Exposure to Secondhand Smoke in Exempted Areas/Outside Areas and Acute Health Effects in Patients With Chronic Lung Disease
Phase: NA
Conditions: COPD, Asthma, Second Hand Tobacco Smoke
Sponsor: Institut Català d'Oncologia

 
 
 
 
 
 
 
TackSHS WP5 
 
Health Effects of Secondhand Smoke Exposure in Outdoor Smoking 
Areas in Patients With COPD and Asthma ( TackSHSWP5 ) 
 
 
NCT03074734 
 
Date: 21/10/2019 
 
 
 
 
 
 
 
 
 
 
 
 

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Tackling secondhand tobacco smoke and e-cigarette emissions: 
exposure assessment, novel interventions, impact on lung diseases and 
economic burden in diverse European populations (TackSHS) 
Call:  H2020-HCO-2014-2015 / H2020-HCO-2015 
Topic: HCO-06-2015 Global Alliance for Chronic Diseases. Prevention and 
treatment of lung diseases 
WP: 5 
Date: 21/10/2019 
Authors: S Keogan/ L Clancy  
Lead Beneficiary: TFRI 
Dissemination level: Confidential 
 
 

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and that the Commission is not responsible for any use that may be made of the information it contains.    
 
 
 
 
 
This project has received funding from the European Union’s 
Horizon 2020 research and innovation programme under grant 
agreement No. 681040 
 
 
Disclaimer 
This document was developed by the TackSHS Project Consortium and does not necessarily 
reflect the views of the European Commission. The European Commission is not responsible 
for any use that may be made of the information that contains in this document. 
Acknowledgement 
This document was developed under the Project TackSHS that received funding from the 
European Union’s Horizon 2020 research and innovation programme under grant agreement 
No 681040. 
More information 
Public reports of the TackSHS Project and other information pertaining to the project are 
available through TackSHS public Web Site: http://www.tackshs.eu.  
 
 

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Protocol title: Exposure to Secondhand smoke in exempted areas/outside areas and 
acute health effects in patients with chronic lung disease.  
EU Horizon 2020 Grant NUMBER — 681040  
Aug 2016 
Version 2 
DIT Ethics Ref: 15-103 
 
• 
Funded by EU Horizon 2020 
• 
Name and title of the  WorkPackage 5 Leader:  
Professor Luke Clancy. 
 
 
 
 
Co-Investigator/co-ordinator: Sheila Keogan 
• 
TobaccoFree Research Institute Ireland (TFRI), DIT, Focas Research Institute, 
Kevin Street, Dublin 8. 
• 
Ph. + 353868364337, +353876887678  
• 
Email lclancy@tri.ie; skeogan@tri.ie 
 
 
 
 
 
This document is confidential and the property of TobaccoFree Research Institute Ireland.  No 
part of it may be transmitted, reproduced, published, or used without prior written authorization 
from the institution. 
 
 
 
 
 

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Table of Contents 
 
DELIVERABLE: D5.1 ....................................................................... ERROR! BOOKMARK NOT DEFINED. 
DISCLAIMER ...................................................................................................................................................... 3 
PROTOCOL TITLE: .......................................................................................................................................... 4 
TABLE OF CONTENTS .................................................................................................................................... 5 
STATEMENT OF COMPLIANCE .................................................................................................................... 7 
WP5 PRINCIPLE INVESTIGATOR: .............................................................................................................. 7 
CO-INVESTIGATOR/COORDINATOR: ....................................................................................................... 7 
PROJECT SUMMARY ....................................................................................................................................... 7 
GLOSSARY OF ABBREVIATIONS: ................................................................................................................ 8 
RATIONALE & BACKGROUND INFORMATION ...................................................................................... 9 
STUDY GOALS AND OBJECTIVES .............................................................................................................. 14 
THE PROJECT OBJECTIVES ARE: .............................................................................................................. 14 
STUDY DESIGN ............................................................................................................................................... 14 
POPULATION: ................................................................................................................................................................. 14 
INCLUSION CRITERIA FOR EACH PATIENT GROUP: ................................................................................................... 14 
EXCLUSION CRITERIA: .................................................................................................................................................. 15 
PRIMARY ENDPOINTS: ................................................................................................................................ 15 
SECONDARY ENDPOINTS .............................................................................................................................................. 16 
ETHICS: ............................................................................................................................................................. 16 
METHODOLOGY:............................................................................................................................................ 16 
RECRUITMENT ............................................................................................................................................................... 16 
CONSENT: ......................................................................................................................................................... 17 
DEVICES USED TO GATHER DATA:.......................................................................................................... 17 
CAREFUSION CO MONITOR ......................................................................................................................................... 17 
SPIROMETER: ................................................................................................................................................................. 17 
RESPECK: ....................................................................................................................................................................... 18 
AIRSPECK: ..................................................................................................................................................................... 19 
PATIENT SCHEDULE: ................................................................................................................................... 19 
TABLE OF TASKS ........................................................................................................................................... 20 
OTHER SUPPORT FOR THE PROJECT:.................................................................................................... 21 
ADVISORY PANEL: TACK PROJECT PARTNERS: ................................................................................. 21 
SAFETY CONSIDERATIONS ........................................................................................................................ 21 
DATA MANAGEMENT AND STATISTICAL ANALYSIS: ........................................................................ 21 
QUALITY ASSURANCE: ................................................................................................................................. 22 
EXPECTED OUTCOMES OF THE STUDY ................................................................................................. 22 

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DISSEMINATION OF RESULTS AND PUBLICATION POLICY: ........................................................... 22 
PROBLEMS ANTICIPATED: ........................................................................................................................ 23 
PROJECT MANAGEMENT ............................................................................................................................ 23 
DURATION OF THE PROJECT: ................................................................................................................... 24 
GANTT CHART ................................................................................................................................................ 24 
APPENDIX 1. ................................................................................................................................................... 25 
PATIENT INFORMATION SHEET AND CONSENT FORM ........................................................................................... 25 
APPENDIX 2: ................................................................................................................................................... 29 
RECRUITMENT QUESTIONNAIRE ................................................................................................................................ 29 
APPENDIX 3: ................................................................................................................................................... 32 
CO Monitor ....................................................................................................................................................................... 32 
APPENDIX 4: ................................................................................................................................................... 36 
RESPECK ......................................................................................................................................................................... 36 
APPENDIX 5: ................................................................................................................................................... 42 
AIRSPECK....................................................................................................................................................................... 42 
APPENDIX 6 .................................................................................................................................................... 57 
APPENDIX 7 .................................................................................................................................................... 58 
 
 
 

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STATEMENT OF COMPLIANCE  
 
This document is a protocol for a clinical research study. The study will be conducted in 
compliance with all stipulations of this protocol, the conditions of ethics committee approval, 
the EU Directive 2001/20EC on Clinical Trials and the Note for Guidance on Good Clinical 
Practice (CPMP/ICH-135/95).  
 
WP5 Principle Investigator:  
Professor Luke Clancy 
Co-Investigator/co-ordinator:     Ms Sheila Keogan 
Project summary 
This Project will try to elucidate the comprehensive impact that SHS and e-cigarettes emissions 
have on the European population and how health impacts vary according to socio-economic 
parameters with particular emphasis on specific vulnerable groups (patients suffering from pre- 
existing chronic lung diseases, heavy smokers, and other disadvantaged groups). Thus, we aim 
to develop and integrate implementation research on the determinants of SHS exposure, both 
assessed at the individual level and in the environment, the overall burden of disease caused 
(lung diseases and also cardiovascular diseases), including the specific respiratory health 
changes in patients and healthy people, the economic impact of both mortality and morbidity 
caused by these exposures, the methods to better characterize these exposures and novel 
interventions to reduce them. This will be achieved through a dedicated collaborative research 
effort involving expert, qualified scientists partnering a world class team to develop a 
comprehensive project to tackle secondhand tobacco smoke and e- cigarettes emissions.  
 
 
 

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GLOSSARY OF ABBREVIATIONS: 
Abreviation 
Term 
 
 
TFRI 
TobaccoFree Research Institute Ireland 
DIT 
Dublin Institute of Technology 
FCTC 
Framework Convention for Tobacco Control 
ELF 
European Lung Foundation 
COPD 
Chronic Obstructive Pulmonary disease 
EU 
European Union 
SHS 
Secondhand Smoke 
CO Monitor 
Carbon Monoxide Monitor 
PEFR 
Peak Expiratory Flow Rate 
bpm 
Beats per minute 
RR 
Respiratory Rate 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Rationale & background information 
 
Respiratory disease causes an important worldwide health burden. It is estimated that 235 
million people suffer from asthma, more than 200 million people have chronic obstructive 
pulmonary disease (COPD), 65 million endure moderate-to-severe COPD, 1–6% of the adult 
population (more than 100 million people) experience sleep disordered breathing, 8.7 million 
people develop tuberculosis (TB) annually, millions live with pulmonary hypertension and 
more than 50 million people struggle with occupational lung diseases, accounting for more 
than 1 billion persons suffering from chronic respiratory conditions (GBD 2013 Mortality and 
Causes of Death Collaborators, 2014). In 2020, of 68 million deaths worldwide, 11.9 million 
will be caused by lung diseases, 4.7 by COPD, 2.5 by pneumonia, 2.4 by TB and 2.3 million 
by lung cancer.  
Respiratory diseases burden the healthcare budgets within the European Union with direct costs 
of approximately €47.3 billion. Assuming a total EU expenditure on healthcare of 
approximately €800 billion (estimated from 9% of gross domestic product), the direct costs of 
treating respiratory diseases account for approximately 6% of the total EU healthcare budget. 
The four major respiratory diseases: COPD, asthma, pneumonia and TB have costs totaling 
€38.7, €17.7, €10.1 and €2.1 billion respectively (ERS, 2014).  
Secondhand tobacco smoke (SHS) is a complex mixture of thousands of compounds 
including particulate matter emitted by the combustion of tobacco products and from smoke 
exhaled by smokers (IARC, 2004). It contains about 70 chemicals recognized as known and 
probable human carcinogens, other animal carcinogens, and many toxic and irritant agents (US 
Department of Health and Human Services, 2006). Over the past two decades, scientific 
evidence has accumulated linking SHS exposure to adverse health outcomes, including 
respiratory outcomes in children and adults, acute cardiovascular effects, and lung cancer 
(IARC, 2004; Ott et al., 2006; US Department of Health and Human Services, 2006). Most of 
this evidence is based on long-term SHS exposure research (IARC, 2004). Some recent studies 
have also reported evidence of effects following short-term exposure to tobacco smoke, such 
as eye irritation and respiratory irritation among non-smokers (Junker et al., 2001). Even brief 
and short-term exposures to SHS may generate significant adverse effects on the human 

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respiratory system (Flouris and Koutedakis, 2011). Finally, the effects of acute exposure to 
tobacco smoke on cardiac autonomic function may contribute to pathophysiological 
mechanisms linking exposure to SHS to increased risk of cardiovascular mortality (Pope, III et 
al., 2001).  
Smoke-free policies have been expanding worldwide since the World Health Organization 
(WHO) encouraged countries to follow Article 8 of the Framework Convention on Tobacco 
Control (FCTC) (WHO 2003) to protect people from SHS (Globalsmokefree Partnership, 
2009). Legislation has been widely implemented in indoor public places, workplaces, and 
public transportation (WHO, 2009). Since the implementation of indoor smoke-free 
environments, several studies have demonstrated important reductions of SHS exposure, 
including an 80–90% decrease in previously high-exposure set- tings, such as workplaces and 
hospitality venues like bars and restaurants (IARC 2008). However, indoor smoking 
restrictions may increase the likelihood that smokers will gather at convenient outdoor 
locations such as public areas near building entrances (Kaufman et al. 2010a). In 2007, a 
revision of the FCTC Article 8 guidelines further recommended that quasi-outdoor and outdoor 
public places should be smoke-free under some circumstances, and called upon countries to 
“adopt the most effective protection against exposure wherever the evidence shows that hazard 
exists” (WHO 2009). Recently, some countries have extended smoking bans to some outdoor 
locations (Globalsmokefree Partnership 2009; Repace 2008), particularly health care centers 
and settings where children are present (Globalsmokefree Partnership 2009). However, there 
remain some outdoor locations close to smoke- free areas where people may be exposed to 
SHS, such as terraces and patios in hospitality venues and near entrances to smoke-free 
buildings (Globalsmokefree Partnership 2009).  
Some controversy exists regarding whether smoking should be prohibited in outdoor settings 
(Chapman, 2008; Thompson et al., 2008). Health concerns about SHS exposure, nuisance from 
SHS, litter, fire hazards, concern about establishing positive smoke-free models for youth, and 
reducing youth opportunities to smoke (Bloch and Shopland, 2000; Brennan et al., 2010; 
Cameron et al. 2010; Chapman, 2008; Repace, 2008; Thompson et al., 2008; Thomson et al. 
2009) exemplify the reasons why smoking should be banned in selected outdoors locations. 
Outdoor smoking bans might also support smokers who are trying to quit by limiting their 

