A Study of Physical Activity paTTerns and Major Health Events in Older People With Implantable Cardiac Devices
Conditions: Physical Activity, Aging, Cardiac Disease
Sponsor: University of Manchester

PATTErn Study Description 
and Protocol V4.3, 10th 
August 2018 
A study of Physical Activity paTTerns and major health Events in older people with implantable 
cardiac devices  
 
 
Date: 10th August 2018  
Version: 4.3 
 
IRAS ID: 215964 
CI: Dr Adam Greenstein 
PI: Dr Joanne Taylor 
Sponsor: University of Manchester 
Host: Manchester University NHS Foundation Trust 
 
 
 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
2
Study Title 
 
A study of Physical Activity paTTerns and major health Events in older people with implantable 
cardiac devices (PATTErn) 
 
Chief Investigator 
Dr Adam Greenstein (AG), Clinical Senior Lecturer, Division of Cardiovascular Sciences, University of 
Manchester 
 
Principle Investigator and Researcher 
Dr Joanne K Taylor (JKT), MD student, University of Manchester, Clinical Fellow, Manchester 
University NHS Foundation Trust 
 
Supervisors 
Dr Adam Greenstein (AG), Clinical Senior Lecturer, Division of Cardiovascular Sciences, University of 
Manchester 
Professor Niels Peek, (NP), Professor of Health Informatics, University of Manchester 
Dr Fozia Ahmed (FA), NIHR CRN Greater Manchester Cardiovascular Speciality Lead, Consultant 
Cardiologist, Central Manchester University Hospitals 
 
Additional Members of the Reporting Team 
Neil Pendleton (NP), Professor of Medical Gerontology, Division of Neuroscience & Experimental 
Psychology, University of Manchester 
Dr Nathaniel Hawkins (NH) , Clinical Assistant Professor, Heart Failure and Cardiac Electrophysiology, 
University of British Columbia 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
3
 
Sponsor 
The University of Manchester (UoM) 
Sponsor contact: Ms Lynne Macrae, Faculty Research Practice Governance Co-ordinator 
Address: Faculty of Biology, Medicine and Health, 1.21a Simon Building, University of Manchester, 
M13 9PL 
Email: FBMHethics@manchester.ac.uk 
Telephone: 0161 275 5436 
 
Host 
Manchester University NHS Foundation Trust 
Host Contact: Ms Sarah Mackie, Senior clinical trials co-ordinator 
Address: Research Office, Manchester Heart Centre, Manchester Royal Infirmary, M13 9WL 
Email: Sarah.Mackie@mft.nhs.uk> 
Telephone: 0161 276 3336 
 
Funder 
Medtronic Limited  
Sponsor contact: Mark Elsey, Senior Finance Director, UK, Ireland and NBP 
Address: Building 9, Croxley Park, Hatters Lane, Watford, Herts, WD18 8WW 
Telephone: 01923 212213 
 
Proposed Start Date 
4th June 2018 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
4
Contents 
 
Contents ...................................................................................................................................... 4 
Supporting Documents ................................................................................................................. 7 
Abbreviations .............................................................................................................................. 8 
Summary ................................................................................................................................... 10 
Background ........................................................................................................................................... 10 
Research question ................................................................................................................................. 10 
Aims ...................................................................................................................................................... 10 
Method ................................................................................................................................................. 11 
Scientific and medical opportunities .................................................................................................... 11 
Expected outcomes ............................................................................................................................... 11 
Further avenues for research ............................................................................................................... 12 
Rationale/Background ............................................................................................................... 13 
Context .................................................................................................................................................. 13 
CIED cohort ........................................................................................................................................... 13 
CIED remote monitoring ....................................................................................................................... 13 
Existing evidence ................................................................................................................................... 14 
Frailty .................................................................................................................................................... 15 
Considerations/Limitations ........................................................................................................ 16 
Accuracy of PA data from CIEDs ........................................................................................................... 16 
Impact of CIED implantation on results ................................................................................................ 16 
Validity of frailty assessments .............................................................................................................. 17 
Generalisability of results ..................................................................................................................... 17 
Sample Size Calculations ............................................................................................................ 18 
Study Objectives ........................................................................................................................ 19 
Study Design .............................................................................................................................. 20 
Type ....................................................................................................................................................... 20 
Setting ................................................................................................................................................... 20 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
5
Duration of Study .................................................................................................................................. 20 
Participants ........................................................................................................................................... 20 
Inclusion Criteria ............................................................................................................................... 20 
Exclusion Criteria .............................................................................................................................. 20 
Justification of Study Design ................................................................................................................. 20 
Study design ...................................................................................................................................... 21 
Selection Bias .................................................................................................................................... 21 
Methodology ............................................................................................................................. 22 
Study timeline ....................................................................................................................................... 22 
Screening and recruitment strategy ..................................................................................................... 22 
Rationale for screening and recruitment strategy ................................................................................ 24 
Consent process .................................................................................................................................... 25 
Data collection process ......................................................................................................................... 25 
CIED data download ......................................................................................................................... 25 
Self-report questionnaire (paper format) ......................................................................................... 26 
Physical frailty assessment ............................................................................................................... 26 
Hospital electronic medical records .................................................................................................. 28 
NEHA Episodes: DARS ....................................................................................................................... 28 
Rationale for data collection process ................................................................................................... 29 
Involvement of third party (Medtronic technical team) ................................................................... 29 
Use of USB stick ................................................................................................................................ 29 
Use of Southampton protocol for hand grip strength ...................................................................... 29 
Use of HES data ................................................................................................................................ 29 
Summary of data to be collected ................................................................................................ 30 
Withdrawal from Study .............................................................................................................. 35 
Statistical Analysis ..................................................................................................................... 36 
Basic variables from CIED data ............................................................................................................. 36 
Descriptive analyses .............................................................................................................................. 36 
PA patterns ........................................................................................................................................... 36 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
6
PA and major health events .................................................................................................................. 37 
Risks to Study ............................................................................................................................ 38 
Recruitment .......................................................................................................................................... 38 
Missing data .......................................................................................................................................... 38 
Data Management Plan .............................................................................................................. 39 
Description of the data ......................................................................................................................... 39 
Data collection / generation ................................................................................................................. 39 
Data management, documentation and curation ................................................................................ 39 
Data security and confidentiality .......................................................................................................... 41 
Data archiving, sharing and access ....................................................................................................... 42 
Responsibilities ..................................................................................................................................... 43 
Dissemination of Results and Publication Policy .......................................................................... 44 
Project Management ................................................................................................................. 44 
Ethics ......................................................................................................................................... 44 
Funding and Resources .............................................................................................................. 44 
References ................................................................................................................................. 45 
 
Appendices 
Appendix 1: Southampton protocol for adult grip strength measurement  
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
7
Supporting Documents 
 
 
Consent to contact form 
 
Invitation letter 
 
Patient information sheet 
 
Consent form 
 
Study participant questionnaire 
 
SP-36 
 
RAPA 
 
Consent withdrawal form 
 
HRA schedule of events form 
 
HRA statement of activities form 
 
Screening log 
 
Enrolment log 
 
Case report form 
 
IRAS form 
 
UoM Data management plan 
 
Funding letter - Medtronic 
 
Panman form 
 
Chief Investigator’s CV 
 
Principle investigator’s CV 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
8
Abbreviations 
 
.pdd 
programmer desktop document 
ADL 
Activities of daily living 
AF 
Atrial fibrillation 
CI 
Confidence interval 
CIED 
Complex implantable electronic device 
CF 
Consent form 
CRF 
Case report form 
CRT 
Cardiac resynchronisation therapy 
CRT-D 
CRT with defibrillator 
CRT-P 
CRT with pacemaker 
CtC 
Content to contact 
CWF 
Consent withdrawal form  
DARS 
Data access request service, NHS Digital 
DMP 
Data management plan 
DOA 
Date of hospital admission; 
DOEDA  
Date of emergency department attendance 
DOD 
Date of discharge 
eGFR 
Estimated glomerular filtration rate 
GCP 
Good Clinical Practice 
GP 
General Practice 
HCU 
Healthcare utilisation 
HeRC 
Health eResearch Centre, University of Manchester 
HES 
Hospital episode statistics 
HF 
Heart failure 
HR 
Hazard ratio 
ICD 
Implantable cardiac defibrillator 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
9
IL 
Invitation letter 
IRAS 
Human Research Authority Integrated Research Application System 
IT 
Information technology 
LOS 
Length of stay 
m 
Meter(s) 
MDPA 
Mean daily physical activity 
MFT 
Manchester University NHS Foundation Trust 
NICE 
National Institute for Health and Care Excellence 
NIHR 
National Institute for Health Research 
NHS 
National Health Service 
ONS 
Office of National Statistics 
PA 
Physical activity 
PI 
Principal investigator 
PIS 
Patient informed sheet 
RAPA 
Rapid Assessment of Physical Activity  
RCT 
Randomised control trials 
SF-36 
Medical Outcomes Study Questionnaire Short Form 36 Health Survey  
SPQ 
Study participant questionnaire  
SPN 
Study participant number 
UoM 
University of Manchester 
QOL 
Quality of life 
 
