Sedentary behaviour levels in adults with an intellectual disability: a systematic review protocol

Rationale

According to the World Health Organisation (WHO, 2013), non-communicable diseases account for almost 71% of world deaths. Non-communicable diseases are non-infectious and chronic but can be prevented. Of these, cardiovascular disease (CVD) is one the largest causes of preventable death worldwide with over 17.9 million dying annually. CVD can manifest as increased blood pressure or elevated blood lipid levels, leading to heart attack or stroke. One of the main contributors to CVD is lack of physical activity (Forouzanfar et al., 2016). Physical activity is any bodily movement which uses skeletal muscles and results in energy expenditure (WHO, 2019) while a sedentary lifestyle is one which has low levels of physical activity and consequently low levels of energy expenditure. In general, people with intellectual disability (ID) have poorer health than their non-disabled contemporaries (Emerson et al., 2016) and often experience health disparities (Krahn & Fox, 2014). However, the real state of the science regarding sedentary behaviour and people with ID is not known. Further investigation is essential to understand if sedentary behaviour contributes to these health differences.

It is necessary to understand some of the known contributors to CVD, obesity and physical inactivity, as well as sedentary behaviours because these are all modifiable and inter-related health risks factors.

Sedentary behaviour. Sedentary comes from the Latin word sedere which means to sit and can describe a wide range of distinct activities which require low levels of energy expenditure in any setting (Thorp et al., 2011). The first real attempt to define the term ‘sedentary’ was made in 2012 (Tremblay et al., 2017). This was in an effort to avoid confusion by standardising the terms to refer to sedentary or inactive behaviours used in journals. A metabolic equivalent (MET), known as the resting metabolic rate, is an objective measurement scale used to classify activity types and levels. A MET is the amount of oxygen (O₂) burned at rest and is the equivalent of 3.5ml O₂ per kg body weight per minute (Jette et al., 1990) or 1kilocalorie per kg of body weight per hour (Newton et al., 2013). Tremblay et al. (2017) proposed to define sedentary behaviour as ‘any waking behaviour characterized by an energy expenditure of ≤1.5 METs while in a sitting or reclining posture’ for example watching television or working on a computer. Hence sedentary behaviour constitutes too much sitting or stationary activity as opposed to physical inactivity which is too little exercise or physical movement. Tudor-Locke et al. (2013) found a link between reduced steps per day (less than 5,000) and being more sedentary. In addition, sitting for prolonged periods (more than 3 hours per day) has been found to have adverse health effects (Pinto Pereira et al., 2012).

Sedentary behaviour has been linked to adverse health conditions in older adults, increased cardio metabolic risks, increased obesity and mortality in both men and women, as well as increased cancer risk (de Rezende et al., 2014; Patel et al., 2010; Same et al., 2016; Thorp et al., 2011). Self-reported studies have shown that high levels of sedentary behaviour, even if minimum exercise guidelines are met, show increased metabolic risk (Patel et al., 2010). This impact of sedentary behaviour can be mollified by interspersing periods of physical activity throughout the day (Healy et al., 2008).

An ecological model of sedentary behaviour for older adults without an ID, proposed by Owen et al. (2011), could be used to assess the sedentary behaviours of people with ID. This model classed sedentary behaviour into four categories:

Sedentary behaviour and people with ID

In a systematic review by Melville et al. (2017), it was proposed that studies to determine sedentary behaviour in people with ID did not use enough randomly selected samples and sample sizes were too small, meaning that results could not be generalised for the ID population as a whole. Furthermore, insufficient studies have distinguished between sedentary behaviour and inadequate physical activity. Consequently, it is not clear what the actual sedentary behaviour of people with ID is.

Older people with an intellectual disability have been shown to have higher rates of multi-morbidity, obesity and inactivity than the general population (Gawlik et al., 2018; McCarron et al., 2013). In 2016 approximately 70,000 people, 1.4% of the overall Irish population (Census, 2016), were shown to have an ID. In an analysis of secondary data, Harris et al. (2017) deduced that people with ID were sedentary for over 70% a day. According to Graham & Reid (2000), adults with ID are more susceptible to age-related health risks, where sedentary behaviour could be a contributing factor.

While breaking up time spent doing sedentary activity has been shown to increase daily living activities and physical independence in older adults (Sardinha et al., 2015), there is no similar information on adults with ID. Increased sedentary behaviour has been linked to obesity levels and increased likelihood of multi-morbidity (Melville et al., 2017), but inconsistent evidence exists on links of sedentary behaviour to level of ID (Oppewal et al., 2018). Often studies use proxy measures (e.g. watching TV) to determine sedentary behaviour which may be inaccurate, especially with regards to people with ID, as people with a more severe ID may be less likely to watch TV due to sensory or cognitive impairments (Owen et al., 2011). Level of ID has been shown to be directly related to physical activity but not sedentary behaviour (Oppewal et al., 2018).

