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Cost-Effectiveness and Value of Information Analysis of Brief Interventions to Promote Physical Activity in Primary Care

  • Vijay Singh GC
    Correspondence
    Address correspondence to: Vijay Singh GC, Health Economics Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK.
    Affiliations
    Health Economics Group, Norwich Medical School, University of East Anglia, Norwich, UK
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  • Marc Suhrcke
    Affiliations
    Health Economics Group, Norwich Medical School, University of East Anglia, Norwich, UK

    UKCRC Centre for Diet and Activity Research, University of Cambridge School of Clinical Medicine, Cambridge, UK

    Centre for Health Economics, University of York, York, UK
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  • Wendy Hardeman
    Affiliations
    School of Health Sciences, University of East Anglia, Norwich, UK
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  • Stephen Sutton
    Affiliations
    Behavioural Science Group, Primary Care Unit, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK
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  • Edward C.F. Wilson
    Affiliations
    Health Economics Group, Norwich Medical School, University of East Anglia, Norwich, UK

    Cambridge Centre for Health Services Research, Institute of Public Health, University of Cambridge School of Clinical Medicine, Cambridge, UK

    Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
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  • on behalf of the Very Brief Interventions Programme Team
Open ArchivePublished:August 16, 2017DOI:https://doi.org/10.1016/j.jval.2017.07.005

      Abstract

      Background

      Brief interventions (BIs) delivered in primary care have shown potential to increase physical activity levels and may be cost-effective, at least in the short-term, when compared with usual care. Nevertheless, there is limited evidence on their longer term costs and health benefits.

      Objectives

      To estimate the cost-effectiveness of BIs to promote physical activity in primary care and to guide future research priorities using value of information analysis.

      Methods

      A decision model was used to compare the cost-effectiveness of three classes of BIs that have been used, or could be used, to promote physical activity in primary care: 1) pedometer interventions, 2) advice/counseling on physical activity, and (3) action planning interventions. Published risk equations and data from the available literature or routine data sources were used to inform model parameters. Uncertainty was investigated with probabilistic sensitivity analysis, and value of information analysis was conducted to estimate the value of undertaking further research.

      Results

      In the base-case, pedometer interventions yielded the highest expected net benefit at a willingness to pay of £20,000 per quality-adjusted life-year. There was, however, a great deal of decision uncertainty: the expected value of perfect information surrounding the decision problem for the National Health Service Health Check population was estimated at £1.85 billion.

      Conclusions

      Our analysis suggests that the use of pedometer BIs is the most cost-effective strategy to promote physical activity in primary care, and that there is potential value in further research into the cost-effectiveness of brief (i.e., <30 minutes) and very brief (i.e., <5 minutes) pedometer interventions in this setting.

      Keywords

      Introduction

      Physical inactivity is a major public health problem associated with a significant burden of chronic disease, including type 2 diabetes, cardiovascular disease, some cancers, and mental health problems [
      Department of Health
      At Least Five a Week: Evidence on the Impact of Physical Activity and Its Relationship with Health. A Report from the Chief Medical Officer.
      ,
      • Lee I.M.
      • Shiroma E.J.
      • Lobelo F.
      • et al.
      Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy.
      ,
      • Waxman A.
      World Health Assembly
      WHO global strategy on diet, physical activity and health.
      ]. Despite the well-documented health benefits of physical activity [
      Department of Health
      UK Physical Activity Guidelines.
      ,
      • Warburton D.E.
      • Nicol C.W.
      • Bredin S.S.
      Health benefits of physical activity: the evidence.
      ,
      • Warburton D.E.
      • Charlesworth S.
      • Ivey A.
      • et al.
      A systematic review of the evidence for Canada’s Physical Activity Guidelines for Adults.
      ,
      • Penedo F.J.
      • Dahn J.R.
      Exercise and well-being: a review of mental and physical health benefits associated with physical activity.
      ], in 2010, 33% of adults aged 18 years and older in high-income countries were insufficiently active, that is, they did not meet the current World Health Organization recommendations [
      World Health Organization
      Global Status Report on Noncommunicable Diseases 2014.
      ]. In England, using self-reported measures in 2012, 61% of adults aged 19 years and older met the current UK guideline [
      Department of Health
      Start Active, Stay Active: A Report on Physical Activity from the Four Home Countries’ Chief Medical Officers.
      ] for moderate/vigorous physical activity [
      • Scholes S.
      • Mindell J.
      Physical activity in adults.
      ], a figure virtually unchanged since the 2008 Health Survey for England (HSE), reporting 59%. Nevertheless, when physical activity was measured objectively using accelerometers, in 2008 only 6% of men and 4% of women aged 16 years and older met the recommended physical activity level [
      • Craig R.
      • Mindell J.
      • Hirani V.
      Health Survey for England 2008, Vol. 1: Physical Activity and Fitness.
      ].
      Physical inactivity is also associated with a considerable economic burden, accounting for 1.5% to 3% of total direct health care costs in high-income countries [
      • Oldridge N.B.
      Economic burden of physical inactivity: healthcare costs associated with cardiovascular disease.
      ]. The annual societal cost of physical inactivity in England (comprising the National Health Service [NHS] costs plus the value of morbidity/premature mortality-related lost productivity) is estimated at £8.2 billion per year, with an additional £2.5 billion for the contribution of physical inactivity to obesity-related costs [
      Department of Health
      At Least Five a Week: Evidence on the Impact of Physical Activity and Its Relationship with Health. A Report from the Chief Medical Officer.
      ].
      Intensive face-to-face physical activity interventions delivered in primary care or community settings targeting sedentary adults can be effective at increasing activity levels [
      • Muller-Riemenschneider F.
      • Reinhold T.
      • Nocon M.
      • et al.
      Long-term effectiveness of interventions promoting physical activity: a systematic review.
      ]. They have been found to represent good “value for money” because they can increase self-reported physical activity at reasonable cost [
      • Muller-Riemenschneider F.
      • Reinhold T.
      • Willich S.N.
      Cost-effectiveness of interventions promoting physical activity.
      ,
      • Garrett S.
      • Elley C.R.
      • Rose S.B.
      • et al.
      Are physical activity interventions in primary care and the community cost-effective? A systematic review of the evidence.
      ]. In recent years, there has been interest in brief interventions (BIs), defined as having a maximum duration of 30 minutes [
      National Institute for Health and Care Excellence
      Four Commonly Used Methods to Increase Physical Activity: Brief Interventions in Primary Care, Exercise Referral Schemes, Pedometers and Community-Based Exercise Programmes for Walking and Cycling. Public Health Intervention Guidance No. 2.
      ,
      • West D.
      • Saffin K.
      ], to promote physical activity in a primary care setting [