 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
10
Summary 
 
Background 
Expanding clinical indications and an aging population have resulted in an increased number of 
implanted cardiac devices (CIEDs) being used worldwide. CIEDs record patient activity (PA) on a daily 
basis in addition to other physical parameters. Despite routine use in clinical practice, the utility of 
integrated PA diagnostics to monitor health in an older population remains relatively unexplored. 
This novel, interdisciplinary study aims to utilise easily accessible data from CIED downloads to 
improve our understanding of the relationship between PA and major health events (as measured by 
non-elective hospital attendance episodes) in an older population with heart disease. The PATTErn 
study will recruit 150 people aged ≥60 years with a CIED from the Manchester University NHS 
Foundation Trust over an 18-month period. Their observational data will be analysed to explore 
longitudinal PA patterns, correlations with markers of physical ageing such as frailty, and the 
relationship between PA and non-elective hospital attendance episodes (NEHAs). In essence, this 
study will bring together expertise in Geriatric Medicine and Cardiology, to exploring how this ‘non-
cardiac’ device data correlates with health status.  
Results will directly guide local pilot studies testing the impact of personalised, actionable clinical 
summaries from CIED data downloads. It will also have wider impact across the research community, 
accelerating our knowledge of the interaction between PA and health outcomes in an older cohort. 
This may lead to future projects looking at the use of this data to proactively identify older people at 
immediate risk of hospitalisation. 
Research question 
 
What are the normal variations in daily PA in later life, and how does PA associate with major health 
events? 
 
Aims 
1. Describe the relationship between PA and NEHAs in an older CIED cohort 
2. Describe daily variability of PA in an older CIED cohort  
3. Describe the relationship between PA and NEHAs, and examining whether frailty moderates this 
relationship  

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
11
4. Confirm/refute the clinical observation that a drop in PA tends to precede episodes of NEHA in 
later life, and if so, evaluate predictive value 
 
Method 
 
Study Type: observational, investigator led 
Study Design: observational model: cohort (retrospective) 
Population: 150 people over 60 years of age with a Medtronic CareLink® compatible CIED in situ 
recruited from the Manchester University NHS Foundation Trust, England, UK 
Data collection: daily PA measures, demographic details, co-morbidity status, physical frailty 
assessment, functional status, quality of life (QOL) and NEHA data 
Consent: for participation in the study, physical frailty assessment, additional collection of 
retrospective CIED data, and access to electronic patient records 
Data sources: CIED downloads, self-report questionnaire, physical measurements (height, weight, 
hand grip strength, gait speed), electronic hospital records, HES (hospital episode statistics) data 
Results analysis: data will be analysed to investigate: daily PA variability; the association between PA 
and NEHAs, and the impact of frailty on PA patterns 
Scientific and medical opportunities 
 
This study will collect a unique set of new data for analysis. Combining expertise in Geriatric 
Medicine and Cardiology, this work will aim to provide a scientific basis for some of the fundamental 
assumptions made regarding health behaviours in later life. Little is known about normal day-to-day 
fluctuations in PA in an older population. This objective data will shed light on these patterns, and 
the temporal relationship between PA and NEHAs. Not only will this enhance our clinical knowledge, 
but it will provide the opportunity to develop proactive monitoring systems – potentially using PA as 
a proxy marker of health in certain older patient groups.  
 
Expected outcomes 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
12
This study expects to find that NEHAs are commonly preceded by a demonstrable acute decline in 
PA. Various models will be tested to demonstrate predictive validity. This association is expected to 
vary by patient sub-groups defined by baseline health status (co-morbidity burden, degree of frailty), 
and demonstrated underlying activity pattern. 
 
Further avenues for research 
 
Following on from this study, we hope results will guide the development of accurate, clinically 
meaningful early warning systems for older people at risk of physical decline using accelerometer 
data. Older people with CIEDs in situ will act as a ‘testing ground’ for this research, using device 
download data. Avenues for further research will be two-fold; improved specificity of pre-existing 
heart failure decompensation alert systems using PA data processing techniques, and the creation 
and pilot testing of personalised, actionable clinical summaries from CIED data. More widely, if this 
study finds PA is a clinically useful marker of health in this ‘test’ population, there is scope to expand 
this work into other older populations at risk of decline and dependency using external wearable 
accelerometry e.g. post hospital discharge.  
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
13
Rationale/Background 
 
Context 
 
Older people are the biggest users of both emergency medical and social services in the UK (1). This 
is not simply due to a higher prevalence of disease; rather the impact of age-associated morbidity on 
physiological and functional coping mechanisms. This vulnerability to functional decline or ‘reduced 
mobility’ in the face of seemingly minor stressors is now recognised as the frailty syndrome (2). This 
new conceptual framework has raised the possibility that physical activity (PA) could be used as a 
proxy for health status in older age. PA is now easily measureable in real-time using ubiquitous 
technologies such as smart watches, paving the way for continuous monitoring systems. Predicting 
impending ‘dependency crisis’ triggered by frailty or ill health could help target evidence-based 
upstream intervention, shown to prevent functional decline (3), falls (4, 5) nursing home admissions 
(4, 6, 7) and unnecessary hospitalisation (4, 8-11), particularly in those with chronic diseases such as 
heart failure (12, 13). 
Although these systems have huge potential to revolutionise the care of older people in the 
community, they remain in their infancy, hindered by poor understanding of the normal patterns of 
daily activity in older age. Obligate data from complex implantable electronic devices offers a unique 
opportunity to study the temporal patterns of functioning and frailty in a real-world setting. 
 
CIED cohort 
 
Every day, a huge volume of physiological data, including daily PA, is routinely collected by most 
modern cardiac implantable electronic devices (CIEDs). These devices are fitted in patients with 
cardiac conduction disorders and/or heart failure (HF). The average age of a patient undergoing 
implantation of a CIED in the UK is approximately 70 year old (14, 15). Older people with CIEDs are a 
cohort of patients with a particularly high risk of clinical frailty, hospitalisation and mortality (16-19).  
This therefore makes an ideal population to study PA patterns and temporal relationships with 
major health events. 
 
CIED remote monitoring 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
14
Modern CIEDs continuously collect data for a host of physiological parameters relevant to cardiac 
function, including; heart rate, rhythm and variability, thoracic impedance, pacing time and PA. This 
data is stored within the device until it is ‘downloaded’ for analysis (for up to 14 months). For 
example, the Medtronic CareLink® Network allows patients to manually download data via a home 
monitor system as well as automatic programmed downloads ordered by clinicians (usually every 3 
months from home, and an annual hospital based CIED check). Reports are then forwarded on to 
care providers for action. PA is considered ‘low’ if patients are ‘active’ (defined as >70 steps/min) for 
less than 60 minutes over a 1-week period (non-overlapping). 
 
Existing evidence 
 
Algorithms using CIED data to predict impending healthcare utilisation (HCU) have been in existence 
for a number of years, but are not yet integrated into standard UK clinical practice. The PARTNERS-
HF study (20) tested a prediction algorithm based on a combination of 8 parameters (AF duration, 
ventricular rate during AF, fluid index, patient activity, night heart rate, heart rate variability, % of 
pacing CRT and ICD shocks) in HF patients with CRT defibrillators. Patients with parameters outside 
the norm for 2 or more domains triggered an alert for clinical action. Cowie et al. 2013 expanded the 
PARTNERS-HF dataset, totalling 921 HF patients, mean age 67-68 years (21). 30-day HF 
hospitalisation rates in the high-risk group were 10 times higher than in the low risk groups, HR 
(hazard ratio): 10.0; 95% CI: 6.4–15.7, P, 0.001. ‘Low’ versus ‘high’ PA was the most common domain 
trigger, contributing to over 80% of high-risk alerts. Low PA as a single domain was associated with a 
2-3 fold increased risk of HF hospitalisation (HR 2.5, P<0.001). 
 
