Abstract
Background:
Sedentary behaviour (SB) is associated with adverse health outcomes in the general population. Replacing sedentary time with light intensity physical activity (LPA) has been linked with improvements in all-cause and cardiovascular disease mortality in adults. People with Rheumatoid Arthritis (RA) typically spend long periods of time sedentary, but the health consequences of ‘too much sitting’, and possible benefits of LPA, have not been fully explored in this population. Moreover, little is known regarding the determinants of these behaviours among people living with RA, and such knowledge is required for the development of effective behavioural interventions.
Aims:
To examine longitudinal relationships between: 1) objectively-assessed SB/LPA with health outcomes in RA, 2) hypothesised determinants of SB/LPA with objectively-assessed SB/LPA in RA.
Methods:
This longitudinal study will secure assessments at baseline (Time 1) and 6-month follow-up (Time 2) from RA patients. At both time points, physical assessments will be undertaken, and questionnaires administered to measure physical (e.g., percentage body fat, disease activity, physical function, pain) and psychological (e.g., depression, anxiety, vitality) health outcomes. Additional questionnaires will be administered to establish hypothesised determinants (i.e., psychosocial, individual differences, and physical environmental). Participants will wear the ActiGraph GT3X accelerometer and activPAL3μTM for 7 days to objectively measure SB and LPA.
Discussion:
Findings will elucidate the health correlates of SB in RA, as well as the relevance of interventions targeting reductions in SB by promoting LPA. Results will also assist in identifying intervention targets (i.e., determinants), with the potential to encourage SB change in RA.
Keywords: sedentary behaviour, light physical activity, rheumatoid arthritis, accelerometry, activPAL, correlates
INTRODUCTION
Rheumatoid Arthritis
Rheumatoid Arthritis (RA) is a chronic autoimmune disease that affects approximately 0.3–1% of people worldwide.1 RA is characterised by high-grade local and systemic inflammation, which leads to severe joint pain, stiffness and swelling in synovial joints of the body.2–4 People living with RA are exposed to a 50% increase in cardiovascular risk,5 with heightened universal inflammation implicated in the common development of cardiovascular disease (CVD) -related morbidity and mortality in this patient group.6,7–9 In addition, this elevated inflammation contributes to an increased risk of poor mental health in RA.10
Sedentary Behaviour
Sedentary behaviour (SB) is defined as ‘any waking behaviour characterised by an energy expenditure ≤1.5 metabolic equivalents (METS) while in a sitting, reclining, or lying posture’.11,12 It is distinct from physical inactivity, which refers to lack of regular engagement in moderate-to-vigorous intensity physical activity (MVPA [≥3 METS]) in accordance with physical activity recommendations (i.e., 30 minutes × 5 days per week for adults). Examples of common SBs include sitting watching television, reading a book, working at a computer and travelling in a vehicle.13 In the general population, SB has been consistently associated with increased inflammation, and it has been proposed that this may represent a mechanism through which SB leads to an increased risk of poor health.14,15 For example, prospective studies demonstrate high levels of SB to associate with worsened cardiovascular and cardiometabolic health; both of which are linked to heightened systemic inflammation.16–25 In addition, a recent systematic review and dose-response meta-analysis reported that total sitting time and television viewing time were associated with a greater risk of several major chronic disease outcomes, including all-cause, CVD, and cancer mortality, as well as incident diabetes.26 Importantly, such adverse health outcomes associated with engagement in SB have been shown to occur, despite the level of MVPA an individual engages in.27,28 That is, SB represents an independent risk factor for poor health.26
Sedentary Behaviour and Rheumatoid Arthritis
It has been reported that people living with RA typically spend long periods of time sedentary,3,29,30 and recent accelerometry studies suggest people with this condition can spend up to 9 waking hours sedentary per day.31,32 On the basis of emerging evidence for the association between SB and inflammation, Fenton and Kitas29 hypothesised that high levels of SB in RA may exacerbate already elevated systemic inflammation in these patients, and contribute to the progression of RA outcomes. Akin to this proposition, Fenton et al.31 summarised results of non-RA studies, demonstrating the adverse links between SB with inflammation and chronic diseases with an inflammatory component (e.g., CVD, type 2 diabetes), underlining the relevance of these findings for RA. That is, as evidence for the associations between SB, inflammation and poor health continues to accumulate, it is important to evaluate the role of SB as an independent risk factor for disease outcomes in RA – a population experiencing compromised health and at high risk of comorbidity.
Whilst studies to date are yet to determine the role of SB for inflammation in RA, research has begun to examine the implications of SB for broader RA outcomes. Such investigations have employed either device-based, or self-report methods to quantify SB. For example, Khoja et al.33 reported positive associations between accelerometer-assessed SB with disease activity, and Greene et al.34 and Giles et al.35 showed that high levels of self-reported SB was associated with poorer physical function in RA patients. In addition, Prioreschi et al.3 used accelerometry to determine SB patterns in people living with RA and found an inverse association between SB and bone mass. More recently, research studies have indicated that higher levels of accelerometer-assessed SB are associated with more pain and fatigue,36 and a higher risk of CVD.37 Specifically, Fenton et al.37 found that total sedentary time was adversely associated with 10-year CVD risk in a sample of RA patients. Interestingly, this study was the first to examine whether the manner in which sedentary time was accumulated held implications for health outcomes in RA. Findings revealed a positive relationship between sedentary time accumulated in bouts ≥20 minutes and 10-year CVD risk.
