic study of atherosclerosis. Psychosomatic Medicine. 2009;71(7):756–62.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Gallo LC, Fortmann AL, Roesch SC, Barrett-Connor E, Elder JP, Espinosa de los Monteros K, et al. socioeconomic status, psychosocial resources and risk, and cardiometabolic risk in Mexican-American women. Health Psychology. 2012;31(3):334–42.CrossRefPubMedGoogle Scholar
  16. 16.
    Adler NE, Stewart J. Health disparities across the lifespan: meaning, methods, and mechanisms. Annals of the New York Academy of Sciences. 2010;1186(1):5–23.CrossRefPubMedGoogle Scholar
  17. 17.
    Myers HF. Ethnicity- and socio-economic status-related stresses in context: an integrative conceptual model. Journal of Behavioral Medicine. 2009;32(1):9–19.CrossRefPubMedGoogle Scholar
  18. 18.
    Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome - an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112(17):2735–52.CrossRefPubMedGoogle Scholar
  19. 19.
    Gallo LC, Bogart LM, Vranceanu AM. Socioeconomic status, resources, psychological experiences, and emotional responses: a test of the reserve capacity model. Journal of Personality & Social Psychology. 2005;88(2):386–99.CrossRefGoogle Scholar
  20. 20.
    Gallo LC, Matthews KA. Understanding the association between socioeconomic status and physical health: do negative emotions play a role? Psychological bulletin. 2003;129(1):10–51.CrossRefPubMedGoogle Scholar
  21. 21.
    Matthews KA, Räikkönen K, Gallo LC, Kuller LH. Association between socioeconomic status and metabolic syndrome in women: testing the reserve capacity model. Health Psychology. 2008;27(5):576–83.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Gallo LC, de los Monteros KE, Ferent V, Urbina J, Talavera G. Education, psychosocial resources, and metabolic syndrome variables in Latinas. Annals of Behavioral Medicine. 2007;34(1):14–25.CrossRefPubMedGoogle Scholar
  23. 23.
    Avendano M, Kawachi I, Van LF, Boshuizen HC, Mackenbach JP, Van den Bos GA, et al. Socioeconomic status and stroke incidence in the US elderly: the role of risk factors in the EPESE study. Stroke. 2006;37(6):1368–73.CrossRefPubMedGoogle Scholar
  24. 24.
    Bosma H, Van Jaarsveld CH, Tuinstra J, Sanderman R, Ranchor AV, van Eijk JT, et al. Low control beliefs, classical coronary risk factors, and socio-economic differences in heart disease in older persons. Social science & medicine. 2005;60(4):737–45.CrossRefGoogle Scholar
  25. 25.
    Bosma H, Schrijvers C, Mackenbach JP. Socioeconomic inequalities in mortality and importance of perceived control: cohort study. British Medical Journal. 1999;319(7223):1469–70.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Matthews KA, Gallo LC. Psychological perspectives on pathways linking socioeconomic status and physical health. Annual Review of Psychology. 2010;62(1):501–30.CrossRefGoogle Scholar
  27. 27.
    Matthews KA, Gallo LC, Taylor SE. Are psychosocial factors mediators of socioeconomic status and health connections? Annals NY Acad Sci. 2010;1186(The Biology of Disadvantage: Socioeconomic Status and Health):146–73.Google Scholar
  28. 28.
    Sorlie PD, AvilÈs-Santa LM, Wassertheil-Smoller S, Kaplan RC, Daviglus ML, Giachello AL, et al. Design and implementation of the Hispanic community health study/study of Latinos. Annals of epidemiology. 2010;20(8):629–41.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    LaVange LM, Kalsbeek WD, Sorlie PD, AvilÈs-Santa LM, Kaplan RC, Barnhart J, et al. Sample design and cohort selection in the Hispanic community health study/study of Latinos. Annals of epidemiology. 2010;20(8):642–9.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Bild DE, Bluemke DA, Burke GL, Detrano R, Ez Roux AV, Folsom AR, et al. Multi-ethnic study of atherosclerosis: objectives and design. American Journal of Epidemiology. 2002;156(9):871–81.CrossRefPubMedGoogle Scholar
  31. 31.
