1 Primary Care Centre Versus Arthritis, School for Primary, Community and Social Care, Keele University, Staffordshire, UK.
Find articles by Emma L Healey2 Department of Medicine & Thurston Arthritis Research Center. The University of North Carolina at Chapel Hill, USA.
3 Center of Innovation to Accelerate Discovery and Practice Transformation, Department of Veterans Affairs Healthcare System, Durham, NC, USA
Find articles by Kelli D Allen4 Centre for Health Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Australia.
Find articles by Kim Bennell5 Institute of Bone and Joint Research, Kolling Institute, The University of Sydney, Sydney, Australia.
Find articles by Jocelyn L Bowden1 Primary Care Centre Versus Arthritis, School for Primary, Community and Social Care, Keele University, Staffordshire, UK.
Find articles by Jonathan G Quicke6 School of Nursing, The University of Hong Kong, Hong Kong.
Find articles by Robert Smith1 Primary Care Centre Versus Arthritis, School for Primary, Community and Social Care, Keele University, Staffordshire, UK.
2 Department of Medicine & Thurston Arthritis Research Center. The University of North Carolina at Chapel Hill, USA.
3 Center of Innovation to Accelerate Discovery and Practice Transformation, Department of Veterans Affairs Healthcare System, Durham, NC, USA
4 Centre for Health Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Australia.
5 Institute of Bone and Joint Research, Kolling Institute, The University of Sydney, Sydney, Australia.
6 School of Nursing, The University of Hong Kong, Hong Kong.All authors drafted the article, revised it critically for important intellectual content, and approved the final version to be published.
Corresponding author: Dr Emma Healey, Primary Care Centre Versus Arthritis, School for Primary, Community and Social Care, Keele University, Staffordshire, ST5 5BG, UK., ku.ca.eleek@yelaeh.e, Tel: +44 01782 734843
The publisher's final edited version of this article is available at Arthritis Care Res (Hoboken)Many people with musculoskeletal (MSK) conditions can reduce their pain and improve their quality of life by being more physically active (1). Physical activity (PA) is internationally recommended as a core treatment for common MSK conditions such as osteoarthritis (OA) (2–5). However, people with MSK conditions are often less active than those without such conditions (6–8), with less than half of adult patients with OA meeting PA guidelines (9).
PA levels can be measured using objective methods such as accelerometry and pedometers or subjective methods such as self-report measures. Use of self-report PA measures is a popular approach as they are easy to use and are low cost (10). Two systematic reviews have evaluated the measurement properties of self-report PA measures in patients with OA (11,12), but to date, there is still no consensus regarding which self-report PA measure is the most suitable for use in MSK populations.
This paper aims to summarise and critically assess the most widely used self-report PA measures in studies of common MSK conditions. The authors selected measures based on the following criteria: 1) administered by self-report, 2) most commonly cited for use in MSK populations (e.g. OA, low back pain (LBP), rheumatoid arthritis (RA), ankylosing spondylitis (AS), fibromyalgia (FM)) in the last 5 years, and 4) have evidence of psychometric data in MSK populations. A two-stage computerized literature search using Medline and Embase were performed. In the first search, medical subject headings for MSK conditions and MSK pain were used in conjunction with terms for exercise and PA to identify studies that used self-report PA measures. A second search was then conducted to retrieve studies that evaluated measurement properties of the self-report PA measures identified from the first search.
The measures reviewed below include the Baecke Physical Activity Questionnaire (BPAQ), the International Physical Activity Questionnaire (IPAQ) (3 versions), the Physical Activity Scale for the Elderly (PASE) and the Short Questionnaire to Assess Health-Enhancing Physical Activity (SQUASH). Details on the content and structure of each measure are presented (number of items, recall period, response options, presence of translations, and adaptations) (see table 1 ). Information on cost and how to obtain the measures is also provided, where available. Important practical information is presented, including evidence for psychometric properties of each measure in common MSK populations (where available) (see table 2 ). A critical appraisal of each measure is provided and the review concludes with a summary and recommendations specific to the rheumatology community.