This finding37 is aligned with novel prospective and experimental studies, which indicate that shorter sedentary bouts (i.e., the duration of uninterrupted sedentary periods) and more frequent sedentary breaks (i.e., interruptions in sedentary time)with light physical activity (LPA [1.6 – <3 METS]), are beneficially linked to health outcomes in non-clinical and clinical populations.20,27,38–43 For example, regularly breaking up SB with LPA has been associated with better cardiometabolic and cardiovascular health in adults (≥20 years)20,38 and older adults,27 and has also been linked with a reduced risk of disability among older adults.41 A recent systematic review and meta-analysis of experimental and observational studies also revealed that LPA (overall, and sedentary breaks) could play a role in improving adult cardiometabolic health and reducing mortality risk.44 In RA, recent studies have reported beneficial associations between daily LPA with CVD risk,37 depression and vitality.45
Such findings illustrating the potential health benefits of LPA are particularly relevant to people with RA, given the pain and physical dysfunction characteristic of this condition. Indeed, approaches to reduce SB by promoting LPA (‘sedentary breaks’), may be better tolerated than those targeting MVPA, the traditional focus of physical activity behaviour change interventions in this patient group. Moreover, the strong inverse correlation between SB and LPA in studies with RA patients, certainly points to the potential of behavioural interventions which aim to displace SB with LPA among this population.33,45
Whilst studies on this topic are beginning to emerge, a number of important limitations mark the extant literature in this field, which should be addressed in future research. First, a heavy reliance on self-report measures to assess SB and physical activity within this patient group brings issues regarding social desirability, errors in participant recall, and a tendency to underestimate levels of SB and overestimate levels of physical activity.16,46 Second, whilst several studies have employed accelerometers in an attempt to address the limitations of self-report, sedentariness is often incorrectly defined as activity ≤1 MET33 or <1 MET.47 As a result, SBs that require between 1–1.5 METS (e.g., sitting whilst watching television or reading) may be misclassified as LPA,48,49 meaning the true significance of SB for RA outcomes cannot be accurately determined. Similarly, SB is sometimes defined as ‘lack of engagement in purposeful physical activity’ (i.e., physical inactivity, not meeting MVPA guidelines), which is inconsistent with the definition used by the SB Research Network (i.e., waking behaviour ≤1.5 METS, whilst sitting/reclining/lying),11,12 employed almost globally across the SB literature. Finally, the majority of studies that identify associations between SB and health outcomes in RA are cross-sectional, posing a challenge when inferring a causal direction of these associations.31 As such, research is required to address these methodological limitations, and generate important knowledge regarding the consequences of SB in RA for inflammatory burden and related RA outcomes. It is also essential that high quality studies investigate the potential benefits of LPA participation for people living with RA, in order to elucidate the potential relevance of interventions that focus on displacing SB with LPA.
Determinants of Sedentary Behaviour and Light Physical Activity
In order to prevent the potential adverse consequences of SB for health in RA, interventions promoting SB change should target factors that influence this behaviour (i.e., determinants). If interventions are to focus on displacing SB with engagement in LPA, they will also need to consider the determinants of LPA.
Psychosocial determinants and individual difference factors
Self-efficacy. Social Cognitive Theory (SCT)50 regularly serves as the theoretical basis for health behaviour change interventions.51 Self-efficacy (i.e., situational-specific confidence), an underlying composite of SCT, has been identified as a consistent predictor of physical activity engagement.51 Indeed, it has been well documented that self-efficacy is a significant determinant for the adoption and adherence of physical activity in different populations.52–54 Contrastingly, few studies have considered self-efficacy as a determinant for SB change, particularly in the RA population. One study by Huffman and colleagues47 indicated that self-efficacy for exercise was negatively and positively associated with SB and physical activity respectively in this patient group. As highlighted above, a criticism of this study is that an incorrect definition of SB as <1 MET was employed.
Quality of motivation. Self-Determination Theory (SDT)55 proposes that variability in the reasons ‘why’ a person chooses to engage (or not to engage) in a behaviour, holds important implications for levels of engagement. Specifically, SDT suggests that an individual’s motivation may vary in its degree of relative autonomy, with more autonomous reasons for engagement (e.g., fun, enjoyment, personally important) linked to an increased likelihood of adopting and persisting with engagement in a behaviour (e.g., physical activity). In contrast, more controlled reasons for participation (e.g., other people’s approval, feeling guilty) are linked to a lesser chance of sustaining behaviour. The implications of quality of motivation have been demonstrated in a considerable amount of physical activity research with different populations,56 including RA.57
SDT also postulates that humans have three basic psychological needs; namely, autonomy, competence and social relatedness. Fulfilment of these needs leads to fostering more autonomous motivation towards a behaviour, as well as benefits in mental health (e.g., vitality and well-being).58 SDT suggests the social environment is central to the satisfaction of these three basic needs, and holds implications for encouraging behaviour change through promoting more autonomous motivation. Specifically, the provision of autonomy support from an ‘important other’ (e.g., peer, parent, spouse, health professional) is reported to hold positive implications for need satisfaction, quality of motivation and behavioural engagement.59–62 Fenton et al.45 demonstrated perceptions of autonomy support from an ‘important other’ to be positively associated with LPA, in turn benefiting psychological health (i.e., depression and vitality), in RA patients. However, no studies have examined the role of the basic psychological needs and quality of motivation in this relationship, nor have the determinants of SB accumulation been explored in the RA population through a SDT lens. For example, it is possible that more autonomous motivation for reducing SB (e.g., identification with health benefits) is associated with lower levels of sedentariness.
Physical environmental determinants
The physical environment has been identified as a modifiable determinant of SB.63 Furthermore, the Systems of SBs framework emphasises the importance of prioritising investigation into physical environmental factors, and their relationship with SB accumulation in different populations. Distinguishing between specific factors within the physical environment (e.g., home, workplace, neighbourhood) has been stressed as important when examining the determinants of SB, in order to ensure interventions can be properly targeted. At present, research studies examining the physical environmental determinants of SB often fail to be domain-specific, which can lead to contradicting evidence.63
The Systems of SBs framework also depicts that elements of the physical environment may influence SB engagement in different ways, among different populations. For example, examining the determinants of SB in the workplace might not be as relevant to an older adult population as the home or neighbourhood environment might be.63 Indeed, aspects of the home environment, such as the number of televisions and motorised vehicles, have been positively correlated with levels of SB in adults (mean age = 57.5 years).63 Furthermore, aesthetic features outside of the home environment (e.g., public parks, trees) have been inversely associated with leisure-time SB in adults (mean age = 52.2 years).65
To date, no studies have investigated the physical environmental correlates of SB and LPA in the RA population. This patient group is highly heterogeneous, representing different ages, variability in disease activity, physical function and employment status. This underlines the need to examine physical environmental determinants across multiple domains of SB and LPA engagement (i.e., in the home, workplace and neighbourhood).
Aims of this research
This study will address the knowledge gaps and limitations of the existing literature, in order to build an evidence base regarding the health-related correlates of SB and LPA, and hypothesised determinants of these behaviours in RA.
The aims of this study are twofold:
To investigate the longitudinal relationships between objectively-assessed SB patterns (i.e., overall sedentary time, sedentary bouts and sedentary breaks) and LPA, with health outcomes in people living with RA. On the basis of evidence establishing an association between SB and inflammation in non-RA cohorts, we hypothesise that SB may contribute to disease outcomes in RA via perpetuating heightened systemic inflammation. As such, our primary health outcomes are: inflammatory biomarkers (e.g., Tumour Necrosis Factor-alpha [TNF-α], Interleukin 6 [IL-6], high-sensitivity C-Reactive Protein [CRP], Erythrocyte Sedimentation Rate [ESR]), which have previously been used as primary endpoints in non-RA studies examining the association between SB, inflammation and health.14,15 Secondary health outcomes are: broader disease-related outcomes (e.g., disease activity, CVD risk, pain, fatigue, physical function), indices of psychological wellbeing (e.g., depression, anxiety, vitality, satisfaction with life, positive and negative affect), and quality of life.