Although these studies have produced promising results, a recent review has highlighted that the 
predictive accuracy of PA in isolation remains poor (22). Trajectories have not been analysed, 
‘normal’ remains unclear, and there has been no evaluation of the impact of ageing or frailty. 
Studies have also taken a narrow approach to data collection and outcome measures, capturing 
episodes of hospitalisation triggered by HF decompensation only, and death primarily due to HF. 
Bearing in mind people living with HF have on average four additional comorbidities (23), this study 
will take a holistic approach – measuring all-cause HCU (as measured by non-elective hospital 
attendance episodes). 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
15
Frailty 
 
Frailty describes a state of reduced physiological reserve in older adults (2). Seen as a measure of 
biological ageing, frailty represents a vulnerability to physical and functional decline in the face of 
seemingly minor stressors – exemplified by the clinical presentation of ‘reduced mobility’ (often 
previously termed ‘acopia’). Three broad models of frailty exist in the literature: the phenotypic 
model, the cumulative deficit model, and pragmatic clinical assessments. The phenotypic model is 
based on pioneering work by Fried et al., who described the five cardinal syndromic features of 
frailty: unintentional weight loss, exhaustion, weakness, slow walking speed, and low physical 
activity (24). This phenotypic model is based upon clinical assessment, and correlates well with 
markers of functional and physical health (25). Following on from this, Rockwood and colleagues 
developed the cumulative deficit model – a mathematical index based measure based on the 
accumulation of ‘symptoms, signs, diseases, and disabilities’ associated with ageing (26). This has 
progressed to an electronic frailty index (eFI), - a score based on 36 deficits constructed using over 
two thousand primary care read-codes (27). This model is population based, calculated remotely. In 
addition to these two models, a number of validated pragmatic clinical assessment methods exist, 
for example the Clinical Frailty Scale (28) and the Edmonton Frail Scale (29). 
 
For the PATTErn study, frailty assessments will be based on the Fried phenotypic model. This 
method was chosen as it demonstrates closest clinical correlation on an individual level, and is the 
most established in clinical and research practice (30). It also allows for the additional definition of 
‘pre-frail’ i.e. patients at high risk of developing clinical frailty in the near future. 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
16
Considerations/Limitations 
 
Accuracy of PA data from CIEDs 
 
A number of reviews have examined the accuracy of various types of wearable activity trackers (31-
34). PA is measured in CIEDs using an embedded single-axis accelerometer. Single-axis 
accelerometers only measure activity performed in the superior/inferior motion, thus are sensitive 
for postural movements such as walking, but less so for other forms of activity. For this reason 
single-axis devices may underestimate overall energy expenditure. With regard to accuracy in an 
older cohort, all activity monitors tend to be less accurate in participants with slower gait speed (33). 
In terms of differentiating ‘active’ versus ‘sedentary’ time, single-axis accelerometers have been 
shown to be accurate even in an older disabled cohort (35). A study by Pressler et al. 2013 (36) 
compared PA measurements from CIEDs with external tri-axial accelerometer in 73 participants 
(mean age 60±20 years). Correlation between the devices was strong (mean total daily activities, 
r=0.64; p<0.001) as was intra-individual correlation in the majority of participants (70%, r>0.7, 
p<0.05).  However, Bland Altman plot analysis suggested significant variations in total daily activity 
measurements. Therefore, use of this data for pattern/trend analysis (as in this study) has been 
shown to be sufficiently accurate, however direct comparison of discrete PA measurements should 
be made with certain caveats. 
 
Impact of CIED implantation on results 
 
CIEDs include pacemakers, implantable cardiac defibrillators (ICDs), and cardiac resynchronisation 
devices (CRT). NICE guidelines (National Institute for Health and Care Excellence) recommend CRT 
device implantation as a treatment option for HF patients with prolonged QRS interval on 
electrocardiogram and an ejection fraction of 35% or less (37). CRTs have been shown to improve 
symptoms and slow progression of HF in randomised control trials (RCT) (38, 39). The MIRACLE study 
(38) (an RCT of 453 patients with moderate to severe HF randomised to CRT versus medical therapy) 
demonstrated CRT patients experienced a median change in ‘distance walked in 6 minutes’ of +39m 
at 6-month follow up (median: +39m, 95% CI +26 to +54, compared to control median: +10m 95% CI 
0 to +25, where CI = confidence interval). Evidence suggests the additional endurance effect of CRTs 
is diminished by 12 weeks of implantation (40), thus data from this time period will be excluded 
from analysis as PA data may not represent normal behaviour. This will also allow time for the 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
17
patient to recover from the procedure, and allow the device time to synchronise its settings. It is 
unclear how pacemaker or ICD implantation would affect PA, however one could expect some 
positive impact through better control of symptoms, or perhaps a psychological ‘safety-net’ for a 
more active lifestyle. Although some devices with tight pacing settings may limit exertion. We expect 
this impact to be very small.  
 
Validity of frailty assessments 
 
The Fried phenotypic based frailty assessments are well validated in the general population. We 
estimate over half of the patients recruited into the PATTErn study will have some degree of HF. One 
domain of frailty that may be less reliable in a HF cohort is weight loss – as this may fluctuate with 
fluid status. Significant weight loss within the criteria is defined as over 10% body weight in the 
preceding four years (or a measured BMI < 18.5 kg/m2). These criteria would not tend to capture 
patients whom have lost weight simply due to diuresis, therefore we do not anticipate this will alter 
validity of results. The Fried criteria have been used in a number of studies which include 
participants with HF (19, 41). The original validation study itself included patients with HF, who 
represented 4.5% of the original Cardiovascular Health Study cohort (42). Therefore, this assessment 
has validity in this context.   
 
Generalisability of results 
 
By the nature of this study, participants are limited to those with a compatible CIED i.e. have some 
form of cardiac disease (the majority HF and/or conduction disorders). Cardiovascular disease is a 
fundamental pillar of ageing, lying on the spectrum of physiological frailty – inherent, and relevant 
to all older people (43). Therefore, on balance we do believe this population sample is generalisable 
to an older population with comparable functional status and degree of frailty. This study will be 
looking at temporal pattern analysis, using this population as a cohort of older people with a 
common age-associated long-term condition (heart disease). There is no reason to expect this group 
of patients would behave any differently with regard to PA and major health events compared with 
any other older patient group. Therefore we believe these results will be of interest out with the 
cardiac community.   
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
18
Sample Size Calculations 
 
Using data from previous analyses (21, 22) sample size calculations have been performed based on 
the Kesley and Fleiss formulas for ‘cohort studies’, using exposure = low/decline in PA and outcome 
= NEHA within 6 weeks. We considered a relative risk (RR) of 2.5-3 to be clinically significant in terms 
of a PA trigger signifying increased risk of NEHA. Sample size estimate thus = 150 participants (using 
inputs: 95% significance, 80% power, RR 2.5-3, output: 88 – 198, see Table 1).  
 
Table 1: Sample size calculations 
Variables 
Option 1 
Option 2 
Option 3 
Option 4 
Two-sided significance level (1-alpha) 
95 
95 
95 
95 
Power (1-beta, % chance of detecting): 
80 
80 
90 
90 
Percent of Unexposed with Outcome: 
17 
17 
17 
17 
Percent of Exposed with Outcome: 
43 
51 
43 
51 
RR 
2.5 
3 
2.5 
3 
Ratio of sample size, unexposed/exposed: 
4.4 
4.4 
4.4 
4.4 
Sample size: Kesley 
133 
81 
177 
108 
Sample size: Fleiss 
145 
88 
198 
121 
 
The SENSE HF study (enrolling adult patients with chronic, stable HF, 6 months follow-up) captured 
224 ‘unscheduled hospital visits’ in 132 patients over 659 patient years, i.e. an annual rate of 34%. 
(44) Using this as a guide, with a sample size of 150 (150 patient years) we would expect to capture 
approximately 51 NEHA episodes from retrospective data collection.  
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
19
Study Objectives 
1. Describe the relationship between PA and NEHAs in an older CIED cohort 
2. Describe daily variability of PA in an older CIED cohort  
3. Describe the relationship between PA and NEHAs, and examining whether frailty moderates 
this relationship 
4. Confirm/refute the clinical observation that a drop in PA tends to precede episodes of NEHA 
in later life, and if so, evaluate predictive value 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
20
Study Design 
 
Type 
Observational study 
Setting 
Manchester University NHS Foundation Trust (MFT). This includes the trust’s 2 main sites: 
Manchester Royal Infirmary and Wythenshawe Hospital. 
Duration of Study 
18 months 
Participants 
Inclusion/exclusion criteria are shown below, with reference to Table 2 that lists compatible CIEDs 
for this study. 
Inclusion Criteria 
1. Age 60+ years 
2. Functioning CIED in situ for at least 6 months 
3. Medtronic manufactured device compatible with CareLink® Cardiac Compass application 
(platform which measures and stores physiological parameters) 
4. Lives in the Greater Manchester area 
5. Able and willing to given written, informed consent to enter the study 
Exclusion Criteria 
1. Faulty or incompatible device 
2. Immobile (unable to walk in upright position) 
3. Active participant in a clinical trial which does not allow concurrent recruitment into this study 
4. People unable to consent in the English language (including the understanding of study 
literature which is published in English only) 
 
Patients may be entered into registries, observational studies or clinical trials while also participating 
in PATTErn. 
 