      National Institute for Health and Care Excellence. Physical activity: brief advice for adults in primary care [PH44]. Available from: https://www.nice.org.uk/guidance/ph44. [Accessed July 10, 2015].

      ,
      Matrix Research and Consultancy
      NICE Rapid Review of the Economic Evidence of Physical Activity Interventions.
      ,
      • Campbell F.
      • Blank L.
      • Messina J.
      • et al.
      National Institute for Health and Clinical Excellence (NICE) Public Health Intervention Guidance Physical Activity: Brief Advice for Adults in Primary Care. Review of Effectiveness Evidence.
      ]. Systematic reviews and meta-analyses of randomized controlled trials (RCTs) showed that BIs, for example, brief exercise advice/counseling delivered in primary care, increase physical activity [
      • Campbell F.
      • Blank L.
      • Messina J.
      • et al.
      National Institute for Health and Clinical Excellence (NICE) Public Health Intervention Guidance Physical Activity: Brief Advice for Adults in Primary Care. Review of Effectiveness Evidence.
      ,
      • Lamming L.
      • Pears S.
      • Mason D.
      • et al.
      What do we know about brief interventions for physical activity that could be delivered in primary care consultations? A systematic review of reviews.
      ] and are cost-effective [
      • Garrett S.
      • Elley C.R.
      • Rose S.B.
      • et al.
      Are physical activity interventions in primary care and the community cost-effective? A systematic review of the evidence.
      ,
      • GC V.
      • Wilson E.C.
      • Suhrcke M.
      • et al.
      Are brief interventions to increase physical activity cost-effective? A systematic review.
      ] over the short-term (12 months or less). Nevertheless, the evidence on the longer term costs and consequences of BIs has been sparse to date.
      Findings from published RCTs of physical activity interventions are not sufficient on their own to inform decision makers about the cost-effectiveness of intervention strategies [
      • Sculpher M.J.
      • Claxton K.
      • Drummond M.
      • et al.
      Whither trial-based economic evaluation for health care decision making?.
      ]. Evidence on the long-term cost-effectiveness of health interventions is essential to inform resource allocation decisions aimed at maximizing health gains to the population from limited available resources [
      • Muller-Riemenschneider F.
      • Reinhold T.
      • Willich S.N.
      Cost-effectiveness of interventions promoting physical activity.
      ]. Using a discrete event simulation model, we aim to evaluate the long-term cost-effectiveness of BIs to promote physical activity in adults eligible for an NHS Health Check in primary care.
      If BIs are cost-effective, this raises the question of whether “very brief interventions” (VBIs) could also be cost-effective. VBIs, defined as lasting no more than 5 minutes [

      National Institute for Health and Care Excellence. Physical activity: brief advice for adults in primary care [PH44]. Available from: https://www.nice.org.uk/guidance/ph44. [Accessed July 10, 2015].

      ], are of interest as they can be delivered as part of a primary care consultation such as the NHS Health Check [

      Public Health England. NHS Health Check implementation review and action plan. Available from: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/224805/NHS_Health_Check_implementation_review_and_action_plan.pdf. [Accessed November 19, 2014].

      ]. This is offered every 5 years to all adults in England aged 40 to 74 years without known pre-existing vascular disease and is intended to assess the risk of certain conditions, including type 2 diabetes and heart disease, and provide preventative advice and interventions when indicated [

      Department of Health. What is an NHS Health Check? Available from: http://www.nhs.uk/Conditions/nhs-health-check/Pages/What-is-an-NHS-Health-Check.aspx. [Accessed November 19, 2014].

      ].
      In this article, we present an economic evaluation of three classes of BIs (plus no intervention), reporting the incremental cost per quality-adjusted life-year (QALY) gained over 10 years. We also report a value of information analysis, a method to predict the return on investment in further research [
      • Eckermann S.
      • Karnon J.
      • Willan A.R.
      The value of value of information: best informing research design and prioritization using current methods.
      ,
      • Claxton K.
      • Posnett J.
      An economic approach to clinical trial design and research priority-setting.
      ,
      • Tuffaha H.W.
      • Gordon L.G.
      • Scuffham P.A.
      Value of information analysis in healthcare: a review of principles and applications.
      ]. This information will inform the design of further research into the effectiveness and cost-effectiveness of VBIs delivered as part of the NHS Health Check.

      Methods

      Study Population

      We used data from the 2011 HSE to generate a simulated cohort of 10,000 adults aged 40 to 74 years who do not have an existing diagnosis of diabetes, hypertension, cardiovascular disease, or renal disease, representing the NHS Health Check population [

      Department of Health. What is an NHS Health Check? Available from: http://www.nhs.uk/Conditions/nhs-health-check/Pages/What-is-an-NHS-Health-Check.aspx. [Accessed November 19, 2014].

      ].