To examine the longitudinal associations between hypothesised determinants (i.e., psychosocial [e.g., autonomy support], individual differences [e.g., self-efficacy] and physical environmental [e.g., home and neighbour-hood environment]) of SB and LPA (i.e., for: 1) reducing overall SB, 2) regularly breaking up SB and 3) physical activity), with objectively-assessed SB patterns (i.e., overall sedentary time, sedentary bouts and sedentary breaks) and LPA, in people living with RA.
METHODOLOGY
Participants and Recruitment
Participants will be recruited from Rheumatology Outpatient Clinics in a hospital in Dudley, England. Inclusion criteria will be: a clinical diagnosis of RA according to the American College of Rheumatology-European League Against Rheumatism (ACR-EULAR) Criteria and aged ≥18 years old. Exclusion criteria will be: wheelchair users and those unable to ambulate independently with the use of an assistive device.
Eligible patients will be approached about the study during Rheumatology Outpatient Clinics. A member of the research team will provide patients with information about study procedures and patients will be given the opportunity to ask the researcher any questions. Willing patients will provide informed consent to participate in the study. This study has been approved by the local National Health Service Research Ethics Committee (West Midlands – Black Country Research Ethics Committee 16/WM/0371).
Protocol
This study will adopt a longitudinal design. Participants will be asked to visit the hospital at 2 time points; baseline (Time 1) and 6-month follow-up (Time 2). At each time point, participants will be asked to undertake 2 visits (i.e., visits 1 and 2) separated by a 7-day period. Specific protocols to be followed are described below.
Visit 1
Participants will visit the hospital to undertake physical assessments and complete questionnaires. At the end of Visit 1, participants will be fitted with the ActiGraph (GT3X) accelerometer and activPAL3μTM posture sensor to wear for the subsequent 7 days. The researcher will give verbal and written instructions, plus a demonstration, regarding how to wear each device. Participants will also be given the Bouchard Physical Activity Record (BAR)66 to complete on 3 of the days during which they wear the GT3X and activPAL3μTM.
Visit 2
After 7 days, participants will re-visit the hospital to provide a fasted blood sample, and return the GT3X accelerometer, activPAL3μTM posture sensor and BAR to the researcher. During this visit, they will also be asked to complete questionnaires with specific reference to their experiences of pain and fatigue over the previous 7 days.
Measures
Visit 1
Participant characteristics. Information will be recorded pertaining to participants’ age, gender, ethnicity, marital status, education, date of diagnosis, existing chronic conditions (e.g., heart disease, diabetes, depression), current medical treatment, smoking status and living arrangements.
Anthropometrics. Taken in duplicate, height, weight and body composition will be measured with participants bare-foot, whilst wearing light and loose-fitting clothing. Height will be measured to the nearest 0.1cm using a stadiometer (SECA, Leicester Height Measure). Weight will be measured to the nearest 0.1kg using the Tanita Body Composition Scales (Tanita BC-418 MA P). Body Mass Index (BMI) will be calculated as weight (kg)/height2 (m2). Percent body fat and muscle mass will be measured using the Tanita Scales via bioelectrical impedance analysis.
Resting blood pressure. Resting blood pressure (systolic and diastolic) will be taken in duplicate with an automatic blood pressure machine (Mindray Accutorr PLUS). The blood pressure cuff will be placed over the brachial artery as standard, after the participant has rested in a supine position for 5 minutes.36,67
Physical function. Gait speed will be assessed using the 20-Metre Timed Walk Test to provide an objective measure of physical function. Participants’ time to walk from a ‘start line’ to a ‘finish line’, 20 metres apart, will be recorded using a stopwatch. The stopwatch will be started once the participant begins to walk from the ‘start line’, and will be stopped once the participant’s heel has completely crossed the ‘finish line’. Gait speed has been previously used in studies of RA to provide an objective measure of physical function (e.g., walking 15.24 metres).68 Research has also shown that slower gait speed is associated with RA outcomes, such as pain, fatigue and joint deformation.69 As such, it may also serve as a proxy for other important aspects of RA health, directly related to physical function.
Questionnaires. Validated questionnaires will be administered to the participant to assess self-reported RA outcomes and hypothesised determinants of SB and physical activity (see Table 1). Questionnaire scores will be calculated according to validated scoring instructions (e.g., mean scores will be calculated for the ‘autonomous motivation for reducing SB’ dimension in the Behavioural Regulation in Exercise Questionnaire-2).
Table 1.
Outcome | Questionnaire | Description | Example |
---|---|---|---|
Pain | McGill Pain Questionnaire | Questions pertaining to the past 2 weeks
|
|
Fatigue | Multidimensional Assessment of Fatigue Scale | Questions pertaining to the past 2 weeks
|
|
Fatigue | Multidimensional Fatigue Inventory | Questions pertaining to the past 2 weeks
|
|
Physical function | Health Assessment Questionnaire | Questions pertaining to the past 2 weeks
|
|
Physical function | Dartmouth Coop Functional Assessment Charts | Questions pertaining to the past 2 weeks
|
|
Sleep | Pittsburgh Sleep Quality Index | Questions pertaining to the past 2 weeks
|
|
Satisfaction with life | Satisfaction With Life Scale | Questions pertaining to the present time
|
|
Vitality | Subjective Vitality Scale | Questions pertaining to the past 2 weeks
|
|
Anxiety and depression | Hospital Anxiety and Depression Scale | Questions pertaining to the past 2 weeks
|
|
Positive and negative affect | Positive and Negative AffectSchedule | Questions pertaining to the past 2 weeks
|
|
Quality of life | World Health Organisation Quality Of Life Scale | Questions pertaining to the past 2 weeks
|
|
Support from participant-identified other for physical activity, reducing sedentary behaviour and breaking up sitting time | Important Other Climate Questionnaire |
|
|
Support from consultant for physical activity, reducing sedentary behaviour and breaking up sitting time | Health Care Climate Questionnaire |
|
|
Need satisfaction for physical activity | Psychological Need Satisfaction For Exercise Scale | Questions pertaining to the past 4 weeks
|
|
Self-efficacy for physical activity and breaking up sitting time | Self-Efficacy for Exercise Scale |
|
|
Motivation for physical activity, reducing sedentary behaviour and breaking up sitting time | Behavioural Regulation in Exercise Questionnaire-2 | Questions pertaining to the past 4 weeks
|
|
Motivation to limit screen time | Motivation to Limit Screen Time Questionnaire | Questions pertaining to the past 4 weeks
|
|
Environmental perceptions – active travel and physical activity | Assessing Levels of Physical Activity Scale |
|
|
Actigraph GT3X. The GT3X triaxial accelerometer (27g; 3.8cm × 3.7cm × 1.8cm) will be attached to an adjustable elastic belt and worn on the right hip in a vertical position.70–74 Participants will be asked to remove the device only for sleeping and water-based activities (e.g., swimming, bathing), and to record all replacement/removal in wear time logbooks provided. The GT3X will record accelerations in 1-second epochs, which will be converted into activity counts. These counts will then be interpreted to determine the frequency, intensity and duration of SB and physical activity.