Emerging evidence is highlighting the importance of reducing sedentary behaviour for improving cardio-metabolic health and adopting a holistic public health approach to improve activity levels as well as sedentary behaviour (van der Ploeg & Hillsdon, 2017).

For the purposes of this systematic review, sedentary behaviour will be defined as:

‘low physical activity as identified by MET or step levels or as measured by the Rapid Assessment of Physical activity questionnaire (RAPA) or the International Physical Activity questionnaire (IPAQ) or sitting for more than 3 hours per day’

Obesity. Globally almost 38% of the world’s population, a greater than 100% increase since 1980, and two-thirds of the American population, are either overweight or obese, with a BMI of greater than 25.0 kg/m² (Fryar et al., 2012; Ng et al., 2014; WHO, 2009). In Ireland, almost 23% of adults are obese with 50% of women and 66% of men being overweight (Ng et al., 2014. This is a huge concern given the proven link between obesity and cancers, higher rates of type II diabetes, CVD and CVD mortality (Bhaskaran et al., 2014; Hossain et al., 2009; Ortega et al., 2016).

Obesity and people with ID

A 2017 review found that the prevalence of overweight and obesity in people with ID varies from 28%–71% and 17%–43%, respectively (Ranjan et al., 2018). The IDS-TILDA study found that overweight and obesity in people with ID increased from 66% in wave2 to 79.7% in wave3 and that 64% of participants considered themselves to be at the right weight (Burke et al., 2017). According to a US based longitudinal study on people with ID women appear to be at a greater risk of developing morbid obesity while men were more likely to be overweight (Hsieh et al., 2014)).

According to Fock & Khoo (2013), excessive calorific intake and increased sedentary behaviour are the main contributors to increased obesity levels but obesity levels may be ameliorated by a combination of healthy eating, a reduction in sedentary behaviour and an increase in physical activity

Physical inactivity. Physical inactivity is classified as not meeting the minimum activity requirements. According to the American College of Sports Medicine, moderate-intensity aerobic physical activity (PA) of between 150 and 250 minutes per week is the minimum necessary for health and weight management in adults (Donnelly et al., 2009; Health Service Executive, 2009; US Department of Health, 2018). Insufficient PA or physical inactivity contributes to adverse health issues like obesity, CVD and cancer as well as increased mortality (Lee et al., 2012). According to the World Health Organisation (WHO, 2009), physical inactivity is the fourth leading risk factor for all-cause mortality, with over three million deaths annually. Of concern is that Ireland is one of the least active countries in Europe (Loyen et al., 2016).

Physical inactivity and people with ID

For People with an ID, the amount of moderate PA done, and the number of hours spent watching TV was found to be significantly associated with obesity level (Hsieh et al., 2014). A 2016 Australian based study found that over 66% of participants did not meet minimum exercise guidelines (Koritsas & Iacono, 2016), while another study found that 77% of participants did not meet the minimum exercise recommendations (Barnes et al., 2013). It must be noted that physical impairments leading to the use of walking aids or wheelchairs may inhibit physical activity for some people with an ID (Ranjan et al., 2018).

Hence sedentary behaviour and physical inactivity are different and should be addressed separately with distinct guidelines for each. While specific recommendations for movement and physical activity levels in adults have been long established, corresponding recommendations for sedentary behaviour have not. The recommendations from emerging evidence are to minimise the amount of time being sedentary, but no specifics have yet been established for the general population or people with ID (WHO, 2020).

Developing the question

A focused and well-defined question avoids bias in literature searches, ensures clarity and therefore ensures the identification of the concepts for the focused search. PICo, which is used for qualitative studies is being used to define the question as follows (Schardt et al., 2007):

The research question to be addressed by this systematic review protocol is

‘What are the sedentary behaviour levels of older Adults with an Intellectual Disability?’.

Eligibility criteria

The criteria for inclusion in the review are as follows:

The criteria for exclusion in the review are as follows:

Information sources

Databases

The following four databases will be used to perform the search:

In addition, the following sources will be explored for grey literature sources:

Search strategy

The search strategy was refined into two concepts following the application of PICo. Concept 1 is ‘Sedentary behaviour or inactivity’ and Concept 2 is ‘Intellectual Disability’. Each of the two concepts will be searched using MESH terms and keywords and then combined using OR. Then the total results of each concept will be combined using AND (See Figure 1). This search will be repeated for each of the four databases. The resulting article list will be the complete combined database search results. This list will be screened for inclusion.