Justification of Study Design 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
21
Study design 
Research question: what are the normal variations in daily PA in later life, and how does PA change 
surrounding major health events? 
This question is best answered by an observational study, as we are attempting to establish normal 
patterns of behaviour. Measuring PA using sensor devices allows for the passive collection of 
objective data. The use of retrospective PA data from CIED devices minimises participation bias (i.e. 
inadvertently influencing behaviour). Using a cohort of older people with CIEDs limits analysis to a 
particular cohort of older people, however as explained below in the ‘generalizability of results’ 
section, this analysis will still be of great value, and on the whole generalizable to the broader 
population. Specific considerations related to the PATTErn study are discussed in more detail below. 
 
Selection Bias 
Selection bias will be introduced by our inclusion/exclusion criteria and recruitment method in the 
following ways: 
1. Inclusion criteria 
1.1.  compatible CIED in situ 
1.1.1. The most frail patients would not be eligible/suitable for CIED implantation 
1.2. Able to provide written, informed consent 
1.2.1. Patients with significant cognitive impairment or physical disabilities affecting ability to 
sign the consent form will thus be excluded from the study 
2. Exclusion criteria 
2.1. immobile (unable to walk in upright position) 
2.1.1. The most frail patients will therefore be excluded 
2.2. People unable to consent in the English language  
2.2.1. This is a practical constraint due to limited funding and resources, but we anticipate 
will result in reduced representation of people born outside the UK. 
3. Recruitment method 
3.1. Consent to contact step 
3.1.1. In inclusion of a CtC step is an ethical requirement given patients will be approached 
for recruitment remotely. This step adds potential selection bias as we suspect the 
most ‘engaged’ patients would fill in and return the forms. We have tried to mitigate 
this by adding the option of contacting the team via post, email or phone to boast 
recruitment, and minimise time/effort required to express an interest.  

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
22
3.2. hospital-based  
3.2.1. The most frail patients would be on remote-only follow-up e.g. nursing home resident 
with very poor mobility 
 
Exclusion criteria have been kept to a minimum to reduce the impact of any selection bias. 
Methodology 
 
Study timeline 
 
See Figure 1 below. This study will have an 18-month recruitment window. Screening and invitation 
will take place at the beginning of the study, and again at 6 months.  
 
Figure 1: Study timeline 
 
Screening and recruitment strategy 
 
Basic details for all patients registered with the Manchester University NHS Foundation Trust (MFT) 
are held on an IT system called “Cardex”. This system allows for filtering of patients by a variety of 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
23
demographics and medical information, thus can facilitate the isolation of all patients on “annual 
device follow-up” meeting the first four study inclusion criteria. This process will generate a list of 
patient for postal invite. The screening process will be performed by the care team at the beginning 
of the study, and will be repeated at 6 months (see Figure 2). 
 
All patients identified for postal invite will be sent a ‘consent to contact’ form (CtC) and invitation 
letter (IL). Following the second screening process at 6 months, this newly generated patient list will 
be cross-referenced with the initial screening list to exclude all patients already contacted (i.e. each 
patient will only be approached once). 
 
 
Figure 2: Screening and recruitment flowchart 
 
The CtC form (see supporting documents) will include a phone number for the research team, as 
well as a slip form for the patient to send back to the research office (a freepost envelope will be 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
24
provided). All patients whom indicate they have consented to be contact (either by directly 
contacting research team or by returning a slip form) will be noted in the screening log (“screening+, 
CtC+”). The researcher will contact the patient in their preferred method of communication (phone, 
email, letter) and send out a patient information sheet (PIS). The patient will then be re-contacted 
(in their preferred method of communication) to establish if they are willing to come in 20-
30minutes prior to their scheduled annual device follow-up appointment to speak to the researcher. 
A further list of “screening+, CtC+, engagement+” patients will be documented in the screening log. A 
reminder contact will be made 1-2 weeks prior to their scheduled appointment.  
 
The researcher will meet all “screening+, CtC+, engagement+” patients 20-30 minutes prior to their 
scheduled appointment to allow time to obtain written, informed consent. After a patient has 
consented to participate, they will be allocated a unique randomly generated study participant 
number (SPN). A list of consented patients with corresponding study numbers will be kept in the 
enrolment log in the site file. All study data there in will be identified using this SPN.  
 
18 months has been allocated for recruitment. It is anticipated all recruitment will be undertaken by 
the PI. Recruitment will not cease once 150 participants have been recruited; rather cease at the end 
of this 18-month window. This will maximise impact of results and reduce risk to the study if number 
of captured NEHAs is less than expected. 
 
Rationale for screening and recruitment strategy 
 
Recruitment at annual device follow-up clinic 
In order to minimise burden of participation for patients and reduce the cost of the study, we have 
chosen to recruit patients whilst they attend hospital for pre-scheduled clinic. It is not anticipated 
this will cause any significant delays/inconvenience for the normal running of the clinic. 
Screening by care team 
Although the PI is a member of the care team and research team, we have separated out these roles 
to make it clear how data will be handled.  
Consent to contact 
Although the PI is a member of the care team and research team, this additional step will ensure the 
boundary between clinical and research care is maintained. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
25
PIS sent after CtC 
The PIS is a relatively long document, thus we have chosen not to send this out with the initial 
invitation letter. This will reduce printing costs, and potentially avoid patients feeling overwhelmed 
by paperwork.   
 
Consent 
 
The researcher will obtain explicit, written consent from all participants on entering the study. 
Consent forms have been produced in line with UoM and MFT guidelines. Reasonable arrangements 
have been made to minimise exclusion of capacitous, potentially eligible patients. 
1. PIS documents will be available in large font format for sight-impaired individuals 
2. Participants whom struggle with writing will be given the alternative option of providing 
verbal responses to the questionnaires (arranged prior to attending appointment to ensure 
extra time has been allocated, administered by the PI). 
Unfortunately due to resource limitation we are unable to provide translators for persons unable to 
consent in the English language. 
 
Data collection process 
 
CIED data download 
CIED downloads will be performed by a trained member of staff (usually a cardiac physiologist or 
clinician). The operator uses a specialised CIED “programmer” to process wireless transmitted data. 
A receiver loop is placed over the patient’s cardiac device (which sits below the skin and muscle 
layers on the chest wall). The receiver loop wirelessly connects to the programmer, where 
physiological and device parameters (alongside patient identifiers) are immediately summarised on 
the screen. In normal clinical practice, a USB stick is then used to transfer the downloaded processed 
data onto a standard computer for storage. For this study, a copy of raw CIED data is required for 
download in a .pdd (programmer desktop document) file (raw data is not usually required to be 
downloaded during routine assessments). In order to pseudo-anonymise participant data at source, 
the researcher will temporarily replace patient identifiers (patient name and patient ID) with the 
unique SPN on the programmer screen before download. This means all downloaded .pdd files will 
be pseudo-identified. All .pdd files will be directly downloaded from the programmer into an 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
26
encrypted trust USB stick. This will be immediately transferred onto the shared drive on an NHS 
desktop computer before being deleted from the USB stick. Personal details on the programmer will 
then be re-entered. This process takes approximately 1-2 minutes. The SPN-coded .pdd file will then 
be send via secure end-to-end encrypted email to the technical team at the Medtronic Bakken 
Research Centre, Maastricht for processing. This SPN-coded data file is returned (following re: 
encryption) via secure email to the PI in an .xml file.  
 
Self-report questionnaire (paper format) 
Each participant will complete a study participant questionnaire (SPQ), a Medical Outcomes Study 
Questionnaire Short Form 36 Health Survey (SF-36) (45), and a Rapid Assessment of Physical Activity 
(RAPA) form (46). Participants will be asked to fill in the questionnaire whilst attending clinic and 
handed back to the researcher. We will allow a 2-week window period for participants to fill in the 
questionnaire in any circumstances where a participant is unable to do so in clinic (for example, 
running late for transport). Pre-paid envelopes to return the forms within this 2-week window will 
be provided. If the participant cannot perform this task independently, then the researcher will 
assist the completion of the questionnaire. 
 
Physical frailty assessment 
Frailty assessments will be based on the Fried phenotypic criteria as described above (24).  
Assessments will be performed by the researcher in a clinical room (except gait speed which will be 
measured in the corridor). See Table 2 below. 
 
