EL ANONIMATO EN LAS REDES: EL USO DE TOR

The participants within the ABC-Knee study represent a community sample of older adults with knee pain, many of whom had not consulted in the last year for their knee pain. They represent a different sample to the BEEP trial participants (described in chapter 4, sections 4.4.2 and 4.5.1), most of whom were recent knee pain consulters. Considering the sociodemographic characteristics of the 611

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ABC-Knee participants with knee pain in comparison to the BEEP trial participants, those in the ABC-Knee sample were slightly older (mean age 65.5 compared to 63 years old) and more likely to be female (57% compared to 51%). The differences in mean age are expected given that RCTs tend to underrepresent the oldest adults often due to inclusion and exclusion criteria (for example the exclusion of those who have undergone TKR who tend to be older) (Peat et al, 2011). Fewer participants in the ABC-Knee sample were obese (20% compared to 39%) perhaps due to the associations between obesity and pain severity (Garver et al, 2014), and pain severity and consultation behaviour (Bedson et al, 2007) whilst fewer were currently employed (32% compared to 42%), potentially due to their older age. Overall, the knee problems of the ABC-Knee participants were also less clinically severe than the BEEP trial participants, with lower mean WOMAC pain (4.6 compared to 8.4), and function scores (15.6 compared to 28.1),

indicating less pain and higher physical functioning. This finding is expected given that the ABC-Knee sample contained less healthcare consulters who are

associated with greater clinical severity (Bedson et al, 2007). In terms of duration of knee pain, ABC-Knee participants had pain of a shorter duration overall, with the majority reporting pain for less than three months (58%), in comparison to the BEEP trial participants, who mostly reported pain for greater than a year (75%).

Overall levels of physical activity were low with 44% being sufficiently active to meet current guideline recommendations according to the self-report STAR.

However, this is a higher proportion compared to most other existing studies measuring physical activity level in older adults with knee pain using

accelerometry or pedometry (Wallis et al, 2013) (see chapter 2, section 2.10.3) and also higher than a study that measured self-report physical activity level (Shih

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et al 2006). Whilst there may be some fluctuation in physical activity across different samples, this may also suggest the STAR questionnaire has a tendency to over-estimate physical activity level when compared to other methods of measuring physical activity such as accelerometry and pedometry. This

phenomenon has also been suggested in non-knee pain populations (Matthews et al, 2005). Comparing the ABC-Knee STAR physical activity levels to the self-report PASE physical activity levels within the BEEP trial is not currently possible since they are incommensurable.

Comparing the attitudes and beliefs about physical activity scale scores to other similar samples, the TSK scores in the BEEP trial sample (mean 35.5) were higher than a younger sample (mean age 51) of older adults with pain generally attributed to OA (mean TSK 28.3) (Heuts et al, 2004) and similar to a large sample of older adults with knee or hip pain (mean age 71.5, mean TSK 38.7) (Shelby et al, 2012). It is not possible to compare the ABC-Knee OPAPAEQ findings to other samples of older adults with knee pain, due to the lack of studies measuring this to the author’s knowledge. The ASES “other” scale score was also similar to a

number of samples of older adults with knee pain (Brand et al, 2013). In summary, the attitudes and beliefs about physical activity appear roughly

generalizable to other populations of older adults with knee pain though different sample characteristics such as age may influence attitudes and beliefs between samples.

5.5.2 Considerations for future thesis research questions

The proportions of missing data in the ABC-Knee dataset for key thesis variables were generally very low, and ranged from 3 to 8%. Hence, complete-case

analysis was considered appropriate for the ABC-Knee analysis in Part 3 of the

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thesis, since the risk of bias from such low levels of missing data is likely to be very low. The sample sizes of 611 was initially considered sufficient to carry out multivariable analyses, although post-hoc power calculations further investigated this and are discussed in chapter 7.

Strengths of the ABC-Knee dataset include its’ easily interpretable physical activity level measure (Matthews et al, 2005), whilst it also includes theoretically

important, and mutually exclusive measures of attitude and beliefs about physical activity that were not included within the BEEP trial dataset. The community sampling frame may represent a broader range of the total population of older adults with knee pain in the community in comparison to that of the BEEP trial dataset, and may also include older adults with less positive attitudes and beliefs towards physical activity who were less likely to enter an exercise trial (Bartlett et al, 2005). Using the findings from both the BEEP and ABC-Knee datasets will increase the generalisability of inferences about the relationship between attitudes, beliefs and physical activity in older adults with knee pain.