      The Physical Activity Cost-Effectiveness Model

      We developed a discrete event simulation model, the Physical Activity Cost-Effectiveness model, using the R software (R Foundation for Statistical Computing, Vienna, Austria) [
      R Core Team
      R: A Language and Environment for Statistical Computing.
      ] to estimate the cost-effectiveness of BIs. The model first generates a cohort of 10,000 representative individuals of the English population. It then follows each individual, predicting the incidence of chronic disease, mortality, and associated costs and outcomes over 10 years, specified with risk equations and data derived from the literature [
      • Clarke P.M.
      • Gray A.M.
      • Briggs A.
      • et al.
      A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68).
      ,
      • Anderson K.M.
      • Odell P.M.
      • Wilson P.W.
      • et al.
      Cardiovascular disease risk profiles.
      ,
      • Schnabel R.B.
      • Sullivan L.M.
      • Levy D.
      • et al.
      Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study.
      ,
      • Wilson E.
      • Fordham R.
      A Model to Estimate Health Impacts and Costs of Obesity in Norfolk Over the Next 10 Years—Final Report.
      ,
      • Eastman R.C.
      • Javitt J.C.
      • Herman W.H.
      • et al.
      Model of complications of NIDDM, I: model construction and assumptions.
      ,
      • Frazier A.L.
      • Colditz G.A.
      • Fuchs C.S.
      • et al.
      Cost-effectiveness of screening for colorectal cancer in the general population.
      ,
      • Johnston K.
      Modelling the future costs of breast screening.
      ,

      Cancer Research UK. Cancer Research UK CancerStats. Available from: http://info.cancerresearchuk.org/cancerstats/types/kidney. [Accessed January 8, 2013].

      ,

      Office for National Statistics. UK interim life tables, 1980–82 to 2009–2011. Available from: http://www.ons.gov.uk/ons/rel/lifetables/interim-life-tables/2009-2011/rft-england.xls. [Accessed April 17, 2013].

      ]. The model includes type 2 diabetes and associated complications, heart disease, stroke, and cancers related to physical inactivity and obesity (breast, colorectal, lung, or kidney cancer). Increased physical activity is assumed to influence risk factors such as reduced blood pressure, cholesterol level, and glycated hemoglobin. Modification of these risk factors leads to changes in the risk of chronic disease and comorbidities, such as reduced risk of cardiovascular disease. A decrease in chronic disease and comorbidities leads to a reduction in costs and to the prevention of a decrease in quality of life (Fig. 1). Effectiveness data for each comparator are entered in the model as an increase in metabolic equivalent (MET)-hours per week compared with no intervention, which, in turn, influences the risk of chronic disease. The random search method [
      • Vanni T.
      • Karnon J.
      • Madan J.
      • et al.
      Calibrating models in economic evaluation: a seven-step approach.
      ] was used to calibrate the model against seven calibration targets. Weighted mean deviation was used to assess the goodness of fit of calibration results [
      • Taylor D.C.
      • Pawar V.
      • Kruzikas D.
      • et al.
      Methods of model calibration: observations from a mathematical model of cervical cancer.
      ]. Full details of the model and calibration are provided in Appendix 1 in Supplemental Materials found at doi:10.1016/j.jval.2017.07.005.
      Fig. 1
      Fig. 1A schematic of the Physical Activity Cost-Effectiveness model. QALY, quality-adjusted life-year.

      Data Inputs and Sources

      Model Inputs

      Data on demographic characteristics of individual participants (age, sex, and ethnicity) were derived from the UK Office for National Statistics [

      Office for National Statistics. Vital statistics: population and health reference tables—spring 2011 update. Available from: http://www.ons.gov.uk/ons/publications/re-reference-tables.html?edition=tcm%3A77-213289. [Accessed December 1, 2011].

      ,

      Office for National Statistics. 2011 census: key statistics for local authorities in England and Wales. Available from: http://www.ons.gov.uk/ons/rel/census/2011-census/key-statistics-for-local-authorities-in-england-and-wales/. [Accessed December 12, 2012].

      ]. The risk factor profile (systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, body mass index, smoking status, and glycated hemoglobin) and prevalence of type 2 diabetes and cardiovascular events (ischemic heart disease, myocardial infarction, stroke, and heart failure) for individual participants in the cohort were generated using data from the 2011 HSE [
      • Craig R.
      • Mindell J.
      Health Survey for England 2011.
      ]. The severity of breast cancer was classified according to the Nottingham Prognostic Index prognostic groups—ductal carcinoma in situ, excellent, good, moderate, and poor [
      • Blamey R.W.
      • Ellis I.O.
      • Pinder S.E.
      • et al.
      Survival of invasive breast cancer according to the Nottingham Prognostic Index in cases diagnosed in 1990–1999.
      ]—and age-specific prevalence data for breast cancer were taken from the estimates for 2008 in the United Kingdom [
      • Maddams J.
      • Brewster D.
      • Gavin A.
      • et al.
      Cancer prevalence in the United Kingdom: estimates for 2008.
      ]. The baseline parameter values for colorectal cancer were derived from Frazier et al. [
      • Frazier A.L.
      • Colditz G.A.
      • Fuchs C.S.
      • et al.
      Cost-effectiveness of screening for colorectal cancer in the general population.
      ] and applied to the baseline population to generate prevalence data for colorectal cancer. The baseline prevalence data of lung and kidney cancers were based on estimates from Cancer Research UK [

      Cancer Research UK. Cancer Research UK CancerStats. Available from: http://info.cancerresearchuk.org/cancerstats/types/kidney. [Accessed January 8, 2013].

      ,

      Cancer Research UK. Average number of new cases per year and age-specific incidence rates, UK, 2007–2009. Available from: http://www.cancerresearchuk.org/cancer-info/cancerstats/types/lung/incidence. [Accessed January 2, 2013].

      ].

