Using visualisation techniques to
analyse engagement with digital
interventions
Prof Lucy Yardley, L.Yardley@soton.ac.uk
Centre for Applications of Health Psychology,
University of Southampton
23rd February, 2015, CBC Conference, UCL, London
Visual analysis of usage data
• Plots what parts of the intervention each user has viewed, in
what order, and for how long.
• R statistical software environment + our own web-based
user interface
• Designed for interventions created using LifeGuide
software (www.lifeguideonline.org), but will accept any
usage data converted to required format
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How does the Visualisation Tool work?
Upload 4 files:
• File with usage data (page
flow, with duration of each
user on every page)
• File with user data (e.g.
demographics, questionnaire
entries)
• File with codes for analysis
(i.e. page groupings)
• File giving colour for each
code
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Example 1: POWeR
Online intervention to support weight management
Weekly access to:
a) in-depth advice sessions – first 3 are ‘core’ (tunneled),
then can choose from 9 ‘optional’ sessions (self-regulation
‘POWeR tools’ available from each session)
b) goal and weight review – tailored feedback based on
weight and goal progress entered
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Example 1: Feasibility trial of POWeR
• Feasibility trial in primary care
• 131 participants accessed at least the first POWeR session
• Usage automatically recorded by LifeGuide software
• Visualisation explored:
– Extent and impact of access to core vs. optional content
– Nature and impact of content re-use
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Use of optional content
Regular return to complete
goal and weight review
n=62 (47%) also used at least
one optional session
n=58 (44%) just used goal
and weight review
No differences in weight loss
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Re-use of POWeR Tools
Tools related to eating plans
(light pink, dark red, grey) reused most frequently (n=106,
80%)
Completed more goal and
weight reviews
No difference in weight loss
Minimal re-use of tools related
to physical activity plans (n=21,
16%)
Those who did lost more weight
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Example 2: POWeR + POWeR Tracker
• POWeR Tracker:
Android smartphone
application to
accompany the online
programme
– Usage of website and app
automatically recorded by
LifeGuide
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Observational study
• POWeR + POWeR Tracker
disseminated via workplaces in
collaboration with local public
health teams in NE England
• N=942 registered participants
• Examined whether usage patterns
of POWeR Tracker users differs
from POWeR only users
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Proportion viewing extra
intervention content (orange):
Proportion viewing problem
solving / support tools (red):
App users: 11/60 (18%)
Web-only users: 36/882 (5%)
App users: 20/60 (33%)
Web-only users: 82/882 (9%)
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Visualising usage data: Advantages
• Facilitates faster exploratory analyses (can perform
follow-up statistical analyses for precise estimates)
• Identifies patterns of intervention usage and dropout
• Identifies ‘active’ components that may mediate
health-related outcomes
• Useful as part of a mixed-methods approach
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Acknowledgements
• The Southampton team: Dr Leanne Morrison,
Dr Mary Steele, Dr Judith Joseph, Dr Laura
Dennison, Dr Charlie Hargood, Dr Danius
Michaelides, Dr Sharon Lin, Prof Peter Smith,
Dr Katherine Bradbury, Dr Emily Smith, Dr
Mark Weal, Prof Paul Little
• External collaborators: Dr Emily Arden-Close
(Bournemouth), Prof Susan Michie (UCL)
• Funders: NIHR, MRC and EPSRC
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