Practical applications for self-report PA measures
| Measure | Number of items | Content/Domains | Method of administration | Recall period | Response format | Range of scores | Score interpretation | Availability of normative data | Cross-cultural validation |
|---|---|---|---|---|---|---|---|---|---|
| BPAQ | 16 | Work related, leisure time and sport activities | Self-administered | Not clear | Multiple choice answers | Not clear | No scoring interpretation | Not reported | Not reported |
| IPAQ-LF | 27 | Job related activities, transportation related activities, housework related activities, leisure time activities and sitting activities | Self-completed, telephone administered | Last 7 days | Number of days, number of hours and number of minutes spent active | NA | Metabolic equivalents week/minutes | NA | Multiple languages available with evidence for cross cultural validity |
| IPAQ-SF | 7 | Moderate, vigorous intensity physical activity, walking and sitting activities | Self-completed, telephone administered | Last 7 days | Number of days, number of hours and number of minutes spent active | NA | Metabolic equivalents week/minutes | NA | Multiple languages available with evidence for cross cultural validity |
| IPAQ-E | 4 | Moderate, vigorous intensity physical activity, walking and sitting activities | Self-administered | Last 7 days | Number of days, number of hours and number of minutes spent active | NA | Metabolic equivalents week/minutes, caution should be used when converting to METs for older adults | NA | Available in English and Swedish |
| PASE | 12 | Activities during leisure time, occupational and household settings | Self-complete or telephone | Last 7 days | Multiple choice | 0–400+ | No scoring interpretation | Not reported | Some studies reported translations, but these are not freely available |
| SQUASH | 14 | Commuting activities, activity at work or school, household activities, leisure time activities and sports | Self-administered | Usual week in the past months | Days per week, average time per day, intensity of activity | NA | Total time spent physically active over 2.0METS in a week | Not reported | Original in Dutch, English, Turkish and Japanese available although cross-cultural validation only reported in Japanese version |
NA = not applicable
Psychometrics (MSK populations)
| Measure | Floor, ceiling effects | Reliability | Validity | Responsiveness | Minimally important differences | Generalizability | Used in RCTs |
|---|---|---|---|---|---|---|---|
| Baecke | 0 | ICC=0.87 females with hip disorders (14), ICC=0.77 LBP (22) | Correlation to accelerometers=0.30–0.49 females with hip disorders (14), correlation to accelerometers= 0.18 LBP (22) | 0 | 0 | 0 | (23) |
| IPAQ-LF | 0 | ICC=0.65, SEM=2668 METS-minute/week, SDC=1115 METS-minute/week, THR and/or TKR (31), ICC=0.83 AS (32), ICC=0.77 females with FM (33), ICC= 0.37 LBP (22) | Correlation to accelerometer = 0.43 THR and/or TKR (31), correlation to accelerometer = 0.38 AS (32), concordance correlation with accelerometer= 0.04 females with FM (33), compared to accelerometer, individual overestimate in IPAQ-LF RA (38), correlation to accelerometer =0.33 LBP (22) | 0 | 0 | 0 | (42) |
| IPAQ-SF | 0 | ICC=0.76 THR, ICC=0.87 TKR (36). ICC=0.51 THR and/or TKR (31). SEM=2487METS-minute/week, SDC= 1039METS-minute/week THR and/or TKR (31), ICC=0.64, SEM=3532, SDC=9791 in a hip, knee, foot and hand OA sample (37) | Correlation to accelerometer = 0.29 THR and/or TKR (31), correlation to PASE=0.61 hip OA (40), correlation to accelerometer =0.40 RA (41), correlation to PASE=0.56 (37) | ES=−0.14, SRM=−0.21, RR=0.12 in a hip, knee, foot and hand OA sample (37) | 0 | 0 | (43–48) |
| IPAQ-E | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| PASE | 0 | ICC=0.77 hip OA (40), ICC=0.77 TKA men, ICC=0.58 TKA women (66), ICC=0.77 THR (60), ICC=0.68, SEM=46.7, SDC=129.6 in a hip, knee, foot and hand OA sample (37), SEM=31, SDC=87 hip OA (67), SEM=32–35%, SDC=89–97% TKR (67), SEM=23% THR (60) | Correlation with accelerometer = 0.30 hip OA (67), correlation with accelerometer = 0.45 TKR men =0.06 TKR women (66), correlation with accelerometer = 0.27 THR (60) | ES= −0.16, SRM=0.21 and RR= 0.09 in a hip, knee, foot and hand OA sample (37) | 0 | 0 | (40, 43, 70–76) |
| SQUASH | 0 | ICC= 0.89 AS (32), Spearman’s correlation=0.57 THR (84) | Correlation to accelerometer =0.35 AS (32), correlation to accelerometer ranged= r=0.28–0.49 Knee OA (89), correlation to accelerometer =0.20–0.67 THA (84) | 0 | 0 | 0 | (86, 90, 91) |
Key: Effect size = ES, ICC = intraclass correlation, responsiveness ratio = RR, smallest detectable change = SDC, standard error of the measurement = SEM, standard responsiveness measure = SRM, LBP = low back pain, TKR = total knee replacement, THR = total hip replacement, OA = Osteoarthritis, AS = Ankylosing Spondylitis, FM = Fibromyalgia RA = Rheumatoid Arthritis, 0 = no existing evidence.
