Effectiveness of the nurse-led Activate intervention in patients at risk of cardiovascular disease in primary care: a cluster-randomised controlled trial

Heleen Westland, Marieke J Schuurmans, Irene D Bos-Touwen, Marjolein A de Bruin-van Leersum, Evelyn M Monninkhof, Carin D Schröder, Daphne A de Vette, Jaap Ca Trappenburg, Heleen Westland, Marieke J Schuurmans, Irene D Bos-Touwen, Marjolein A de Bruin-van Leersum, Evelyn M Monninkhof, Carin D Schröder, Daphne A de Vette, Jaap Ca Trappenburg

Abstract

Background: To understand better the success of self-management interventions and to enable tailoring of such interventions at specific subgroups of patients, the nurse-led Activate intervention is developed targeting one component of self-management (physical activity) in a heterogeneous subgroup (patients at risk of cardiovascular disease) in Dutch primary care.

Aim: The aim of this study was to evaluate the effectiveness of the Activate intervention and identifying which patient-related characteristics modify the effect.

Methods: A two-armed cluster-randomised controlled trial was conducted comparing the intervention with care as usual. The intervention consisted of four nurse-led behaviour change consultations within a 3-month period. Data were collected at baseline, 3 months and 6 months. Primary outcome was the daily amount of moderate to vigorous physical activity at 6 months. Secondary outcomes included sedentary behaviour, self-efficacy for physical activity, patient activation for self-management and health status. Prespecified effect modifiers were age, body mass index, level of education, social support, depression, patient provider relationship and baseline physical activity.

Results: Thirty-one general practices (n = 195 patients) were included (intervention group n = 93; control group n = 102). No significant between-group difference was found for physical activity (mean difference 2.49 minutes; 95% confidence interval -2.1; 7.1; P = 0.28) and secondary outcomes. Patients with low perceived social support (P = 0.01) and patients with a low baseline activity level (P = 0.02) benefitted more from the intervention.

Conclusion: The Activate intervention did not improve patients' physical activity and secondary outcomes in primary care patients at risk of cardiovascular disease. To understand the results, the intervention fidelity and active components for effective self-management require further investigation.Trial registration: ClinicalTrials.gov NCT02725203.

Keywords: Behaviour change wheel; cluster-randomised controlled trial; nurse-led intervention; physical activity; primary care; self-management.

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Study design of the Activate cluster-randomised controlled trial.
Figure 2.
Figure 2.
CONSORT flowchart of general practices and participants assigned to the intervention and control group. *Data available for at least 4 week days and one weekend day for 8 hours.
Figure 3.
Figure 3.
Estimated means of the multidimensional scale of perceived social support.
Figure 4.
Figure 4.
Estimated means of baseline physical activity.

