Data analysis

3. Data cleaning

The sensor data was checked for inconsistencies resulting from technological failures, and these were removed

4. Aggregation into proxy activity measures

Proxy activity measures were formed from either individual sensors data, or by combining data from a number of sensors

5. Data analysis

The relationship between measures of activity and health was subjected to statistical testing

4A.7 Analysis Strategy

The aim of the quantitative analysis was to explore the relationship between the proxy activity and self-reported health measures.

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The level of statistical significance was set at less than 0.05, on the basis that this is a standard cut off point. Due to the very many tests undertaken, there are considerable issues with multiple testing, i.e. we are very likely to observe significant values, even in the absence of an association. In order to counter this, P-values were used as a measure of the strength of the association, and in those with strong associations the trends in values were explored.

I undertook the following steps:- a) Visual Checking by House

Before embarking on statistical analysis, the first stage was to check the data for any apparent associations between activity and health measures within each house.

i) Trend Charts by House

The available activity and health measures were plotted on trend charts, by house, in order to look at the measures over the whole study period, and identify

whether there were any variations in the activity data that appeared to correspond to fluctuations in self-reported health.

ii) Locally Weighted Scatter Plot Smoothing (LOESS) Graphs by House

In order to establish visually whether there were any variations in levels of activity across the study period the data were plotted in LOESS graphs, for each house.

The graph fits a curve to the plots using a window which samples subsets of the data (Wikipedia 2010).

b) Exploring Relationships between Activity and Health by Living Arrangements Consideration was given to the relationship between home activity and health amongst the two types of living arrangement (participants living alone and as a

couple), in order to quantify any differences between the two domestic arrangements.

i) Scatterplots by Living Arrangements

Average activity and health scores were calculated for each house for the whole study period. The health scores were calculated according to the previously described methodology. The degree of correlation between the two types of variables was tested using Pearson’s correlation co-efficient which calculates the degree to which the two variables fit a linear relationship. This association is

87 represented by the letter r, with the potential for r² to range from 0 to 1 (+ or -).

An r² of 0 would mean that no linear relationship was present between the two variables; and in contrast an r² of 1 would represent a perfect linear relationship (Julious 2009). The strength of the association between measures of activity and health was graded according to the methodology described by Cohen and Halliday (1982), which grades the association from very low to very high (using the value of r²); see table below.

Table 4A.7: Grading of Scatterplot Associations Value of r² Strength of Association

0 to 0.19 Very Low 0.20 to 0.39 Low 0.40 to 0.69 Modest 0.70 to 0.89 High 0.90 to 1 Very High (Cohen and Halliday 1982)

c) Testing for Significant Variations in Proxy Activity Levels across the Increments of Self-Reported Health, by House

Tests were undertaken to assess whether activity levels varied according to self-reported health.

i) Analysis of Variance (ANOVA) and t-Tests by House

Tests were carried out to ascertain whether there was any difference in the level of recorded proxy activity across the increments of self-reported health. ANOVA and t-tests were undertaken (depending on the type of variable) to establish if there were any statistically significant differences in the mean activity levels, between the grades of health (Campbell, Machin, & Walters 2007).

d) Post-Hoc Tests to Explore the Nature of Significant differences in Activity, by House ANOVA and t-tests provide evidence of significant differences; post-hoc tests are utilised in order to describe the nature of these differences, for example, whether activity was observed at higher or lower levels when worse health was reported, and whether patterns of activity were observed across the whole sample (Pallant 2007).

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i) Post-Hoc Tests on Statistically Significant Variations in Activity According to Health, by Single Occupant Houses

Statistical advice was sought on the most appropriate method of analysis, and the student-Newman-Keuls (SNK) test was recommended as a way of identifying which comparisons with an ANOVA contribute to the significance. This test proved suitable for the majority of associations, however it was not appropriate where the activity levels were unevenly distributed across the health/symptom

groupings; in this case the Tukey’s Honestly Significant Difference (HSD) test was recommended. In some cases this additional post-hoc test also did not find any significant differences between the health groupings, and further statistical advice was sought. It was recommended that if both tests had proved unsuitable, and the ANOVA level of significance was above the 1% cut off, then these correlations would be considered not to be different. As a result of this fourteen correlations were excluded, and were also removed from the ANOVA analysis.

In addition to this, a further forty two correlations were discarded because of small numbers. Either one group was very small, and the remaining groups were not significantly different; or when the small group was excluded there was only one remaining group. These exclusions were also removed from the ANOVA analysis. A total of 177 associations were examined via post-hoc tests.

Results from post-hoc tests can be very detailed, exploring the relationship between each health increment in greater detail than could be summarised;

especially as there were so many associations. It was decided to focus on

identifying which levels of health were associated with significantly higher or lower activity, in order to find out whether poorer reported health, or better self-reported health, was associated with lower or higher activity levels. In a few cases there was middle range of activity which occurred between high and low activity and was significantly different from the two outer groups; this was recorded as a middle level significant group. In addition the activity levels that were not significantly different were recorded as not different.

The sequence of analysis is represented by the following diagram (4A.2).