The BPAQ (13) is a self-report PA measure developed, originally, for use in epidemiological studies to assess levels of PA in young adults.
Self-reported responses across three domains are used to assess physical activity levels in a typical week: a) occupational physical activities (8 questions); b) sport (4 questions); c) leisure (4 questions); and total PA is the sum of all indices.
16 questions divided across 3 domains and summed for a total level of PA.
Questions are scored on a five-point Likert scale ranging from ‘never’ to ‘always or very often’.
Questionnaire and scoring available within the appendix of original article (13).
Each domain (occupation, sports, leisure) can receive a score from 1 through to 5, which is achieved through a scoring formula for that domain (see below). Within domains each question is also given a score from 1 through to 5 with the exception of questions asking main occupation and types of sports played. Total score is a sum of the scores for each domain and can range between 3 and 15 with a higher score corresponding to a higher PA level.
For the work domain, the respondent’s occupation is given a score of 1 (low activity), 3, (moderate activity) or 5 (high activity) based on work activity ratings from the Netherlands Nutrition Council. The overall work index is calculated using a formula: ((6 − Q2) + (Q1 + Q3 + Q4 + Q5 + Q6 + Q7 + Q8))/8.
For the sport domain, the first question involves calculating a “simple sports score” based on the respondent’s sport activity (a calculation of their two most frequently played sports). First it is determined what two sports the respondent plays most frequently. Sports are subdivided into three categories each of which has a value corresponding to intensity (average energy expenditure). Second, it is determined how frequently the respondent engages in the sport in hours per week, with different numbers of hours given a different value. Finally, it is determined how many months in the year the respondent plays their most frequently played sports. A value is given for different numbers of months per year. Once these three values are determined, the sport score is determined using the formula: ((value for intensity of most frequent sport)*(value for weekly time of most frequent sport)*(value for yearly proportion of most frequent sport))*((value for intensity of second sport)*(value for weekly time of second sport)*(value for yearly proportion of second sport). Finally, an overall sport index can be calculated using the formula: (simple sport score + Q10 + Q11 + Q12)/4. For the leisure domain the overall leisure index is calculated using the formula: ((6 − Q13) + (Q14 + Q15 + Q16))/4.
Scores cannot be interpreted in relation to other metrics of PA.
Minimal burden. Quick to complete as all items are multiple choice.
Time to administer is short and scoring can be done quickly by totalling scores for each index, and a total score summing all of these indices.
There are no repositories of the BPAQ to identify all the translations, but the questionnaire has been used internationally in different populations with multiple languages including Japanese (14), Dutch (15), Persian (16), French (17), Flemish (18), Greek (19), Korean (20), and Portuguese (21).
Not reported in any studies
Intrasession intraclass correlation coefficient (ICC) 0.77 (95% confidence interval [95% CI] 0.65, 0.84) in patients with LBP (22). ICC 0.87 in adult women with hip disorders (14).
Not reported in any studies
Not reported in any studies.
Significant and fair correlation in people with LBP (rho 0.18) between BPAQ and number of steps and vector magnitude on Actigraph wGT3X-BT accelerometer (22). In adult females with hip disorders there was significant but low correlation (rho 0.30–0.49) across 3 measures of the BPAQ and a higher correlation between step counts and total score on BPAQ (rho 0.49) (14).
Not reported in any studies.
Not reported in any studies.
Originally developed for epidemiological research and tested in a young Dutch male and female population (aged 32 years and under). Translation in multiple languages but its application in older populations and in MSK populations is limited.
No randomised controlled trials (RCTs) in MSK populations that used the BPAQ questionnaire could be identified, only one protocol (23).
Items are closed-response items that allow for easy completion and scoring. Scores can be broken down into three indices which allow for interpretations on how individuals are loading their PA. Total scores of the BPAQ appear to have adequate reliability (ICC>0.7).
Limited to two studies of evidence in measurement properties in MSK populations, low correlations to objective measures. No evidence on sensitivity to changes/responsiveness. Scores cannot be interpreted in relation to recommended levels of PA.
Quick and easy to administer and score. Individual scores across indices can be used to identify where individuals are most and least active (occupation, sport or leisure).
