Data Analysis
4.8 Data Integration
Integration is defined as “the interaction or conversation between the qualitative and quantitative components of the study” in order to gain insights greater than the sum of the parts (O’Cathain et al 2010, page 1147). The strength and uniqueness of mixed methods derives from the points of integration, whereby there is interplay between the two methods, either to influence the research design or at the point of data analysis or in later
interpretation. In the past integration of qualitative and quantitative findings has been limited (Bryman 2007), but over time, as the literature describing differing methods of integration has grown, so have the studies more fully integrating mixed methods (Bazeley 2009). At the analysis stage mixed methods integration can take place using a variety of methods, such as, a matrix approach drawing together all data sources in order to analyse data (O’Cathain et al 2010); or utilising a technique called following a thread (Moran-Ellis et al 2006) whereby inferences and themes are followed across the project and data sources, moving iteratively between elements of the project (O’Cathain et al 2010). In this study the initial quantitative data analysis informed the qualitative data collection and analysis, with a focus on finding out whether both data sources told the same story of complexity, or whether activity patterns were apparent. The qualitative data collection focused on broader influences of everyday activity than the quantitative, and therefore it was not possible to return to the quantitative data to reanalyse the data.
The second potential point of integration occurs at the data interpretation stage. Farmer et al (2006) described a method of triangulation whereby the data sources are assessed to identify points of convergence and divergence within common themes, and thereby to illuminate and explore the themes in greater depth. This approach was undertaken as a methodology to integrate the differing data sources within this study in order to gain the beneficial insights from integration; although it is acknowledged that whilst this method was followed, the results were limited by the general trend of complex patterns in the quantitative findings. However Farmer et al (2006) acknowledged that there are challenges to integration, since the differing
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purposes and nature of data sources will mean that some sources are more suited to address particular questions than others, and thus at some points one source will gain more weight than another. In addition, further challenges can arise from participant samples that are not matched in the data sources, since results may arise from the differing participants (Moffatt et al 2006). Therefore taking all these potential impediments to integration into account a pragmatic approach was undertaken, since as Moffatt et al (2006) argue, even where
integration is not achieved, valuable insights can be gained from simultaneous interpretation of qualitative and quantitative data sources, “We advocate treating qualitative and
quantitative datasets as complementary rather than in competition for identifying the true version of events” (page 9). For this study, where integration was possible this was
undertaken, and where this was not possible the varied data sources were used in a complementary way to gain a greater depth in answering the research question.
4.9 Conclusion
This chapter described the mixed methods approach undertaken for this study, and in keeping with the sequential approach outlined the following chapters detail the quantitative and then qualitative methodologies. Data integration is detailed in chapter 10.
73 Diagram 4.2: Schematic Diagram of the Research Design
Quantitative Research hypothesis: – Qualitative
Everyday activities vary according to the state of health in heart failure
Phase 1
Statistical testing to ascertain whether proxy levels of home activity vary according to self-reported levels of health
(T-test and analysis of variance)
Phase 2
Post-hoc tests to explore the nature of the statistically significant associations between proxy measures of home activity and self-reported health, e.g. are activity levels higher when better health is reported?
Participant Interviews
Design interview questions based on findings from the quantitative analysis to explore everyday activity under differing health states, and other factors influencing activity
Validation Exercise at the Heart Failure Support Group
Validation of themes from the participant interviews, and insights from group members
Nurse Focus Group/Interviews
Insights from Heart Failure Specialist nurses
Inferences from both Methods
To garner insights from both methodologies
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Chapter 4A: Quantitative Methodology 4A.1 Introduction
This section presents the rationale for the quantitative element of the study; and a description of the data-set utilised, including sampling, participant recruitment, data collection methods, and ethical considerations of this secondary data. The final section outlines the data analysis undertaken to explore a key research hypothesis of this study, with data preparation and methods of statistical analysis described.
4A.2 Rationale
The key aim of the study was to explore the relationship between everyday activities and the health state in older people, in order to test the assumption that activities change when health deteriorates. The quantitative phase of the study aimed to explore the following research question:-
Do levels of recorded proxy activity captured by lifestyle monitoring sensors vary according to self-reported levels of health?
The quantitative research phase had three key aims. Firstly, to subject measures of activity and health to statistical testing, in order to ascertain whether there were statistically significant variations in the levels of activity according to self-reported levels of health. This first step aimed to find out whether there was a statistical association between the measures of activity and health within this sample; and should this be the case, the further analysis sought to explore the nature of this association. Secondly, to explore the nature of any significant differences in activity according to health (should they occur), to find out whether recorded activity levels were higher or lower according to the self-reported health state;
whether recorded activity levels were higher when health was reported as good or poor.
