Evaluations of the NHS Health Trainer Service

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%)
Measures
Pre- 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])
Results
1) 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):
Q2
Q3
Q4
Q5 (least deprived)
1836 (43.2%)
1093 (25.7%)
688 (16.2%)
405 (9.5%)
230 (5.4%)
Results
1) Who uses the HTS?
• Ethnicity:
–
–
–
–
White
Asian
Black
Mixed or other
(UK 2001 = 93% White)
3647 (83.2%)
485 (11.1%)
175 (4.0%)
79 (1.8%)
Results
1) Who uses the HTS – and for what purpose?
• Weight status:
– Obese
– Overweight
– Normal weight
2717 (72.3%)
824 (22.4%)
218 (5.8%)
• PHP focus:
– Diet
– Physical activity
3346 (75.7%)
1072 (24.3%)
Results
2) 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
Results
2) 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
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
[email protected]