No RCTs in MSK populations that used the BPAQ questionnaire could be identified, only one protocol (23). The BPAQ questionnaire was designed for use in epidemiological studies only. Several observational studies in MSK populations have used the BPAQ questionnaire (24–27).
The purpose of the International Physical Activity Questionnaire (IPAQ) is to measure internationally comparable physical activity levels in adult populations (28). It was designed primarily for surveillance of PA at a population level, and has been predominantly used in studies of people with OA. It is not recommended for use as an outcome measure in small scale intervention studies.
The IPAQ is available for use with young and middle aged individuals (15–69 years) in Long (IPAQ-LF) and Short Forms (IPAQ-SF). There is also a short form version validated for use in older adults (IPAQ-E, 65+ years) (29).
Covers five activity domains asked as separate sections. Namely, PA undertaken related to: a) work; b) transportation; c) housework; d) leisure time activities; and e) time spent sitting. The IPAQ-LF asks questions around the frequency (days) and duration (minutes) spent in each of these domains, with a focus on three types of activity; vigorous intensity, moderate intensity, and time spent walking (28).
ask about the four specific activity types (vigorous intensity, moderate intensity, time spent walking and time spent sitting), undertaken during any work, transportation, housework, or leisure activity. Time spent sitting is asked as a separate question and used as an indicator of sedentary behaviour (see scoring manual, www.ipaq.ki.se).
Has 27 items, however some can be skipped if the individual does not participate in any activities for a given domain (e.g. no work-related vigorous or moderate activities undertaken).
Has six items, seven if the individual reports any time spent walking.
Covers the same items as the SF, but is presented as only four questions.
All forms ask people completing the survey to indicate the number of days per week, and hours and minutes per day they spend doing the PA within each domain. Individuals may indicate they are not sure of the activity undertaken.
Past week (last 7 days).
All versions are free to use and open access (www.ipaq.ki.se).
The IPAQ forms are freely available in multiple languages, either in pdf or word document format.
The IPAQ-LF and IPAQ-SF are available as either telephone administered or self-administered. The IPAQ-E is only available in a self-administrated format.
An English version of the scoring protocol for the LF and SF is freely available. The scoring protocol for the SF can be used to score the IPAQ-E. Automatic scoring templates and reports are available for select languages on the website.
Walking, moderate and vigorous scores are converted into METS (metabolic equivalent)-minute/week using the Ainsworth Compendium (30). An average MET score is based on time spent on each activity intensity then added together to make a total. This can then be converted into a categorical score of three categorical levels; low, moderate and high levels of activity. Definitions of these categories are outlined in the IPAQ scoring protocol.
Provides separate domain specific scores for vigorous-intensity, moderate-intensity and walking within the 4 activity related domains (excluding sitting). Computation of the total scores for this form involves summation of the frequency (days) and duration (minutes), for all activities in all domains. Domain specific scores require summation of the scores of the different intensity activities within the specific domain. Activity specific sub-scores can also be calculated by summing the scores of the specific types across the domains.
Provides separate scores for vigorous-intensity activity, moderate-intensity activity and walking. However, domain specific estimates cannot be calculated. Data for time sitting is not included in the summed score of PA, but should be reported and used to categorise activity into low, moderate or high levels (see the SF scoring protocol).
Scored similarly to the SF, however Hurtig-Wennlof and colleagues (29) suggest caution should be used when converting to MET due to lower metabolic rates in older adults. Rather min/day in each intensity can be reported.
Scores are given in total METS-minute/week giving an estimate to energy expenditure in a week. Categorical score can be interpreted in terms of PA recommendations, those scoring low are below recommendations, moderate scores are meeting recommendations, and high scores are exceeding the recommendations.
Time to complete has not been reported, however as the IPAQ-SF and IPAQ-E have less than 7 short response items, time to complete is minimal. The LF is longer to administer, although still takes a relatively short time to complete.
Time to administer the questionnaires is very short as only a print out and a pen is required to complete. Scoring can be completed by hand or calculator, or on a simple spreadsheet. No additional equipment or software is required.
There are currently 24 translated versions available (www.ipaq.ki.se), although not for all form types. The website includes a guide for translating the IPAQ into languages not currently available.
Not reported in any studies
Not reported in any studies.
ICC=0.65 in patients with total hip replacement (THR) and/or total knee replacement (TKR) (31), ICC=0.83 in patients with AS (32), ICC=0.77 in females with FM (33), ICC= 0.37 in patients with LBP (25). Standard error of the measurement (SEM) =2668 METS-minute/week, minimal detectible change = 1115 METS-minute/week in patients with THR and/or TKR (31). Comparison across 12 countries ICC ranged from 0.96 to 0.46 (34). The IPAQ-LF has shown weak reliability for sedentary behaviour and moderate to vigorous activity in healthy older adult populations, therefore care should be taken when using it to classify PA levels in older populations (35).