There a number of limitations to the ABC-Knee dataset including its cross-sectional nature, non-response bias, the broad screening method for knee pain, the sampling frame, and the clinimetric properties of the STAR and OPAPAEQ.

Firstly, since the ABC-Knee pain dataset is cross-sectional in nature it is not possible to infer causation from its’ data analysis since the temporal relationship between variables is not known (Hill, 1965; Szklo & Nieto, 2014).

Secondly, non-response to the ABC questionnaire may affect the generalisability of the findings. Although the response rate was considered reasonable (59%), because 41% of individuals who were sent questionnaires did not reply, the data is

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at risk of non-response bias (Armstrong & Overton, 1997). Non-response bias occurs when those who respond to questionnaires and provide data are

systematically different to those who do not (Armstrong & Overton, 1997; Holden et al, 2015). Other observational studies of community samples of older adults with knee pain have suggested those who do not respond may fall into two categories, those who are younger, still in employment with minor episodes of knee pain and the most elderly with severe knee pain and comorbidities (Herzog &

Rodgers, 1988; Peat, 2006b). Although these groups may be somewhat

underrepresented in the ABC-Knee dataset, the descriptive statistics (see table 5.2) reveal a broad range of age, pain and comorbidities were still captured within the dataset so the effect of any non-response bias from the aforementioned groups may not be of critical concern to the generalisability of findings from future analyses. However, since it was clear the questionnaire was about attitudes, beliefs and physical activity it is possible that the least active older adults would be least interested, more likely not to respond and hence under-represented (Holden et al, 2015). Any non-response bias resulting from this is difficult to confirm or disprove but may have contributed to the relatively high levels of physical activity compared to other studies of older adults with knee pain (discussed in 5.5.1).

Thirdly, due to the broad method of screening for individuals with knee pain (any knee pain in the previous 12 months), some knee pain within the ABC-Knee

dataset will likely be from causes other than OA, for example, pain associated with a recent injurious fall or pain associated with a recent joint replacement. This needs to be considered when drawing inferences about the generalisability of the findings. A further point regarding generalisability is that the participants were sampled from a single GP practice register in one area of the UK (Holden et al,

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2015). This should be considered when applying the findings to other UK older adults with knee pain as the socio-demographics of registered patients vary between GP practices. For example, the Cheshire sample has a lower ethnic mix than the entire UK (Holden et al, 2015).

Although the majority of ABC-Knee dataset variables key to the analyses within this thesis have been validated in older adults with joint pain, the OPAPAEQ and STAR and have only been validated in general older adult populations (Terry et al, 1997; Matthews et al, 2005). The OPAPAEQ measures a broad range of attitude and beliefs about physical activity including perceived health benefits and one item regarding pain. Although, it is likely that the OPAPAEQ will remain reasonably valid for use in older adults with knee pain, given its general physical activity

theoretical underpinnings and the inclusion of an item relating to pain (Matthews et al, 2005) its’ clinimetric properties in this population are unknown. The STAR measure is easily interpretable and considered suitable for measuring physical activity at a population level, yet it is also associated with substantial individual classification errors (Matthews et al, 2005), and can only crudely differentiate individuals into three broad categories of physical activity level. As a result of its low number of discriminatory categories and since the vast majority (96%) of ABC-Knee participants were classified into just two categories (“insufficiently active” and

“meeting current recommended levels of physical activity”), it may not ideally suited to detect associations with attitude and belief variables.

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5.6 Chapter summary

This chapter summarised the cross-sectional ABC-Knee dataset of older adults with knee pain (n=611) containing sociodemographic, clinical, physical activity level and attitudes and beliefs about physical activity variables that will be used in Part 3 of this thesis. Descriptive statistics showed that less than half of the sample were meeting guideline recommended levels of physical activity. The relationship between attitudes and beliefs about physical activity and those who are “inactive”,

“insufficiently active” and those “meeting current recommended levels of physical activity” will be investigated in chapter 7, adjusting for potential confounders. The subsequent chapter uses longitudinal BEEP data to explore if change in physical activity is associated with future clinical outcome.

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Chapter 6

Change in physical activity level and future clinical