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

This work was undertaken as part

of a BPS DHP consultancy to the

Department of Health

(2003-2010)

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?

Questions

1) Who uses the HT service?- Are we reaching ‘hard to reach’ clients?

2) Does (diet and activity) behaviour change following use of HT service?

3) Do all clients benefit equally?

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

Data availability

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%)

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])

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%)

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%)

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%)

Results2) Diet change following diet PHP achievement

Outcome Number of clients

Pre-HTS mean

Post-HTS mean

% change

Daily fruit & veg

(portions)

2376 3.08 5.23 70% increase

No. of daily fried snacks

1869 1.99 0.79 60% decrease

BMI 3164 34.33 32.45 6% decrease

Results2) Activity change following activity PHP achievement

Outcome N Pre-HTS mean

Post-HTS mean

% change

Weekly moderate sessions

921 3.06 4.77 56% increase

Weekly intensive sessions

637 0.63 1.71 171% increase

BMI 595 32.46 31.24 4% decrease

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)

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

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

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

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

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

Thank you