      Interventions

      We selected three classes of BIs: pedometer interventions, advice/counseling in primary care, and action planning interventions. Evidence of effectiveness was extracted from published meta-analyses of RCTs [
      • Belanger-Gravel A.
      • Godin G.
      • Amireault S.
      A meta-analytic review of the effect of implementation intentions on physical activity.
      ,
      • Bravata D.M.
      • Smith-Spangler C.
      • Sundaram V.
      • et al.
      Using pedometers to increase physical activity and improve health: a systematic review.
      ,
      • Orrow G.
      • Kinmonth A.L.
      • Sanderson S.
      • et al.
      Effectiveness of physical activity promotion based in primary care: systematic review and meta-analysis of randomised controlled trials.
      ]. The three classes are somewhat heterogeneous, and therefore descriptions of the classes (and associated costings) hereafter reflect the scope of interventions included in the respective meta-analyses. This selection of BIs was based on the strength of evidence of effectiveness and their relevance in a primary care setting. Full details are provided in Appendix 2 in Supplemental Materials found at http://dx.doi.org/10.1016/j.jval.2017.07.005. We also included current practice in which no physical activity intervention is delivered.

      Pedometer Interventions

      Participants were given a pedometer to wear and were encouraged to view and record their daily step counts. They were also asked to set a physical activity goal such as to walk 20 minutes on all or most days of the week, or walk 10,000 steps on 5 days of the week. In some interventions, participants received individualized exercise feedback or additional “behavioral counseling” from a nurse or physiotherapist.

      Advice/Counseling in Primary Care

      Typically, participants received written materials with exercise advice or an exercise prescription; they also received two or more sessions of advice/counseling on physical activity. Advice/counseling was mostly delivered face-to-face, in some cases by phone, or at times both. In most cases, the interventions were delivered by primary care doctors or nurses.

      Action Planning Interventions

      These BIs used “implementation intentions,” a commonly used form of action planning. Participants were asked to formulate an action plan for physical activity in the format of what, when, and where, and to record their action plan in a logbook or calendar. In most interventions, participants were encouraged to write down an action plan assisted by a trained interviewer.

      Current Practice

      Participants received no intervention.

      Short-Term Effectiveness of Interventions

      The intervention effects of the BIs were converted to MET-hours by estimating the time spent in activities with higher MET intensities as a result of the intervention [
      • Wu S.
      • Cohen D.
      • Shi Y.
      • et al.
      Economic analysis of physical activity interventions.
      ]. We extracted physical activity outcomes from individual studies included in the three meta-analyses that were translated into MET-hours by selecting the estimates from the compendium of physical activity [
      • Ainsworth B.E.
      • Haskell W.L.
      • Herrmann S.D.
      • et al.
      2011 Compendium of Physical Activities: a second update of codes and MET values.
      ]. Finally, we updated the meta-analysis using translated values (MET-hours). It was, however, not possible to translate intervention effects into MET-hours for 5 of the 19 RCTs included in the meta-analysis of action planning interventions [
      • Belanger-Gravel A.
      • Godin G.
      • Amireault S.
      A meta-analytic review of the effect of implementation intentions on physical activity.
      ] because either these studies did not provide details on changes in intensity, duration, and/or frequency of activity required for MET-hours translation or the outcome was expressed in composite units (e.g., a sum of scores when responses were rated on a scale). Thus, we excluded those five studies.

      Intervention Costs

      We first extracted resource use data on the basis of the intervention description provided for individual studies in the meta-analyses. We then costed each intervention on the basis of the quantities of resources used multiplied by the unit cost of each resource component. The cost per participant was then evaluated as weighted average of intervention costs from each RCT in the meta-analysis (Table 1). Full details on translating the intervention effects into MET-hours and costing of each intervention are provided in Appendix 2 in Supplemental Materials.
      Table 1Intervention effects and costs associated with implementing BIs promoting physical activity
      Brief interventionsNo. of studiesTotal no. of participantsMedian (range) duration of follow-upUnit of measurement; intervention effect (95% CI)Effect (in MET-hours per day)Intervention costs
      All costs are inflated to 2011 UK pound sterling using the Hospital and Community Health Services index [53].
      Source
      Advice/counseling in primary care9 RCTs344512 moStandardized mean difference; 0.25 (0.11–0.38)0.33 (0.16–0.49)£71.26
      • Orrow G.
      • Kinmonth A.L.
      • Sanderson S.
      • et al.
      Effectiveness of physical activity promotion based in primary care: systematic review and meta-analysis of randomised controlled trials.
      Action planning interventions14 RCTs186410 (2–52) wkStandardized mean difference; 0.23 (0.10–0.35)0.05 (0.02–0.08)£33.21
      • Belanger-Gravel A.
      • Godin G.
      • Amireault S.
      A meta-analytic review of the effect of implementation intentions on physical activity.
      Pedometer interventions8 RCTs27711 (4–24) wkIncrease in steps per day; 2491 (1098–3885)1.06 (0.47–1.65)£54.33
      • Bravata D.M.
      • Smith-Spangler C.
      • Sundaram V.
      • et al.
      Using pedometers to increase physical activity and improve health: a systematic review.
      Current practice (“doing nothing”)
      BI, brief intervention; CI, confidence interval; MET, metabolic equivalent; RCT, randomized controlled trial.
      low asterisk All costs are inflated to 2011 UK pound sterling using the Hospital and Community Health Services index
      • Thompson Coon J.
      • Hoyle M.
      • Green C.
      • et al.
      Bevacizumab, sorafenib tosylate, sunitinib and temsirolimus for renal cell carcinoma: a systematic review and economic evaluation.
      .