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overall cigarette consumption (Williams et al., 2009). Selected outdoor smoking bans should 
also help to denormalise smoking in outdoor areas (Thompson et al., 2008). In a number of 
jurisdictions, the majority of the public supports restricting smoking in various outdoors 
settings, and this support appears to be increasing over time (Thomson et al., 2009). However, 
those who oppose outdoor smoking bans argue that it is ethically unsustainable because it does 
not respect the principle of freedom and autonomy of individuals, and that there is insufficient 
evidence that SHS in these environments has an impact on health (Chapman, 2000; Chapman, 
2008). Outdoor SHS has been scarcely evaluated. It has been hypothesized that the introduction 
of indoor smoking bans has led to a relocation of smokers to outdoor areas, with a subsequent 
increase of tobacco smoke levels in outdoor places (Sureda et al., 2011).  
SHS exposure has been commonly studied in different indoor locations, especially in 
workplaces such as hospitality venues or health care centers (IARC, 2009); mainly before 
launching smoke-free legislation or, in a few countries, for the evaluation of smoke-free laws. 
In the US, it is estimated that SHS exposure causes more than 41,000 deaths among 
nonsmoking adults and 400 deaths in infants each year, and approximately $5.6 billion annually 
in lost productivity. Although population exposure to SHS has declined over the past two 
decades (Homa et al., 2015), many non-smokers remain exposed to SHS in workplaces, public 
places, homes, and vehicles (Max et al., 2012). The Eurobarometer survey has incorporated a 
few questions on exposure to secondhand smoke in recent editions, and previous work from 
some of the TackSHS partners have studied the exposure to secondhand smoke in some 
European countries to be between 20 and 60% of the non-smoker population, both using a 
population- based approach and cross-sectional surveys [Lukshenkova et al., 2008; Martínez-
Sánchez et al., 2009; Sureda et al., 2014 (TackSHS Spanish partner) in Spain, Gorini et al., 
2008; Martínez-Sánchez et al., 2012; (TackSHS Italian partners) in Italy, Wallner et al., 2010 
in Austria, Vardavas et al., 2012 (TackSHS Greek partner) in Greece, Goodman et al., 2007 
(TackSHS Irish partner) in Ireland], or performing environmental measures of particulate 
matter or airborne nicotine as SHS markers [Semple et al. 2007 (TackSHS UK partner) in 
Scotland, Schneider et al., 2008 et al. in Germany, Vardavas et al., 2014 (TackSHS Greek 
partner) in Greece, and Nebot et., 2005 and Lopez et al., 2008 in 8 countries in previous 
SANCO and FP7 projects.  

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An inclusive understanding of the exposure to SHS in European populations and its societal 
mortality, morbidity and economic burden is however lacking. A comprehensive survey using 
validated questions across countries will provide an actual picture of the exposure to this 
hazard, with the possibility to compare countries with different income and vulnerable groups, 
such as people with chronic dis- eases, and specifically chronic pulmonary diseases. Moreover, 
we will be able to gain information about the attitudes of the population towards SHS, and the 
degree of agreement with smoke-free legislation. In addition to the survey, we aim at 
objectively measuring markers of secondhand smoke in non-regulated settings such as private 
places and outdoor public places. In a recent review of 18 studies that assessed secondhand 
smoke levels (Sureda et al. 2013), we found that SHS levels in outdoor smoking areas were not 
negligible, especially in areas that are semi-enclosed. This review clearly indicated the 
potential for high SHS exposures at some outdoor settings and indoor locations adjacent to 
outdoor smoking areas. We showed, moreover, that high smoker density, highly enclosed 
outdoor are as, low wind conditions, and close proximity to smokers generate higher outdoor 
SHS concentrations. Thus, our aim in the TackSHS project is to perform new measurements in 
the same countries where we will be conducting the survey –hence with the possibility to 
compare individual perceptions and assess concentrations of SHS markers-- and to take into 
account those factors that modify the exposure. Moreover, the project will quantify the 
proportion of Europeans exposed to secondhand tobacco smoke, thus enabling the estimation 
of the morbidity and mortality attributable to this hazard, and to estimate its economic burden 
by means of “Social Return on Investment” models.  
Since 2007, the popularity of electronic cigarettes (e-cigarettes) has grown rapidly around 
the world. Among the general population of the United States (US) (King et al., 2013) the 
prevalence of ever-use, according to a web-based survey, showed a twofold increase between 
2010 and 2011 (from 3.3% to 6.2%). Double similar scale of increase was also observed in US 
adolescents (Dutra and Glantz, 2014) and in middle and high school students (MMWR, 2013) 
between 2011 and 2012. In Europe, there is certain variability in the prevalence of use among 
studies, depending on the population and the questions used in the surveys (Dockrell et al., 
2013; Goniewicz et al., 2012; Douptcheca et al., 2013; Mar- tínez-Sánchez et al., 2014). Using 
data from the 2012 Eurobarometer, Vardavas et al. 2014 (TackSHS Greek partner) showed that 
7% of the European citizens have tried the e-cigarettes with experimentation significantly 

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higher among current smokers. In Italy, a survey conducted by Gallus et al. 2014 (TackSHS 
Italian partner) showed that awareness of e-cigarettes was 91.1%, lowest among some 
vulnerable populations such as women, the elderly, and less educated subjects. In Italy, ever e-
cigarette use was 6.8% overall and was inversely related to age, whereas no difference was 
observed according to sex. In Spain, studies form the TackSHS coordinators found an 
awareness of e-cigarettes lower than in Italy (82.3%) but a similar prevalence of ever e-
cigarette use of 6.5% (1.6% current use, 2.2% past use and 2.7% only e-cigarette 
experimentation) and the predominant ever and current e-cigarette use was among current 
smokers (75% of ever e-cigarette users were current smokers) (Martínez-Sánchez et al. 2014a 
and 2014b)  
Finally, reducing population exposure to secondhand tobacco smoke is a significant health 
priority. While evidence suggests that there has been an increase in the percentage of smokefree 
homes from 16% in 1998 to almost 50% in 2008 in England and Wales (Jarvis et al., 2012), 
and data from Scotland indicate that about 12% of children are regularly exposed to second-
hand smoke within their own home (Akhtar et al., 2007). This is particularly the case for 
children living in socioeconomically dis- advantaged communities (Akhtar et al., 2009). Even 
though families across all levels of deprivation have shown increased restrictions towards 
reducing SHS exposure in their homes, homes with greater disadvantage tend to have fewer 
restrictions (Phillips et al., 2007). The Scottish Government is the first country in the EU to 
introduce a national target with the aim of reducing the proportion of children exposed to 
second-hand smoke by one-half by 2020. There is a clear and significant challenge to the public 
health community on how to deliver reductions in both frequency and intensity of children’s 
exposure to SHS, particularly within socio-economically deprived communities. One known 
effective motivational tool in aiding change in smoking behaviour is the use of instruments to 
measure levels of PM2.5 as a marker of SHS, with some recent qualitative work suggesting 
that personalized biofeedback of exposure to SHS may be a key motivator to household 
smoking behaviour change (Jones et al., 2011). Additionally, there is evidence from the 
occupational health literature that suggests that measurement and feedback of information on 
personal exposure to hazards can help change behavior (Rosen et al., 2005). Thus, feedback on 
air quality within the home setting could work as a powerful tool to change the perceptions and 
behaviours of smokers. The Scottish partner of TackSHS used personalised feedback of PM2.5 

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values in addition to other methods aimed at reducing children’s SHS exposure (the REFRESH 
intervention) (Wilson et al., 2013). This feasibility study of an air quality feedback behavioural 
intervention was based on the evidence that people can make changes in response to receiving 
personalised health-based data along with evidence suggesting some effectiveness in reducing 
SHS exposure through counseling.  
Study goals and objectives 
The project objectives are: 
 
• To Measure Respiratory Health effects when exposed to SHS in  
 Patients with Asthma 
 Patients with COPD 
• Exposure levels to Second Hand Smoke (SHS) in smoking areas provided by Bars 
Casinos and Bingo halls etc. 
Study Design 
 
Population:   
The study population will consist of 30 Asthmatic and 30 Chronic Obstructive Pulmonary 
Disease (COPD) subjects to be recruited through the European Lung Foundation (ELF) in 
Hungary Spain and Ireland.  (20 subjects in each country) 
Inclusion criteria for each patient group: 
 
Confirmed Dr Diagnosed COPD patients 
 
 
Current or ex-smokers. 
 
 
Fully ambulatory. 
 
 
Frequent smoking areas* 
 
 
Between 50yrs and 70yrs 

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Confirmed Dr Diagnosed Asthmatic patients 
 
 
Fully ambulatory 
 
 
Frequent smoking areas 
 
 
Over 18yrs 
 
 
 
*Irish law has defined an outdoor smoking area as: A place or premises, or part of a 
place or premises that, is wholly uncovered by any roof, fixed or mobile. An out door 
place or premises that is covered by a roof, so long as not more than 50% of the 
perimeter (outside) is covered by a wall, windows, gate or similar. 
Exclusion criteria: 
 
 
Under 18yrs 
 
On Oxygen therapy 
 
Never smokers in COPD patient group 
 
Undergoing treatment for acute exacerbations. 
 
Pregnant women 
Primary Endpoints: 
 
• To monitor personal exposure to SHS in areas exempted from legislation in pubs, 
bars and casinos etc. using novel monitoring technologies.  
• To simultaneously monitor the respiratory effects as indicated by changes in 
respiratory rate and flow and activity levels. 
 
 

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Secondary Endpoints 
 
• Any increase in medication usage 
• Unscheduled Primary Care /Hospital visits 
• Breath Carbon Monoxide (CO) monitoring pre and post study 
• Spirometry pre and post exposure 
Ethics: 
 
Ethical approval will be sought from DIT Ethics Committee prior to beginning of the 
project.  All documentation relating to the project will be provided for ethical review. There 
are no perceived Ethical issues as patients will be fully informed and there will be no 
intervention other than monitoring. It was considered that requesting subjects to go into 
outside smoking areas might be a problem so only subjects who already frequent such areas 
will be recruited. 
 
All participants will be provided with a patient information leaflet and asked to sign an 
informed consent form. (Appendix 1) 
Methodology: 
Recruitment 
 
Relevant patient organisations will be identified in each country through European Lung 
Foundation (ELF) (Mr Dan Smith) and project partners.  
Patients will be contacted through ELF national organisation with input from national/project 
partners. Inclusion and exclusion criteria, Information sheet and a short recruitment 
questionnaire (Appendix 1 and 2) will be distributed to possible patient volunteers. 

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Consent:  
 
Once eligibility has been determined patients will be given an appointment (in a research 
room either Clinic/Hospital or University based, venue to be confirmed) where they will 
receive an information leaflet.  Once this has been read and any concerns of the participant 
addressed and explained, they will then be asked to provide written consent prior to their 
study enrolment. Consent will be obtained from all participants. Consent is on-going and 
participants can revoke consent at any stage of the project. (Appendix 1) 
Technicians and researchers will be trained in the use of monitoring devices by TFRI staff.  
The 60 patients will  be fitted with equipment which will measure their exposure to 
pollutants including Secondhand smoke as well as measuring the effects on their  breathing 
rate. 
Devices used to gather data: 
Carefusion CO Monitor 
The CO Monitor features single-button operation and coloured light indicators to simplify 
patient understanding of the process: (Appendix 3) 
• 
Green: 0 to 6ppm 
• 
Amber: 7 to 10ppm 
• 
Red:  11 to 20ppm 
• 
Flashing red and alarm: >20ppm 
Spirometer: 
 
Spirometry measures the volume of air that the patient is able to expel from the lungs after 
maximal inspiration.  
Spirometry is invaluable as a screening test of general respiratory health, similar to BP 
measurement in cardiovascular disease. It is a reliable method of diagnosing and differentiating 
between obstructive airways disorders (e.g. COPD, asthma) and restrictive diseases (where the 
size of the lung is reduced, e.g. fibrotic lung disease).  