Data collected by the GT3X will be cleaned and analysed using Actilife Software (version 6). For inclusion in analyses, participants will be required to have worn the GT3X for ≥4 days, for ≥10 hours per day, including a weekend day.25,75–78 Non-wear (e.g., 60 minutes of ‘zero counts’) will be defined in accordance with previous research among older adults75,78 and people living with RA.45 Time spent in SB, LPA and MVPA will be determined using cut-points that have been validated in previous research with adults (e.g., Troiano et al.),74 and used in studies investigating SB and physical activity in the general population,79 in people with rheumatic diseases80 and in osteoarthritis.81
ActivPAL3μTM. The activPAL3μTM posture sensor (9g; 2.35cm × 4.3cm × 0.5cm) will be initialised using activPAL3μTM software and attached by the researcher to the front of the right thigh, in a mid-anterior position, with a waterproof, adhesive Tegaderm dressing. Participants will be asked to wear the activPAL3μTM for 24 hours a day to enable assessment of time spent sitting/lying, standing and stepping, as well as sit-to-stand transitions.82–85 activPAL3μTM software will be used to download the activPAL3μTM data. Sleep time will be determined using self-reported information from the wear time logbooks and BAR, in conjunction with non-wear periods identified via algorithms applied to GT3X data. Specifically, the GT3X output generated by Actilife details at which time the participant removes (e.g., at bedtime) and replaces (e.g., at waking) the accelerometer throughout the 7-day period. This will be checked against the BAR, which details (over 3 days) when the participant woke up and went to bed.
Participants will be required to have worn the activPAL3μTM for ≥4 days, for ≥10 hours per day, including a weekend day, to be included in analyses.76,86–88 Variables derived for analyses will include: time spent sitting/lying, standing and stepping (i.e., hours per day), as well as the number of sit-to-stand transitions (i.e., sedentary breaks per day).
Bouchard Physical Activity Record. Participants will be asked to self-report the dominant activity undertaken every 15 minutes, over 3 days of the study week, including a weekend day (e.g., Thursday, Friday and Saturday).66,89,90 They will be asked to report this information in real-time, in order provide information regarding the context in which SB and physical activity were undertaken. For this study, the BAR has been adapted to include visual analogue scales, in which participants will be asked to report their average, minimum and maximum pain and fatigue on each of the 3 days. Specifically, at the end of each day, participants will be asked to mark a vertical line along a 100mm continuum from ‘no pain/fatigue’ to ‘extreme pain/fatigue’.
Visit 2
Fasted blood sample. Blood will be taken from the inside of the arm and collected in appropriate vacutainers. Standard laboratory procedures and Enzyme-Linked Immunosorbent Assays (ELISAs) will be used to measure: serum biomarkers of inflammation (i.e., TNF-α, IL-6, high-sensitivity CRP, ESR), plasma lipids (i.e., total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides), plasma glucose and insulin.
Disease activity score-28. The number of swollen and tender joints in 28 joints of the body (i.e., hands, wrists, elbows, shoulders and knees) will be examined. Tenderness will be assessed via participants’ self-report when light pressure is applied to the joint by the researcher. The degree of swelling will be visually assessed and self-reported by the researcher. The number of swollen and tender joints will be used in conjunction with patients’ ESR and a self-reported degree of overall health ranging from 0 (very good) to 100 (very poor), to determine patients’ disease activity score-28 (DAS-28).91
Questionnaires. Validated questionnaires will be administered to the participant on visit 2 to assess RA-related pain and fatigue during the study week (see Table 2). Questionnaire scores will be calculated according to validated scoring instructions (e.g., pain scores from the McGill Pain Questionnaire will be calculated by summing the intensity rank values for descriptors of sensory, affective and total pain).
Table 2.
Outcome | Questionnaire | Description | Example |
---|---|---|---|
Pain | McGill Pain Questionnaire | Questions pertaining to the past week
|
|
Fatigue | Multidimensional Assessment of Fatigue Scale | Questions pertaining to the past week
|
|
Fatigue | Multidimensional Fatigue Inventory | Questions pertaining to the past week
|
|
Power Calculation
Power calculations were conducted with G*Power (version 3.1.9.3) using data collected from the Physical Activity in Rheumatoid Arthritis (PARA) randomised controlled trial (Trial Number: ISRCTN04121489). In the PARA study, accelerometers were utilised to measure SB, LPA and MVPA in a subsample of RA participants, and high-sensitivity CRP was measured as a biomarker of systemic inflammation. Cross-sectional accelerometer data were available for n = 61 participants. A priori power calculation from this data indicated that a sample size of n = 125 would be sufficient to detect statistically significant relationships (power = 0.80, α error of probability = .05), between daily SB and LPA with high-sensitivity CRP (a key inflammatory biomarker in RA).
To ensure the robustness of our calculations for detecting significant changes in broader RA outcomes, we also conducted power calculations for physical function, overall cardiovascular risk score and vitality. For this, cross-sectional accelerometer data, Health Assessment Questionnaire scores (physical function), QRisk-2 scores (cardiovascular risk), and vitality scores (vitality) were available for n = 61, n = 61 and n = 59 participants respectively. A priori power calculation confirmed minimum sample sizes of n = 82 (physical function), n = 14 (QRisk-2) and n = 114 (vitality), would ensure adequate statistical power (power = 0.80, α error of probability = .05) to detect the hypothesised associations.
Statistical Analyses
SPSS (version 24) will be used to compute descriptive statistics for all measured variables. These will include information regarding participant characteristics (e.g., gender, mean age, ethnicity), health outcomes (e.g., DAS-28, BMI), determinants (e.g., quality of motivation, self-efficacy), as well as levels of SB and LPA among the RA sample. Missing value analyses will be conducted via multiple imputation92 or expectation maximization93 methods in SPSS, where missing data does not exceed 5%.
Cross-sectional associations between RA participants’ objectively-assessed SB patterns and LPA, with health outcomes (Aim 1) and proposed determinants (Aim 2) will be examined using correlation and regression analyses. Longitudinal associations from baseline (Time 1) to 6-month follow-up (Time 2) will be analysed using regression models.