Thirdly, to explore whether any variations in activity according to health were present in an observable pattern across the sample, or whether variations were individual in their nature.
In order to answer the research hypothesis a dataset was required that recorded measures of activity and health over-time, in order to explore activity within differing self-reported health-states. Given that both time and financial constraints prevented the commissioning of a Lifestyle Monitoring (LM) project to capture longitudinal data specifically for this project, an existing LM dataset (the Barnsley LM project (BLM)) was identified as the best available source for the following reasons. BLM had sample of older participants with heart failure, and the available dataset had both measures of health and activity captured over-time. The project
75 had seventeen participants, and therefore this provided the scope to explore analysis both between and within cases. In addition, as the project had been undertaken by the
Rehabilitation and Assistive Technology Group, this facilitated both access to the dataset, and expertise from colleagues who had been involved in the both commissioning the project, and data analysis for other projects.
4A.3 Ethical Issues
Ethical approval for the original study had been gained from NHS Research ethics, and research governance approval was gained from Barnsley hospital (Biddiss et al 2009).
Consideration was given to ethical issues that arise out of the analysis of secondary data (Cresswell 2009). Participant confidentiality was protected as the dataset was anonymised, and participants were referred to in this study by house number only; and when referring to participants any potential identifiers were removed. The dataset was stored safely on a password protected drive on the university network.
The analysis was undertaken with an awareness of the ethical issues around home monitoring of older people (appendix paper 1).
4A.4 Barnsley Lifestyle Monitoring (BLM) Dataset
This section describes the processes of data collection of the BLM secondary data, as documented by Biddiss et al (2009). Whilst I was not involved in the data collection, I undertook the data preparation and analysis of the raw dataset (as described from section 4A.5 onwards). The wider data collection process is described it was from this broader group of participants that the LM data for this research originated.
The BLM project was undertaken with the primary purpose of exploring the value of health and physiological data collected within the home, to predict key medical events in participants with heart failure. Forty five participants were recruited for the study, after being identified from a review of patient records at Barnsley hospital, according to the following inclusion and exclusion criteria:-
Inclusion Criteria
1. Age 60 years and over 2. Living at home
3. Heart failure diagnosed by echocardiogram
4. Conventional symptoms of heart failure, including dyspnoea and oedema 5. New York Heart Failure classification of II, III, or IV
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Exclusion criteria
1. Ejection fraction >40%
2. Unstable angina 3. Under the age of 60
4. Severe dementia or another other debilitating psychiatric disorder 5. Inability to read and understand words on an electronic screen 6. Any planned coronary revascularization procedure
7. On a waiting list for heart transplant
8. Participation in any other heart failure research study that would conflict with this project
9. Lack of an operational phone line and electrical socket close by 10. Living in a residential or nursing home
Six participants died, and eight returned their equipment, leaving a total of 31remaining participants, with the following characteristics (Table 4A.1). :-
Table 4A.1: Sample Characteristics of BLM Project (n=31)
Gender Age NYHA Scale
Male (83%) 60-69 (35.5%) II (43%) Female (17%) 70-79 (50%) II-III (17%) 80-89 (14.5%) III (40%)
These participants were monitored for an average of eighteen months.
The NYHA scale refers the New York Heart Association’s grading of the severity of heart failure (The Criteria Committee of the New York Heart Association 1994). Grade II is defined as mild, with ordinary activity likely to result in some heart failure symptoms; and grade III defined as moderate, with symptoms arising from less than ordinary activity
The data analysed for this study came from a subset of 17 participants from this larger sample, who had additional LM sensor data installed into their homes for the study period. The characteristics of this subset were as follows:-
Table 4A.2: Sample Characteristics of BLM Subset
Gender Living Arrangements Age NYHA Scale
Male - 14 (82%) Alone - 7 (41%) 60-69 (41%) II (59%) Female - 3 (18%) Couples - 10 (59%) 70-79 (41%) II-III (12%) 80-89 (18%) III (29%)
77 4A.4.1 BLM Data Collected
Data were collected from December 2005 to October 2006, from entry to the study to drop out, or study end. The following health and activity data was collected:-
4A.4.1.1 Health Data
Health data was collected by means of a Telehealth health monitor (Docobo 2014) whereby participants input symptom and health scores. The following table shows the symptom data (collected daily) which enabled the participants to record the scale of their self-reported symptoms, ranging from no symptoms, to worse than usual symptoms. The questions were devised from a British Heart Foundation Heart Failure Plan (Lewin et al 2005), and from advice from Heart Failure Specialist nurses involved with the project (SB 2011).