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Spirometry can also be used to determine the severity of asthma and COPD. This is important 
because the severity of these diseases cannot be predicted simply from the clinical signs and 
symptoms. The FEV1 is the maximal volume of air exhaled in the first second of a forced 
expiration from a position of full inspiration (expressed in litres at BTPS). The forced vital 
capacity (FVC) is the maximal volume of air exhaled with maximally forced effort from a 
maximal inspiration (expressed in litres at body temperature and ambient pressure saturated 
with water vapour – BTPS).  
RESpeck: 
 
RESpeck is a monitoring device, which collects data via low power wireless link for 
processing at base-station in patient’s home. RESpeck had been verified to be a reliable 
measure of RR when compared with RR derived from a nasal cannula in anaesthetised post-
operative patients; instantaneous RESpeck RR matched the routine clinical measurement of 
RR within two breaths per minute (bpm) an acceptable limit of accuracy employed 
previously—on 86 % of occasions, with a mean absolute difference of 0.6 bpm. 
(15. Drummond GB, Bates A, Mann J, Arvind DK. Validation of a new non-invasive 
automatic monitor of respiratory rate for post-operative subjects (Br J 
Anaesth. 2011;107:462–469. doi: 10.1093/bja/aer153). It will be transmitted to a server 
at the Centre for Speckled Computing via broadband internet where the data will be analysed 
RESpeck files will be downloaded from the iPod or Android tablet and the gross activity 
and breathing signals will be plotted. The raw data files will then be processed post hoc 
using proprietary software (University of Edinburgh), which produces a separate file 
containing time and validated RR data. The software will actively exclude any data captured 
in excess of a pre-defined movement threshold. This means that only the fine movements 
associated with breathing will be analysed, and that larger movements not associated with 
breathing will be omitted. The number of validated breaths captured before, during and after 
exposure to SHS will be noted. The maximum, minimum and mean number of validated 
breaths captured before and after SHS exposure per minute will also be calculated; zero data 
(i.e. where no validated breaths are produced per minute) will be included in the mean data. 
Finally, maximum, minimum, and the mean (±1 SD) RR will be recorded. (Appendix 4), 

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AIRSpeck: 
AIRSpeck Personal/mobile monitor will be used for exposure measurements (Appendix 5) 
• 
Includes humidity sensor and improved WiFi range 
• 
Detect indoor fine particulate matter (dust, smoke, exhaust, etc.) 
• 
Plot trends over time with our free visualization tools 
• 
Access your home's data from your smart phone or computer anywhere, anytime 
• 
Compare indoor and outdoor* air quality readings on screen 
Patient Schedule: 
 
Each patient will visit the centre on 2 occasions the first at enrolment and training in use of 
equipment, this should be at least 2 to 4 hours prior to exposure.  The second will be at least 
24hrs post exposure to secondhand smoke at, at least one outing to an outdoor smoking area.  
The duration of time to be spent in the outdoor smoking area is to be a minimum of 15 mins 
with a preferable time of 30-60 mins.  
Spirometry and Breath Co are to be done for at least 2 to 4 hours prior to exposure and 24 
hrs post exposure, at the second visit to the research centre..  At the follow up visit all data 
will be downloaded and checked and any anomalies will be addressed and clarified with the 
patient. 
 
National Partners will be trained in the use of devices measuring exposure using AirSpeck 
and respiratory rate using RESpeck. Data will be transmitted to server at the 
Centre for Speckled Computing via Internet or mobile phone technology where the data will 
be analysed.  
Subsequently patients in each of 3 countries will be shown how to wear and carry the 
personal monitor devices , ensuring data is recorded. 
Data collection, analysis and report writing: This will be carried out by TFRI in 
collaboration with Project Partners and in consultation with Prof DK Arvind. 

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Table of tasks 
 
  
Baseline visit Minimum 
of 2 to 4 hrs pre 
exposure 
Post exposure visit 
Minimum 24hrs post 
exposure 
Consent and information 
sheet 
 
Recruitment Questionnaire 
 
  
CAT/ACT form 
 
 
CO Reading 
 
 
Spirometry 
 
  
Training of fitting and use of 
Respiratory device 
 
  
Training and fitting of 
Particle monitor 
 
  
Retrieve equipment and 
check data recorded 
 
Check patient entries and 
resolve any data queries 
  
 
Download data and send to 
TFRI 
  
 
Check devices working 
correctly for next subject 
  
 
 

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Other support for the Project: 
Prof DK Arvind 
ELF 
Asthma society Ireland 
Advisory Panel: Tack Project Partners: 
Dr Esteve Fernandez 
Dr Silvano Gallus 
Dr Sean Semple 
Dr Ario Alberto Ruprecht  
Dr. Joan B Soriano 
Dr. Constantine Vardavas  
Safety Considerations 
 
The safety of all research participants will be considered at all times, they will be seen in an 
insured medical practice. All personnel dealing with patients will be fully trained and 
insured. 
The Project will be registered at www.clinicaltrials.com  
Data Management and Statistical Analysis: 
 
All data will be stored on a secure server and will be de-identified. Data will be entered giving 
each participant a unique identifier code; neither their name, address nor date of birth will be 
recorded in order to protect the confidentiality of the participant. Analysis will be carried out 
on a blinded dataset using SPSS.  Data will be presented as mean and standard deviation (±SD) 
for continuous variables, or percentage for qualitative variables, as appropriate, or their 95% 
confidence interval. Differences within groups will be compared with Chi2 tests for categorical 

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variables and Student t test for continuous variables. A p value lower than 0.05 will be 
considered statistically significant. 
Quality Assurance: 
 
Baseline and post exposure data re CO readings, Peak flows any change in medication usage 
or unscheduled hospital or Dr visits will be entered at source and will be verified by the 
clinical nurse specialist.  Patients will be asked to keep a record of any increase medication 
usage as well as document any Doctor or Hospital visits as well as description of outdoor 
smoking area visited.  (Appendix 6) 
All digitally recorded data will be downloaded from the device to a secure server. A Post 
Doc and or a Research assistant working with TFRI with advice from partners will further 
examine study data. Any anomalies found will be addressed and resolved from source data. 
Expected Outcomes of the Study 
• Exposure levels in the home 
• Exposure levels in Smoking areas 
• Respiratory symptoms pre and post exposure 
Dissemination of Results and Publication Policy: 
 
It is intended that research findings will be published in the form of peer reviewed research 
articles in first-line high impact or open access journals, and also the presentation of the 
main results in international tobacco control conferences, such as the European Tobacco or 
Health Conference, or the Annual or the European Meetings of the Society for Research in 
Nicotine and Tobacco. There will be a final TackSHS conference in which the main findings 
will be presented to patients, stakeholders, and policy makers. Together with this, 
dissemination materials (i.e., factsheets, press releases) will be produced and published in 

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the TackSHS website as well as disseminated through the websites of the collaborating 
partners and stakeholders. The results will also be EU deliverables for TackSHS project. 
Problems Anticipated: 
 
Recruitment 
Co-operation of partners in Spain and Hungary at the appropriate time. 
Lag time of symptoms post exposure.  
Proof of smoking on site, photograph of venue will be needed to verify this 
Translation and local adaption of documents 
Project Management 
 
Luke Clancy is WorkPackage 5 Leader.  
Sheila Keogan will be responsible for the management of the project, obtaining consent of 
all participants in the project, project equipment and gathering and safety of all data. A Post 
Doc and research assistant working with TFRI will aid in verifying validity of data and the 
statistical analysis. 
 
 
 
 
 
 
 

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Duration of the Project: 
 
The project is to run for a 4-year period starting November 2015 and completion is expected by  
October 2019.   
Gantt chart  
 
Due Month Number 
  
  
  
  
  
12 
13 
14 
15 to 30 
48 
Protocol 
  
  
  
  
  
Local Ethical approval 
  
  
  
  
  
Study registration number 
  
  
  
  
  
Ethical approval of Spain and Hungary 
  
  
  
  
  
National Level Training in use of sensors   
  
  
  
  
Recruitment and data collection 
  
  
  
  
  
Report of Results 
  
  
  
  
  
  
  
  
  
  
  

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Appendix 1.  
Patient Information Sheet and Consent Form 
Tackling secondhand tobacco smoke and e-cigarette emissions: exposure assessment, novel 
interventions, impact on lung diseases and economic burden in diverse European populations. 
TACK SHS Horizon 2020 
Exposure to Secondhand smoke in exempted areas/outside areas and acute health effects in 
patients with chronic lung disease. 
 
What is this study about?  
Smokefree legislation has been successful in its primary aim of eliminating smoking in public 
indoor places. However, the exemptions allowed for within the law (i.e. smoking shelters in pubs, 
smoking in prisons and nursing homes, and smoking within the home) raise further challenges. 
Along with adults, children are often exposed to secondhand smoke in homes, cars, and sporting 
venues. Our previous research has also shown that children are also exposed to Secondhand 
Smoke in cars in sporting venues and at home. As partners in the EU funded IMPASHS project 
we are aware of the undesired complications of the exemptions to the legislation. 
To date, it has been very difficult to gather accurate, objective data on Secondhand smoke 
exposure among adults in Ireland. This study aims to measure Secondhand Smoke exposure 
during everyday activities in 3 European countries (Ireland, Spain and Romania) and monitor the 
associated health effects.  
How do you measure air pollution? 
Air pollution will be measured through the use of a cutting-edge, small portable instrument that 
can continuously and rapidly record changes in the air. Breathing will be measured by a peak flow 
meter and exhaled breath gases (e.g. Carbon Monoxide). Every participant will be given a peak 
flow meter and asked to blow into it at pre-designated times. A small sensor attached to each 

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participant’s chest will measure respiratory rate, this will transmit a signal to a tablet device where 
the data will be recorded. (RESpeck). This will not cause any undue discomfort or inconvenience 
to participants. 
What does participation involve?  
Step One: A researcher will contact you by phone to discuss participation in further detail and 
answer any questions you may have.  
 
Step Two:  If you agree to take part: A researcher will arrange an appointment for you to attend 
St James’s Hospital or DIT Clinical Measurement Lab, where  
• Any queries you may have will be addressed.  
• You will be provided with an information sheet and a consent form, which you will be 
asked to read. 
• The recording devices will be demonstrated to you and fitted in place. 
• Carbon monoxide (CO) readings which is a breath test, will be carried out at both the 
initial and second visit. 
• Breathing tests (Spirometry) will be carried out at both the initial and second visit 
• You will be asked to complete a short de-identified questionnaire (which will ensure that 
your information is private).  
• An agreed appointment will be made for the return of the devices 24hrs later when repeat 
breathing tests will be carried out  as well as downloading of the recorded data from the 
supplied smartphone.  
 
Do I have to participate?  
Absolutely not, participation is 100% voluntary. No one will be included in any stage of the 
research unless they have given consent. Participants can revoke consent at any stage of the 
process.  
Will this be confidential?  

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All information that is gathered in this study remains 100% confidential. Your information will 
be stored in a de-identified form on a secure computer that is only used by members of the research 
team. No one will have access to the information gathered in this study aside from the researchers 
and it will only be used for research purposes. There will be no identifiable information stored in 
the computer at any stage during this research. 
Who is running this study?  
This project is part of a European Study funded by EU Grant Horizon 2020.  The co-ordinator of 
the project is Dr. Estevez Fernandez of the Institut Catala D’Oncologia in Barcelona Spain.  The 
part of the project you will be involved in is being run by the TobaccoFree Research Institute 
Ireland. The TFRI was formed on the basis of a partnership between the Office of Tobacco Control 
and ASH Ireland. The Institute supports the development of a tobacco free society by engaging 
in research in all aspects of tobacco from a public health perspective to provide the evidence base 
for action.  
It is only by conducting studies such as these that we are able to understand the impact of 
Secondhand Smoke on our airways. Through studies like this, we are able to work towards 
promoting the health of people in Ireland and abroad.  
 
We hope that you can support us in our work and we would thank you, in anticipation, for your 
help. 
 
 
 
 
 
 
 
 
 

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Participant Consent 
 
I have read the information sheet pertaining to the ‘Exposure to secondhand smoke in exempted 
areas/outside areas and acute health effects in patients with chronic lung disease.’ project, 
 
I understand that research is being conducted by The TobaccoFree Research Institute Ireland and 
that my participation is completely voluntary. 
 
I understand that by agreeing to take part in the study that it will involve me attending for at least 
2 scheduled visits to the research centre.  Visit one will involve: written consent, explanation and 
demonstration on the use of the recording device, and the keeping of an event diary. Visit two 
will involve the returning of the device and the handing in of the event diary. 
 
Participant Name (Print) 
_________________________________________ 
 
Participant Signature: ________________________________________, 
 
Date; ____________________________, 
 
Investigator Name: (Print)  __________________________________________, 
 
Investigator Signature: 
__________________________________________, 
 
Date: ____________________________ 
 
 

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Appendix 2: 
Recruitment Questionnaire 
 
 
 
 
    
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
    
Tackling secondhand tobacco smoke and e-cigarette emissions: exposure assessment, 
novel interventions, impact on lung diseases and economic burden in diverse European 
populations. The TackSHS Project.  
New approaches to monitoring exposure to air pollution and health effects 
Researcher: Sheila Keogan 
This is a short questionnaire to ascertain personal smoking status, other sources of 
exposure, average weekly exposure in hospitality premises, and experience of 
respiratory symptoms.  Please circle your response where appropriate. 
  
Unique Identifier code:  XXXX 
 
1. Are you  
Male 
 
Female 
2. What is your age?  
 
3. Do you ever smoke cigarettes/tobacco? 
 
Yes/No 

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If yes  
• for how many years? 
• At what age did you start to smoke? 
• Do you smoke everyday 
 
 
 
Yes/No 
• How many do you smoke on an average per day? 
4. Do you currently live with any smokers? 
 
Yes/No 
If yes  
• how many? 
• do they smoke in the home? 
5. Are you exposed to smoking in cars? 
 
Yes/No 
If yes  
• How many trips per week? 
• What is the average duration of these trips? 
6. Do you visit smoking areas in the hospitality sector such as Pubs or Restaurants? 
Yes/No 
If yes: 
• How often per month? 
7. Have you been diagnosed by a doctor with?  
 Asthma  
COPD  
 
 
 
 
 
 
 
 
Neither 
If yes how long is it since your diagnosis? 
 