      Disease Costs and Health Outcomes

      All costs in the study were inflated to 2011 UK pound sterling. The annual costs associated with each health state were derived from previous studies [
      • Wilson E.
      • Fordham R.
      A Model to Estimate Health Impacts and Costs of Obesity in Norfolk Over the Next 10 Years—Final Report.
      ,
      • Johnston K.
      Modelling the future costs of breast screening.
      ,
      • Godfrey C.
      • Ali S.
      • Parrott S.
      • et al.
      Economic Model of Adult Smoking Related Costs and Consequences for England.
      ,
      • Thompson Coon J.
      • Hoyle M.
      • Green C.
      • et al.
      Bevacizumab, sorafenib tosylate, sunitinib and temsirolimus for renal cell carcinoma: a systematic review and economic evaluation.
      ,
      • Tappenden P.
      • Chilcott J.
      • Eggington S.
      • et al.
      Option appraisal of population-based colorectal cancer screening programmes in England.
      ,
      • Clarke P.
      • Gray A.
      • Legood R.
      • et al.
      The impact of diabetes-related complications on healthcare costs: results from the United Kingdom Prospective Diabetes Study (UKPDS Study No. 65).
      ,
      Joint Formulary Committee
      British National Formulary (BNF) 64.
      ,
      • Gordois A.
      • Scuffham P.
      • Shearer A.
      • et al.
      The health care costs of diabetic nephropathy in the United States and the United Kingdom.
      ,
      • Ward S.
      • Lloyd Jones M.
      • Pandor A.
      • et al.
      A systematic review and economic evaluation of statins for the prevention of coronary events.
      ,
      National Clinical Guideline Centre
      Hypertension: Clinical Management of Primary Hypertension in Adults (Update). Clinical Guideline 127.
      ,
      National Institute for Health and Care Excellence
      NICE Clinical Guideline 36—Atrial Fibrillation: The Management of Atrial Fibrillation.
      ,
      • Ara R.
      • Brennan A.
      ,
      • Ara R.
      • Pandor A.
      • Stevens J.
      • et al.
      Early high-dose lipid-lowering therapy to avoid cardiac events: a systematic review and economic evaluation.
      ,

      Department of Health. Reference costs guidance for 2011–12. Available from: http://www.dh.gov.uk/prod_consum_dh/groups/dh_digitalassets/@dh/@en/documents/digitalasset/dh_133719.pdf. [Accessed October 22, 2012].

      ]. The health outcomes were evaluated in QALYs. Utilities for health states included in the model were obtained from published sources [
      • Clarke P.
      • Gray A.
      • Holman R.
      Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62).
      ,
      • Sullivan P.W.
      • Slejko J.F.
      • Sculpher M.J.
      • et al.
      Catalogue of EQ-5D scores for the United Kingdom.
      ,
      • Lidgren M.
      • Wilking N.
      • Jonsson B.
      • et al.
      Health related quality of life in different states of breast cancer.
      ,
      • Ness R.M.
      • Holmes A.M.
      • Klein R.
      • et al.
      Utility valuations for outcome states of colorectal cancer.
      ,
      • Zhang P.
      • Brown M.B.
      • Bilik D.
      • et al.
      Health utility scores for people with type 2 diabetes in U.S. managed care health plans: results from Translating Research Into Action for Diabetes (TRIAD).
      ,
      • Coffey J.T.
      • Brandle M.
      • Zhou H.
      • et al.
      Valuing health-related quality of life in diabetes.
      ]. Details on costs and utilities related to comorbid disease conditions in the model are available in Appendix 1 in Supplemental Materials. We performed the analysis from the English NHS perspective to estimate the costs and benefits of BIs over a 10-year time horizon. All health outcomes and costs were discounted at 3.5% per annum [
      National Institute for Health and Care Excellence
      Guide to the Methods of Technology Appraisal.
      ].

      Probabilistic Sensitivity Analysis

      We performed a Monte-Carlo simulation (n = 10,000 iterations) to simultaneously account for uncertainty in all input parameters (see Appendix 1 in Supplemental Materials). The net monetary benefit (NMB; UK 2011 pound sterling), that is, the health effect (QALYs) multiplied by a societal willingness to pay (WTP) per QALY gained minus the cost was calculated for each BI [
      • Stinnett A.A.
      • Mullahy J.
      Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis.
      ]. At any given WTP threshold, the cost-effective intervention was identified as the one with the highest expected NMB, which is mathematically identical to sequentially identifying the most cost-effective intervention with an incremental cost-effectiveness ratio less than the threshold compared with the next best nondominated alternative. We constructed cost-effectiveness acceptability curves (CEACs) to illustrate the decision uncertainty surrounding the adoption of BIs, conditional on the WTP per QALY [
      • Fenwick E.
      • Claxton K.
      • Sculpher M.
      Representing uncertainty: the role of cost-effectiveness acceptability curves.
      ]. The NMBs were estimated at a WTP of £20,000/QALY.

      Scenario Analyses

      The sustainability of intervention effects over time plays an important role in the cost-effectiveness analysis. There is considerable uncertainty about the maintenance of any effects of BIs because very few studies have follow-up measurement beyond 1 year. We performed sensitivity analysis with decay rates for the intervention effect ranging between 0% (lifelong behavior change) and 100% (behavior change reversed to baseline after the first year post-intervention). In the base-case, we assumed that the intervention effects were sustained for the first year, but decayed at a rate of 55% per annum thereafter. We also evaluated the cost-effectiveness of BIs if they were repeated once every 2, 5, and 10 years, and finally explored the direct impact of a short-term quality-of-life boost from increased physical activity. Only a few studies have measured these short-term improvements in the general population [
      • Pavey T.G.
      • Anokye N.
      • Taylor A.H.
      • et al.
      The clinical effectiveness and cost-effectiveness of exercise referral schemes: a systematic review and economic evaluation.
      ,
      • Bize R.
      • Johnson J.A.
      • Plotnikoff R.C.
      Physical activity level and health-related quality of life in the general adult population: a systematic review.
      ], because most studies focus on older adults and people with chronic conditions [
      • Haskell W.L.
      • Lee I.M.
      • Pate R.R.
      • et al.
      Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association.
      ]. A pragmatic RCT evaluating the national exercise referral scheme in Wales estimated a utility boost of 0.03 ± 0.023 [
      • Murphy S.M.
      • Edwards R.T.
      • Williams N.
      • et al.
      An evaluation of the effectiveness and cost effectiveness of the National Exercise Referral Scheme in Wales, UK: a randomised controlled trial of a public health policy initiative.
      ]. We added this in the first year of intervention to reflect the short-term benefits of physical activity.