Aim 1. For cross-sectional analyses, objectively-assessed SB patterns and LPA will be independent variables. Dependent variables will include biomarkers of inflammation, disease activity, CVD risk, pain, fatigue, physical function, depression, anxiety, vitality, satisfaction with life, positive and negative affect, and quality of life. For longitudinal analyses, regression models will examine if changes in objectively-assessed SB patterns significantly predict change in health outcomes from baseline (Time 1) to 6-month follow-up (Time 2). Health outcomes will be examined in separate regression models, but analyses will be adjusted for other factors which may influence these associations, as appropriate (e.g., disease duration, age, gender, current medication, and GT3X and activPAL3μTM wear time).
Aim 2. For cross-sectional analyses, the determinants of SB in RA (e.g., autonomy support and self-efficacy for physical activity, reducing SB and breaking up SB) will be independent variables, and objectively-assessed SB patterns and LPA in RA will be dependent variables. For longitudinal analyses, regression models will examine if changes in hypothesised determinants of SB and LPA (e.g., autonomy support for reducing SB) significantly predicts change in objectively-assessed SB patterns and LPA from baseline (Time 1) to 6-month follow-up (Time 2). As above, all analyses will be adjusted for potential confounders (e.g., 7-day pain and fatigue [to consider the possibility of bi-directional relationships], GT3X and activPAL3μTM wear time).
Subsequent analyses will use AMOS (version 24) to conduct path analyses and structural equation modelling, in order to examine multivariate relationships and hypothesised process models. For example, psychological processes (e.g., autonomy support for reducing SB) proposed to underlie objectively-assessed accumulated sedentary time and LPA engagement, in turn influencing RA disease outcomes (e.g., DAS-28) will be explored.37,45,94,95
DISCUSSION
To date, there is a paucity of research on SB conducted in the RA population. The current study – investigating potential physical and psychological health consequences, as well as potential determinants, of SB and LPA in RA patients – is taking steps to address the limitations of previous studies, whilst simultaneously addressing important knowledge gaps in the field. Firstly, this study is employing two novel devices to measure SB, according to the SB Research Network definition,11,12 and LPA in RA patients. The present study will also provide the first longitudinal evidence regarding possible changes in health consequences associated with changes in sedentariness and LPA in this patient group. Furthermore, this longitudinal study is the first to comprehensively explore the determinants (i.e., psychosocial, individual differences and physical environmental) of SB and LPA in RA. Indeed, this study will identify modifiable factors that might influence SB and LPA in this patient group above and beyond, for example, RA-related disease activity and physical function, which may demonstrate bi-directional associations with SB and LPA. These data will elucidate targets for intervention that have the potential to support people living with RA, to reduce their time spent sedentary (e.g., by regularly breaking up their sedentary time and displacing it with LPA).
Future Research Directions
Building on findings from this study, future research that aims to ascertain the health outcomes and determinants of SB in the RA population should be consistent in their approach. Specifically, studies should accurately define and conceptualise SB, and follow recommended protocols that utilise validated measures (subjective and objective) to measure sedentariness and its correlates in RA.31 Additionally, future research studies should seek to further explore the ‘sedentary-inflammation hypothesis’,31 which postulates cyclical relationships between SB, inflammation and adverse health outcomes in RA. Finally, interventions assessed by Randomised Controlled Trials must be developed, implemented and evaluated, to definitively test the relationships in this study in order to infer causality. Data from the present study will generate an evidence base which will inform the development of such interventions, and optimise their potential to encourage SB change and improve health outcomes among people living with RA.
FUNDING
Funding from the Russells Hall Hospital Charitable Research Fund was received to carry out the work described in this manuscript.
CONFLICT OF INTEREST
The authors have declared no conflicts of interest.
REFERENCES
- 1.World Health Organisation Chronic rheumatic conditions [Internet]. 2018. [cited 2018 June 19]. Available from: http://www.who.int/chp/topics/rheumatic/en/
- 2.Cooney J K, Law R J, Matschke V, Lemmey A B, Moore J P, Ahmad Y, et al. Benefits of exercise in rheumatoid arthritis. J Aging Res 2011;2011:1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Prioreschi A, Makda M A, Tikly M, McVeigh JA. Habitual Physical Activity, Sedentary Behaviour and Bone Health in Rheumatoid Arthritis. Int J Sports Med 2015;36:1021–6. [DOI] [PubMed] [Google Scholar]
- 4.Rouse P C, Veldhuijzen Van Zanten J J, Metsios G S, Ntoumanis N, Yu C-a, Koutedakis Y, et al. Fostering autonomous motivation, physical activity and cardiorespiratory fitness in rheumatoid arthritis: protocol and rationale for a randomised control trial. BMC Musculoskelet Disord 2014;15:1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Aviña-Zubieta J A, Choi H K, Sadatsafavi M, Etminan M, Esdaile J M, Lacaille D. Risk of cardiovascular mortality in patients with rheumatoid arthritis: a meta-analysis of observational studies. Arthritis Rheum 2008;59:1690–7. [DOI] [PubMed] [Google Scholar]
- 6.Innala L, Möller B, Ljung L, Magnusson S, Smedby T, Södergren A, et al. Cardiovascular events in early RA are a result of inflammatory burden and traditional risk factors: a five-year prospective study. Arthritis Res Ther 2011;13:R131–R. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Avina-Zubieta J A, Thomas J, Sadatsafavi M, Lehman AJ, Lacaille D. Risk of incident cardiovascular events in patients with rheumatoid arthritis: a meta-analysis of observational studies. Ann Rheum Dis 2012;71:1524–9. [DOI] [PubMed] [Google Scholar]
- 8.Kitas G D, Gabriel S E. Cardiovascular disease in rheumatoid arthritis: state of the art and future perspectives. Ann Rheum Dis 2010;70:8–14. [DOI] [PubMed] [Google Scholar]
- 9.Kerola A M, Kauppi M J, Kerola T, Nieminen T V M. How early in the course of rheumatoid arthritis does the excess cardiovascular risk appear? Ann Rheum Dis 2012;71:1606–15. [DOI] [PubMed] [Google Scholar]