------Years  
-------Months 
 
8. Do you suffer from any of the following? 
Wheeze 
 
 
 
Never  
Sometimes   
Often 
Cough 
 
 
 
Never  
Sometimes   
Often 

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Shortness of breath  
 
Never  
Sometimes   
Often 
Stinging or irritation of the eyes 
Never  
Sometimes   
Often 
 

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Appendix 3:  
CO Monitor 
 

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Appendix 4:  
Respeck  
 
 
 

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J Clin Monit Comput (2016) 30:23–32 
DOI 10.1007/s10877-015-9673-z 
 
OR IGINAL  RESEARCH 
  
 
Lightweight  physiologic sensor performance during  pre-
hospital care  delivered  by ambulance clinicians 
  
Alasdair J. Mort  •  David 
Fitzpatrick  • Philip M. J. 
Wilson •  Chris  Mellish • 
Anne Schneider 
  
 
 
 
Received: 16 July 2014 / Accepted: 23 February 2015 / Published online: 25 March 2015 
  The Author(s) 2015. This article is published with open access at Springerlink.com 
 
Abstract   The aim of this study was to explore the impact 
of motion generated by ambulance patient management on 
the performance of two lightweight physiologic sensors. 
Two physiologic sensors were applied to pre-hospital pa- 
tients. The first was the Contec Medical Systems 
CMS50FW finger pulse oximeter, monitoring heart  rate 
(HR) and blood oxygen saturation (SpO2). The second was 
the RESpeck respiratory rate (RR) sensor, which was 
wireless-enabled with a Bluetooth     Low Energy protocol. 
Sensor data were recorded from 16 pre-hospital patients, 
who were monitored for 21.2 ± 9.8 min, on average. Some 
form of error was identified on almost every HR and SpO2 
trace.  However, the  mean  proportion of  each  trace  ex- 
hibiting error was \10 % (range \1–50 % for individual 
patients). There appeared to be no overt impact of the gross 
motion associated with road ambulance transit on the in- 
cidence of HR or SpO2   error. The RESpeck RR sensor 
delivered an average of 4.2 (±2.2) validated breaths per 
minute, but did not produce any validated breaths during 
the gross motion of ambulance transit as its pre-defined 
motion threshold was exceeded. However, this was many 
more data points than could be achieved using traditional 
manual assessment of RR. Error was identified on a ma- 
jority of pre-hospital physiologic signals, which empha- 
sised the need to ensure consistent sensor attachment in this 
unstable and unpredictable environment, and in developing 
intelligent methods of screening out such error. 
 
Keywords    Physiologic monitoring   Pre-hospital   
Ambulance clinicians   Motion artefact 
  
 
1 Introduction 
 
The  pre-hospital  context  is  a  notoriously difficult envi- 
ronment in which to measure patient physiology accurately 
and reliably. If the patient is trapped (e.g. following a road 
traffic collision) then it may be difficult to make appro- 
priate manual assessments or to apply electronic monitor-
                                                                                                       ing equipment. There is often movement of unpredictable
 
A. J. Mort   P. M. J. Wilson   C. Mellish   A. Schneider 
dot.rural Digital Economy Hub, King’s College, 
University of Aberdeen, Aberdeen, Scotland, UK 
 
A. J. Mort (&)   P. M. J. Wilson 
The Centre for Rural Health, University of Aberdeen, 
Centre for Health Science, Old Perth Road, Inverness, 
Scotland, UK 
e-mail: a.mort@abdn.ac.uk 
 
D. Fitzpatrick 
Scottish Ambulance Service, Gyle Square, Edinburgh, 
Scotland, UK 
 
C. Mellish 

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Department of Computing Science, King’s College, University 
of Aberdeen, Aberdeen, Scotland, UK 
amplitude and acceleration in multiple directions. For ex- 
ample, the patient may be moved in the process of im- 
mediate, potentially life-saving management. This might 
include clearing the patient’s airway, inserting a device that 
protects the airway, conducting chest compressions where 
the patient is in cardiac arrest, or moving an unconscious but 
breathing patient into the recovery position. 
Patients must also be moved to a location where they can 
receive definitive treatment for their illness and/or injury. 
However, it may take more than one journey and there may be 
intercurrent treatment at more than one site before definitive 
care is reached. In most cases pre-hospital patients are trans- 
ported by emergency ambulance, which in the United King- 
dom are staffed by a mixture of qualified Paramedics and

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Technicians; those qualified to a lower level than Paramedics 
with a smaller skill-set. The time taken to transport patients to 
hospital can vary greatly, influenced mainly by the geographic 
site of the emergency. That is, it can take much longer to 
transport rural patients to hospital than it might do in urban 
centres. Much of the patient assessment and physiologic 
monitoring conducted by rural ambulance clinicians thus 
takes place during road and air transit. 
Standard physiologic parameters (e.g. blood pressure, 
heart rate, respiratory rate) play a key role in the triage of 
pre-hospital patients as they may indicate present and fu- 
ture patient deterioration. For example, validated systems 
such as the National Early Warning Score are now widely 
used [1]. Hillman et al. [2] reported serious physiological 
abnormalities in 29 % of patients in the 8 h prior to death 
(excluding cardiac arrests and deaths whilst in intensive 
care). Also, one-third of patients who did not have ‘do not 
resuscitate’ orders had persistently abnormal physiology 
for  2 days  prior  to  death.  The  physiologic  abnormality 
reported most often was hypotension, followed by 
tachypnoea, then tachycardia. Other studies have also re- 
ported considerable instability in standard physiologic 
measures prior to a major, life-threatening event (e.g. res- 
piratory arrest, cardiac arrest, haemorrhagic hypotension) 
[3–5], although this is not always the case [6, 7]. 
The physiologic monitoring systems operated by ambu- 
lance clinicians most often take the form of a single device 
that measures several parameters. For example, the Scottish 
Ambulance Service operated the Philips HeartStart MRx 
system (Philips, Netherlands), which apart from being a 
defibrillator measured blood pressure on the upper-arm, and 
blood oxygen saturation and heart  rate  through a  pulse 
oximeter attached to a finger. The monitor also captured a 
12-lead ECG, and could be linked via Bluetooth     to a mo- 
bile phone from where the ECG was transmitted to a coro- 
nary care unit for expert advice. Monitors like these are 
suitable for ambulance use as they are rugged and can be 
removed from the vehicle to conduct monitoring where a 
patient is located (e.g. in their house, by the roadside). 
However, they tend to be relatively heavy (due to battery 
requirements) and  the  sensors  are  wired. Wires  can  be 
snagged, pulling on the site of attachment resulting in spu- 
rious readings and even pulling the sensor(s) off the patient 
altogether. There have also been anecdotal reports of emer- 
gency workers accidentally cutting cables during the extri- 
cation 
process. 
Such 
systems 
provide 
continuous 
monitoring, although the recording of individual values to 
care provider files is usually performed manually, limiting 
the volume of data recorded to perhaps two or three data 
points (depending upon the duration of patient transport). 
There is evidence that having a much larger volume of 
physiologic data is a better predictor of later mortality than 
relying upon a single value [8]. 

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Lightweight wireless physiologic monitors are now in 
existence, developed partly through an international effort to 
enable and enhance the monitoring of patients in their own 
homes (i.e. telehealth). We propose that such monitors could 
play a major role in the future of pre-hospital care, employed 
by ambulance clinicians where lightweight, wireless 
monitoring could convey an advantage over current heavy, 
wired systems. We also contend that these monitors may be 
beneficial to  Community  First  Responders (CFRs)  who 
volunteer to deliver basic first-aid for ambulance services 
whilst an emergency ambulance is on its way. Such devices, 
if simple to apply and use, could greatly increase the volume 
of data that such personnel are able to capture, and poten- 
tially alert them to patient deterioration that they may be able 
to  address within  their  limited  skill-set. Facilitating  the 
capture of more physiologic data is particularly important 
given the general lack of evidence to support CFR activity; 
this would help to inform their practice and policy. However, 
this is only applicable if such technology can deliver accurate 
data reliably during the unstable and unpredictable context of 
pre-hospital care. 
Commonly-recorded physiologic parameters are influ- 
enced by motion and in turn display artefact or ‘noise’; 
physiologic waveforms deviate from their ‘normal’, char- 
acteristic patterns and erroneous data are potentially re- 
ported to the user. Such error could take the form of falsely 
low  or  high  readings, often  triggering  alarms  that  then 
distract the operator from keeping a physical watch on the 
patient. Most physiologic sensors include some form of 
signal processing so that they can continue to deliver data 
in the face of motion or extreme physiologic compromise. 
However, it is reasonable to postulate that there is a 
threshold beyond which a physiologic sensor will no longer 
be  able  to  deliver  accurate  data;  for example,  if  finger 
perfusion is so low that it is impossible to generate valid 
blood oxygen saturation and pulse data. 
The aim of our study was to explore the impact of am- 
bulance clinician patient management and transport on de- 
fined parameters recorded from patients by two lightweight 
physiologic sensors. This contributed to the University of 
Aberdeen Managing Information in Medical Emergencies 
(MIME) project (see www.dotrural.ac.uk/mime), which 
developed and evaluated technology to support CFRs at the 
scene of rural medical emergencies [9]. 
 
 
2 Materials and methods 
 
2.1 Design 
 
The study employed a ‘field-function’ design, which in- 
volved controlled ‘pseudo-deployment’ of the physiologic 
sensors in a real-life situation [10].

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2.2 Setting 
 
The  study  took  place  at  a  Scottish  Ambulance Service 
station in the north of Scotland, UK (Fig. 1). The station 
responded to a variety of types of call-outs originating from 
urban and rural areas. 
 
2.3 Participant identification and recruitment 
 
2.3.1 Ambulance clinicians 
 
Clinicians from two station ‘watches’ were invited to take 
part. Clinicians attended an evening presentation at which 
the study and their proposed role in the research were de- 
scribed. All clinicians who volunteered to take part were 
asked to provide written, informed consent. 
 
2.3.2 Pre-hospital patients 
 
The first approach to patients was made by recruited am- 
bulance   clinicians  according  to  set  inclusion  criteria 
(Table 1). The decision of whether or not to approach each 
patient was made ultimately at clinicians’ discretion. They 
were only to approach patients if the application of the 
physiologic sensors did not interfere with the timely de- 
livery of their ‘usual’ care. 
The first stage of consent involved clinicians describing 
the study to patients verbally. Patients were given the op- 
portunity to ask any questions, and if they were happy to 
proceed they provided verbal informed consent on-scene. 
A preliminary verbal informed consent was deemed ap- 
propriate considering the relatively low risk nature of the 
study. The second stage of consent involved sending pa- 
tients an opt-out form to their home address within a study 
information pack, at least 2 weeks after their contact with 
  
 
 
 
Fig. 1  Scottish Ambulance Service emergency vehicle 

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the research. Whilst consent mechanisms based on opting 
out are not the norm, these have previously been carried 
out in other pre-hospital emergency care research where 
patients were in a vulnerable state immediately after their 
emergency care visit, and are well recognised as being a 
difficult group to make contact with [11, 12]. We consid- 
ered  opt-out consent to  be  satisfactory  in  this  situation 
where there was no significant risk of harm to participants 
and no risk to patient confidentiality. An opt-in approach 
could have resulted in lower recruitment and therefore lower 
generalisability of the results [13]. 
The local  NHS Health  Board was contacted  prior to 
mailing study information packs in order to establish 
whether or not patients had been discharged from hospital. 
Patients who had died and those who completed the opt-out 
form were excluded from the study. All patients who par- 
ticipated  were  given  a  unique  identification number  in 
order to anonymise their involvement. 
 
2.4 Physiologic parameters 
 
Three  physiologic parameters  were selected to  monitor; 
respiratory rate (RR), heart rate (HR) and blood oxygen 
saturation  (SpO2).  RR  was  chosen as  it  is  an  essential 
clinical parameter that traditionally is difficult to monitor 
both reliably and repeatedly in anything but a resting, 
motionless patient.  Indeed, RR has previously been  de- 
scribed as the ‘neglected vital sign’ [14]. In the pre-hospital 
environment, ambulance clinicians will monitor RR by 
counting the rise and fall of the patient’s chest/abdomen 
and/or the  misting  and  de-misting  of  a  non-rebreathing 
oxygen mask (not including RR monitored using capnog- 
raphy in the intubated  patient). This means that only a 
small number of RR data points are recorded during the time 
that ambulance clinicians are with the patient. HR and SpO2  
were selected as they are also parameters that are monitored 
ubiquitously in the pre-hospital environment using pulse 
oximetry. 
Respiratory rate, HR and SpO2  formed the basis of a 
novel pre-hospital physiologic monitoring system that we 
developed within our research group for use by Ambulance 
Service CFRs. They are all parameters that can be mon- 
itored using lightweight physiologic sensors that are simple 
and quick to apply by non-medical experts. 
 
2.5 Physiologic sensors 
 
Two lightweight, non-invasive physiologic sensors were 
selected for application to ambulance patients. The first 
was the Contec Medical Systems CMS50FW pulse oximeter 
(Contec Medical Systems, Qinhuangdao, China), which 
monitored SpO2, HR and also displayed the photo- 
plethysmograph to the user.