      Value of Information Analysis

      We had originally intended to conduct a value of information analysis on the entire decision problem. Nevertheless, because of computational difficulties for such a complex model, we limited this to a comparison between the pedometer BIs and current practice. This was chosen because we selected a pedometer intervention as the most promising VBI for trial evaluation in our wider research program [
      • Pears S.
      • Bijker M.
      • Morton K.
      • et al.
      A randomised controlled trial of three very brief interventions for physical activity in primary care.
      ]. Thus, the analysis established whether or not there was an economic case for a trial of a pedometer BI.
      We estimated the expected value of perfect information (EVPI) as the difference between the expected value of decision (i.e., the maximum expected NMB) with perfect information and that with current information [
      • Wilson E.C.
      A practical guide to value of information analysis.
      ]. Because the value for additional information is related to the size of the eligible population, the EVPI was multiplied with the estimated Health Check population to estimate the population EVPI. Given that approximately 30% of the population is on a primary care disease register [

      Public Health England. NHS Health Check Single Data List Returns: a brief guide for local authorities. Available from: http://www.healthcheck.nhs.uk/document.php?o=537. [Accessed November 14, 2014].

      ], the eligible Health Check population (i.e., adults aged 40–74 years) over 10 years equates to approximately 19.1 million. The population EVPI provides the upper bound on the value of future research. To identify a parameter or group of parameters that contribute to most of the overall decision uncertainty and for which future research is the most promising, we estimated the expected value of partial perfect information (EVPPI) [
      • Wilson E.C.
      A practical guide to value of information analysis.
      ,
      • Briggs A.
      • Claxton K.
      • Sculpher M.J.
      Decision Modelling for Health Economic Evaluation.
      ,
      • Coyle D.
      • Oakley J.
      Estimating the expected value of partial perfect information: a review of methods.
      ]. For this, we grouped model parameters into the following six subgroups: research on 1) intervention effects, 2) health state utilities, 3) costs, 4) risk of myocardial infarction, 5) risk of stroke, and 6) parameters used in systolic blood pressure equation. The EVPPI was calculated using a two-level Monte-Carlo sampling loop, in which the parameter(s) of interest was sampled 500 times in the outer loop, and for every iteration the remaining parameters were sampled 1000 times in the inner loop [
      • Tappenden P.
      • Chilcott J.B.
      • Eggington S.
      • et al.
      Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-beta and glatiramer acetate for multiple sclerosis.
      ,
      • Brennan A.
      • Kharroubi S.
      • O’Hagan A.
      • et al.
      Calculating partial expected value of perfect information via Monte Carlo sampling algorithms.
      ]. Multiplying the EVPPI values per patient with the eligible Health Check population resulted in the population EVPPI.

      Results

      Cost-Effectiveness Analysis

      In the base-case, the point estimates for per-person costs and QALYs for all interventions were similar (Table 2). Pedometer BIs dominated both advice/counseling and action planning BIs; that is, pedometer BIs were both less expensive and more effective. When compared with current practice, all three BIs were both more effective and more costly.
      Table 2Cost-effectiveness of brief interventions over 10 y (base-case costs, QALYs, and NMBs)
      Brief interventionMean cost (SE)Mean QALY (SE)Mean NMB
      NMB calculated at a WTP of £20,000/QALY.
      (SE)
      ICER
      Current practice£1712 (583)7.848 (0.228)£155,254 (5072)
      Action planning£1738 (583)7.851 (0.228)£155,291 (5079)Extendedly dominated
      Advice/counseling in primary care£1758 (580)7.857 (0.229)£155,378 (5084)Dominated by pedometers
      Pedometer interventions£1723 (579)7.864 (0.229)£155,549 (5097)£687.50
      ICER, incremental cost-effectiveness ratio; NMB, net monetary benefit; QALY, quality-adjusted life-year; SE, standard error; WTP, willingness to pay.
      low asterisk NMB calculated at a WTP of £20,000/QALY.

      Analysis of Uncertainty

      The scatterplot of incremental costs versus incremental QALYs (comparing each BI with current practice) for the 10,000 iterations showed points scattered across all four quadrants of the cost-effectiveness plane, with most of the points overlapping with each other (Fig. 2A). The CEAC (Fig. 2B) showed that pedometer BIs were the optimal option in 56% of the 10,000 model simulations at a WTP of £20,000/QALY. Advice/counseling intervention was optimal in 22% of the iterations at a WTP of £20,000/QALY. Current practice and action planning interventions had similar CEACs showing less than 13% probability of being the most cost-effective.
      Fig. 2
      Fig. 2(A) Cost-effectiveness plane showing incremental costs and effects of BIs as compared with current practice (10,000 simulations); circles represent mean point estimates. (B) CEACs showing probability of interventions being optimal by threshold value. (C) Sensitivity analysis of intervention cost-effectiveness to decay in intervention effects at a WTP of £20,000/QALY. (D) Sensitivity analysis of intervention cost-effectiveness to the intervention repeat year at a WTP of £20,000/QALY. BI, brief intervention; CEAC, cost-effectiveness acceptability curve; QALY, quality-adjusted life-year; WTP, willingness to pay.

      Scenario Analysis

      As the intervention decay rate increases, BIs become less cost-effective (Fig. 2C). At higher intervention decay rates, the expected NMBs of all interventions were quite similar, ultimately dropping below that of current practice. This is to be expected because the treatment effect declines to that of current practice. For the scenario analysis with the interventions being repeated once every 2, 5, and 10 years (Fig. 2D), respectively, pedometer BIs were found to be the optimal option for all three repeat-year scenarios. The expected NMB for pedometer interventions was highest when the intervention was repeated once every 2 years.
      The inclusion of short-term health gains (utility boost) from increased physical activity (see Appendix Table 4.1 in Supplemental Materials found at http://dx.doi.org/10.1016/j.jval.2017.07.005) had similar results as in the base-case analysis, with pedometer BIs as the most cost-effective intervention. Nevertheless, there was a parallel shift upward for the three BIs. The probabilities of pedometer and advice/counseling BIs being cost-effective at a WTP of £20,000/QALY increased to 61% and 24%, respectively, up from 56% and 21% in the base-case scenario.