- 10.Margaretten M, Julian L, Katz P, Yelin E. Depression in patients with rheumatoid arthritis: description, causes and mechanisms. Int J Clin Rheumatol 2011;6:617–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tremblay M S, Aubert S, Barnes J D, Saunders T J, Carson V, Latimer-Cheung A E, et al. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act 2017;14:75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sedentary Behaviour Research Network Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab 2012;37:540–2. [DOI] [PubMed] [Google Scholar]
- 13.Ainsworth B E, Haskell W L, Whitt M C, Irwin M L, Swartz A M, Strath S J, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32:S498–504. [DOI] [PubMed] [Google Scholar]
- 14.Falconer C L, Cooper A R, Walhin J P, Thompson D, Page A S, Peters T J, et al. Sedentary time and markers of inflammation in people with newly diagnosed type 2 diabetes. Nutr Metab Cardiovasc Dis 2014;24:956–62. 10.1016/j.numecd.2014.03.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Henson J, Yates T, Edwardson C L, Khunti K, Talbot D, Gray L J, et al. Sedentary time and markers of chronic low-grade inflammation in a high risk population. PLoS One 2013;8:e78350 10.1371/journal.pone.0078350 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Atkin A J, Gorely T, Clemes S A, Yates T, Edwardson C, Brage S, et al. Methods of Measurement in epidemiology: Sedentary Behaviour. Int J Epidemiol 2012;41:1460–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Bankoski A, Harris T B, McClain J J, Brychta R J, Caserotti P, Chen K Y, et al. Sedentary activity associated with metabolic syndrome independent of physical activity. Diabetes care. 2011;34:497–503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chinapaw M J, Proper K I, Brug J, van Mechelen W, Singh A S. Relationship between young peoples’ sedentary behaviour and biomedical health indicators: a systematic review of prospective studies. Obes Rev 2011;12:621–32. [DOI] [PubMed] [Google Scholar]
- 19.Esbensen B A. SP0022 Reduction of Sedentary Behaviour in Patients with Rheumatoid Arthritis – Experiences from An Intervention Study. Ann Rheum Dis 2015;74:6–7. [Google Scholar]
- 20.Healy G N, Matthews C E, Dunstan D W, Winkler E A, Owen N. Sedentary time and cardiometabolic biomarkers in US adults: NHANES 2003–06. Eur Heart J 2011;32:590–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Owen N, Healy G N, Matthews C E, Dunstan D W. Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 2010;38:105–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Proper K I, Singh A S, van Mechelen W, Chinapaw M J. Sedentary behaviors and health outcomes among adults: a systematic review of prospective studies. Am J Prev Med 2011;40:174–82. [DOI] [PubMed] [Google Scholar]
- 23.Thorp A A, Owen N, Neuhaus M, Dunstan D W. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996–2011. Am J Prev Med 2011;41:207–15. [DOI] [PubMed] [Google Scholar]
- 24.Veerman J L, Healy G N, Cobiac L J, Vos T, Winkler E A, Owen N, et al. Television viewing time and reduced life expectancy: a life table analysis. Br J Sports Med 2012;46:927–30. [DOI] [PubMed] [Google Scholar]
- 25.Wilmot E G, Edwardson C L, Achana F A, Davies M J, Gorely T, Gray L J, et al. Sedentary time in adults and the association with diabetes, cardiovascular disease and death: systematic review and meta-analysis. Diabetologia 2012;55:2895–905. [DOI] [PubMed] [Google Scholar]
- 26.Patterson R, McNamara E, Tainio M, de Sa T H, Smith A D, Sharp S J, et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol 2018. 10.1007/s10654-018-0380-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Healy G N, Dunstan D W, Salmon J, Cerin E, Shaw J E, Zimmet P Z, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes care 2008;31:661–6. [DOI] [PubMed] [Google Scholar]
- 28.Mansoubi M, Pearson N, Biddle S J, Clemes S. The relationship between sedentary behaviour and physical activity in adults: a systematic review. Prev Med 2014;69:28–35. [DOI] [PubMed] [Google Scholar]
- 29.Fenton S A M, Kitas G D. Rheumatoid arthritis: Sedentary behaviour in RA [mdash] a new research agenda. Nat Rev Rheumatol 2016;12:698–700. [DOI] [PubMed] [Google Scholar]
- 30.Thomsen T, Beyer N, Aadahl M, Hetland M L, Loppenthin K, Midtgaard J, et al. Sedentary behaviour in patients with rheumatoid arthritis: A qualitative study. Int J Qual Stud Health Well-being 2015;10:28578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Fenton S A M, Veldhuijzen van Zanten J J, Duda J L, Metsios G S, Kitas G D. Sedentary behaviour in rheumatoid arthritis: definition, measurement and implications for health. Rheumatology (Oxford, England) 2017;57:213–26. [DOI] [PubMed] [Google Scholar]
- 32.Gilbert A L, Lee J, Ma M, Semanik P, DiPietro L, Dunlop D, et al. Comparison of subjective and objective measures of sedentary behavior using the Yale Physical Activity Survey and accelerometry in patients with rheumatoid arthritis. J Phys Act Health 2016;13:371–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Khoja S S, Almeida G J, Chester Wasko M, Terhorst L, Piva S R. Association of Light-Intensity Physical Activity With Lower Cardiovascular Disease Risk Burden in Rheumatoid Arthritis. Arthritis Care Res 2016;68:424–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Greene B L, Haldeman G F, Kaminski A, Neal K, Lim S S, Conn D L. Factors affecting physical activity behavior in urban adults with arthritis who are predominantly African-American and female. Phys Ther 2006;86:510–9. [PubMed] [Google Scholar]
- 35.Giles J T, Bartlett S J, Andersen R E, Fontaine K R, Bathon J M. Association of body composition with disability in rheumatoid arthritis: impact of appendicular fat and lean tissue mass. Arthritis Rheum 2008;59:1407–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Thomsen T, Aadahl M, Beyer N, Hetland M L, Loppenthin K, Midtgaard J, et al. The efficacy of motivational counselling and SMS reminders on daily sitting time in patients with rheumatoid arthritis: a randomised controlled trial. Ann Rheum Dis 2017;76:1603–06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Fenton S A M, Veldhuijzen van Zanten J J, Kitas G D, Duda J L, Rouse P C, Yu C A, et al. Sedentary behaviour is associated with increased long-term cardiovascular risk in patients with rheumatoid arthritis independently of moderate-to-vigorous physical activity. BMC Musculoskelet Disord 2017;18:131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Carson V, Wong S L, Winkler E, Healy G N, Colley R C, Tremblay M S. Patterns of sedentary time and cardiometabolic risk among Canadian adults. Prev Med 2014;65:23–7. [DOI] [PubMed] [Google Scholar]
- 39.de Rezende L F, Rey-Lopez J P, Matsudo V K R, Luiz O C. Sedentary behavior and health outcomes among older adults: a systematic review. BMC Public Health 2014;14:333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Dempsey P C, Owen N, Biddle S J, Dunstan D W. Managing sedentary behavior to reduce the risk of diabetes and cardiovascular disease. Curr Diab Rep 2014;14:522. [DOI] [PubMed] [Google Scholar]