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Table 1  Pre-hospital patient inclusion and exclusion criteria 
 
Inclusion criteria                                                                 Exclusion criteria 
 
Adults (18 years and above)                                              Unable to understand verbal explanations given in English—also including those with 
special communication needs 
Males and females                                                              Patients with injuries or in a position that prevented application of the sensors 
Ability to represent own interests and to provide verbal, 
informed consent 
Able to apply the sensors to the patient 
  
 
The CMS50FW had Bluetooth     capability to send data 
wirelessly. However, this facility was turned off in this 
study and the data stored on the device instead (NOT in- 
cluding photoplethysmograph data). Capturing data wire- 
lessly would have necessitated a separate laptop computer 
and time-consuming device pairing, both of which were 
inappropriate in  the  space-restricted and time-dependent 
emergency ambulance environment. Non-averaged HR and 
SpO2 data were recorded in a comma separated value file at 
a rate of 1 Hz. 
The second monitor was the RESpeck RR sensor 
(University of Edinburgh Department of Speckled Com- 
puting, School of Informatics, Scotland), which was an 
encapsulated tri-axial accelerometer positioned on the left 
side of the abdomen just under the costal margin. RESpeck 
recorded changes in abdominal position in three orthogonal 
axes, relative  to gravity. These data were automatically 
integrated and differentiated into a derived ‘activity’ sig- 
nal, and a RR signal with a shape similar to inspiratory and 
expiratory flow. RESpeck had been verified to be a reliable 
measure of RR when compared with RR derived from a 
nasal cannula in anaesthetised post-operative patients; in- 
stantaneous RESpeck RR matched the routine clinical 
measurement of RR within two breaths per minute (bpm)—
an acceptable limit of accuracy employed previ- ously—on 
86 % of occasions, with a mean absolute dif- ference of 0.6 
bpm [15]. However, our study was the first time that 
RESpeck had been implemented in the pre-hos- pital 
environment. RESpeck was entirely wireless and all data 
were transmitted using a Bluetooth     4.0 Low-Energy 
protocol to an iPod (Apple, Cupertino, CA, USA) on which 
the data were displayed and recorded. Accelerometer data 
were recorded at a rate of 12.5 Hz. Figure 2 displays both 
medical  sensors and  their  method  of  attachment  to  the 
body. 
 
2.6 Study protocol 
 
Upon arrival on-scene, ambulance clinicians approached 
each patient and carried out a primary survey of their clinical  
status.  If  the  patient  provided  verbal  informed 

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Fig. 2  Medical sensors employed in the pre-hospital fieldwork 
 
 
consent the aim was to apply the sensors as early as ap- 
propriate. The pulse oximeter was attached first by ambu- 
lance clinicians to patients’ index fingers. A stopwatch was 
started at the point that pulse oximeter data recording was 
initiated, which provided a ‘time zero’ reference. Secondly, 
the  RESpeck  RR  sensor  was  enclosed  in  a  protective 
plastic sleeve (to meet infection control requirements) and 
attached to the abdomen using TransporeTM  medical tape 
(3 M Healthcare, USA). It was then paired with the iPod 
and  data  recording  commenced  30 s  after  the  pulse 
oximeter data stream began. Data were recorded from both 
sensors until the end of ambulance clinician management, 
which was most often at the point of handover to hospital 
Emergency Department staff. However, there were a small 
number of occasions where patients were transported di- 
rectly by ambulance to a receiving ward (i.e., for referrals 
by General Practitioners), or were not transported by am- 
bulance at all if it was deemed appropriate to leave them at 
home. 
A researcher (AM) travelled as an observer in the am- 
bulance, and only came into contact with patients if they had 
provided verbal consent. The researcher oversaw the

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application of the sensors by ambulance clinicians and was 
responsible  for  starting  and  stopping  data  recording  on 
each device. He also carried a Getac Z710 rugged tablet 
computer (Getac, Irvine, CA, USA) that ran software 
(University of Aberdeen) that captured the input of con- 
textual information about patient management and clinical 
status. This was essential in order to explore the effect of 
patient management on sensor data. A copy of the elec- 
tronic Patient Report Form (ePRF) was retrieved for each 
patient in order to gather as much contextual data about each 
patient and their management as possible. The ePRF 
contained   all   clinical   data,   including   interventions, 
recorded by ambulance clinicians. The form permitted the 
formal handover of information between ambulance clin- 
ician and Emergency Department staff on arrival at hos- 
pital.  Each  record  contained  its  own  unique  incident 
number that enabled calls to be traced and patients iden- 
tified at a later date if necessary. 
Data collection proceeded until reasonable ‘‘saturation’’ 
(defined as the point at which no new patterns of data were 
emerging) was achieved, assessed by author AM. This 
approach to sampling is commonly used in qualitative re- 
search and was appropriate in this exploratory study. 
 
2.7 Data analysis 
 
2.7.1 Pulse oximetry 
 
Heart rate and SpO2 data were initially reviewed and plotted to 
explore for any gross deviations from ‘normal’ physiologic 
values. In particular, the pulse oximeter logged a non- 
physiologic value of 255 BPM for HR and 127 % for SpO2 
when the sensor was removed from the finger, or if finger 
attachment was sub-optimal. The frequency of such values 
was noted. The maximum, minimum, mean and standard 
deviation for HR and SpO2 were then calculated on a patient- 
by-patient basis. Further analyses explored for the presence 
and length of any periods of pulse oximeter data that appeared 
‘abnormal’. Our definition of ‘abnormal’ also included any 
incidence of a sudden, apparently non-physiologic, increase or 
decrease in blood oxygen saturation and/or heart rate from a 
stable value. The total time that each sensor exhibited ‘ab- 
normality’ was expressed as a percentage of the total 
monitoring time. This was also expressed as the proportion of 
‘abnormality’ at nominal rest, and the proportion of ‘abnor- 
mality’ during ambulance transit, in order to explore for any 
apparent impact of gross motion on signal quality. 
 
2.7.2 Respiratory rate 
 
RESpeck files were downloaded from the iPod and the gross 
activity and breathing signals were plotted. Patient 
management data were plotted on top of each trace, noting 

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in particular the start and end of gross motion associated 
with ambulance transit. The raw data files were then pro- 
cessed post hoc using proprietary software (University of 
Edinburgh), which produced a separate file containing time 
and validated RR data. The software actively excluded any 
data captured in excess of a pre-defined movement 
threshold. This meant that only the fine movements asso- 
ciated with breathing were analysed, and that larger 
movements not associated with breathing  were omitted. 
The number of validated breaths captured before, during and 
after ambulance transit were noted. The maximum, 
minimum and mean number of validated breaths captured 
before and after ambulance transit per minute was also 
calculated; zero data (i.e. where no validated breaths were 
produced per minute) were included in the mean data. Fi- 
nally, maximum, minimum, and the mean (±1 SD) RR were 
recorded. 
 
2.8 Ethical approval 
 
The study was approved by an NHS Research Ethics 
Committee and by the Scottish Ambulance Service’s Re- 
search Governance Group. 
 
 
3 Results 
 
3.1 Patients 
 
A total of 20 pre-hospital patients gave verbal consent to 
take part in the study. Data for four patients were excluded: 
three patients opted out, and one patient died sometime 
after admission to hospital. This left a total of 16 patient data 
sets for inclusion (ten male, six female; age range 
42–96 years). Patients were managed by ambulance clin- 
icians for a wide variety of suspected medical problems 
and  injuries  (Table 2);  there  were  12  emergency  calls, 
three urgent call-outs (requested by local General Practi- 
tioners) and one patient transfer from hospital to a local 
airport. A majority of patients (n = 13) were transported to 
the local hospital (10 to the Emergency Department and 
three to an acute receiving ward). Two patients (both 
emergency calls) were not transported to the Emergency 
Department; one was a diabetic whose blood sugar levels 
returned to normal after treatment, and the other was a 
bariatric patient who had fallen but on assessment did not 
have any injury or illness. 
 
3.2 Monitoring time 
 
Mean  sensor  monitoring  time  was  21.2 ± 9.8 min  (to- 
tal = 5.7 h; range 7.4–41.5 min). For patients who were 
transported to hospital, mean monitoring time at nominal

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Table 2  Pre-hospital patient clinical status 
 
Patient ID 
PM—male 
Working clinical assessment 
Response 
Airway 
Breathing rate 
range (breaths 
Pulse rate 
range (beats 
SpO2 
range 
Glasgow 
coma 
PF—female 
per min) 
per min) 
(%) 
scale 
PM1 
Central chest pain 
Alert 
Clear 
14–20 
46–62 
99–100 
15 
PM3 
Road traffic collision injuries 
Alert 
Clear 
16 
74–76 
98 
15 
(some pain, abrasions 
and contusions) 
PM4 
Chest pain 
Alert 
Clear 
16 
97–105 
97–100 
15 
PM5 
NA—transfer from hospital 
to airport after discharge 
Alert 
Clear 
16–17 
73–74 
93–95 
15 
PM7 
Non-traumatic back pain 
Alert 
Clear 
16–24 
80–100 
94–99 
15 
PM8 
Unknown problem 
Alert 
Clear 
16 
106 
100 
15 
PM9 
Diabetic 
Alert 
Clear 
16 
70–75 
93–96 
15 
PM10 
Sick person 
Alert 
Clear 
12–14 
62–65 
94 
14 
PM11 
Diabetic 
Alert 
Clear 
14–16 
100–110 
95–97 
15 
PM12 
Fall 
Alert 
Clear 
15 
114 
92 
15 
PF1 
Fall 
Alert 
Clear 
Not recorded 
81 
98 
15 
PF2 
Back pain 
Alert 
Clear 
16–24 
70–88 
98–99 
15 
PF3 
Sick person 
Alert 
Clear 
20 
101 
100 
15 
PF6 
Stroke, numbness, paralysis, 
or movement problems 
Responding to pain 
Clear 
15 
70 
95 
14–15 
PF7 
Abdominal pain 
Alert 
Clear 
32 
74 
99 
14 
PF8 
Stroke history 
Alert 
Clear 
19 
64 
98 
15 
NB these are the clinical working assessments recorded by ambulance clinicians immediately prior to ending their management 
 
 
rest (i.e. before and after gross motion associated with am- 
bulance transit) was 11.4 ± 9 min (range 3.3–36.8 min), 
and mean road transport time was 12.9 ± 8.2 min (range 
3–27.4 min). 
 
3.3 Heart rate and blood oxygen saturation 
 
The pulse oximeter was applied successfully to all patients. 
Mean HR was 83.7 ± 10.5 BPM (maximum = 166 BPM, 
minimum = 49  BPM).  Mean  SpO2    was  93.9 ± 0.9 % 
(maximum = 99 %, minimum = 79 %). Non-physiologic, 
‘abnormal’ values and signal patterns were present on a 
majority  of  HR  and  SpO2   traces  (n = 12/16  for  HR, 
n = 13/16  for  SpO2).  HR  traces  mirrored  the  ‘abnor- 
malities’ seen on SpO2 traces, and vice versa, on all but one 
occasion. 
 
3.3.1 Heart rate 
 
The most frequent non-physiologic HR value returned was 
255 BPM (n = 12/16), which was what the sensor recorded 
in its memory when it was removed from the finger. 
However, it was apparent from observing the monitoring 
process that the pulse oximeter was removed from patients’ 
fingers in the middle of monitoring on only two occasions. 

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Hence, 255 BPM was recorded when the pulse oximeter 
monitoring conditions were sub-optimal, whilst the sensor 
was still attached to the finger. The other type of apparent 
error noted (n = 3/16) was rapid, non-physiologic drops 
and increases in HR, with periods in-between where HR 
remained artificially static. This was contrary to the normal 
physiologic undulations in HR that were evident in other 
parts of the trace for the same patient (Fig. 3). 
The absolute duration of individual HR errors ranged 
from 2 s up to almost 20 min in the most extreme case 
(mean  80.9 ± 237.5 s). HR error occurred between one 
and five times for each patient. The proportion of each HR 
trace that exhibited apparent error ranged from under 1 to 
almost 50 % (mean 8.8 ± 13.9 %); however, the propor- 
tion of error was \10 % on 9/12 occasions, and \5 % on 
7/12 occasions, where error presented. There appeared to 
be no effect of gross motion associated with ambulance 
transit on the incidence of HR error, or on the duration of 
individual HR errors. 
 
3.3.2 Blood oxygen saturation 
 
The non-physiologic SpO2  value recorded most frequently 
was 127 % (n = 12/16). SpO2  traces also exhibited fluc- 
tuations in the form rapid drops and rises, a minority of

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Blood oxygen saturation (%) 
Heart rate (BPM) 
Blood oxygen saturation (%) 
 
250 
 
225 
 
200 
 
175 
 
150 
 
125 
 
100 
 
75 
 
50 
 
25 
 
0 
00:07:00         00:12:00         00:17:00         00:22:00         00:27:00 
Time (hr:min:sec) 
 
Fig. 3  Example of pulse oximeter HR abnormality (Patient PM7) 
100 
 
 
95 
 
 
90 
 
 
85 
20 
 
10 
  
0 
00:00:00                00:05:00                00:10:00                00:15:00 
Time (hr:min:sec) 
 
Fig. 5  Example of ‘normal’ SpO2  fluctuation (Patient PF3)
 
100 
 
 
95 
 
 
90 
 
 
85 
20 
 
10 
 
0 
00:17:00          00:22:00          00:27:00          00:32:00          00:37:00 
Time (hr:min:sec) 
 
Fig. 4  Example of SpO2  apparent error (Patient PM7) 
 
 
which  appeared  to  be  error  (n = 3/16)  (Fig. 4),  whilst 
others appeared physiologically feasible (Fig. 5). SpO2 was 
more stable for some patients, and less so for others. 
The range of duration of individual SpO2   errors was 
almost exactly the same as for HR, with each error oc- 
curring between one and four times for each patient. The 
proportion of each SpO2  trace that included apparent error 
ranged   from    under   1 %    to    almost    50 %    (mean 
7.6 ± 13.2 %); 10/13 traces \10 %, and 8/13 traces \5 %. 
Just as for HR, there appeared to be no effect of gross motion 
associated with ambulance transit on the incidence of SpO2  
error, or on the duration of individual errors. HR and SpO2  
errors tended to occur at the same time. 
 