      Value of Information Analysis

      At a WTP of £20,000/QALY, the base-case per-person EVPI associated with a decision between pedometer BIs and current practice was £97 (or £1.85 billion in total) to the NHS Health Check population (Fig. 3A). This means that at a WTP of £20,000/QALY, the upper limit for research into which intervention is most cost-effective is £1.85 billion. Among the groups of different parameters, intervention effects had the highest population EVPPI of £708 million, followed by costs (£690 million) and risk of stroke (£684 million) parameters at a WTP of £20,000/QALY (Fig. 3B).
      Fig. 3
      Fig. 3(A) EVPI against varying WTP values for cost-effectiveness. (B) Partial EVPI results for the base-case at a WTP of £20,000/QALY. EVPI, expected value of perfect information; QALY, quality-adjusted life-year; SBP, systolic blood pressure; WTP, willingness to pay.

      Discussion

      What This Study Shows

      In this study, we estimated the expected long-term costs and health outcomes of BIs that could potentially be used to increase physical activity among apparently healthy adults who are eligible for NHS Health Check in primary care in England. The health benefits of increasing physical activity levels were simulated using effects of BIs reported in the meta-analyses of RCTs and synthesized in a decision-analytic cost-effectiveness model. In the base-case analysis, we found that pedometer BIs were dominant; that is, they were less costly and had better outcomes (QALY gains) than other BIs. The value of information analysis for pedometer BIs versus current practice showed that the expected value of conducting further research to eliminate decision uncertainty was £1.85 billion, assuming a time horizon of 10 years, and that further research that would eliminate uncertainty in intervention effects for the NHS Health Check population would be worth £708 million (Fig. 3B). We explored the impact of repeating the BI, and repetition every 2 years seems to be the most efficient interval.
      Care must be taken in interpreting the value of information statistics. A new study will not eliminate uncertainty, but is expected to reduce it. Therefore, the expected value of sample information (EVSI) of such a study will be less than the EVPI. We attempted to calculate the EVSI and the expected net gain of sampling (defined as the EVSI less the total cost of a proposed study) but because of computational demands we were unable to generate meaningful and stable results. Nevertheless, given the size of the EVPI, it may be reasonable to suggest that there is scope for further research to be efficient. Although all parameter groups had a similar EVPPI, the highest was in intervention effectiveness.

      Implications for Policy

      Our results show that using pedometer BIs appears to be a cost-effective way of promoting physical activity when compared with using other BIs such as advice/counseling in primary care. Delivering the pedometer BI once every 2 years appears to be the most efficient repeat interval. Nevertheless, this is contingent on the assumed “decay rate” of the intervention effect and the ability of repeat contacts to maintain physical activity.
      The value of information analysis suggests that there may be value in further exploring the effectiveness of a general class of pedometer BIs: the population EVPI of £1.85 billion is certainly way higher than the cost of any plausible design of an RCT, suggesting that further research could be efficient. Nevertheless, a definitive answer requires calculation of the expected net gain of sampling for a particular trial. Given that this analysis is part of a research program on VBIs, the logical next question is whether it is worth investigating whether a shortened VBI pedometer intervention, incorporated as part of the NHS Health Check, would be of value. Given that it is highly likely that future research into pedometer BIs is efficient and that less is known about the effectiveness and cost-effectiveness of VBIs, it is reasonable to suggest that exploration of the effectiveness and cost-effectiveness of a pedometer VBI is efficient.

      Comparison with Other Studies

      Our cost-effectiveness results were mostly more favorable than the results of Over et al. [
      • Over E.A.
      • Wendel-Vos G.W.
      • van den Berg M.
      • et al.
      Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study.
      ] and Gulliford et al. [
      • Gulliford M.C.
      • Charlton J.
      • Bhattarai N.
      • et al.
      Impact and cost-effectiveness of a universal strategy to promote physical activity in primary care: population-based cohort study and Markov model.
      ] studies of pedometer and brief exercise advice/counseling interventions, respectively, but were not as favorable as reported by Cobiac et al. [
      • Cobiac L.J.
      • Vos T.
      • Barendregt J.J.
      Cost-effectiveness of interventions to promote physical activity: a modelling study.
      ] for pedometer interventions. This could be because intervention costs in our study were lower than in the studies by Over et al. [
      • Over E.A.
      • Wendel-Vos G.W.
      • van den Berg M.
      • et al.
      Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study.
      ] and Gulliford et al. [
      • Gulliford M.C.
      • Charlton J.
      • Bhattarai N.
      • et al.
      Impact and cost-effectiveness of a universal strategy to promote physical activity in primary care: population-based cohort study and Markov model.
      ]. Nevertheless, Cobiac et al. [
      • Cobiac L.J.
      • Vos T.
      • Barendregt J.J.
      Cost-effectiveness of interventions to promote physical activity: a modelling study.
      ] used much lower intervention costs per person than used in our model. In addition, differences in the target population, modeling methods, inclusion of diseases/comorbidities and short-term health gains, and assumptions on intervention decay could explain the observed differences in the cost-effectiveness results. Two modeling studies [
      • Gulliford M.C.
      • Charlton J.
      • Bhattarai N.
      • et al.
      Impact and cost-effectiveness of a universal strategy to promote physical activity in primary care: population-based cohort study and Markov model.
      ,
      • Cobiac L.J.
      • Vos T.
      • Barendregt J.J.
      Cost-effectiveness of interventions to promote physical activity: a modelling study.
      ] were driven by a disease burden modeling strategy, and Anokye et al. [
      • Anokye N.K.
      • Lord J.
      • Fox-Rushby J.
      Is brief advice in primary care a cost-effective way to promote physical activity?.
      ] included short-term mental health gains in their cost-effectiveness analysis. Nevertheless, in their analyses [
      • Gulliford M.C.
      • Charlton J.
      • Bhattarai N.
      • et al.
      Impact and cost-effectiveness of a universal strategy to promote physical activity in primary care: population-based cohort study and Markov model.
      ,
      • Anokye N.K.
      • Lord J.
      • Fox-Rushby J.
      Is brief advice in primary care a cost-effective way to promote physical activity?.
      ] they did not take into account any decay in the intervention effects over time.