Table 4A.3: Symptom Questions
Question Possible Answers
Do you have a cough? 1) No
2) New cough 3) Same cough 4) Worse cough 1) How many episodes of angina have you had today?
2) Did you sleep well last night?
3) How often did you wake up during the night due to shortness of breath?
4) Have you had any shortness of breath today?
5) Have you felt more tired today?
6) Have your ankles or feet been swollen today?
7) Have you passed less urine than usual today?
8) Have you eaten well today?
9) How anxious have you been today?
10) Have you felt dizzy today?
1) No/None 2) Less than usual 3) As usual
4) More than usual
Did you need extra pillows to sleep with last night? 1) No 2) Yes
Health-related quality of life (HRQoL) was measured using the EQ-5D (Cheung et al 2009) delivered via the Telehealth device. Participants completed the EQ-5D statement questions twice weekly to provide a measure across five dimensions of health: mobility, self-care, usual activity, pain/discomfort, and anxiety/depression (table 4A.4). The responses for each dimension were scored from 1 to 3, with a score of 1 indicating no problems, 2 moderate problems, and a score of 3 indicating extreme problems. The EQ-5D visual analogue scale (VAS) was completed daily to glean a measure of perceived health, from the worst imaginable health-state of zero, to the best of one hundred. EQ-5D was chosen as it is short and easy to
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fill out, and had been validated as a measure with similar samples (Ellis et al 2005, Morgan et al 2007, Brazier et al 1996). However, the use was unusual since EQ-5D is generally used as a cross-sectional measure, rather than longitudinally.
4A.4.1.2 Activity Data
A central tenet of LM is that home activity should be measured unobtrusively, with sensors placed around the home; rather than the alternative more direct approach of requiring participants to wear sensors (Kowalski et al 2012). Activity data was therefore collected via sensors which were devised to record an interaction with participants, in the form of a triggering of the sensor. The sensor data is assumed to represent a home activity based on two attributes: the type of sensor, and the siting of the sensor. Sensors of differing types were developed to detect specific activities within the home, such as mattress sensors that detect when the bed is occupied, or an electrical appliance sensor which detects when a particular electrical appliance has been switched on or off. Other types of sensor have broader purposes, in terms of indicating that home activity is taking place, such as the movement sensors, which are designed to record general movement in a room. The siting of the sensor also offers clues to what type of activity is being undertaken, for example, sensors activated in the kitchen would be assumed to relate to food preparation activity. Thus electronic
activations from the sensors formed the raw data collected from BLM.
The Barnsley sensor platform consisted of passive infrared movement sensors (PIR), door contact sensors, electrical appliance sensors, and bed and chair occupancy sensors (table 4A.4). PIR sensors are based on the same technology used in burglar alarm room sensors that are wall mounted. The aim of the PIR is to capture the presence of a person in the room and the sequence of general movement around the home, and potentially to record levels of activity (Tunstall 2012). Door contact sensors capture the activations of opening and closing of doors around the home, with the aim of quantifying usage. Electrical appliance sensors are fitted on the plug socket and record the sequence of on and off activations; the level and duration of appliance usage can therefore be calculated. Kettle, fridge, toaster, and television appliances were monitored (Tunstall 2012a). The bed and chair sensor takes the form of a pressure pad recording when the bed/chair is occupied, and the time when the occupancy comes to an end (Tunstall 2011).
79 Table 4A.4: BLM Sensor Platform
Sensor Type Room
Bed/Chair Living room, and master bedroom Door contact Food cupboard, and fridge
Electrical appliance Kettle, microwave, toaster, and TV PIR
Dining room, downstairs toilet, garage,
hall/stairs/landing, kitchen, landing, living room, main bathroom, master bedroom, and second bedroom
4A.5 Development of proxy measures of activity
Rather than analysing the data captured by individual sensors, a decision was made to
combine the sensors into groups to form proxy measures of activity. There were three reasons for this decision. Firstly, the measures were informed by the literature review, which had identified activities which had the potential to provide an indication of the health-state.
Secondly, it was decided to treat the sensors as building blocks and build them up as necessary into more concrete proxy measures, that reflected meaningful activity in the home (such as Food Activity). Thirdly, the measures were also informed by the methods utilised by previous LM studies to measure activity. The table 4A.5 summarises the motives for the choice of proxy activity measures, and details the sensors contained within each measure.
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Table 4A.5: Composition and Motives for Proxy Activity Measures Proxy
Activity Measure
Sensor Type
Room/Object Justification 1 – Meaningful