3.4 Respiratory rate 
 
The  RESpeck sensor was applied  successfully to  14/16 
patients. On two occasions the patient’s clothing impeded 

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application to the abdomen. On average, 40 % of activity 
data  were below  the  pre-defined activity  threshold, and 
60 % (±18.9, 1 SD) were above it. This meant that 60 % of 
data were actively excluded from the RESpeck post hoc 
analysis. However, the proportion of data in excess of the 
activity threshold ranged between 27.4 and 86.5 % in in- 
dividual patients, meaning that the level of motion varied 
widely. 
The total number of validated breaths captured at nominal 
rest (i.e., without the gross motion associated with 
ambulance transit) during individual patient management 
ranged from 5 to 255, with an average of 54.6 breaths 
captured per patient (±65, 1 SD). The maximum number of 
validated  breaths returned by RESpeck for each  patient 
ranged between 3 and 18 breaths per minute. The mean 
number of validated breaths reported per minute, including 
zero values for minutes where no validated breaths were 
returned, ranged from 0.5 to 7.9 (overall mean across all 
patient  data = 4.2 ± 2.2,  1  SD).  Breathing  rate  ranged 
from 5.3 to 35.3 breaths per minute, whilst mean breathing 
rate for each patient ranged from 9.7 to 21.2 breaths per 
minute. 
Only 29 validated breaths were captured during ambu- 
lance transit across 3/11 patients (n = 15, 12, 2), compared 
to 765 breaths recorded at nominal rest from 14 patients. 
 
 
4 Discussion 
 
This was the first study of its kind to robustly measure the 
impact  of  pre-hospital motion  on  commonly  monitored 
physiologic parameters. Our research study identified some 
form of error in nearly every blood oxygen saturation, heart 
rate and breathing rate signal. For most pre-hospital pa- 
tients error accounted for a relatively small proportion of

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pulse oximeter recording. The breathing rate sensor de- 
livered  considerably  less  data,  but  still  produced  many 
more data points than would be achievable through manual 
assessment alone. 
Signal artefact  is  a  key  limitation  of  pulse oximeter 
technology, which can arise from low signal-to-noise ratio 
and from false signals [16]. Pulse oximetry relies on the 
assumption that all of the pulsating blood is arterial. 
However, motion  mobilises  venous  blood, which  has  a 
lower SpO2 and mixes with the arterial component. Motion 
thus tends to lower SpO2  and produce false alarms [17]. 
This effect is exaggerated if there is low perfusion to the 
site of monitoring. Clinical studies have demonstrated this 
effect. For example, Wikilund et al. [18] noted that, post- 
anaesthesia, the pulse oximeter alarmed every eight min- 
utes,  on  average.  Some  77 %  of  oximeter  alarms  were 
found to be false, with motion indicated as one contributing 
factor. It also appeared that finger pulse oximeters 
demonstrated a poorer true/false ratio than ear pulse 
oximeters (18 vs. 29 %). Tsien and Fackler [19] reported 
that [90 % of SpO2  and heart rate alarms generated by 
pulse oximeters in an intensive care unit were false posi- 
tives. Generally speaking, pulse oximeters result in more 
false-positive alarms than other physiologic monitoring 
systems. The clinically-relevant positive predictive value 
(=number of clinically-relevant true positives divided by 
clinically-relevant    true    positives ? clinically-irrelevant 
true  positives ? false  positives) for pulse oximetry  has, 
accordingly, been reported to be very low (B6 %, for both 
SpO2 and the derived heart rate value, Ibid). However, it is 
important to note that the threshold of alarms will vary 
from study to study, and the threshold is open to adjustment 
by users. 
Langton and Hanning studied the ability of four differ- 
ent pulse oximeters to identify simulated hypoxaemia in 
healthy volunteers during two levels of controlled vibration 
(sine wave 4 Hz and intermittent 8 Hz; the 8 Hz condition 
was representative of the motion experienced during pa- 
tient transport) [20]. The vibration sometimes resulted in 
false decreases in SpO2 in 3/4 oximeters, which was similar 
to the current study, but was not identical as such patterns 
were not identified across all of our 16 patients. Vibration 
also lengthened the time taken for the pulse oximeters to 
detect hypoxaemia. There were also differences between 
the  individual  pulse oximeters under test,  reflecting the 
varying capacities of the different algorithms to deal with 
motion. 
Perhaps the seminal piece of research on characterising 
motion in a very wide variety of clinical environments was 
that conducted by Tobin et al. [21]. Some 350 patients were 
monitored, of whom 70 exhibited motion (20 %); 35/70 
moving patients were instrumented for detailed analysis. 
This included three patients who had been transported by 

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ambulance.  Ambulance transit  at  reasonably high speed 
(60 mph) resulted in a very noisy pulse oximeter signal; 
indeed one of the largest in the study’s clinical cohort. 
However, the investigators did note that the magnitude of 
disturbance to  the  underlying photoplethysmograph was 
not directly related to the absolute force of movement. For 
example, a patient flexing their foot resulted in more 
oximeter signal deformation than that caused when the leg 
twitched. This indicated that there were underlying, vas- 
cular  mechanisms  at  play.  One  of  the  difficulties  in 
studying pulse oximetry during the gross motion of am- 
bulance transit is that it is difficult to ascertain just how 
much error is due to vehicle motion, and how much is due 
to the patient moving the site of monitoring; using an ac- 
celerometer as a reference sensor may be an appropriate 
solution. Silbergleit et al. [22] attempted to quantify the 
forces  experienced  during  road  ambulance  transport  at 
35 mph.  They  identified that  road  ambulance  vibration 
varied greatly—generally occurring \1 Hz and from 10 to 
15 Hz—and was highest in the inferior and superior axes. 
The largest peak accelerations were also in the inferior and 
superior planes (0.8 and 0.7 g respectively). 
This was the first application of the RESpeck breathing 
rate sensor in a pre-hospital context. However, no validated 
breathing rates were delivered by the sensor during am- 
bulance transit; the fine excursions of the abdomen with 
breathing were lost amongst the large, random movements 
of the ambulance. Also, even the entry and exit of clin- 
icians into and from the ambulance (without the patient) was 
sufficient for it not to deliver validated data. Despite this, 
only reporting on rhythmic, regular breaths is more 
diagnostic than reporting on irregular, noisy breathing rate 
data. For example, Chen et al. [23] employed the technique 
of impedance pneumography (measuring the changes in 
resistance across the chest with breathing, using a con- 
ventional electrocardiogram trace) combined with a novel 
algorithm (applied retrospectively) on 898 trauma patients 
monitored during helicopter transit. Breathing rate based 
upon reliable breaths only was a better predictor of a pa- 
tient receiving a respiratory intervention at a later stage, 
and of identifying patients with haemorrhage. Impedance 
pneumography (available on some pre-hospital multi-pa- 
rameter monitoring systems) will inevitably capture more 
data on respiratory rate than the RESpeck during gross 
motion. However, our proposed model of employing the 
RESpeck on pre-hospital patients managed by Community 
First Responders will  not  mean  exposure to  ambulance 
transit. As such, the RESpeck may be an effective way for 
non-experts to gather considerably more breathing rate data 
than would be achievable using manual methods alone. It 
may also be more objective. 
There  were  some  limitations  to  this  study.  Pulse 
oximeter photoplethysmograph data were displayed on the

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screen of the commercially-available pulse oximeter, but 
the device did not permit recording of the raw photo- 
plethysmograph to file in ‘non-wireless’ mode. Wireless 
data transfer for the pulse oximeter under test was time- 
consuming and necessitated a separate laptop computer; 
there was neither sufficient time nor space to effect this in 
the emergency ambulance. This prevented any detailed 
analysis of the impact of motion on the underlying signal 
(e.g. motion artefact issues during driving). However, some 
data to this effect have been reported previously [21]. Our 
next  study  will  be  a  ‘laboratory-function’  experiment 
where  there  will  be  more  time  and  space  to  conduct 
wireless data transfer and capture raw data. A single pulse 
oximeter was tested in this study; it is likely that other 
sensors employ different algorithms and thus respond to 
motion differently. The study was also a proxy for the 
proposed context of employing the sensors in our MIME 
system; i.e. patients managed by ambulance clinicians, and 
not Community First Responders. This meant that the 
motion that the sensors was exposed to was much greater, 
although the  study  did  still  include  periods  of  reduced 
motion before and after ambulance transit. However, con- 
ducting the study in emergency ambulances was the 
quickest and safest method for collecting our data; first 
responders see  relatively  few  patients  and  have  limited 
first-aid training. Finally, the study only included patients 
who did not have immediately life-threatening medical 
conditions or trauma (i.e.  only those who were able to 
provide verbal informed consent). 
To conclude, this study identified that all of the 
physiologic sensors exhibited some error on almost every 
patient recording during ambulance clinician patient man- 
agement. However, this accounted for a relatively small 
proportion of the total monitoring time, on average. Error 
mostly took the form of non-physiological blood oxygen 
saturation and heart rate values, and rapid step changes often 
to much lower values. The RESpeck breathing rate sensor 
did not exhibit error per se. Rather, if the pre-de- fined 
threshold of motion was crossed it did not deliver breathing 
rate data, which was as it was designed to do. It was very 
positive that many more validated breathing rate points were 
achieved through employing RESpeck than via manual 
assessment. The almost complete lack of validated breathing 
rate data during ambulance transit was inconve- nient, but 
would not be problematic in our proposed model of use by 
CFRs (i.e. where there is no ambulance transit). 
Future work in this area should focus on sensor-pro- 
duced breathing rate data during motion, which is currently 
not recorded during ambulance transit. The development of 
a single sensor to monitor all three parameters (RR, HR 
and SpO2) would also be valuable, minimising the time 
taken to apply equipment and simplifying the process for 
non-expert users such as CFRs. 

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Acknowledgments    We  would  like  to  extend  our  thanks  to  the 
Scottish Ambulance Service for granting us permission to undertake 
this research. We would also like to thank the ambulance clinicians 
who took part, and the patients. We also recognise the non-financial 
support given to us by the developers of the RESpeck sensor 
(University of Edinburgh). The research described here was supported 
by the award made by the RCUK Digital Economy programme to the 
dot.rural Digital Economy Hub; award reference: EP/G066051/1. 
 
Conflict of interest     None declared. 
 
Open  Access   This  article  is  distributed under  the  terms  of  the 
Creative Commons Attribution License which permits any use, dis- 
tribution,  and  reproduction in  any  medium,  provided the  original 
author(s) and the source are credited. 
 
 
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amplitude and acceleration in multiple directions. For 
ex- ample, the patient may be moved in the process of 
im- mediate, potentially life-saving management. This 
might include clearing the patient’s airway, inserting a 
device that protects the airway, conducting chest 
compressions where the patient is in cardiac arrest, or 
moving an unconscious but breathing patient into the 
recovery position. 
Patients must also be moved to a location where they 
can receive definitive treatment for their illness and/or 
injury. However, it may take more than one journey and 
there may be intercurrent treatment at more than one site 
before definitive care is reached. In most cases pre-
hospital patients are trans- ported by emergency 
ambulance, which in the United King- dom are staffed 
by a mixture of qualified Paramedics an

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Appendix 5:   
AIRSpeck  
The AirSpeck family of static and mobile wireless 
air quality monitors 
 
 
D K Arvind, Janek Mann 
Centre for Speckled Computing, School of Informatics 
University of Edinburgh, Scotland, UK. 
dka@inf.ed.ac.uk 
Andrew Bates and Konstantin Kotsev 
Centre for Speckled Computing, School of Informatics 
University of Edinburgh, Scotland, UK.
 