    Jain A, Tandri H, Dalal D, Chahal H, Soliman EZ, Prineas RJ, et al. Diagnostic and prognostic utility of electrocardiography for left ventricular hypertrophy defined by magnetic resonance imaging in relationship to ethnicity: the multi-ethnic study of atherosclerosis (MESA). American heart journal. 2010;159(4):652–8.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Gallo LC, Penedo FJ, Carnethon M, Isasi C, Sotrez-Alvarez D, Malcarne VL, et al. The Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study: Sample, design, and procedures. Ethnicity Dis. 2016.Google Scholar
  33. 33.
    Cohen S, Mermelstein R, Kamarck T, Hoberman HM. Measuring the functional components of social support. In: Sarason IG, Sarason BR, editors. Social support: theory research and applications. Dordrecht: Martinus Nijholt; 1985. p. 73–94.CrossRefGoogle Scholar
  34. 34.
    Cohen S, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JM, Jr. Social ties and susceptibility to the common cold. JAMA. 1997;277(24):1940–4.CrossRefPubMedGoogle Scholar
  35. 35.
    Moos RH, Moos BS. Family environment scale (3rd ed) manual. Palo Alto, CA: Consulting Psychologists Press; 1994.Google Scholar
  36. 36.
    Scheier MF, Wrosch C, Baum A, Cohen S, Martire LM, Matthews KA, et al. The life engagement test: assessing purpose in life. Journal of Behavioral Medicine. 2006;29(3):291–8.CrossRefPubMedGoogle Scholar
  37. 37.
    Rosenberg M. Society and the adolescent self-image. Princeton, NJ: Princeton University Press; 1965.CrossRefGoogle Scholar
  38. 38.
    Scheier MF, Carver CS, Bridges MW. Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): a reevaluation of the life orientation test. Journal of Personality & Social Psychology. 1994;67(6):1063–78.CrossRefGoogle Scholar
  39. 39.
    Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). American journal of preventive medicine. 1994;10(2):77–84.PubMedGoogle Scholar
  40. 40.
    Spielberger CD, Sydeman SJ. State-trait anxiety inventory and state-trait anger expression inventory. In: Maruish ME, editor. The use of psychological testing for treatment planning and outcome assessment. Hillsdale, NJ: Erlbaum; 1994. p. 292–321.Google Scholar
  41. 41.
    Barefoot JC, Dodge KA, Peterson BL, Dahlstrom WG, Williams RB, Jr. The cook-medley hostility scale: item content and ability to predict survival. Psychosomatic Medicine. 1989;51(1):46–57.CrossRefPubMedGoogle Scholar
  42. 42.
    Hughes ME, Waite LJ, Hawkley LC, Cacioppo JT. Ashort scale for measuring loneliness in large surveys: results from two population-based studies. Research on Aging. 2004;26(6):655–72.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Everson SA, Goldberg DE, Kaplan GA, Cohen RD, Pukkala E, Tuomilehto J, et al. Hopelessness and risk of mortality and incidence of myocardial infarction and cancer. Psychosomatic Medicine. 1996;58(2):113–21.CrossRefPubMedGoogle Scholar
  44. 44.
    Katzmarzyk PT, Bray GA, Greenway FL, Johnson WD, Newton RL, Jr., Ravussin E, et al. Ethnic-specific BMI and waist circumference thresholds. Obesity. 2011;19(6):1272–8.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Lopez-Jaramillo P, Velandia-Carrillo C, Gomez-Arbelaez D, Aldana-Campos M. Is the present cut-point to define type 2 diabetes appropriate in Latin-Americans? World journal of diabetes. 2014;5(6):747–55.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Tillin T, Sattar N, Godsland IF, Hughes AD, Chaturvedi N, Forouhi NG. Ethnicity-specific obesity cut-points in the development of type 2 diabetes - a prospective study including three ethnic groups in the United Kingdom. Diabetic medicine : a journal of the British Diabetic Association. 2015;32(2):226–34.CrossRefGoogle Scholar
  47. 47.
    Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Prevalence of the metabolic syndrome among U.S. adolescents using the definition from the international diabetes federation. Diabetes Care. 2008;31(3):587–9.CrossRefPubMedGoogle Scholar
  48. 48.
    Ballantyne CM, Hoogeveen RC, McNeill AM, Heiss G, Schmidt MI, Duncan BB, et al. Metabolic syndrome risk for cardiovascular disease and diabetes in the ARIC study. International journal of obesity. 2008;32 Suppl 2:S21–4.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Llabre MM, Arguelles W, Schneiderman N, Gallo LC, Daviglus ML, Chambers EC, et al. Do all components of the metabolic syndrome cluster together in U.S. Hispanics/Latinos? Results from the Hispanic community health study/study of Latinos. Annals of epidemiology. 2015;25(7):480–5.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Muthén LK, Muthén BO. Mplus. Los Angeles: Muthén & Muthén; 2006.Google Scholar
  51. 51.
    Yuan KH, Bentler PM. Three likelihood-based methods for mean and covariance structure analysis with Nonnormal missing data. Sociological Methodology. 2000;30(1):165–200.CrossRefGoogle Scholar
  52. 52.
    Bentler PM. On tests and indices for evaluating structural models. Personality & Individual Differences. 2007;42(5):4.CrossRefGoogle Scholar
  53. 53.
    MacKinnon DP, Fritz MS, Williams J, Lockwood CM. Distribution of the product confidence limits for the indirect effect: program PRODCLIN. Behav Res Methods. 2007;39(3):384–9.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Schneiderman N, Llabre M, Cowie CC, Barnhart J, M. C, Giachello a, et al. prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic community health study/study of Latinos (HCHS/SOL). Diabetes Care. 2014;37(8):2233–9.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Gallo LC, Fortmann AL, Espinosa de los Monteros K, Mills PJ, Barrett-Connor E, Roesch SC, et al. individual and neighborhood socioeconomic status and inflammation in Mexican American women: what is the role of obesity? Psychosomatic Medicine. 2012;74(5):535–42.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Fortmann AL, Gallo LC, Roesch SC, Mills PJ, Barrett-Connor E, Talavera GA, et al. Socioeconomic status, nocturnal blood pressure dipping, and psychosocial factors: A cross-sectional investigation in Mexican-American women. Annals of Behavioral Medicine. 2012.Google Scholar
  57. 57.
    Whittaker KS, Krantz DS, Rutledge T, Johnson BD, Wawrzyniak AJ, Bittner V, et al. Combining psychosocial data to improve prediction of cardiovascular disease risk factors and events: the National Heart, Lung, and Blood Institute--sponsored Women’s ischemia syndrome evaluation study. Psychosom Med. 2012;74(3):263–70.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Boyle S, Michalek JE, Suarez EC. Covariation of psychological attributes and incident coronary heart disease in U.S. Air Force veterans of the Vietnam war. Psychosomatic Medicine. 2006;68(6):844–50.CrossRefPubMedGoogle Scholar
  59. 59.
    Smith TW, Glazer K, Ruiz JM, Gallo LC. Hostility, anger, aggressiveness, and coronary heart disease: an interpersonal perspective on personality, emotion, and health. Journal of Personality. 2004;72(6):1217–70.CrossRefPubMedGoogle Scholar
  60. 60.
    Gallo LC, Ghaed SG, Bracken WS. Emotions and cognitions in coronary heart disease: risk, resilience, and social context. Cognitive Therapy and Research. 2004;28(5):669–94.CrossRefGoogle Scholar
  61. 61.
    Ryff CD, Singer BH, Dienberg Love G. Positive health: connecting well-being with biology. Philos Trans R Soc Lond B Biol Sci. 2004;359(1449):1383–94.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Havranek EP, Mujahid MS, Barr DA, Blair IV, Cohen MS, Cruz-Flores S, et al. Social Determinants of Risk and Outcomes for Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation. 2015.Google Scholar
  63. 63.
    Bijker R, Agyemang C. The influence of early-life conditions on cardiovascular disease later in life among ethnic minority populations: a systematic review. Intern Emerg Med. 2015.Google Scholar
  64. 64.
