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Aging Clinical and Experimental Research

, Volume 31, Issue 2, pp 163–173 | Cite as

Self-monitoring to increase physical activity in patients with cardiovascular disease: a systematic review and meta-analysis

  • Yuji Kanejima
  • Masahiro Kitamura
  • Kazuhiro P. IzawaEmail author
Review

Abstract

Background and aims

It is important to encourage physical activity in patients with cardiovascular disease (CVD), and self-monitoring is considered to contribute to increased physical activity. However, the effects of self-monitoring on CVD patients remain to be established. In this study, we examined the influence of self-monitoring on physical activity of patients with CVD via a systematic review and meta-analysis.

Methods

Screening of randomized controlled trials only was undertaken twice on PubMed (date of appraisal: August 29, 2017). The inclusion criteria included outpatients with CVD, interventions for them, daily step counts as physical activity included in the outcome, and self-monitoring included in the intervention. Assessments of the risk of bias and meta-analysis in relation to the mean change of daily step counts were conducted to verify the effects of self-monitoring.

Results

From 205 studies retrieved on PubMed, six studies were included, with the oldest study published in 2005. Participants included 693 patients of whom 541 patients completed each study program. Their mean age was 60.8 years, and the ratio of men was 79.6%. From these 6 studies, a meta-analysis was conducted with 269 patients of 4 studies including only RCTs with step counts in the intervention group and the control group, and self-monitoring significantly increased physical activity (95% confidence interval, 1916–3090 steps per day, p < 0.05). The average intervention period was about 5 months. Moreover, four studies involved intervention via the internet, and five studies confirmed the use of self-monitoring combined with other behavior change techniques.

Conclusion

The results suggest that self-monitoring of physical activity by patients with CVD has a significantly positive effect on their improvement. Moreover, the trend toward self-monitoring combined with setting counseling and activity goals, and increased intervention via the internet, may lead to the future development and spread of self-monitoring for CVD patients.

Keywords

Self-monitoring Cardiovascular disease Physical activity Steps Systematic review Meta-analysis 

Abbreviations

BCTs

Behavior change techniques

BMI

Body mass index

CI

Confidence interval

CVD

Cardiovascular disease

MD

Mean difference

RCT

Randomized controlled trial

SMD

Standardized mean difference

Notes

Acknowledgements

This study was benefitted by the support and encouragement of Taku Shinoda (Kobe University Graduate School of Health Sciences, Kobe University), Masashi Kanai (Kobe University Graduate School of Health Sciences), and Masato Ogawa (Kobe University Graduate School of Health Sciences). We also thank Dr. Minato Nakazawa, Department of International Health, Graduate School of Health Sciences, Kobe University, for statistical support in the present study.

Funding sources

This work was supported by a Grant from JSPS KAKENHI (No. JP17K01500). Neither the authors nor their associated institutions report any financial relationships with industry relevant to this study.

Compliance with ethical standards

Conflict of interest

All authors declare no conflicts of interest in relation to the work reported in this manuscript.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study, formal consent form is not required.