 
Abstract—   The   Automatic   Urban   and   Rural   Network 
(AURN) [1] is a set of high quality reference monitoring sites for 
recording air quality in the United Kingdom. They are costly to 
install  and  expensive  to  run,  and  are  therefore  limited  in 
numbers. The data from these networks are used to inform 
regulatory compliance with the Ambient Air Quality Directives [2].   
There is also a requirement to monitor air pollution at sufficiently 
high spatial and temporal resolutions around people to estimate 
personal exposure to particulates, and gases such as Nitrogen  
Dioxide  and  Ozone  for  better  understanding  their health 
impacts. Such high resolution measurements can also be used for 
validating the air quality models’ estimates of variability over 
space and time due to complex interactions.   Networks of air-
quality monitors using inexpensive sensors offer a cost- effective 
alternative approach for recording trends in air quality at a higher 
spatial resolution, albeit not as accurately as the reference 
monitoring sites. This paper describes the design, implementation, 
and deployment of a family of air quality monitors: stationary 
(AirSpeck-S) monitors for measuring ambient air quality, and 
mobile wearable AirSpeck-P for monitoring personal exposure to 
air borne particulates (PM10, PM2.5  and PM1), and the gases - 
Nitrogen Dioxide and Ozone. Results are presented for 
characterising the ambient air quality in public spaces gathered 
from people wearing the AirSpeck-P monitors who are out and 
about in two cities as pedestrians (Edinburgh, Scotland) and as car 
passengers (Delhi, India). The paper demonstrates the viability of 
using inexpensive static and mobile AirSpeck monitors for 
mapping trends in particulate concentrations in urban spaces. 
Results are presented for comparisons of the mobile personal 
exposure data from pedestrians  with  static  AirSpeck-S  monitors  
along  the  same route, and the characterization of urban spaces 
based on levels of particulate concentration using the AirSpeck-P 
monitor. 
 
Keywords— Ambient air quality; wearable sensors; AirSpeck; k- 
NN classifier. 
  
I.   INTRODUCTION 
Progress   in   electrochemical   sensors   and   laser-based 
compact optical particle counters, such as Alphasense’s OPC- 
N2 [3], has led to the development of low-cost air quality 
monitors. This paper presents the AirSpeck platform for 
connecting particulate counters and electrochemical gas sensors, 
for processing the raw sensor data and transmitting them 
wirelessly to remote servers for analysis. The platform is 

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configured in two ways: AirSpeck-S is a static version tethered 
to street furniture such as lamp posts (Fig. 1(L)), or attached to 
vehicles such  as  buses  and  cars;  AirSpeck-P is  a  personal 
exposure monitor worn as a belt by pedestrians (Fig. 1). 
 
AirSpeck platform is equipped with two wireless radios for 
transferring sensor data to the server: a Bluetooth Low Energy 
(BLE) radio connection to a mobile device such as a phone or a 
tablet for onward transmission using a WiFi link to the 
broadband internet; an on-board GPRS radio for  uploading 
data via the cellular network. Stationary air quality monitors 
would   normally  use   the   GPRS   radio   whereas  personal 
exposure monitors would use BLE to transfer to an App on the 
mobile phone to store and forward to the server. 
 
The wearable AirSpeck-P personal exposure monitors can 
be used to map ambient air quality in public spaces based on 
numerous personal exposure readings taken when the wearers 
are out and about. The preliminary experiments in fingerprinting 
the urban environment using particulate data was conducted  in  
the  Meadows  area  in  Edinburgh  and  then extended to a route 
in the city which spanned six urban environments. 
 
The novel contributions of this paper are the design of the 
AirSpeck platform which can be easily configured for stationary 
measurements of ambient air quality and for mobile 
measurements of personal exposure,  and pedestrians and car 
passengers contributing data to characterise air quality in urban 
spaces. 
 
II.  THE AIRSPECK PLATFORM 
The principal consideration in the design of the AirSpeck-S 
(Fig. 1) is the ability to deploy for a period of  several weeks 
powered by a battery pack housed in a water-resistant enclosure. 
The stationary configuration supports the OPC particulate 
counter, up to 4 gas sensors, an auxiliary fan to circulate the 
flow of air to the gas sensors, a battery pack, a switch to turn off 
the system between deployments, and data transmission over the 
GSM network (Figure 2). 
 
In contrast, the design of AirSpeck-P as a body-worn 
personal exposure monitor gives prominence to size, weight 
and wearablity issues and an App running on the mobile device 
orchestrates its operation to measure particulate matter and two 
gases (NO2 and O3).

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Fig 1. (Top-left) AirSpeck-S monitor attached to a lamp post in the Meadows 
area in Edinburgh (Top-right); (Bottom-left) AirSpeck-P monitor worn as a belt 
(Bottom-right). 
 
 
 
 
Fig 2. Block diagram of the principal components of the AirSpeck hardware 
platform. 
  
A.  Sensors 
Optical Particle Counter (OPC): The OPC is connected to 
the AirSpeck using an SPI interface and powered from the 5V 
rail along with the other external sensors. It provides particle 
counts in 16 bins categorised in terms of ranges of spherical 
equivalent sizes between 0.38μm to 17μm (Figure 3). It uses a 
high intensity laser source to illuminate the particle as it passes 
through the detection chamber and the redirected light is detected 
by a photo detector. It also outputs derived concentrations in PM1, 
PM2.5, and PM10 equivalent ratios. The 5V rail supplying the 
OPC and other external sensors are switched off between 
measurement sessions to extend the battery lifetime. 

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Fig. 3. A “spectrum” of particulate numbers (Y-axis) over time (X-axis) 
ranging in diameter sizes between 0.38μm and 17.0μm distributed across 
16 bins (inset) detected by the laser-based Optical Particle Counter (OPC). 
 
Temperature & Humidity Sensors: A Sensirion SHT21 
temperature and humidity sensor [6] is included on the 
AirSpeck PCB to enable integrated sensing of temperature 
and humidity without requiring the connection of external 
sensors. 
 
FS200 Temperature & Humidity Sensor: An interface is 
provided for the FS200 waterproof temperature and  
humidity sensor, comprising a Sensirion SHTXX series 
temperature and humidity sensor [7] in a waterproof (sintered 
metal) enclosure. This sensor is placed to protrude outside the 
enclosure either into the ambient air or into the soil for 
measuring soil moisture content. 
 
 
B. 
 
Processing 
and 
Communication 
Bluetooth Low Energy (BLE): The NRF51822 SoC [8] has 
an in-built Bluetooth LE radio which communicates with a 
custom   Android   Application  on   the   mobile   device   for 
receiving and stamping the sensor data with time and location 
(GPS) information and then forwarding them to the server 
via the cellular network or WiFi. 
 
GPRS module:  A SimCom SIM900 GPRS module [9] in 
the AirSpeck platform is used to transfer sensor data to the 
server over   the   GSM   network   for   the   stationary   
AirSpeck-S monitors.  Transmissions  take  place  every  third  
sampling period (once every 5 minutes) to reduce battery 
drain due to the GPRS module.  It consumes considerably 
more energy to connect to the GSM network, and initialise an 
HTTP connection, compared to transmitting each additional 
byte of data once connected.

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C.  Power Supply 
In  the  AirSpeck-P  configuration,  a  compact,  rechargeable 
7.4V  (2S)  LiPoly battery pack  is  used.  For  the  stationary 
AirSpeck-S, an 8400mAh, 13.2V (4S) rechargeable LiFePo4 
battery pack is used with sufficient capacity for around 20 
days of operation. LiFePo4 chemistry was chosen as it has a 
higher intrinsic safety than LiIon type of batteries, while still 
providing a high energy density compared to sealed lead acid 
and other rechargeable battery types. 
The AirSpeck board requires 3 power rails, 3.3V for the 
microcontroller, BLE radio, the on-board sensors and sensor 
interfaces; 4.2V for the SIM900 GPRS module; and 5V for 
external  sensors and  the  auxiliary fan.  Each power  rail  is 
supplied using an LMZ12003 DC/DC converter module 
capable of sourcing up to 3A of current. A key switch enables 
the system to be turned off between deployments. 
The OPC and the auxiliary fan each consumes around 200mA 
when active, and the GPRS module around 100-150mA, 
depending on its state (idle/transmitting) of operation. Due to 
their higher power consumption, care is taken to only enable 
them for  the  minimum period of time. The AirSpeck-S is 
configured for long battery life and operates at an average 
current of around 30mA, whereas AirSpeck-P keeps the OPC 
turned on continuously for sampling when mobile and 
consumes around 250mA. 
  
D. Firmware 
At the start of each measurement cycle, the firmware turns on 
the 5V rail used for the OPC and the gas sensors, then initiates 
the measurement cycle of the OPC, including turning on the 
built-in fan in the OPC. A sequence of OPC measurements is 
taken at 1second intervals and the results aggregated.    Once 
the OPC measurement cycle has completed, the OPC fan is 
turned off and the gas sensor fan is turned on for 2 seconds. At 
the conclusion of the gas sensing period the output voltages 
from the gas sensor interface board are read using the ADS1115 
ADCs and the 5V power supply rail is turned off. The  
temperature  and  humidity  sensor  readings  are  next initiated 
for the measurement period and the sensor results are read out. 
Results from all the sensors are aggregated in a results array 
and uploaded using the GPRS module every third sensing cycle. 
The 4.2V rail for the GPRS module is turned on when an upload 
event is scheduled and instructed to connect to the GPRS 
service of the service provider set up for the SIM card inserted 
in the AirSpeck board. Once connected to the network, an 
HTTP service connection is set up, the data is uploaded to the 
GPRS module and instructed to perform a HTTP POST request 
to the server. At the conclusion of the HTTP request, the 
AirSpeck enters a low-power state waiting for the timer event 
to start a new measurement cycle. 
The firmware for the AirSpeck-P version operates in a fashion 
similar to the AirSpeck-S firmware; however, unlike the 
stationary firmware, the personal one keeps the fan for the 
OPC turned on permanently and performs readings from the 
OPC at 1 second intervals.   In contrast to the stationary 
firmware, the GPRS module is not normally used and instead 
the Bluetooth LE radio is used to transmit the sensor readings 
to the mobile device at the conclusion of each measurement 
interval.
 
III. POWER OPTIMISATIONS 
The AirSpeck platform is  required to  operate for  extended 
periods in the field, without the opportunity to recharge the 
batteries or with reliable access to solar energy (in the northerly 
latitudes), and therefore an important consideration is the 
optimisation of the energy consumption by the hardware and 
firmware. 
  
 
Fig. 4. Block diagram of the principal components of the AirSpeck firmware. 
 
The   AirSpeck   firmware   has   been   designed   to   operate 
primarily in a sequential fashion which simplifies the code for 
interfacing with the sensors, and increasing the reliability of 
operations. A timer peripheral supports both low-power delays 
during sequential operation as well as global events such as 
starting a new measurement cycle. Once initialised, the 
firmware sets up interfaces to the sensors   (I2C for the 
temperature and humidity sensors, SPI for the OPC, I2C 
for the ADCs for reading the output from the gas sensor 
interface module, and UART for the GPRS module), as well 
as setting up the startup power supply states for the 5V, 4.2 
and 3.3V rails. 

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A.  Hardware Power Optimisation 
The use of switching DC/DC converter modules allows for efficient 
interfacing to different battery types. By utilising three separate 
DC/DC converters, the efficiency of the energy conversion is 
maintained for each of the separate power domains in the design. 
 
During  optimisation of  the  power  consumption of  the  gas 
sampling subsystem of AirSpeck-S it was found that replacing the 
whole volume of air inside the enclosure was consuming 
considerable energy, as the fan needed to be operated for an 
extended period of time to replace the full volume of air in the 
enclosure. 
 
A custom baffle was designed in which the gas sensors were placed 
and connected directly to the outside of the AirSpeck-S enclosure. 
A smaller fan is adequate to replace only the volume of air inside 
the baffle. The lower-power fan (100mA at 5V)

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was required to be turned on for only 2s compared with 5s for 
the larger fan (200mA at 5V), while still ensuring sufficient 
turn-over of the air in the gas sampling chamber. Additionally, 
the baffle is equipped with a series of apertures designed to 
trap rain falling on the ingress port, allowing the baffle to be 
directly ported to the exterior of the enclosure without risking 
water entering into the system. 
 
B.  Firmware power optimisations 
Optimising the energy consumption in the firmware was an 
important   consideration   in   its   design.   Key   parameters 
affecting energy consumption are the sensor sampling interval 
and the duration of each sampling period. The NRF51822 SoC 
used at the core of the system is based around a very energy- 
efficient ARM Cortex-M0 microcontroller core. 
The firmware has been designed to put the microcontroller to 
sleep between the different state machine transitions. The 
Cortex-M0  core  consumes  around  4.1mA  while  executing 
code from Flash, but less than 5uA while in sleep state (with the 
32.768kHz oscillator running). 
Data collection from the OPC sensor was optimised to achieve 
a tradeoff between energy consumption and accurate data 
collection. The OPC sensor is typically used in a continuous 
monitoring scenario, and will aggregate readings over  periods 
of time. The sensor is equipped with an internal fan which 
requires a startup period before providing accurate readings. 
The firmware first activates the fan in the OPC and then takes 
a succession of readings at 1s interval, aggregating the results 
from the sensor. Such a sampling approach was found to result 
in  more  accurate readings  when  optimising the  sensor  for 
lower average energy consumption.   In the AirSpeck-S, the 
OPC is sampled for a 30s period in each 5-minute interval, 
thus giving readings with a high degree of accuracy. 
The electro-chemical gas sensors used in AirSpeck consume 
less energy than the OPC. However, for accurate readings it is 
important that the gas sensor is exposed to air for a period of 
time which is achieved by a fan pulling fresh air into the 
sampling chamber in which the gas sensors are placed. 
The   proportion   of   energy   consumed   in   the   different 
subsystems of the AirSpeck-S were as follows: OPC sensor – 
54%; GPRS module – 36.5%; Gas Sensors – 4%; Power 
distribution – 3%; Gas fan – 2%; NRF51822 SoC – less than 
0.5%. 
 