Table 2: Frailty domains and criteria 
Characteristic 
Measure 
Assessment 
Interpretation 
Weight loss 
Weight, 
BMI 
(body 
mass 
index) 
1. Single question: have you lost 
more than ….kg (10% body weight) in 
the last 4 years? 
2. Height (cm) 
3. Weight (kg) 
One point in case of either: 
1. Positive response to single question 
2. BMI < 18.5 kg/m2 (47) 
Muscle 
weakness 
Grip 
strength 
(48, 49) 
Jamar J00105 hydraulic hand 
dynamometer 
Southampton protocol (49) 
One point if ‘low’ 
Cut-offs stratified by gender and BMI: 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
27
 
Men 
BMI  ≤ 24: ≤ 29kg 
BMI 24.1–26: ≤30kg 
BMI 26.1–28: ≤30kg 
BMI ≥  28: ≤32kg 
Women 
BMI ≤ 23: ≤17kg 
BMI 23.1–26: ≤17.3kg 
BMI 26.1–29: ≤18kg 
BMI ≥ 29: ≤21kg 
Exhaustion 
Self-
reported 
 
2 questions derived from CES-D 
questionnaire (50) 
- How often in the past week did you 
feel like everything you did was an 
effort? 
- How often in the past week did you 
feel like you could not get going?  
Responses: 
- Often [i.e., ≥3 days] 
- Not often [i.e., 0–2 days] 
One point in case of  ‘often’ response 
to either question 
Slow gait speed 
Gait 
speed 
(50-52)  
Participant asked to walk 5 metres 
(m) at normal walking speed. See 
below for further detail. 
. 
One point if gait speed <0.6m/s, or 
unable to perform task. 
Also subcategories into: 
Slow: <0.8 m/s (>6s)  
Very slow: <0.65 m/s (>7.7 s) 
Extremely slow: <0.50 m/s (>10 s) 
Low PA level 
 
Self-
reported 
PA 
The Rapid Assessment of Physical 
Activity (RAPA) questionnaire (53) 
One point if RAPA 1 Aerobic score <6 
 
Interpretation of results: 
 
>/= 3 criteria: frailty 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
28
 
1-2 criteria: ‘pre- frail’ 
 
0 criteria: no frailty 
 
Height will be measured using a standard height measure. Weight will be measured using a set of 
standing scales. Grip strength will be measured using a JAMAR hand hydraulic dynamometer 
supervised by the researcher following the Southampton protocol (49) (see Appendix 1). Exhaustion 
will be assessed using 2 questions derived from CES-D questionnaire as per the Fried criteria used in 
the validation study (24) These will be asked verbally. For gait speed, the participant will be asked to 
walk at a normal pace in the corridor. A start line and finish line will be marked on the corridor wall, 
alongside a smaller marker 1m on either side.  The participant will start 1m before start line, and will 
stop 1m after the finish line.  The researcher will start the stopwatch as soon as the participant’s 
foot crosses the start line and stop recording when the participant’s second foot crosses the finish 
line. Three repetitions will be performed and the mean result used (52). The RAPA questionnaire 
was chosen to record self-reported PA as it is short, intuitive to use and well validated (53). 
 
Hospital electronic medical records 
Accessed by researcher on designated NHS computers.  
 
NEHA Episodes: DARS 
A request will be made to DARS (data access request service, NHS Digital) for access to NHS Digital 
Hospital Episode Statistics (HES) data for each participant in the preceding 12 months. HES data will 
be requested twice during the study – at 6 months, and 18 months (a fee is associated with each 
request). HES data includes: 
 
Emergency department (ED) attendances 
o Date 
o Discharge destination 
o Main diagnostic codes from attendance 
 
Non-elective hospitalisation episodes 
o Date of admission 
o Date of discharge 
o Discharge destination 
o Main diagnostic codes from admission 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
29
o Discharge destination 
 
Rationale for data collection process 
 
Involvement of third party (Medtronic technical team) 
Conversion of the CIED download file from .pdd to .xml format can only be done by Medtronic staff 
using internal software. Only pseudo-anonymised files will be shared with Medtronic. Please see 
data management plan (DMP) for further detail.  
 
Use of USB stick 
Transfer of data from the CIED processor to a computer can only be done using a portable USB stick. 
Please see DMP for further clarification on data security. 
 
Use of Southampton protocol for hand grip strength 
The Southampton protocol for measuring handgrip strength is well established and has been used 
widely in large epidemiological studies involving older people. It is recommended by the European 
Working Party on Sarcopenia in Older People (49).  
 
Use of HES data 
MFT is a tertiary centre for Cardiology, therefore the local hospital for many device patients may be 
elsewhere within Greater Manchester. In order to ensure no NEHAs are missed, it is necessary to 
collect this data from NHS Digital, which monitors all hospital attendances on a national level.  
 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
30
Summary of data to be collected 
 
 Personal data (hospital records, self-report questionnaire) 
1. Age (hospital records, verbally confirmed with patient) 
2. Sex (self-report questionnaire) 
3. Ethnicity (self-report questionnaire) 
4. Postcode (hospital records) 
4.1. Index of deprivation measure 
5. Type of accommodation (self-report questionnaire) 
6. Living circumstances (self-report questionnaire) 
7. Mobility aids (self-report questionnaire) 
8. Care provision (self-report questionnaire) 
 
Events/changes in care needs in preceding 12 months  
9. Falls (self-report questionnaire) 
10. Use of rehabilitation facilities in preceding 12 months (self-report questionnaire) 
11. Frequency of use of primary care services (self-report questionnaire) 
12. NEHA Episodes (HES) 
12.1. 
A&E attendances  
12.1.1. Date 
12.1.2. Discharge destination 
12.1.3. Main diagnostic codes from attendance 
13. Non-elective hospitalisation episodes  
13.1.1. Date of admission 
13.1.2. Date of discharge 
13.1.3. Discharge destination 
13.1.4. Main diagnostic codes from admission 
13.1.5. Discharge destination 
 
Functional data (self-report questionnaire) (46) 
14. Assistance required with activities of daily living (ADLs)                                                 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
31
14.1. 
Dressing 
14.2. 
Walking across a room 
14.3. 
Bathing or showering 
14.4. 
Eating 
14.5. 
Getting out of bed 
14.6. 
Using the toilet 
15. Assistance required with instrumental activities of daily living (IADLs) 
15.1. 
Using a map 
15.2. 
Preparing a hot meal 
15.3. 
Shopping for groceries 
15.4. 
Making phone calls 
15.5. 
Taking medications 
15.6. 
Doing work around the house 
15.7. 
Managing money 
 
Medical data 
16. Multi-morbidity (hospital records, self-report questionnaire) 
16.1. 
Presence of the long term conditions (LTC) as listed in Table 3 (54) 
17. Presence of multi-morbidity (2+ LTCs) 
18. Anti-arrhythmic medication (hospital records) 
 
Cardiac data  
19. Most recent left ventricular ejection fraction, systolic and diastolic function  (echo parameter) 
(hospital records) 
20. NYHA HF score  (I - IV)(55), (self-report questionnaire) 
21. AF/pAF/no AF (CIED download) 
 
Device data, (Cardex, CIED download) 
22. Type of device 
23. Date of insertion 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
32
24. Indication for insertion 
25. Pacing settings  
 
CIED physiological data, (CIED download) 
26. Heart failure risk status (low, medium, high), on day of download 
27. OptiVol fluid index, daily 
28. Patient activity, daily 
29. Average night heart rate, daily 
30. Heart rate variability (HRV), daily (note NOT computed if atrial paced >80% of time 
31. % Pacing/day, daily 
31.1. 
Atrial, ventricular 
32. Hours a day in AF, daily 
 
Frailty (physical assessment, self-report questionnaire) 
33. Physical frailty assessment – Fried criteria (24) 
 
Self-reported PA 
34. RAPA score (aerobic, strength and flexibility) (53) 
 
Quality of life 
35. Medical Outcomes Study Questionnaire Short Form 36 Health Survey (45) 
 
Table 3: Long term conditions 
Long Term Condition 
Definition for study 
Hypertension 
 
Self-report  
 
Coded on electronic medical records 
Hyperlipidaemia  
 
Self-report 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
33
 
 
Coded on electronic medical records 
 
non HDL-cholesterol concentration >7.5mmol/L 
Ischemic heart disease  
 
Self-report 
 
Coded on electronic medical records 
 
Previous myocardial infarction, coronary stent, or 
coronary artery bypass graft 
Diabetes (type 1, type 2) 
 
Self-report 
 
Coded on electronic medical records 
 
HbA1c >48mmol/mol 
Arthritis (osteoarthritis, inflammatory 
arthritis) 
 
Self-report 
 
Coded on electronic medical records 
Heart failure (left ventricular failure, 
heart failure with preserved ejection 
fraction) 
 
Self-report 
 
Coded on electronic medical records 
 
Echo demonstrating ejection fraction <40% 
Depression 
 
Self-report 
 
Coded on electronic medical records 
Chronic kidney disease 
 
Self-report 
 
Coded on electronic medical records 
 
eGFR (estimated glomerular filtration rate) <60% 
Osteoporosis 
 
Self-report 
 
Coded on electronic medical records 
 
T score < -2.5 on DEXA scan 
Dementia (plus subtype if available) 
 