References

  1. 1.
    Roger VL (2013) Epidemiology of heart failure. Circ Res 113:646–656CrossRefGoogle Scholar
  2. 2.
    WHO (2017 ) The top 10 causes of death. WHO. http://www.who.int/mediacentre/factsheets/fs310/en/. Accessed 30 Oct 2017
  3. 3.
    Kalogeropoulos VV, Georgiopoulou SB, Kritchevsky BM, Psaty NL, Smith AB, Newman et al (2009) Epidemiology of incident heart failure in a contemporary elderly population: the health, aging, and body composition study. Arch Intern Med 169:708–715CrossRefGoogle Scholar
  4. 4.
    DH Cardiovascular Disease Team (2013) Cardiovascular disease outcomes strategy: improving outcomes for people with or at risk of cardiovascular disease. Department of Health, London,Google Scholar
  5. 5.
    Leon AS, Franklin BA, Costa F, Balady GJ, Berra KA, Stewart KJ et al (2005) Cardiac rehabilitation and secondary prevention of coronary heart disease. Circulation 111:369–376CrossRefGoogle Scholar
  6. 6.
    WHO (2010) The atlas of heart disease and stroke. http://www.who.int/cardiovascular_diseases/resources/atlas/en/. Accessed 30 Nov 2017
  7. 7.
    WHO (2013) The World Health Report 2002—reducing risks, promoting healthy life. http://www.who.int/whr/2002/en/. Accessed 30 Nov 2017
  8. 8.
    Ayabe M, Brubaker PH, Dobrosielski D, Miller HS, Kiyonaga A, Shindo M et al (2008) Target step count for the secondary prevention of cardiovascular disease. Circ J 72:299–303CrossRefGoogle Scholar
  9. 9.
    Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM et al (2011) Quantity and quality of exercise for developing and maintain cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults. Med Sci Sports Exerc 43:1134–1359Google Scholar
  10. 10.
    Tudor-Locke C, Craig CL, Aoyagi Y, Bell RC, Croteau KA, De Bourdeaudhuij I et al (2011) How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act 8:80CrossRefGoogle Scholar
  11. 11.
    Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R et al (2007) Using pedometers to increase physical activity and improve health: a systematic review. JAMA 298:2296–2304CrossRefGoogle Scholar
  12. 12.
    US Department of Health and Human Services (1996) Physical activity and health: a report of the surgeon general, US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, AtlantaGoogle Scholar
  13. 13.
    Franco OH (2005) Cardiovascular disease prevention: from meta-analyses to life expectancies. PrintPartners Ipskamp, EnschedeGoogle Scholar
  14. 14.
    Anderson L, Thompson DR, Oldridge N, Zwisler AD, Rees K, Martin N et al. (2016) Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev (1):CD001800.  https://doi.org/10.1002/14651858.CD001800.pub3
  15. 15.
    Bäck M (2012) Exercise and physical activity in relation to kinesiophobia and cardiac risk markers in coronary artery disease. Aidla Trading AB/Kompendiet, GothenburgGoogle Scholar
  16. 16.
    Kesaniemi YK, Danforth E Jr, Jensen MD, Kopelman PG, Lefebvre P, Reeder BA (2001) Dose-response issues concerning physical activity and health: an evidence-based symposium. Med Sci Sports Exerc 33:S351–S358CrossRefGoogle Scholar
  17. 17.
    Nelson ME, Layne JE, Bernstein MJ, Nuernberger A, Castaneda C, Kaliton D et al (2004) The effects of multidimensional home-based exercise on functional performance in elderly people. J Gerontol A Biol Sci Med Sci 59:154–160CrossRefGoogle Scholar
  18. 18.
    Stewart RAH, Held C, Hadziosmanovic N, Armstrong PW, Cannon CP, Granger CB et al (2017) Physical activity and mortality in patients with stable coronary heart disease. J Am Coll Cardiol 70:1689–1700CrossRefGoogle Scholar
  19. 19.
    Fitzgerald JD, Johnson L, Hire DG, Ambrosius WT, Anton SD, Dodson JA et al (2015) Association of objectively measured physical activity with cardiovascular risk in mobility-limited older adults. J Am Heart Assoc 4:e001288CrossRefGoogle Scholar
  20. 20.
    Abraham C, Michie S (2008) A taxonomy of behavior change techniques used in interventions. Health Psychol 27:379–387CrossRefGoogle Scholar
  21. 21.
    Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W et al (2013) The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 46:81–95CrossRefGoogle Scholar
  22. 22.
    Khaled R, James N, Biddle R (2005) An analysis of persuasive technology tool strategies. https://www.researchgate.net/publication/221253988_An_Analysis_of_Persuasive_Technology_Tool_Strategies. Accessed 16 Nov 2017
  23. 23.
    Bird EL, Baker G, Mutrie N, Ogilvie D, Sahlqvist S, Powell J (2013) Behavior change techniques used to promote walking and cycling: a systematic review. Health Psychol 32:829–838CrossRefGoogle Scholar
  24. 24.
    Kang M, Marshall SJ, Barreira TV, Lee JO (2009) Effect of pedometer-based physical activity interventions: a meta-analysis. Res Q Exerc Sport 80:648–655Google Scholar
  25. 25.
    Tudor-Locke C (2010) Steps to better cardiovascular health: how many steps does it take to achieve good health and how confident are we in this number? Curr Cardiovasc Risk Rep 4:271–276CrossRefGoogle Scholar