- 41.Sardinha L B, Santos D A, Silva A M, Baptista F, Owen N. Breaking-up sedentary time is associated with physical function in older adults. J Gerontol A Biol Sci Med Sci 2015;70:119–24. [DOI] [PubMed] [Google Scholar]
- 42.Duvivier B M, Schaper N C, Hesselink M K, van Kan L, Stienen N, Winkens B, et al. Breaking sitting with light activities vs structured exercise: a randomised crossover study demonstrating benefits for glycaemic control and insulin sensitivity in type 2 diabetes. Diabetologia 2017;60:490–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Edwardson C L, Henson J, Bodicoat D H, Bakrania K, Khunti K, Davies M J, et al. Associations of reallocating sitting time into standing or stepping with glucose, insulin and insulin sensitivity: a cross-sectional analysis of adults at risk of type 2 diabetes. BMJ Open 2017;7: e014267 10.1136/bmjop-en-2016-014267 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Chastin S F M, De Craemer M, De Cocker K, Powell L, Van Cauwenberg J, Dall P, et al. How does light-intensity physical activity associate with adult cardiometabolic health and mortality? Systematic review with meta-analysis of experimental and observational studies. Br J Sports Med 2018; bjsports-2017-097563. 10.1136/bjsports-2017-097563 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Fenton S A M, Veldhuijzen van Zanten J J C S, Yu C A, Duda J L. Autonomy support, light physical activity and psychological well-being in Rheumatoid Arthritis: A cross-sectional study. Ment Health Phys Act 2018;14:11–8. [Google Scholar]
- 46.Yu C, Rouse P, Veldhuijzen van Zanten J J C S, Metsios G, Ntoumanis N, Kitas G D, et al. THU0588-HPR Associations between objective and self-reported physical activity data in rheumatoid arthritis patients. Ann Rheum Dis 2014;73:1196. [Google Scholar]
- 47.Huffman K M, Pieper C F, Hall K S, St Clair E W, Kraus W E. Self-efficacy for exercise, more than disease-related factors, is associated with objectively assessed exercise time and sedentary behaviour in rheumatoid arthritis. Scand J Rheumatol 2015;44:106 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Ainsworth B E, Haskell W L, Leon A S, Jacobs D R, Jr, Montoye H J, Sallis J F, et al. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc 1993;25:71–80. [DOI] [PubMed] [Google Scholar]
- 49.Mansoubi M, Pearson N, Clemes S A, et al. Energy expenditure during common sitting and standing tasks: examining the 1.5 MET definition of sedentary behaviour. BMC Public Health 2015;15:516. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Bandura A. Self-efficacy: The Exercise of Control. New York, NY: WH Freeman; 1997. [Google Scholar]
- 51.McAuley E, Mullen S P, Szabo A N, White S M, Wójcicki T R, Mailey E L. Self-regulatory processes and exercise adherence in older adults: executive function and self-efficacy effects. Am J Prev Med 2011;41:284–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Cleland V J, Ball K, Salmon J, Timperio A F, Crawford D A. Personal, social and environmental correlates of resilience to physical inactivity among women from socio-economically disadvantaged backgrounds. Health Educ Res 2010; 25:268–81. [DOI] [PubMed] [Google Scholar]
- 53.McAuley E, Jerome G J, Marquez D X, Elavsky S, Blissmer B. Exercise self-efficacy in older adults: social, affective, and behavioral influences. Ann Behav Med 2003;25:1–7. [DOI] [PubMed] [Google Scholar]
- 54.McAuley E, Morris K S, Motl R W, Hu L, Konopack J F, Elavsky S. Long-term follow-up of physical activity behavior in older adults. Health Psychol 2007;26:375–80. [DOI] [PubMed] [Google Scholar]
- 55.Deci E L, Ryan R M. Intrinsic motivation and self-determination in human behavior. New York, NY: Plenum; 1985. [Google Scholar]
- 56.Teixeira P J, Carraça E V, Markland D, Silva M N, Ryan R M. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act 2012;9:1–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Hurkmans E J, Maes S, de Gucht V, Knittle K, Peeters A J, Ronday H K, et al. Motivation as a determinant of physical activity in patients with rheumatoid arthritis. Arthritis Care Res (Hoboken) 2010;62:371–7. [DOI] [PubMed] [Google Scholar]
- 58.Ryan R M, Deci E L. Overview of self-determination theory: An organismic dialectical perspective. In Deci E L, Ryan R M, editors. Rochester, NY: Rochester University Press; 2002. p. 3–33. [Google Scholar]
- 59.Fortier M S, Duda J L, Guerin E, Teixeira P J. Promoting physical activity: development and testing of self-determination theory-based interventions. Int J Behav Nutr Phys Act 2012;9:20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Rouse P C, Ntoumanis N, Duda J L, Jolly K, Williams G C. In the beginning: Role of autonomy support on the motivation, mental health and intentions of participants entering an exercise referral scheme. Psychol Health 2011;26:729–49. [DOI] [PubMed] [Google Scholar]
- 61.Standage M, Gillison F B, Ntoumanis N, Treasure D C. Predicting Students’ Physical Activity and Health-Related Well-Being: A Prospective Cross-Domain Investigation of Motivation Across School Physical Education and Exercise Settings. J Sport Exerc Psychol 2012;34:37–60. [DOI] [PubMed] [Google Scholar]
- 62.Williams G C, McGregor H A, Sharp D, Levesque C, Kouides R W, Ryan R M, et al. Testing a Self-Determination Theory Intervention for Motivating Tobacco Cessation: Supporting Autonomy and Competence in a Clinical Trial. Health Psychol 2006;25:91–101. [DOI] [PubMed] [Google Scholar]
- 63.Compernolle S, Busschaert C, De Bourdeaudhuij I, Cardon G, Chastin S F M, Van Cauwenberg J. Cross-Sectional Associations between Home Environmental Factors and Domain-Specific Sedentary Behaviors in Adults: The Moderating Role of Socio-Demographic Variables and BMI. Int J Environ Res Public Health 2017;14:E1329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Chastin S F M, De Craemer M, Lien N, Bernaards C, Buck C, Oppert J M, et al. The SOS-framework (systems of sedentary behaviours): An international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: A dedipac-study. Int J Behav Nutr Phys Act 2016;13:83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Compernolle S, De Cocker K, Roda C, Oppert J M, Mackenbach J D, Lakerveld J. Physical Environmental Correlates of Domain-Specific Sedentary Behaviours across Five European Regions (the SPOTLIGHT Project). PLOS One 2016;11:e0164812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Bouchard C, Tremblay A, Leblanc C, Lortie G, Savard R, Thériault G. A method to assess energy expenditure in children and adults. Am J Clin Nutr 1983;37:461–7. [DOI] [PubMed] [Google Scholar]
- 67.Panoulas V F, Douglas K M, Milionis H J, Stavropoulos-Kalinglou A, Nightingale P, Kita M D. Prevalence and associations of hypertension and its control in patients with rheumatoid arthritis. Rheumatology (Oxford) 2007;46:1477–82. [DOI] [PubMed] [Google Scholar]