    Non AL, Rewak M, Kawachi I, Gilman SE, Loucks EB, Appleton AA, et al. Childhood social disadvantage, cardiometabolic risk, and chronic disease in adulthood. Am J Epidemiol. 2014;180(3):263–71.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Bergmann N, Gyntelberg F, Faber J. The appraisal of chronic stress and the development of the metabolic syndrome: a systematic review of prospective cohort studies. Endocr Connect. 2014;3(2):R55–80.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Everson-Rose SA, Lewis TT. Psychosocial factors and cardiovascular diseases. Annual Review of Public Health. 2005;26:469–500.CrossRefPubMedGoogle Scholar
  67. 67.
    Uchino BN. Social support and health: a review of physiological processes potentially underlying links to disease outcomes. Journal of Behavioral Medicine. 2006;29(4):377–87.CrossRefPubMedGoogle Scholar
  68. 68.
    Dokken BB. The pathophysiology of cardiovascular disease and diabetes: beyond blood pressure and lipids. Diabetes Spectrum. 2008;21(3):160–5.Google Scholar
  69. 69.
    Baumeister H, Hutter N, Bengel J. Psychological and pharmacological interventions for depression in patients with diabetes mellitus: an abridged Cochrane review. Diabetic medicine : a journal of the British Diabetic Association. 2014;31(7):773–86.CrossRefGoogle Scholar
  70. 70.
    Smith SM, Sonego S, Ketcheson L, Larson JL. A review of the effectiveness of psychological interventions used for anxiety and depression in chronic obstructive pulmonary disease. BMJ Open Respir Res. 2014;1(1):e000042.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Whalley B, Rees K, Davies P, Bennett P, Ebrahim S, Liu Z, et al. Psychological interventions for coronary heart disease. Cochrane Database Syst Rev. 2011(8):CD002902.Google Scholar
  72. 72.
    Williams MM, Clouse RE, Lustman PJ. Treating depression to prevent diabetes and its complications: understanding depression as a medical risk factor. Clinical Diabetes 2006;24(2):79–86.Google Scholar
  73. 73.
    Gallo LC, Espinosa de los Monteros K, Shivpuri S. Socioeconomic status and health: what is the role of reserve capacity? Current Directions in Psychological Science. 2009;18(5):269–74.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Services TFoCP. Recommendations to increase physical activity in communities. American journal of preventive medicine. 2002;22(4S):67–72.Google Scholar

Copyright information

© The Society of Behavioral Medicine 2017

Authors and Affiliations

  • Jessica L. McCurley
    • 1
  • Frank Penedo
    • 2
  • Scott C. Roesch
    • 3
  • Carmen R. Isasi
    • 4
  • Mercedes Carnethon
    • 5
  • Daniela Sotres-Alvarez
    • 6
  • Neil Schneiderman
    • 7
  • Patricia Gonzalez
    • 8
  • Diana A. Chirinos
    • 7
  • Alvaro Camacho
    • 9
  • Yanping Teng
    • 10
  • Linda C. Gallo
    • 3
    • 11
  1. 1.San Diego Joint Doctoral Program in Clinical PsychologySan Diego State University/University of CaliforniaSan DiegoUSA
  2. 2.Department of Medical Social SciencesNorthwestern UniversityChicagoUSA
  3. 3.Department of PsychologySan Diego State UniversitySan DiegoUSA
  4. 4.Deptartment of Epidemiology and Population HealthAlbert Einstein College of MedicineBronxUSA
  5. 5.Department of Preventive Medicine, Feinberg School of MedicineNorthwestern UniversityChicagoUSA
  6. 6.Collaborative Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public HealthUniversity of North CarolinaChapel HillUSA
  7. 7.Department of PsychologyUniversity of MiamiCoral GablesUSA
  8. 8.Graduate School of Public HealthSan Diego State UniversitySan DiegoUSA
  9. 9.Departments of Psychiatry and Family Medicine and Public HealthUniversity of CaliforniaSan DiegoUSA
  10. 10.Department of Biostatistics, Gillings School of Public HealthUniversity of North Carolina at Chapel HillChapel HillUSA
  11. 11.South Bay Latino Research CenterChula VistaUSA

Personalised recommendations