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MapMySmoke - Primary Care Validation

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Abstract

Since smartphones can log spatial, as well as quantitative and qualitative data related to smoking behaviour, we can support smokers by first understanding their smoking behaviour and then sending dynamic support messages post-quit.
MapMySmoke
A Context Aware Mobile Phone Application
Targeted at Smoking Cessation
Tom Kelsey
School of Computer Science
University of St Andrews
https://tom.home.cs.st-andrews.ac.uk
twk@st-andrews.ac.uk
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 1 / 16
My Research Background
The late effects of treatment on the fertility of survivors of
childhood cancer (1, 2, 6)
Normative models in reproductive endocrinology &
physiology (3, 4, 5, 9, 10)
Normative models of the human ovarian reserve (7, 8)
Selected recent references are at the end of this presentation
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 2 / 16
The Team
Rob Schick – School of Mathematics & Statistics
Gerry Humphris – School of Medicine
Tom Kelsey – School of Computer Science
John Marston – Primary Care physician, Fife
Kay Sampson – Tobacco Co-ordinator, Fife Health & Social
Care Partnership
Funding from NHS Fife R & D, the Fife Health and Social Care
Partnership, and the EPSRC Impact Acceleration Account.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 3 / 16
Objectives
With the increasing availability of smartphones, we sought to
investigate whether:
An app could be deployed with smokers intending to quit
An app would increase understanding of individual and
population level smoking behavoiour
Knowledge of craving and smoking behaviour helps
smokers quit
App-based delivery of support messages could better
support individual quit attempts
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 4 / 16
Background
11,000 people die in Scotland each year from smoking related
causes.
While quitting smoking is relatively easy, maintaining a quit
attempt is very difficult. Pharmaceutical treatments improve
abstinence rates, however they do not address the spatial aspects
of smoking behaviour.
Since smartphones can log spatial, as well as quantitative and
qualitative data related to smoking behaviour, we can support
smokers by first understanding their smoking behaviour and
then sending dynamic support messages post-quit.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 5 / 16
Screenshots from the MapMySmoke app
One data entry screen and two visual summaries
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Methods
We have built a
smartphone app
that works on Android and iOS
platforms.
The deployment of this app within a clinical NHS setting has two
distinct phases:
1
a two-week logging phase where pre-quit patients log all of
their smoking and craving events
2a post-quit phase where users receive dynamic support
messages and can continue to log craving events, and
should they occur, relapse events.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 7 / 16
Methods
Following the initial logging phase, patients will consult with
their GP to review their smoking patterns and to outline an
individualised quit-attempt plan.
We will deploy the app through two feasibility studies within
NHS Fife with 10 and 100 patients, respectively. Phase I
recruitment was done through Dr Kyle & Partners Surgery in
Pittenweem.
Phase II recruitment will be supervised by NHS Fife Tobacco
Services.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 8 / 16
Initial Results
We have successfully deployed the MapMySmoke app in a
clinical setting within NHS Fife.
MapMySmoke collects real-time data on smoking and craving
behaviour.
Initial feedback indicates that use of the app helps make patients
more aware and helps them resist cravings.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 9 / 16
Initial Results
The 10 consented patients have logged 124 craving events and
261 smoking events.
Patients using the MapMySmoke app have reported positive
feedback to Dr. Marston:
“The app is very useful in highlighting smoking behaviour–in
particular I found the heatmap the most helpful.”
“I like the app, and found that seeing a representation of my
smoking behaviour is both surprising and helpful.”
“Being asked to log my cravings has helped me resist
smoking.”
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 10 / 16
Example Smoking Behaviour
Size of circle corresponds to level of satisfaction of each smoking event–larger circle means more satisfaction
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 11 / 16
Digital Health Science
MapMySmoke provides useful data for the patient & smoking
cessation team to monitor and plan.
Data can also be used to trigger interventions by health
psychology clinicians and/or primary care, who have access to
the entire personalised data.
Anonymised data is available for analysis by non-clinical
researchers investigating population-based issues.
Our privacy, confidentiality & information governance scheme
has been approved by the Public Benefit and Privacy Panel for
Health and Social Care, a governance structure of NHS Scotland.
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Context Diagram
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Conclusions & Future Work
This is a complex, pragmatic intervention with technological and
clinical components working in unison.
Full release is planned, subject to careful validation of both the
clinical utility and information governance of the app.
Ethical approval & funding is being sought for studies that use
the same underlying technology
1Dental anxiety – with colleagues in Ireland
2Fear of recurrence of cancer – with colleagues in Scotland
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 14 / 16
References
1: McLaughlin M, et al. Non-growing follicle density is increased following
adriamycin, bleomycin, vinblastine and dacarbazine (ABVD) chemotherapy in
the adult human ovary. Hum Reprod. 2016
2: El Issaoui M, et al. Effect of first line cancer treatment on the ovarian reserve
and follicular density in girls under the age of 18 years. Fertil Steril.
2016;106(7):1757-1762.e1.
3: Andersen CY, et al. Micro-dose hCG as luteal phase support without
exogenous progesterone administration: mathematical modelling of the hCG
concentration in circulation and initial clinical experience. J Assist Reprod
Genet. 2016;33(10):1311-1318.
4: Kelsey TW, et al. A Validated Normative Model for Human Uterine Volume
from Birth to Age 40 Years. PLoS One. 2016 Jun 13;11(6):e0157375.
5: Kelsey TW, et al. A Normative Model of Serum Inhibin B in Young Males.
PLoS One. 2016 Apr 14;11(4):e0153843.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 15 / 16
References cont.
6: Wallace WH, et al. Fertility preservation in pre-pubertal girls with cancer: the
role of ovarian tissue cryopreservation. Fertil Steril. 2016;105(1):6-12.
7: McLaughlin M, et al. An externally validated age-related model of mean
follicle density in the cortex of the human ovary. J Assist Reprod Genet.
2015;32(7):1089-95.
8: Depmann M, et al. The Relationship Between Variation in Size of the
Primordial Follicle Pool and Age at Natural Menopause. J Clin Endocrinol
Metab. 2015;100(6):E845-51.
9: Kelsey TW, et al. A validated age-related normative model for male total
testosterone shows increasing variance but no decline after age 40 years. PLoS
One. 2014;9(10):e109346.
10: Kelsey TW, et al. Ovarian volume throughout life: a validated normative
model. PLoS One. 2013;8(9):e71465.
Tom Kelsey Digital Health Initiative Forum 20 Dec 2016 16 / 16
ResearchGate has not been able to resolve any citations for this publication.
Article
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