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Health behaviour change among users of NHS Health Trainer Services

Health behaviour change among users of NHS Health Trainer Services. Benjamin Gardner 1 , James Cane 1 , Nichola Rumsey 2 & Susan Michie 1 1: University College London; 2: University of the West of England 3 rd July 2012.

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Health behaviour change among users of NHS Health Trainer Services

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  1. Health behaviour change among users of NHS Health Trainer Services Benjamin Gardner1, James Cane1, Nichola Rumsey2 & Susan Michie1 1: University College London; 2: University of the West of England 3rd July 2012

  2. This work was undertaken as part of a BPS DHP consultancy to the Department of Health(2003-2010)

  3. Evaluations of the NHS Health Trainer Service • 2007-09: data from hub leads (‘hub reports’) • Yearly audits of workforce and clients • Who are the HTs? • Is the workforce growing? • Who is using the HT service? (Wilkinson et al, 2007; D Smith et al, 2008) • 2009: DCRS data • Evaluation of service effectiveness • Does behaviour change among users of the HT service?

  4. Questions • Who uses the HT service? - Are we reaching ‘hard to reach’ clients? • Does (diet and activity) behaviour change following use of HT service? • Do all clients benefit equally?

  5. Data • Drawn from DCRS • Period: 1st April 2008 – 31st March 2009 • Data extracted from DCRS v2.4 by BPCSSA • Final extraction for DCRS report: December 2009 • Final extraction for paper mid-2010 • Data recording on DCRS then non-compulsory • At start of time period, estimated from hub report that 62% of HTSs entered data into DCRS • Paper accepted for publication in Dec 2011

  6. Data availability

  7. Drop-out bias? • Setting PHPs: • White clients (35%) and Asian clients (30%) more likely to set PHPs than Black clients (25%) • More PHPs set in least deprived quintile (42%) than others (~36%) • Pre-post HTS data availability: • White clients (35%) more likely to have pre-post than Asian (30%) or Black clients (27%) • More data available in least deprived quintile (45%) than others (~29%)

  8. MeasuresPre- and post-HTS - Baseline demographics - Pre- and post-HTS: • Behaviour measures • BMI (height, weight) • Self-reported behaviour (diet [snacks, fruit & veg], activity [moderate/intensive sessions])

  9. Results1) Who uses the HTS? • 3503 female (79%) (UK population, 2001 = 51% female) • Typical age 36-45 years (22.4%) (UK 2001 = 19%) • Deprivation: • Q1 (most deprived): 1836 (43.2%) • Q2 1093 (25.7%) • Q3 688 (16.2%) • Q4 405 (9.5%) • Q5 (least deprived) 230 (5.4%)

  10. Results1) Who uses the HTS? • Ethnicity:(UK 2001 = 93% White) • White 3647 (83.2%) • Asian 485 (11.1%) • Black 175 (4.0%) • Mixed or other 79 (1.8%)

  11. Results1) Who uses the HTS – and for what purpose? • Weight status: • Obese 2717 (72.3%) • Overweight 824 (22.4%) • Normal weight 218 (5.8%) • PHP focus: • Diet 3346 (75.7%) • Physical activity 1072 (24.3%)

  12. Results2) Diet change following diet PHP achievement

  13. Results2) Activity change following activity PHP achievement

  14. 3) Do all clients benefit equally? • Ethnicity or deprivation differences? • All clients • Deprivation & BMI: • Less BMI reduction in most deprived quintile vs all others (0.28 BMI points) • Diet: • Deprivation & BMI: • Less BMI reduction in most deprived quintile vs all others (0.24 BMI points) • Ethnicity & BMI: • Less BMI reduction in Asian versus White clients (0.55 BMI points)

  15. Conclusions • HTS is reaching disadvantaged clients and changing behaviour • Effects similar across demographic groups • But more PHPs set and more data recorded in less deprived groups

  16. Challenges and recommendations • Missing data problematic • Pre- and post-HTS behaviour data essential • Reliance on self-report • May overestimate behaviour change • Ideally need objective measures, e.g. biochemical verification, objectively measured weight • Whether data self-report or objective should be recorded

  17. Challenges and recommendations • Need to ensure continued fidelity to HTS as originally devised • Qualitative data needed • Quantitative data allows for ‘birds eye view’ group-level analyses • Qualitative data engages with contextualised individual experiences • Would reveal ‘real-life’ benefits of HTS

  18. Challenges and recommendations • Qualitative data needed • Brief interviews with clients/feedback from clients? • How do clients feel they have benefitted? • Written case studies? • Description of individual client’s journey • Need a DCRS repository for qualitative evidence storage

  19. Thank you Acknowledgements: Janet Andelin and Rachel Carse, Dept of Health Jan Smith, CORE, UCL Ertan Fidan & David Hopkinson, Birmingham Primary Care Shared Services Agency For a copy of the published paper, contact me at b.gardner@ucl.ac.uk

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