Self-report 
 
Coded on electronic medical records 
Chronic obstructive pulmonary disease 
 
Self-report 
 
Coded on electronic medical records 
Atrial fibrillation (permanent, 
paroxysmal) 
 
Self-report 
 
Coded on electronic medical records 
 
Device download data 
Cancer  (excluding skin 
squamous/basal cell carcinomas) 
 
Self-report 
 
Coded on electronic medical records 
Asthma 
 
Self-report 
 
Coded on electronic medical records 
Stroke/TIA 
 
Self-report 
 
Coded on electronic medical records 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
34
 
Brain imaging demonstrating previous ischaemic 
or haemorrhagic event 
 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
35
Withdrawal from Study 
 
All participant derived data will be collected on the day of consent, bar the occasional case where a 
participant wishes to take the questionnaire home and post in back at a later date. Given the short 
period of time each participant will be actively enrolled in the study, it is highly unlikely a withdrawal 
of consent will occur. In the event of withdrawal of consent, a consent withdrawal form will be used 
to document this event. All data collected up to the date of consent withdrawal will be retained, but 
no further outstanding data collected. This will be made clear in the study information and consent 
form.  
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
36
Statistical Analysis 
 
Basic variables from CIED data 
CIED data will be processed to create a new set of basic variables for analysis. 
1. Mean daily physical activity (MDPA) – mean PA across dataset 
2. PA intra-individual variability – as measured by:  
 
Range 
 
Interquartile range 
 
Mean absolute deviation 
3. PA surrounding NEHAs – multiple measures.  
DOEDA = date of emergency department attendance; DOA = date of hospital admission; DOD = date 
of discharge; LOS = length of stay (DOD-DOA) 
 
Difference in PA from MDPA on DOEDA/DOA day -1, day -2, day-3, day-7, day -14 
 
Difference in PA from MDPA on DOEDA/DOD day +1, day +2, day+3, day+7, day +14 
 
 ‘Time till PA recovery’ – time from DOEDA/DOD to day regained ≥ MDPA 
 
Descriptive analyses 
All data will be stored and analysed using SPSS and ‘R’ statistical computing software. Basic 
descriptive statistics will be used to compare different sub-groups of participant (e.g. frail v non-frail). 
MDPA will be used to facilitate basic comparative analysis using a continuous variable. Determinants 
of MDPA will be calculated using univariate and multivariable analysis.  
 
PA patterns 
As demonstrated in Figure 3 below, common activity patterns embedded within CIED PA data will be 
identified using clustering algorithms. Using these identified subtypes of PA as exposures in a 
longitudinal model with outcome NEHA, multilevel/hierarchical modelling methods (both classical 
and Bayesian approaches) will be utilised to investigate associations with outcome.  
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
37
 
Figure 3 
 
PA and major health events 
For this study, major heath events are defined as NEHA episodes. Daily PA in the days leading up to 
NEHA episodes will be compared to a participant’s MDPA, in order to define across the cohort when 
any observable change in PA is detected by this method.  To explore trends in PA preceding NEHA 
episodes, a nested case control study will be constructed within the dataset, comparing trends in PA 
preceding NEHA episodes with general trends in PA before index times with no NEHA episodes. This 
will allow the creation of various ‘PA markers’ for diagnostic test evaluation methods 
(sensitivity/specificity, positive and negative predictive value). These PA markers will then be 
investigated to ascertain if they predict PA subtypes i.e. long-term behaviour patterns. We will also 
look at how these ‘markers’ superimposed on identified subtypes can be used to potentially 
personalise alert systems. 
In a similar fashion, we will also explore PA trends following NEHA episodes. There is very little 
published data on ‘recovery time’ following hospitalisation episodes in an older population. This 
study is not powered to investigate this as a primary outcome, however we will measure ‘time till PA 
recovery’ following NEHA subtypes (A&E attendance and non-elective hospitalisation episodes), and 
examine determinants of this. 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
38
Risks to Study 
 
Recruitment 
A snapshot ‘screening’ audit using Cardex data in October 2016 found 341 potentially eligible 
patients on CIED annual follow-up at Manchester Royal Infirmary, an additional 150-250 patients at 
Wythenshawe are anticipated to be eligible. Recruitment will be undertaken by the PI. An 18-month 
period has been allocated for initial recruitment. Based on observational studies in similar 
population, we anticipate a take-up rate of from screening of between 60-80% (56). 
Efforts made to maximise recruitment: 
 
Rigorous screening process 
 
Recruitment from existing clinical visits 
 
Low burden of assessment 
In the event of lower than anticipated take-up, the recruitment window could be extended beyond 
18 months to reach required sample size.  
 
Missing data 
This is always a risk during any clinical study. Missing clinical data from electronic health records is 
expected. Missing data from device downloads is anticipated to be minimal. Self-report 
questionnaires have been designed to be as short as possible, with clear questions and minimal need 
for free-text. Questionnaires have been reviewed by the Manchester Academic Health Science 
Centre Public Patient Involvement panel (MAHSC PPI) to identify any misleading or unclear questions. 
 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
39
Data Management Plan 
 
Description of the data 
Directly acquired participant data will be quantitative, generated from; consent forms, 
questionnaires, physical frailty assessments, CIED downloads and electronic health records. Data 
from linked electronic health records will be ascertained through NHS Digital Hospital Episode 
Statistics. Collection and storage of personal, identifiable data will be kept to a minimum, using SPNs 
wherever possible. 
 
Data collection / generation 
All CtC forms returned to the research team by post will be stored in the site file(s). The screening 
and enrolment log will also be stored in paper format in the site file(s). Three paper copies of the 
signed consent forms will be produced – one for the participant to keep, one to be placed in the 
patient’s hospital care file, and one to be retained by the study team in the site file(s). A paper case 
report form (CRF) will be created for each participant. This will be used by the researcher to record 
data collected during the physical frailty assessment and hospital electronic records. All participant 
questionnaires are in paper format. 
Raw CIED data will be collected via a wireless receiver. See ‘data collection processes above for 
further detail. The process of obtaining data from cardiac devices has been rigorously tested 
previously, and used as standard in clinical and research practice. This raw data will then be de-
identified into SPN-coded format by the PI, before being transferred via secure end-to-end 
encrypted email to the technical team at the Medtronic Bakken Research Centre, Maastricht for 
processing. This SPN-coded data file is returned in the same fashion to the PI in an .xml file.  
Linked HES data will be collected from NHS Digital via the Data Access Request Service (DARS) 
service.  The NHS numbers of all participants (ascertained from health records) will be shared with 
the DARS service (via secure online portal), and linked HES data (with corresponding NHS number) 
will be returned to the PI via the portal. NHS Digital uses NHS standardised coded systems.  
 
Data management, documentation and curation 
See Figure 3 below for an overview of the flow of data through the study. 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
40
 
Figure 3 
 
Identifiable data will be collected on: 
1. Consent to contact (CtC) forms 
2. Consent forms 
3. Screening log 
4. Enrolment log 
5. Linked HES data file 
All other documents will be tagged with the SPN only. 
One copy of the consent forms, the screening log, and the enrolment log are kept in the site file. The 
enrolment log will be used as a reference for linking identifiable data with SPNs. The completed 
SPQs and CRFs will be stored in the study participant files. Data from the SPQs and CRFs will be 
manually transcribed onto the electronic ‘study data’ database. Identifiable electronic data (linked 
HES data) will be stored in three secure locations: MHC research shared drive, an MFT designated 
research PC, and a designated research PC within the UoM. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
41
All study data in paper format will be stored at the MFT. We have restricted the physical movement 
of data from the hospital site to the UoM as this is not required for data access by relevant staff – 
therefore would add an unnecessary complication.  As per standard MHC research policy, the site 
file will be stored in stand-alone locked cabinets held in a secure location at the MFT. Study 
participant files will be stored in the locked MHC research office. 
A single combined ‘research data’ database will be used for analysis (SPN-coded data only). In 
addition to allocated UoM PCs, this database will also be stored on a designated laptop will be 
encrypted by the UoM IT team. Metadata will be produced regarding data collection, management 
and analysis, in order for any secondary users to understand the data. 
The study has been registered with the UoM Research Data Management Service (RDMS), where all 
electronic research data and associated metadata will be stored. This is an internal platform 
facilitation the storage, management, preservation, publication and sharing of research data.  
 