      Strengths and Limitations

      Our model is comprehensive and complex in terms of the inclusion of physical activity dose-response relationship and comorbidities related to physical inactivity and obesity. Our analysis was based on meta-analyses of RCTs evaluating the effect of BIs on physical activity levels, incorporated into a comprehensive decision model to project costs and outcomes associated with each intervention (plus current practice) over the long-term. We included interventions that are low-cost and relevant to a primary care setting.
      Nevertheless, there are limitations to our approach, including the structure of the model and data inputs. Not all the data sources, for example, transition probabilities and health state utilities, were available for the NHS Health Check population. We included only those disease conditions related to physical inactivity and obesity for which dose-response evidence was available. Only one of the meta-analyses [
      • Orrow G.
      • Kinmonth A.L.
      • Sanderson S.
      • et al.
      Effectiveness of physical activity promotion based in primary care: systematic review and meta-analysis of randomised controlled trials.
      ] focused on interventions delivered in primary care, and all three meta-analyses included several studies in which participants had a chronic condition. Nevertheless, on the basis of the intervention descriptions (see Appendix 2 in Supplemental Materials), many of them are arguably amenable to delivery in a primary care setting and suitable for apparently healthy populations. Translating a wide range of physical activity measures used in the primary studies into a common metric (here MET-hours) entails potential for error. It was not always possible to translate intervention effects directly into MET-hours. Although we excluded 5 of the 19 studies included in the meta-analysis of action planning interventions [
      • Belanger-Gravel A.
      • Godin G.
      • Amireault S.
      A meta-analytic review of the effect of implementation intentions on physical activity.
      ], the intervention effect size after excluding those 5 studies did not change.
      The true rate of decline in intervention effects and how it differs across interventions over time are unknown, because much of the evidence is based on studies with short (i.e., 12 months or less) follow-up [
      • Marcus B.H.
      • Dubbert P.M.
      • Forsyth L.H.
      • et al.
      Physical activity behavior change: issues in adoption and maintenance.
      ,
      • Roux L.
      • Pratt M.
      • Tengs T.O.
      • et al.
      Cost effectiveness of community-based physical activity interventions.
      ]. Moreover, the “active ingredients” differ across interventions as well as the extent to which interventions are able to prompt enactment of behavior change techniques in daily life (e.g., goal setting and action planning) and only the meta-analysis of advice/counseling interventions [
      • Orrow G.
      • Kinmonth A.L.
      • Sanderson S.
      • et al.
      Effectiveness of physical activity promotion based in primary care: systematic review and meta-analysis of randomised controlled trials.
      ] included studies with 12 months or longer of follow-up.
      In the base-case analysis, we assumed that the intervention effects are sustained for the first year and decay at a rate of 55% per annum thereafter, irrespective of intervention duration and length of follow-up. This assumption may have overestimated the intervention effects of pedometers and action planning interventions, leading to an underestimation of the incremental cost-effectiveness ratio. In addition, our assumptions on sustained effects of intervention beyond 1 year are arbitrary and may be a strong assumption. Previously reported modeling studies [
      • Over E.A.
      • Wendel-Vos G.W.
      • van den Berg M.
      • et al.
      Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study.
      ,
      • Cobiac L.J.
      • Vos T.
      • Barendregt J.J.
      Cost-effectiveness of interventions to promote physical activity: a modelling study.
      ,
      • Jacobs-van der Bruggen M.A.
      • Bos G.
      • Bemelmans W.J.
      • et al.
      Lifestyle interventions are cost-effective in people with different levels of diabetes risk: results from a modeling study.
      ], however, assumed similar base-case decay rates, varying between 50% and 55%.
      The effect of pedometer BIs was based on a meta-analysis that included only 277 participants in total. In addition, the pooled intervention effects of BIs included in this analysis are based on a broad range of similar interventions and not all the included interventions are sufficiently brief to be used in primary care. Hence, this category represents a broad class of interventions. Although “no intervention” was the typical comparator in the studies included in the three meta-analyses, some of the studies evaluated the intervention against a comparator intervention only (see Appendix 2 in Supplemental Materials).

      Conclusions

      On the basis of currently available data, the use of pedometer BIs appears to be the most cost-effective strategy to increase physical activity in primary care. Nevertheless, there is substantial uncertainty, and BIs yielded only relatively small health benefits. The sensitivity analysis suggests that repeating BIs once every 2 years could be the most efficient repeat interval. If further research were to be conducted, research on the effectiveness of brief and very brief pedometer BIs in primary care would be a worthwhile investment.

      Acknowledgments

      We thank Mr Richard Parker from the University of Edinburgh for his statistical advice in translating intervention effects into MET-hours and Dr Dena Bravata from Stanford University for providing additional data on the pedometer meta-analysis. This study was conducted on behalf of the Very Brief Interventions Programme Team (see http://www.phpc.cam.ac.uk/pcu/research/research-projects-list/vbi/vbi-research-team for a list of the team members). Preliminary findings were presented in part at the first European Health Economics Association PhD Student Supervisor and Early Career Researcher Conference (poster presentation) on September 1–3, 2014, in Manchester, UK. We thank the conference participants for their thoughtful and helpful remarks on this work. Any errors or omissions are those of the authors alone. The research presented in this article was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.
      Source of financial support: This article presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (grant no. RP-PG-0608-10079). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. The funder had no role in study design, data collection, data analysis, data interpretation, the writing of the article, and decision to submit the manuscript for publication. E. C. F. Wilson is funded by the NIHR Cambridge Biomedical Research Centre.

      Supplemental materials

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