Figure 1. Search strategy.

Search string. An example of the search string used for the Medline database is shown in Table 1.

Screening process. All identified articles from each database that is searched, as well as all grey literature sources, will be combined and duplicates removed. Endnote software will be used to store all the identified articles. The articles will be stored in folders which are named after the search process used. Using the inclusion criteria as detailed above, all articles will initially be screened by title and then by abstract. The remaining full text articles will be retrieved and read thoroughly. Those that do not meet the inclusion criteria will be omitted. The remaining articles will then be quality assessed using two separate assessors with a third person as an adjudicator should any discrepancies arise.

Quality assessment and risk of bias. The remaining articles will be assessed using a quality assessment tool for observational cohort and cross-sectional studies from the National Institute of Health. The tool used is available as extended data (Lynch, 2020).

These tools are used to critically assess the internal validity of each article and identify any issues or sources of potential bias. According to Cochrane, effectively evaluating the quality of a study is done by looking at its design, methodology, results, analysis and reporting, and how they relate to the original research question (Higgins et al., 2011).

There are different types of study quality assessment tools for the different study types. For Controlled Intervention Studies and Observational Cohort and Cross-sectional studies, 14 criteria are used to evaluate the study quality, while for Case-Control studies 12 criteria are used. This means that a maximum quality score of 12-14 can be achieved. This quality score will be used to determine if the study should be included in the review. Quality scores are divided into 3 main categories: Good, Fair or Poor. See Table 2 for details.

Any studies that are excluded will be tracked with reasons for rejection.

Quality scoring

Scores are attributed to distinct parts of the study design for example type of study, design and blinding, where a ‘yes’ answer gives a score of ‘1’, a ‘no’ answer a score of ‘0’ and could potentially highlight an issue with the article. See Table 3.

Ethics

This research project is part of the IDS-TILDA project. Full ethical approval for IDS-TILDA has been granted by the Trinity College Dublin Faculty of Health Sciences Research Ethics Committee.

Data management

All search records will be kept in an excel spreadsheet detailing the database, type of search (keyword or MESH terms) and the resulting search numbers. The articles will be stored in Endnote. Each stage of the search and review will be recorded in excel. For each stage of the search process, articles will be stored in an appropriately named folder in EndNote X9 for windows.

Selection process

The selection process of studies for inclusion, which are identified by the search strategy, will be done by two independent review authors [LL and EB]. The initial screening will be done by title and abstract. If eligibility is inconclusive from the title and abstract, the full text of the article will be assessed. Any articles that do not match the inclusion criteria will be excluded. Any differences on article inclusion between the two authors will be resolved by discussions with the third review author [MMc]. Finally, the full-text article of all potential articles that could be included in the review will be independently assessed by the authors for inclusion as above.

Data collection process

An excel spreadsheet will act as the data extraction tool. This will be used to summarise all the shortlisted studies. The categories to be captured are as in Table 4.

Data items

The PICo framework will be used to define what data will be sought from variables as follows:

Outcomes and prioritisation

The outcomes of this investigation into sedentary behaviour will determine the sedentary behaviour levels of older adults with an intellectual disability.

Primary outcome

Data synthesis

All article data will be summarised in a spreadsheet format as seen in Table 4 (McKenzie et al., 2019). If studies are homogenous in nature a meta-analysis may be performed and a forest plot produced to summarise results. A narrative synthesis will be used to summarise all the study article data and relevant information. A thematic analysis of the semantic and latent topics of the articles using a 6-step process (see Table 5), will guide the derivation of a framework for the analysis of the outcome data (Braun & Clarke, 2006).

Statistical comparisons of article data will be reviewed on a case-by-case basis.

Confidence in cumulative evidence

The GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the strength of the body of evidence of the review. In line with the Cochrane methodology, each outcome will be ranked according to whether the quality is high, moderate, low or very low. The GRADE framework will be used to assess each outcome in the following areas: risk of bias, consistency of effect, imprecision, indirectness and publication bias (Schünemann et al., 2019).

Underlying data

No data are associated with this article

Extended data

Harvard Dataverse: Replication Data for: Sedentary behaviour levels in adults with an intellectual disability: a systematic review protocol. https://doi.org/10.7910/DVN/TPS2HU (Lynch, 2020)

This project contains the following extended data:

Reporting guidelines

Harvard dataverse: PRISMA-P checklist for ‘Sedentary behaviour levels in adults with an intellectual disability: a systematic review protocol’ https://doi.org/10.7910/DVN/TPS2HU (Lynch, 2020)