IV. RESULTS 
The AirSpeck-P was used by pedestrians to characterise the air 
quality in the cities of Edinburgh, Scotland and Delhi, India. 
The   pedestrian   data   in   Edinburgh   was   also   used   to 
“fingerprint” a route by classifying it in terms of six urban 
environments based on the OPC particulate data from Bin0 
(particulate diameter ranging between 0.38μm – 0.42μm) as it 
was by far the most active bin in the relatively clean environment 
in Scotland. 
 
A.  Characterising the urban environment in Edinburgh 
Figure 5 shows the visualisation of the quantiles of particulate 
data collected along three routes in the Meadows area in the 
city of Edinburgh. It is a  green space south of the central 

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University campus which is bounded on the north by a path 
(North Meadows Walk) favoured by cyclists and pedestrians, 
and on the south by a road (Melville Drive) with moderately 
busy vehicular traffic. Three routes were walked carrying the 
AirSpeck-P, and the resulting dataset contains 260 data points, 
including 85 readings from within the park, 86 readings on 
North Meadows Walk, and 89 readings along the pavement on 
Melville Drive. Figure 6 shows the differences in Bin 0 counts 
as a boxplot for the three environments and the results of one- 
way ANOVA applied shows significant differences with a p- 
value < 2e-16. 
 
 
 
 
Fig. 5. Characterisation of three routes in the Meadows area in Edinburgh using 
Bin0 (0.38μm – 0.42μm) particulate data. 
  
The walk in the centre of the park is confidently picked up as a 
stretch of points with values below the median. The only 
outliers  were  at  the  intersection of  the  route  with  a  busy 
pedestrian walk in the middle of the park. Finally, the route in 
North Meadow Walk is a mixture of mainly red and yellow 
points with outliers in the zone where all the walkways meet 
which has a heavy footfall from pedestrians. The graphs in 
Figure 7 show the PM10, PM2.5 and PM1 particulate data for 
a period of 70 hours from three stationary AirSpeck-S monitors 
placed along the three routes in Figure 5: Melville Drive (Blue) 
showing diurnal variations due to vehicular traffic, interior of 
the Park  (Green), and the Upper Meadow Walk (Yellow). The 
results in Figure 5-7 confirm that the AirSpeck-P mobile 
personal exposure monitor worn by pedestrians does 
differentiate between the three types of urban environments in 
the Meadows area, and which was validated by the data from 
the stationary AirSpeck-S monitors along the same routes. 
 
Next, a pedestrian route (Figure 13) in the centre of Edinburgh 
was characterised in terms of Bin0 (0.38μm – 0.42μm) 
particulate   data   for   six   types   of   urban   environments 
enumerated in Figure 8:   (i) Indoor environment inside the 
university library; (ii) Middle of the park; (iii) Pedestrian walk; 
(iv) Quiet street - one with little or no traffic; (v) Medium- traffic 
street; (vi) Congested street/junction. Figure 9 shows the box 
plot of the OPC particulate data points for the six different types 
of urban environments. One-way ANOVA test applied to

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the data gives a p-value < 2e-16. The particulate counts are the 
lowest inside the Library and the highest in the street with 
moderately  busy  traffic  and  the  traffic  junction  with  high 
traffic. The environment types inside the park, the park walk, 
and the quiet street have small differences in PM counts. 
 
 
 
Fig. 6. Box plot for the walks along three paths in the Meadows 
  
 
 
 
Figure 7. PM10, PM2.5 and PM1 particulate data for a period of 70 hours 
from three stationary AirSpeck-S monitors placed along Melville Drive 
(Blue),  interior of the Park  (Green), and Upper Meadow Walk (Yellow). 
  
B.  Characterisation of the particulate concentration in Delhi 
 
 
Fig 8. Map of the route in Delhi, India for personal exposure data collection 
using AirSpeck-P indoors in a shop, walk in a park, and the car journey. 
  
The results in the previous section had established that the 
AirSpeck-P was capable of monitoring the personal exposure 
to particulates when attached to the subject moving around. 
The Figure 8 shows the map of the circuit taken in New Delhi 
on 11th May, 2015 during the period from 10:00 to 14:00, 
covering three distinct types of urban environments. Figure 12 
displays this journey to better effect: the top graph shows the 

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speed of the person (in m/s) – three phases of car journeys 
(identified by the elevated speeds), firstly to a shop 
followed by 90 minutes stay within the indoor 
environment; next to a park with roughly 60-minute worth 
of exposure data in the outdoors; and finally the car 
journey back to the starting location. The other three 
graphs in Figure 12 shows the PM10, PM2.5 and PM1 
values measured by the AirSpeck-P during the circuit. The 
three distinct plateaus of high particulate exposure 
coincides with the phases of the car journey, and the 
bottom

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two graphs in Figure 12 shows elevated values for PM2.5 and 
PM1 values during the journey. A comparison of the boxplots 
in Figure 11 and Figure 9 for the two cities reveals that worst 
case   exposure   in   Delhi   during   the   road   journey   is 
approximately 60 times higher compared to the equivalent type 
of environment in Edinburgh. These two graphs are directly 
comparable as they used the same AirSpeck-P device for 
monitoring particulate levels. 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 9. Boxplot of personal exposure using AirSpeck-P in terms of Bin0 
(0.38μm – 0.42μm) particulate count for three environments: inside a shop 
(middle), in a garden (right), and in the car (left) when travelling between the 
two sites. 
 
 
 
 
 
Fig. 10. (Clockwise from top-left) Inside of the University Library; Middle of 
the park; Pedestrian park  walk; Congested street/junction; Moderate traffic 
road; Low traffic street. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Fig. 11. Boxplot for the Bin0 (0.38μm – 0.42μm) particulate dataset for a route 
differentiating six types of urban environments in Edinburgh. 
Fig. 12. (Top) Speed (m/s) of the person carrying the AirSpeck-P personal 
exposure monitor; (Middle) Bin0 (0.38μm – 0.42μm) particulate count during 
the journey; (Bottom) PM10 values during the journey.

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C.  Classification of urban environments in Edinburgh 
Figure 13 summarises the results of personal exposure datasets 
collected over seven walking trips in Edinburgh and the route 
has been automatically classified into six types of urban 
environments described in Figure 10. Data preprocessing 
involved aggregating on the GPS location within each sample 
and applying the  k-means clustering technique to discover 50 
clusters of points based on their location along the route. 
 
 
 
 
 
 
 
 
Fig. 13. Classification of six urban environment types for seven walking trips 
in Edinburgh with the AirSpeck-P personal exposure monitor. 
 
 
The data points are then aggregated by location clusters and 
the mean of all the readings within the cluster was taken. Each 
point in the final data set can be thought of as a summary of 
the sensor readings around the cluster centres. k-means 
clustering technique in R [10] was once again applied to the 
size-resolved  bin  counts  and  total  particulate  counts  to 
discover six types of urban environments with each cluster 
shown as a distinct colour in Figure 13. A visual examination 
of classification of the route tallies with manual classification, 
with the nuances along the route such as air quality effects due 
to waiting at traffic lights and different levels of traffic along a 
long street, being discernable. 
 
V.  RELATED WORK 
Steinle et al [14] present a review of the myriad of issues to be 
considered  for  quantifying personal human  exposure  to  air 
pollution when moving away from static measurements. Cho et 
al. [11] examined the relationship between exposure to 
particulate matter and mortality. Particulate mass (PM10 and 
PM2.5) and size-resolved particulate counts from 0.3μm to 25 
μm  were  measured.  The  interquartile  range  of  fine  and 

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respiratory particles number concentrations were associated 
with  a  5.73%  and  a  5.82%  increase in  respiratory disease 
associated mortality, respectively. Stationary sensors using 
Optical Particle Counters have been used to study the effects 
on the environment at Healthrow airport in the UK [12]. 
 
Pedestrian exposure to particulate matter was studied by Ozgen 
et al. [14] along a selected route in the city centre of Milan. Data 
was collected using an optical particle counter and a GPS 
receiver  carried  in  a   backpack.  Particulate  counts  were 
collected by the OPC every minute in the range from 0.3μm to 
10μm and PM mass concentrations were calculated from the 
counts. Colombi et al. [13] studied passenger exposure in the 
underground transport system in the same city. 
 
 
VI. CONCLUSIONS 
  
The paper has presented the AirSpeck static and mobile air 
quality monitors and methods for pedestrians (Edinburgh) and 
car passengers (Delhi) to characterise urban environments 
wearing  the  AirSpeck-P  personal  exposure  monitors.  The 
same monitor was used to gather data in Edinburgh and Delhi 
for comparison which showed that the exposure to particulates 
in Delhi could be as high as 60 times that of Edinburgh. The 
statistical differences between six locales in the urban 
environments have been characterised for fingerprinting a 
walking route in Edinburgh and the exposure to particulates 
visualized in a map.   The data from the mobile AirSpeck-P 
was  validated  against  equivalent  measurements  along  the 
same route using stationary AirSpeck-S monitors in the 
Meadows area in Edinburgh.  Future work will investigate the 
spatial and temporal predictions of PM2.5 and PM10 
concentrations using data from network of static AirSpeck-S 
monitors located in the Meadows and supplemented by mobile 
data from subjects wearing AirSpeck-P monitors moving within 
the area covered by the static network. 
 
 
REFERENCES 
  
[1]     http://uk-air.defra.gov.uk/networks/network-info?view=aurn 
[2]    http://ec.europa.eu/environment/air/legis.htm 
[3]  http://www.alphasense.com/index.php/products/optical-particle-counter/ 
[4]    http://www.alphasense.com/WEB1213/wpcontent/uploads/2015/05/OX- 
A421.pdf 
[5]     http://www.alphasense.com/WEB1213/wp- 
content/uploads/2015/05/NO2-A42F.pdf 
[6]     http://www.sensirion.com/fileadmin/user_upload/customers/sensirion/D 
okumente/Humidity/Sensirion_Humidity_SHT21_Datasheet_V4.pdf 
[7]     https://www.google.co.uk/webhp?sourceid=chrome- 
instant&ion=1&espv=2&ie=UTF-8#q=Sensirion+SHTXX 
[8]     https://www.nordicsemi.com/eng/Products/Bluetooth-Smart-Bluetooth- 
low-energy/nRF51822 
[9]     http://www.simcom.eu/index.php?m=termekek&prime=1&sub=40 
[10]  J.  A.  Hartigan  and  M.  A  Wong.  A  K-means  clustering  algorithm. 
Applied Statistics 28, pp 100-08, 1979. 
[11]  Yong-Sung Cho, Jong-Tae Lee, Chang-Hoon Jung, Young-Sin Chun, 
and Yoon-Shin Kim. Relationship between particulate matter measured

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Version 2, Aug 2016 
 
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme 
under grant agreement No 681040”. The information featured only reflects the author's view and that the 
Commission is not responsible for any use that may be made of the information it contains.    
 
 
by optical particle counter and mortality in Seoul, Korea, during 2001. Journal of Environmental Health, 71(2):37 – 43, 2008. 
[12]  Sensor Networks for Air Quality at Heathrow Airport. http://snaq.org/. 
Accessed:2015-03-29. 
[13]  C. Colombi, S. Angius, V. Gianelle, and M. Lazzarini. Particulate matter concentrations, physical characteristics and elemental composition 
in the Milan underground transport system. Atmospheric Environment, 70:166 
– 178, 2013. 
[14]  Senem Ozgen, Giovanna Ripamonti1, Alessandro Malandrini1, Martina S. Ragettli, Giovanni Lonati. Particle number and mass 
exposure concentrations by commuter transport modes in Milan, Italy, AIMS Environmental        Science,        2016,        3(2):        168-184.        
doi: 
10.3934/environsci.2016.2.168. 
[15]  S. Steinle, S. Reis, C. E. Sabel. Quantifying human exposure to air pollution—Moving from static monitoring to spatio-temporally 
resolved personal exposure assessment, Science of the Total Environment 443 (2013

 
 
Appendix 6 
TACK SHS Work Package 5 Subject Data collection Form: 
 
Subject code: __ __ - __  __  - __ __  
Date of baseline visit:  __ __ / __ __ / __ __   
Collector Name: ______________________ 
Country : ____________________________ 
City: ________________________________ 
  Date: dd / mm / yy; 3 Time: hh:mm (24h format); 
 
      Please, write down the following information:  
 
Date of going to Smoking area 
__ __ / __ __ / __ __ 
Start of Exposure  time3 
  __ __ / __ __ / __ __   
End of Exposure time3 
__ __ / __ __ / __ __   
Duration time of exposure (Hour/min):  
 __ __ / __ __   
Number of walls in premises 
1 
2  
3 
4 
Number of smokers present 
0 
1-5 
6-10 
11-15 
16-20 
>20 
Number of E cig users present 
0 
1-5 
6-10 
   11-15 
16 -20 
>20 
Any unusual events during monitoring, (please list ) 
Time of event 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Medications 
Amount taken 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Time/date of next appointment: 
 
 
 
 

TackSHS Research Project 
 
DX.X Name of the Deliverable 
58 
 
Appendix 7  
 
 
 
 
 
 
 
 

TackSHS Research Project 
 
DX.X Name of the Deliverable 
59