  26. 26.
    Izawa KP, Watanabe S, Oka K, Hiraki K, Morio Y, Kasahara Y et al (2013) Usefulness of step counts to predict mortality in Japanese patients with heart failure. Am J Cardiol 111:1767–1771CrossRefGoogle Scholar
  27. 27.
    Harris TJ, Owen CG, Victor CR, Adams R, Ekelund U, Cook DG (2009) A comparison of questionnaire, accelerometer, and pedometer: measures in older people. Med Sci Sports Exerc 41:1392–1402CrossRefGoogle Scholar
  28. 28.
    Williams SL, French DP (2011) What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour-and are they the same? Health Educ Res 26:308–322CrossRefGoogle Scholar
  29. 29.
    Ferrier S, Blanchard CM, Vallis M, Giacomantonio N (2011) Behavioural interventions to increase the physical activity of cardiac patients: a review. Eur J Cardiovasc Prev Rehabil 18:15–32CrossRefGoogle Scholar
  30. 30.
    ter Hove N, Huisstede BM, Stam HJ, van Domburg RT, Sunamura M, van den Berg-Emons R et al (2015) Does cardiac rehabilitation after an acute cardiac syndrome lead to changes in physical activity habits? Systematic review. Phys Ther 95:167–179CrossRefGoogle Scholar
  31. 31.
    Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD et al (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343:d5928CrossRefGoogle Scholar
  32. 32.
    Devi R, Powell J, Singh S (2014) A web-based program improves physical activity outcomes in a primary care angina population: randomized controlled trial. J Med Internet Res 16:e186CrossRefGoogle Scholar
  33. 33.
    Izawa KP, Watanabe S, Omiya K, Hirano Y, Oka K, Osada N et al (2005) Effect of the self-monitoring approach on exercise maintenance during cardiac rehabilitation: a randomized, controlled trial. Am J Phys Med Rehabil 84:313–321CrossRefGoogle Scholar
  34. 34.
    Izawa KP, Watanabe S, Hiraki K, Morio Y, Kasahara Y, Takeichi N et al (2012) Determination of the effectiveness of accelerometer use in the promotion of physical activity in cardiac patients: a randomized controlled trial. Arch Phys Med Rehabil 93:1896–1902CrossRefGoogle Scholar
  35. 35.
    Martin SS, Feldman DI, Blumenthal RS, Jones SR, Post WS, McKibben RA et al (2015) mActive: a randomized clinical trial of an automated mhealth intervention for physical activity promotion. J Am Heart Assoc 4:e002239CrossRefGoogle Scholar
  36. 36.
    Reid RD, Morrin LI, Beaton LJ, Papadakis S, Kocourek J, McDonnell L et al (2012) Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease. Eur J Prev Cardiol 19:1357–1364CrossRefGoogle Scholar
  37. 37.
    Thorup C, Hansen J, Gronkjaer M, Andreasen JJ, Nielsen G, Sorensen EE et al (2016) Cardiac patients’ walking activity determined by a step counter in cardiac telerehabilitation: data from the intervention arm of a randomized controlled trial. J Med Internet Res 18:e69CrossRefGoogle Scholar
  38. 38.
    Harris TJ, Owen CG, Victor CR, Adams R, Cook DG (2009) What factors are associated with physical activity in older people, assessed objectively by accelerometry? Br J Sports Med 43:442–450CrossRefGoogle Scholar
  39. 39.
    Klompstra L, Jaarsma T, Strömberg A (2015) Physical activity in patients with heart failure: barriers and motivations with special focus on sex differences. Patient Preference Adherence 9:1603–1610CrossRefGoogle Scholar
  40. 40.
    Slovinec D’Angelo ME, Pelletier LG, Reid RD, Huta V (2014) The roles of self-efficacy and motivation in the prediction of short- and long-term adherence to exercise among patients with coronary heart disease. Health Psychol 33:1344–1353CrossRefGoogle Scholar
  41. 41.
    Zutz A, Ignaszewski A, Bates J, Lear SA (2007) Utilization of the internet to deliver cardiac rehabilitation at a distance: a pilot study. Telemed J E Health 13:323–330CrossRefGoogle Scholar
  42. 42.
    Kanai M, Nozoe M, Izawa KP, Takeuchi Y, Kubo H, Mase K et al (2017) Promoting physical activity in hospitalized patients with mild ischemic stroke: a pilot study. Top Stroke Rehabil 24:256–261CrossRefGoogle Scholar
  43. 43.
    Gleeson-Kreig JM (2006) Self-monitoring of physical activity: effects on self-efficacy and behavior in people with type 2 diabetes. Diabetes Educ 32:69–77CrossRefGoogle Scholar
  44. 44.
    Vooijs M, Alpay LL, Snoeck-Stroband JB, Beerthuizen T, Siemonsma PC, Abbink JJ et al (2014) Validity and usability of low-cost accelerometers for internet-based self-monitoring of physical activity in patients with chronic obstructive pulmonary disease. Interact J Med Res 3:e14CrossRefGoogle Scholar
  45. 45.
    Uman LS (2011) Systematic reviews and meta-analyses. J Can Acad Child Adolesc Psychiatry 20:57–59CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Health Science, Faculty of MedicineKobe UniversityKobeJapan
  2. 2.Department of Physical TherapyKokura Rehabilitation CollegeKitakyushuJapan
  3. 3.Department of Public Health, Graduate School of Health ScienceKobe UniversityKobeJapan
  4. 4.Cardiovascular stroke Renal Project (CRP)KobeJapan

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