89 Diagram 4A.2: BLM Dataset Analysis Strategy

Final Phase of Analysis

Homes Occupied by Couples Single Occupant Homes

Step 1

Aim: To plot activity and health measures and inspect for any observable relationships between the two sets of variables

Method: Trend charts by house

Step 2

Aim: To test whether activity levels varied or were static across the study period Method: LOESS plots by house

Step 3a

Aim: To explore the relationship between activity and health within the single occupant homes, as a group

Method: Scatter plots by single occupant homes

Step 3b

Aim: To explore the relationship between activity and health within couple/family homes, as a group

Method: Scatter plots by multi-occupant homes

Where appropriate to compare and contrast between the two types of house Step 4

Aim: To test whether there were statistically significant differences in activity levels across the levels of self-reported health

Method: ANOVA or t-tests by house

Step 5

Aim: To describe the nature of statistically significant

differences in activity levels across the levels of health Method: Post-hoc tests

BLM Dataset Analysis

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A final stage of analysis was undertaken after a review of the results, in order to explore whether different measures of health would reap more fruitful results in the analysis.

Consideration was given to a narrower symptom index based on the most common symptoms in heart failure (Davis et al 2006), based on the premise that these key symptoms may be more indicative of the health state. An index of daytime breathlessness, night-time breathlessness, tiredness, and swelling was created. The symptom score for each of the separate symptoms were aggregated together using the method previously described, whereby higher scores indicate more troublesome symptoms. In order to carry out the statistical tests the results of the aggregations were divided into three groups: fewest symptoms, middle range symptoms, and worst symptoms. There were 13 different key symptom scores present in the data, ranging from 4 (the level with fewest symptoms) to 16 (the level with worst symptoms). It was decided to split the 13 levels of key symptom scores into two groups of 4, and one of 5, with the worst health group having the greatest number of levels.

In addition, a measure of stair activity was devised. Stairs are the means of accessing the rooms on other floors in the home, and for people incapacitated in any way they can become an impediment to the ease of movement around the house (Hill et al 2000); especially when heart failure symptoms worsen (Clark et al 2005, Hill et al 2000, Ryan and Farrelly 2009). It was acknowledged that a stair measure may also pick up increased traffic to the toilet in response to doses of diuretics (Hill et al 2000), unless participants have access to a downstairs toilet (Davis et al 2006). The efficacy of a stair measure would also depend on the individual circumstances of participants; for example, whether stairs were present in the home and utilised (Barnes et al 2006).

A measure comprising solely of stair use was not available in the BLM data, which necessitated the use of a broader measure of movement in the hall, stairs, and landing areas of the home.

It was acknowledged that this grouping of three areas may potentially limit the usefulness of this activity measure, since it would be recording general movement in the hall and landing as well as the more exerting movements up and down the stairs.

The method previously described of testing for significant variations in activity levels across the levels of health was utilised (see section 4A.7c).

91 4A.8 Issues with the BLM datasets

The following issues were noted with the BLM dataset, which had the potential to impact both on the data analysis and interpretation of the results:-

a) Participants joined the study at different times

The participants in the study did not join and leave at the same time, and thus there is more data for some houses than others.

b) Gaps in Data Collection

There were some gaps in data collection when participants went on holiday, or in some cases chose not to fill out the health measures.

c) Measures collected daily, or twice weekly

Not all the data was collected daily, some measures were collected twice weekly. A previous study which looked at the health data aggregated the data into weekly measures using the average or median values (Biddiss 2009).

Issues a, b and c were circumvented by the use of average measures.

In addition there were the following issues:-

d) There was not a uniform set of sensors in every house

It was not possible to have a uniform set of sensors in each house because the houses were not all the same, and some had rooms and equipment that others did not. The subjects were allowed to choose which sensors they wanted, and the least popular was the bed sensor which was uncomfortable.

e) Technical issues

There were technical issues which impacted on data collection and interpretation of the data (for more information on this issue, see chapter 9, technology chapter).

f) More than one person in the house

In houses where people live as a couple there is no way of knowing whose activity the sensor is recording. In homes where the person lives alone, there may be other family member, friends, or carers who visit the home, and there is no way of distinguishing this activity from the participant.

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g) Indirect Measurement

There was no way of knowing whether the participant was participating in the activity that was implied by the sensor activation, for example, the television may be on, but the participant may not actually be watching (for more information on this issue, see chapter 9, technology chapter).

4A.9 Conclusion

This chapter set out to describe and justify the inclusion of quantitative elements in the research. The quantitative analysis set out to explore whether the levels of recorded LM proxy activity data varied according to self-reported levels of health, as the idea that home activity changes with the health state is a central tenet of lifestyle monitoring. The secondary dataset was chosen for the analysis as it had both LM sensor data captured from the homes of

participants with heart failure, and self-reported health measures, and thus the relationship between the two types of measure could be explored. The planned data analysis sought to explore this relationship via three means. Firstly, by visually checking trend charts for any observable relationships between the two types of variable for each study participant.

Secondly to explore the nature of the relationship between the two types of variable within each housing group (that is single occupant homes, and homes where couples lived) using scatter plots. The final methods of analysis sought to identify any statistically significant differences in proxy activity according to levels of self-reported health, and then to explore the nature of these associations using post-hoc tests.

The following chapter describes the methodology for the qualitative element of the study.

93 Chapter 4B: Qualitative Methodology

4B.1 Introduction

This chapter presents the rationale for the qualitative elements of the study, the methodologies utilised, and the procedures undertaken. Four qualitative studies were

undertaken: participant interviews, a validation exercise to test initial interview findings, both a focus group and interviews with health professionals, and interviews with key informants about telecare technology.

4B.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 hypothesis that activities change when health deteriorates. The qualitative phase of the study aimed to answer the following research questions:-

3) Do everyday activities undertaken in the home by older people with Heart Failure