Data security and confidentiality  
UoM and MFT policies assure information security in line with recognised standards, and support 
compliance with relevant legislation. Personal data relating to the study will be stored securely in 
either paper or electronic format as laid out in Figure 3. All paper copies of study materials will be 
stored in either the site file(s) or study participant files. The site file(s) contain identifiable data, and 
will be kept on-site at the MFT in designated secure, stand-alone cabinets held in a secure location in 
the MHC. Study participant files contain all paper format SPN-coded data and will be stored in the 
MHC research office. The MHC research office is located in a ‘staff-only’ section of the hospital and is 
locked/occupied by research staff at all times. All identifiable study data in electronic format will be 
encrypted and backed-up, stored on secure designated locations only. Access to identifiable data will 
granted to the PI, any additional research staff delegated to the PATTErn study, supervisory team 
and members of the clinical care team (in any case of clinical need). Authorised representatives from 
the UoM, NHS or regulatory bodies may be granted access to personal data for audit and monitoring 
purposes. All staff accessing study data will sign the delegation log in the HRA statement of activities 
file stored at the MHC research office. As per MFT/UoM policy, all members of staff with access to 
study material are fully trained in research ethics and IT security, bound to professional standards 
and accredited with up to date NIHR Good Clinical Practice (GCP) training. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
42
The USB stick used to transfer CIED download .pdd files from the processor onto the trust PCs will be 
encrypted by MFT IT services. Once data has been safely transferred onto the PC, the USB stick will 
be wiped clean. The USB stick will remain on site in the MHC at all times. 
A pseudo-anonymised, SPN-coded ‘research data’ database will be created for all analysis and only 
this data will be used and shared out with the study team as per the terms laid out in ethical 
guidelines, and consent forms. Due to the nature of the data collected, it is not feasible to create a 
fully anonymised database. Whist re-identification will not be possible directly from the database, 
indirectly this may be possible if a person also has access to other confidential healthcare databases 
(for example, electronic patient records). This data will be stored on designated MFT and UoM 
computers. 
PCs within the MFT and UoM have been identified for storing and processing the research data.  
Once the PC has loaded the operating system a local, password protected computer account is 
required to login to the PC.  This account is unique to the primary user of the computer and only the 
account owner knows the password.  Identified PCs have Windows firewall enabled and configured 
to prevent remote access.  The PCs have been configured to automatically update their antivirus 
signatures daily and have been configured to download and install any Microsoft operating system 
and application security patches automatically from the Microsoft update service. The secure MFT 
and UoM servers will be used to store and back-up all electronic data daily. 
In addition, a UoM encrypted laptop will be allocated to store SPN-coded research data and 
associated metadata only for the duration of the study. This will allow the PI to work with the data 
as part of her MD out with the trust/university sites. The PI will take full responsibility for the 
security of the laptop and follow UoM IT security protocols at all times. 
 
Data archiving, sharing and access 
All electronic research data and associated metadata will be stored with the UoM RDMS. Research 
data will be made available for other researchers and the public after results have been validated, 
presented, and published in peer-reviewed journals. Where we engage with commercial or other 
collaborators, and where appropriate, we reserve the right to share the data and analysis under 
Non-Disclosure Agreement. 
Published data will be available through the UoM Current Research Information System. As per UoM 
policy, all peer-reviewed journal articles and conference papers will be open access, and accessible 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
43
via the Open Access Gateway. All Published outputs will be assigned a Digital Object Identifier to 
reference the data in publications. 
Electronic study data will be retained until all study data is collected, processed and stored securely 
as research data (no longer than 24 months from start of study, see Figure 1). All paper documents 
will be retained in line with MFT policy. The consent form which is filed in the patient notes will be 
stored there indefinitely. All research data will be retained on the UoM RDMS, in possession of the 
UoM for a minimum of 20 years. 
 
Responsibilities 
The PI will take day-to-day responsibility for the quality of the data acquired, the datasets, and 
transfer for storage with the RDMS. Overall responsibility for the data will lie with the PI’s primary 
academic supervisor at the UoM. 
 
 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
44
Dissemination of Results and Publication Policy 
 
Results of this study will be disseminated to the academic community by publication in peer 
reviewed scientific journals, conference presentation and presentations at local academic meetings. 
Results will be disseminated to participants by letter, which will provide a summary of findings, and 
links to further information. We will also hold an event for participants and the public to explain the 
results of the study, for example through MICRA (Manchester Institute for Collaborative Research on 
Ageing) public event meetings. The wider public will be informed of findings through UoM media 
channels. 
 
Project Management 
 
This study will be co-ordinated by the PI. Study sponsor is the University of Manchester. Study 
development and delivery is supported by the Research and Innovation Division at Manchester 
University NHS Foundation Trust (Dr Iain McLean PhD Senior Divisional Research Manager). 
 
Ethics 
 
Ethical approval is mandatory for this study to commence. This shall be sought on confirmation of 
funding via the Integrated Research Application System (IRAS). This is a minimal risk observational 
study. Data collection at recruitment and follow-up has been scheduled to cause minimal 
inconvenience to patients and will coincide with appointments that constitute standard clinical care. 
The PI and all members of the research team are trained in assessing capacity to consent in clinical 
research. 
 
Funding and Resources 
 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
45
PI (JKT) salary and data access costs will be funded by Medtronic. Study administrative costs will be 
met by research funds at the MFT. The project has been designed to be cost neutral to the host NHS 
trust. 
References 
 