- 68.Escalante A, Haas R W, del Rincón I. Measurement of global functional performance in patients with rheumatoid arthritis using rheumatology function tests. Arthritis Res Ther 2004;6:R315–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Lusa A L, Amigues I, Kramer H R, Dam T T, Giles J T. Indicators of walking speed in rheumatoid arthritis: relative influence of articular, psychosocial, and body composition characteristics. Arthritis Care Res (Hoboken) 2015;67:21–31. 10.1002/acr.22433 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Aadland E, Ylvisåker E. Reliability of the Actigraph GT3X+ Accelerometer in Adults under Free-Living Conditions. PLOS One 2015;10:e0134606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Kim Y, Barry V W, Kang M. Validation of the ActiGraph GT3X and activPAL Accelerometers for the Assessment of Sedentary Behavior, Measurement in Physical Education and Exercise Science. Meas Phys Educ Exerc Sci 2015;19:125–37. [Google Scholar]
- 72.Matthews C E, Hagströmer M, Pober D M, Bowles H R. Best practices for using physical activity monitors in population-based research. Med Sci Sports Exerc 2012;44:S68–S76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Santos-Lozano A, Santín-Medeiros F, Cardon G, Torres-Luque G, Bailón R, Bergmeir C, et al. Actigraph GT3X: validation and determination of physical activity intensity cut points. Int J Sports Med 2013;34:975–83. 10.1055/s-0033-1337945 [DOI] [PubMed] [Google Scholar]
- 74.Troiano R P, Berrigan D, Dodd K W, Mâsse L C, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008;40:181–8. [DOI] [PubMed] [Google Scholar]
- 75.Arnardottir N Y, Koster A, Van Domelen D R, Brychta R J, Caserotti P, Eiriksdottir G, et al. Objective measurements of daily physical activity patterns and sedentary behaviour in older adults: Age, Gene/Environment Susceptibility-Reykjavik Study. Age Ageing 2013;42:222–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Dunstan D W, Wiesner G, Eakin E G, Neuhaus M, Owen N, LaM-ontagne A D, et al. Reducing office workers’ sitting time: rationale and study design for the Stand Up Victoria cluster randomized trial. BMC Public Health 2013;13:1057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Prioreschi A, Hodkinson B, Avidon I, Tikly M, McVeigh J A. The clinical utility of accelerometry in patients with rheumatoid arthritis. Rheumatology 2013;52:1721–7. [DOI] [PubMed] [Google Scholar]
- 78.Shiroma E J, Cook N R, Manson J E, Buring J E, Rimm E B, Lee I M. Comparison of Self-Reported and Accelerometer-Assessed Physical Activity in Older Women. PLOS One 2015;10:e0145950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Matthews C E, Chen K Y, Freedson P S, Buchowski M S, Beech B M, Pate R R, et al. Amount of time spent in sedentary behaviour in the United States, 2003–2004. Am J Epidemiology 2008;167:875–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Legge A, Blanchard C, Hanly J G. Physical activity and sedentary behavior in patients with systemic lupus erythematosus and rheumatoid arthritis. Open Access Rheumatol 2017;9:191–200. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Timmermans E J, Schaap L A, Visser M, van der Ploeg H P, Wagtendonk AJ, van der Pas S, et al. The association of the neighbour-hood built environment with objectively measured physical activity in older adults with and without lower limb osteoarthritis. BMC Public Health 2016;15:710. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Aguilar-Farías N, Brown W J, Peeters G M. ActiGraph GT3X+ cut-points for identifying sedentary behaviour in older adults in free-living environments. J Sci Med Sport 2014;17:293–9. [DOI] [PubMed] [Google Scholar]
- 83.Berendsen B A, Hendriks M R, Meijer K, Plasqui G, Schaper N C, Savelberg H H. Which activity monitor to use? Validity, reproducibility and user friendliness of three activity monitors. BMC Public Health 2014;14:749. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Edwardson C L, Winkler E A H, Bodicoat DH, Yates T, Davies M J, Dunstan D W, et al. Considerations when using the activPAL monitor in field-based research with adult populations. J Sport Health Sci 2017;6:162–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson P S. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 2011;43:1561–7. [DOI] [PubMed] [Google Scholar]
- 86.Dowd K P, Purtill H, Harrington D M, Hislop J F, Reilly J J, Donnelly A E. Minimum Wear Duration for the activPAL Professional Activity Monitor in Adolescent Females. Pediatr Exerc Sci 2017;29:427–33. [DOI] [PubMed] [Google Scholar]
- 87.Wilmot E G, Davies M J, Edwardson C, Gorely T, Khunti K, Nimmo M, et al. Rationale and study design for a randomised controlled trial to reduce sedentary time in adults at risk of type 2 diabetes mellitus: project STAND (Sedentary Time ANd Diabetes). BMC Public Health 2011;11:908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Winkler E A, Bodicoat D H, Healy G N, Bakrania K, Yates T, Owen N, et al. Identifying adults’ valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol. Physiol Meas 2016;37:1653–68. [DOI] [PubMed] [Google Scholar]
- 89.Saunders T J, Tremblay M S, Després J P, Bouchard C, Tremblay A, Chaput J P. Sedentary behaviour, visceral fat accumulation and cardiometabolic risk in adults: a 6-year longitudinal study from the Quebec Family Study. PLOS One 2013;8:e54225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Sylvia L G, Bernstein E E, Hubbard J L, Keating L, Anderson E J. Practical guide to measuring physical activity. J Acad Nutr Diet 2014;114:199–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Prevoo M L, Van’t Hof M A, Kuper H H, Van Leeuwen M A, van de Putte L B, van Riel P L. Modified disease activity scores that include twenty-eight-joint counts. Development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum 1995;38:44–8. [DOI] [PubMed] [Google Scholar]
- 92.Pinto Pereira S M, Ki M, Power C. Sedentary behaviour and biomarkers for cardiovascular disease and diabetes in mid-life: the role of television-viewing and sitting at work. PLOS One 2012;7:e31132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Ku P, Fox K R, Chen L. Leisure-Time Physical Activity, Sedentary Behaviors and Subjective Well-Being in Older Adults: An Eight-Year Longitudinal Research. Soc Indic Res 2016;127:1349–61. [Google Scholar]
- 94.Elavsky S, McAuley E, Motl R W, Konopack J F, Marquez D X, Hu L. Physical activity enhances long-term quality of life in older adults: efficacy, esteem, and affective influences. Ann Behav Med 2005;30:138–45. [DOI] [PubMed] [Google Scholar]
- 95.Mudrak J, Slepicka P, Elavsky S. Social Cognitive Determinants of Physical Activity in Czech Older Adults. J Aging Phys Act 2017;25:196–204. [DOI] [PubMed] [Google Scholar]