References

    1. Bodenheimer T, Lorig K, Holman H, et al. Patient self-management of chronic disease in primary care. JAMA 2002; 288: 2469–2475.
    1. Barlow J, Wright C, Sheasby J, et al. Self-management approaches for people with chronic conditions: a review. Patient Educ Couns 2002; 48: 177–187.
    1. Craig P, Dieppe P, Macintyre S, et al. Developing and evaluating complex interventions: the new Medical Research Council guidance. Int J Nurs Stud 2013; 50: 587–592.
    1. Jonkman N, Westland H, Groenwold RHH, et al. Do self-management interventions work in patients with heart failure? An individual patient data meta-analysis. Circulation 2016; 133: 1189–1198.
    1. Jonkman NH, Westland H, Trappenburg JC, et al. Characteristics of effective self-management interventions in patients with COPD: individual patient data meta-analysis. Eur Respir J 2016; 48: 55–68.
    1. Jonkman N, Groenwold RHH, Trappenburg JCA, et al. Complex self-management interventions in chronic disease unravelled: a review of lessons learnt from an individual patient data meta-analysis. J Clin Epidemiol 2017; 83: 48–56.
    1. Kemper HGC, Ooijendijk WTM, Stiggelbout M. Consensus over de Nederlandse Norm voor Gezond Bewegen. Tijdschr Soc Gezondheidsz 2000; 78: 180–183.
    1. Williams PT. Physical fitness and activity as separate heart disease risk factors: a meta-analysis. Med Sci Sports Exerc 2001; 33: 754–761.
    1. Burke LE, Dunbar-Jacob JM, Hill MN. Compliance with cardiovascular disease prevention strategies: a review of the research. Ann Behav Med 1997; 19: 239–263.
    1. Westland H, Sluiter J, Te Dorsthorst S, et al. Patients’ experiences with a behaviour change intervention to enhance physical activity in primary care: a mixed methods study. PLoS One 2019; 14: e0212169.
    1. Baan CA, Hutten JBF, Rijken PM. Afstemming in de zorg: een achtergrondstudie naar de zorg voor mensen met een chronische aandoening. [Coordination of care: a study into the care for people with a chronic condition]. Bilthoven, The Netherlands: RIVM, 2003.
    1. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci 2011; 6:42,5908–6–42
    1. Westland H, Bos Touwen I, Trappenburg JCA, et al. Unravelling effectiveness of a nurse-led behaviour change intervention to enhance physical activity in patients at risk for cardiovascular disease in primary care: study protocol for a cluster randomised controlled trial. Trials 2017; 18: 79.
    1. Boter H, van Delden JJ, de Haan RJ, et al. Modified informed consent procedure: consent to postponed information. BMJ 2003; 327: 284–285.
    1. Nederlands Huisartsen Genootschap. Multidisciplinaire richtlijn Cardiovasculair risicomanagement. Houten, The Netherlands: Bohn Stafleu van Loghum, 2011.
    1. Slootmaker SM, Chin A Paw MJM, Schuit AJ, et al. Concurrent validity of the PAM accelerometer relative to the MTI Actigraph using oxygen consumption as a reference. Scand J Med Sci Sports 2009; 19: 36–43.
    1. Everett B, Salamonson Y, Davidson PM. Bandura’s exercise self-efficacy scale: validation in an Australian cardiac rehabilitation setting. Int J Nurs Stud 2009; 46: 824–829.
    1. Shin Y, Jang H, Pender NJ. Psychometric evaluation of the exercise self-efficacy scale among Korean adults with chronic diseases. Res Nurs Health 2001; 24: 68–76.
    1. van der Heijden MMP, Pouwer F, Pop VJM. Psychometric properties of the Exercise Self-efficacy Scale in Dutch Primary care patients with type 2 diabetes mellitus. Int J Behav Med 2014; 21: 394–401.
    1. Hibbard J, Mahoney E, Stockard J, et al. Development and testing of a short form of the patient activation measure. Health Serv Res 2005; 40: 1918–1930.
    1. Kind P. The EuroQol instrument: an index of health-related quality of life. In: Spilker B (ed.) Quality of life and pharmacoeconomics in clinical trials, 2nd edn. Lippincott-Raven, 1996.
    1. Pedersen SS, Spinder H, Erdman RA, et al. Poor perceived social support in implantable cardioverter defibrillator (ICD) patients and their partners: cross-validation of the multidimensional scale of perceived social support. Psychosomatics 2009; 50: 461–467.
    1. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67: 361–370.
    1. Makoul G, Krupat E, Chang C. Measuring patient views of physician communication skills: development and testing of the Communication Assessment Tool. Patient Educ Couns 2007; 67: 333–342.
    1. van der Weegen S, Verwey R, Spreeuwenberg M, et al. It’s LiFe! Mobile and web-based monitoring and feedback tool embedded in primary care increases physical activity: a cluster randomized controlled trial. J Med Internet Res 2015; 17: e184.
    1. Harris T, Kerry S, Limb E, et al. Effect of a primary care walking intervention with and without nurse support on physical activity levels in 45- to 75-year-olds: the Pedometer and Consultation Evaluation (PACE-UP) cluster randomised clinical trial. PLoS Med 2017; 14: e1002210.
    1. Pedišic Ž, Bauman A. Accelerometer-based measures in physical activity surveillance: current practices and issues. Br J Sports Med 2015; 49: 219–223.
    1. Kimura T, Kobayashi H, Nakayama E, et al. Seasonality in physical activity and walking of healthy older adults. J Physiol Anthropol 2015; 34: 33.
    1. Westland H, Koop Y, Schröder C, et al. Nurses’ perceptions towards the delivery and feasibility of a behaviour change intervention to enhance physical activity in patients at risk for cardiovascular disease in primary care: a qualitative study. BMC Fam Pract 2018; 19: 194.
    1. Bellg A, Borrelli B, Resnick B, et al. Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium. Health Psychol 2004; 23: 443–451.
    1. Mutrie N, Doolin O, Fitzsimons C, et al. Increasing older adults’ walking through primary care: results of a pilot randomized controlled trial. Fam Pract 2012; 29: 633–642.
    1. French D, Olander E, Chisholm A, et al. Which behaviour change techniques are most effective at increasing older adults’ self-efficacy and physical activity behaviour? A systematic review. Ann Behav Med 2014; 48: 225–234.
    1. Fitzsimons C, Baker G, Gray S, et al. Does physical activity counselling enhance the effects of a pedometer-based intervention over the long-term: 12-month findings from the Walking for Wellbeing in the west study. BMC Public Health 2012; 12: 206.
    1. Richards D. Complex interventions and the amalgamation of marginal gains: a way forward for understanding and researching essential nursing care? Int J Nurs Stud 2015; 52: 1143–1145.

Source: PubMed

3
Abonnere