1. 
National Service Framework for Older People, Department of Health for England, 2001. 
2. 
Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. The Lancet. 
2013;381(9868):752-62. 
3. 
Gill TM, Baker DI, Gottschalk M, Peduzzi PN, Allore H, Byers A. A program to prevent 
functional decline in physically frail, elderly persons who live at home. New England Journal of 
Medicine. 2002;347(14):1068-74. 
4. 
Beswick AD, Rees K, Dieppe P, Ayis S, Gooberman-Hill R, Horwood J, et al. Complex 
interventions to improve physical function and maintain independent living in elderly people: a 
systematic review and meta-analysis. The Lancet. 2008;371(9614):725-35. 
5. 
Gillespie LD, Gillespie WJ, Robertson MC, Lamb SE, Cumming RG, Rowe BH. Interventions for 
preventing falls in elderly people. Cochrane Database Syst Rev. 2003;4. 
6. 
Stuck AE, Egger M, Hammer A, Minder CE, Beck JC. Home visits to prevent nursing home 
admission and functional decline in elderly people: systematic review and meta-regression analysis. 
Jama. 2002;287(8):1022-8. 
7. 
Lewis G. Predicting who will need costly care. How best to target preventative health, 
housing and social programmes. The Kings Fund 2007. Available at URL: 
http://www.kingsfund.org.uk/sites/files/kf/Predicting book final.pdf. 
8. 
Purdy S. Avoiding hospital admissions. What does the research evidence say?. The King’s 
Fund 2010. 
9. 
Hutt R, Rosen R, McCauley J. Case managing long-term conditions: what impact does it have 
in the treatment of long-term conditions. London: Kings Fund. 2004. 
10. 
Chiu WK, Newcomer R. A Systematic Review of Nurse‐Assisted Case Management to 
Improve Hospital Discharge Transition Outcomes for the Elderly. Professional case management. 
2007;12(6):330-6. 
11. 
Oeseburg B, Wynia K, Middel B, Reijneveld SA. Effects of case management for frail older 
people or those with chronic illness: a systematic review. Nursing research. 2009;58(3):201-10. 
12. 
Gonseth J, Guallar-Castillón P, Banegas JR, Rodríguez-Artalejo F. The effectiveness of disease 
management programmes in reducing hospital re-admission in older patients with heart failure: a 
systematic review and meta-analysis of published reports. European heart journal. 
2004;25(18):1570-95. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
46
13. 
McAlister FA, Stewart S, Ferrua S, McMurray JJ. Multidisciplinary strategies for the 
management of heart failure patients at high risk for admission: a systematic review of randomized 
trials. Journal of the American College of Cardiology. 2004;44(4):810-9. 
14. 
Swindle JP, Rich MW, McCann P, Burroughs TE, Hauptman PJ. Implantable cardiac device 
procedures in older patients: use and in-hospital outcomes. Archives of Internal Medicine. 
2010;170(7):631-7. 
15. 
Kirkfeldt RE, Johansen JB, Nohr EA, Jørgensen OD, Nielsen JC. Complications after cardiac 
implantable electronic device implantations: an analysis of a complete, nationwide cohort in 
Denmark. European heart journal. 2013:eht511. 
16. 
Mitchell P, Marle D, Donkor A, Shote, Aminat, McDonagh T, et al. The national heart failure 
audit April 2013 - March 2014. 2014. 
17. 
Conraads VM, Spruit MA, Braunschweig F, Cowie MR, Tavazzi L, Borggrefe M, et al. Physical 
Activity, Measured with Implanted Devices, Predicts Patient Outcome in Chronic Heart Failure. 
Circulation: Heart Failure. 2014:CIRCHEARTFAILURE. 113.000883. 
18. 
Lien CT, Gillespie ND, Struthers AD, McMurdo ME. Heart failure in frail elderly patients: 
diagnostic difficulties, co‐morbidities, polypharmacy and treatment dilemmas. European journal of 
heart failure. 2002;4(1):91-8. 
19. 
McNallan SM, Singh M, Chamberlain AM, Kane RL, Dunlay SM, Redfield MM, et al. Frailty 
and healthcare utilization among patients with heart failure in the community. JACC: Heart Failure. 
2013;1(2):135-41. 
20. 
Whellan DJ, Ousdigian KT, Al-Khatib SM, Pu W, Sarkar S, Porter CB, et al. Combined heart 
failure device diagnostics identify patients at higher risk of subsequent heart failure hospitalizations: 
results from PARTNERS HF (Program to Access and Review Trending Information and Evaluate 
Correlation to Symptoms in Patients With Heart Failure) study. Journal of the American College of 
Cardiology. 2010;55(17):1803-10. 
21. 
Cowie MR, Sarkar S, Koehler J, Whellan DJ, Crossley GH, Tang WHW, et al. Development and 
validation of an integrated diagnostic algorithm derived from parameters monitored in implantable 
devices for identifying patients at risk for heart failure hospitalization in an ambulatory setting. 
European heart journal. 2013;34(31):2472-80. 
22. 
Hawkins NM, Virani SA, Sperrin M, Buchan IE, McMurray JJ, Krahn AD. Predicting heart 
failure decompensation using cardiac implantable electronic devices: a review of practices and 
challenges. European journal of heart failure. 2015. 
23. 
Manemann SM, Chamberlain AM, Boyd CM, Gerber Y, Dunlay SM, Weston SA, et al. 
Multimorbidity in heart failure: effect on outcomes. Journal of the American Geriatrics Society. 
2016;64(7):1469-74. 
24. 
Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older 
adults evidence for a phenotype. The Journals of Gerontology Series A: Biological Sciences and 
Medical Sciences. 2001;56(3):M146-M57. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
47
25. 
Fried LP, Kronmal RA, Newman AB, Bild DE, Mittelmark MB, Polak JF, et al. Risk factors for 5-
year mortality in older adults: the Cardiovascular Health Study. Jama. 1998;279(8):585-92. 
26. 
Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. The Journals of 
Gerontology Series A: Biological Sciences and Medical Sciences. 2007;62(7):722-7. 
27. 
Clegg A, Bates C, Young J, Ryan R, Nichols L, Teale EA, et al. Development and validation of 
an electronic frailty index using routine primary care electronic health record data. Age and ageing. 
2016;45(3):353-60. 
28. 
Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical 
measure of fitness and frailty in elderly people. Canadian Medical Association Journal. 
2005;173(5):489-95. 
29. 
Rolfson DB, Majumdar SR, Tsuyuki RT, Tahir A, Rockwood K. Validity and reliability of the 
Edmonton Frail Scale. Age and ageing. 2006;35(5):526-9. 
30. 
Bouillon K, Kivimaki M, Hamer M, Sabia S, Fransson EI, Singh-Manoux A, et al. Measures of 
frailty in population-based studies: an overview. BMC geriatrics. 2013;13(1):64. 
31. 
Murphy SL. Review of physical activity measurement using accelerometers in older adults: 
considerations for research design and conduct. Preventive medicine. 2009;48(2):108-14. 
32. 
Yang C-C, Hsu Y-L. A review of accelerometry-based wearable motion detectors for physical 
activity monitoring. Sensors. 2010;10(8):7772-88. 
33. 
Van Remoortel H, Giavedoni S, Raste Y, Burtin C, Louvaris Z, Gimeno-Santos E, et al. Validity 
of activity monitors in health and chronic disease: a systematic review. International Journal of 
Behavioral Nutrition and Physical Activity. 2012;9(1):1. 
34. 
Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-
based research. Medicine and science in sports and exercise. 2005;37(11):S531. 
35. 
Taraldsen K, Askim T, Sletvold O, Einarsen EK, Bjåstad KG, Indredavik B, et al. Evaluation of a 
body-worn sensor system to measure physical activity in older people with impaired function. 
Physical therapy. 2011;91(2):277-85. 
36. 
Pressler A, Danner M, Esefeld K, Haller B, Scherr J, Schömig A, et al. Validity of cardiac 
implantable electronic devices in assessing daily physical activity. International journal of cardiology. 
2013;168(2):1127-30. 
37. 
(NICE) NIFHACE. NICE. Implantable cardioverter defibrillators and cardiac resynchronisation 
therapy for arrhythmias and heart failure. Technology appraisal guidance [TA314]. 25 June 2014. 
38. 
Abraham WT, Fisher WG, Smith AL, Delurgio DB, Leon AR, Loh E, et al. Cardiac 
resynchronization in chronic heart failure. New England Journal of Medicine. 2002;346(24):1845-53. 
39. 
Cleland JG, Daubert J-C, Erdmann E, Freemantle N, Gras D, Kappenberger L, et al. The effect 
of cardiac resynchronization on morbidity and mortality in heart failure. New England Journal of 
Medicine. 2005;352(15):1539-49. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
48
40. 
Kawabata M, Fantoni C, Regoli F, Raffa S, Pastori F, Fratini S, et al. Activity monitoring in 
heart failure patients with cardiac resynchronization therapy. Circulation Journal. 2007;71(12):1885-
92. 
41. 
Boxer R, Kleppinger A, Ahmad A, Annis K, Hager D, Kenny A. The 6‐minute walk is associated 
with frailty and predicts mortality in older adults with heart failure. Congestive Heart Failure. 
2010;16(5):208-13. 
42. 
Gottdiener JS, Arnold AM, Aurigemma GP, Polak JF, Tracy RP, Kitzman DW, et al. Predictors 
of congestive heart failure in the elderly: the Cardiovascular Health Study. Journal of the American 
College of Cardiology. 2000;35(6):1628-37. 
43. 
Lakatta EG. So! What's aging? Is cardiovascular aging a disease? Journal of molecular and 
cellular cardiology. 2015;83:1-13. 
44. 
Conraads VM, Tavazzi L, Santini M, Oliva F, Gerritse B, Yu C-M, et al. Sensitivity and positive 
predictive value of implantable intrathoracic impedance monitoring as a predictor of heart failure 
hospitalizations: the SENSE-HF trial. European heart journal. 2011:ehr050. 
45. 
McHorney CA, Ware Jr JE, Lu JR, Sherbourne CD. The MOS 36-item Short-Form Health Survey 
(SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. 
Medical care. 1994:40-66. 
46. 
Gale CR, Cooper C, Sayer AA. Prevalence of frailty and disability: findings from the English 
Longitudinal Study of Ageing. Age and ageing. 2015;44(1):162-5. 
47. 
Liljas AE, Carvalho LA, Papachristou E, Oliveira CD, Wannamethee SG, Ramsay SE, et al. Self‐
Reported Hearing Impairment and Incident Frailty in English Community‐Dwelling Older Adults: A 4‐
Year Follow‐Up Study. Journal of the American Geriatrics Society. 2017;65(5):958-65. 
48. 
Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, et al. Age-associated 
changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. 
Journal of applied physiology. 2003;95(5):1851-60. 
49. 
Roberts HC, Denison HJ, Martin HJ, Patel HP, Syddall H, Cooper C, et al. A review of the 
measurement of grip strength in clinical and epidemiological studies: towards a standardised 
approach. Age and Ageing. 2011;40(4):423-9. 
50. 
Afilalo J, Alexander KP, Mack MJ, Maurer MS, Green P, Allen LA, et al. Frailty assessment in 
the cardiovascular care of older adults. Journal of the American College of Cardiology. 
2014;63(8):747-62. 
51. 
Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: 
European consensus on definition and diagnosis Report of the European Working Group on 
Sarcopenia in Older People. Age and ageing. 2010:afq034. 
52. 
Lusardi MM, Pellecchia GL, Schulman M. Functional performance in community living older 
adults. Journal of Geriatric Physical Therapy. 2003;26:14-22. 

PATTErn: study description and protocol V4_3 IRAS ID: 215964 
49
53. 
Topolski TD, LoGerfo J, Patrick DL, Williams B, Walwick J, Patrick MMB. Peer reviewed: the 
Rapid Assessment of Physical Activity (RAPA) among older adults. Preventing chronic disease. 
2006;3(4). 
54. 
Salive ME. Multimorbidity in older adults. Epidemiologic reviews. 2013:mxs009. 
55. 
Dolgin M, Association CCNYH. Nomenclature and criteria for diagnosis of diseases of the 
heart and great vessels: the Criteria Committee of the New York Heart Association: Little, Brown; 
1994. 
56. 
Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients 
hospitalized for heart failure: derivation and validation of a clinical model. Jama. 2003;290(19):2581-
7.