Not reported in any studies.
ICC=0.76 in patients with THR, ICC=0.87 in patients with TKA (36). ICC=0.51 in patients with THR and/or TKR (31). SEM=2487 METS-minute/week, minimal detectible change = 1039 METS-minute/week in patients with THA and/or TKA (31). Comparison across 12 countries ICC 0.88 to 0.32 (34). ICC=0.64, SEM=3532 METS-minute/week, smallest detectable change (SDC) =9791 METS-minute/week in patients with OA in a hip, knee, foot and hand OA sample (37).
Not reported in any studies
Not reported in any studies
CSA accelerometers across 12 countries = 0.33 (34)
Correlation to ActiGraph GT1M accelerometer = 0.43 in patients with THR and/or TKR (31), correlation to ActiGraph GT1M=0.38 in patients with AS (32), concordance correlation with SenseWear Pro Armband= 0.04 in females with FM (33), compared to Actigraph GT3X, individual overestimate in RA (38), correlation to Actigraph wGT3X-BT counts=0.33 in LBP (25); compared to the Actigraph older adult self-report had moderate correlations for moderate to vigorous PA (MVPA) (0.43–0.56) and 0.70 to 0.26 for SB, but they tended to underestimate both MVPA and sedentary behaviour (39)
Not reported in any studies.
Correlations to CSA accelerometers across 12 countries = 0.30 (34).
Correlation to ActiGraph GT1M accelerometers = 0.29 in patients with THR and/or TKR (31), correlation to PASE=0.61 in patients with hip OA (40), correlation to Sensewear activity monitor=0.40 in patients with RA (41). Correlation to PASE=0.56 in a hip, knee, foot and hand OA sample (37).
Not reported in any studies
activity domains positively correlated to ActiGraph GT1M=0.28 to 0.47 in older adults (29). A main effect for category (high, med, low) was observed with the high-sensitive serum C-reactive protein biomarker.
Not reported in any studies.
Effect size (ES) = −0.14, standard responsiveness measure (SRM) = −0.21, responsiveness ratio (RR) = 0.12 in 6 in a hip, knee, foot and hand OA sample (37).
Not reported in any studies.
The IPAQ is designed as a population based measure. It is generic and designed to be used across all adults aged 18–65 (IPAQ-LF and SF), or 65+ (IPAQ-E), with or without clinical conditions (28). However, there are studies in certain populations that suggest it should be used cautiously with these groups.
A number of RCTs have used the IPAQ-LF to assess and classify the level of PA in the study population (42) and IPAQ-SF (43–48).
Scores for the SF and LF relate to weekly energy expenditure. Scores can be compared to recommended levels of PA or between different conditions. It has been translated into different languages, is easy to administer and quick to complete. The forms are open access, readily accessible and are free to use. The IPAQ-SF is widely used to research different MSK conditions and has been used in a range of OA and rheumatology studies. It covers activities across multiple domains including work, leisure and home life. The IPAQ-LF has more evidence for reliability and construct validity in relation to objective measures as compared to the IPAQ-SF or IPAQ-E. It was tested and developed in both high and low income countries (34).
There is limited evidence of measurement properties in MSK populations for any of the forms, and there is no evidence in MSK populations for the IPAQ-E. All forms have low correlations to objective measures, and there is no evidence on sensitivity to changes/responsiveness. It is not recommended for use as an outcome measure for small intervention studies. Care should be taken when converting the IPAQ-E to METs (29). The IPAQ has not been validated for use in online studies.
The questionnaires are quick and easy to administer and score, regardless of the form used. Individuals scores can be related to evidence for levels of PA that can lead to health benefits. The IPAQ-E reports good acceptance by older users (29). It may not be appropriate for use with certain patient populations.
The forms can be self-completed or administered via telephone. They have been translated in different languages and can be used in different countries/languages with direct comparisons. The inclusion of the sitting activity scores in the IPAQ-SF can provide data on inactivity and sedentary behaviour in this population. The IPAQ-LF may be more applicable for research that requires more detailed assessments of PA.
While the IPAQ questionnaire has been used in several RCTs it was predominately designed for observational or population based studies. Several observational studies in MSK populations have used the IPAQ-LF (49) and the IPAQ-SF (45,50–54).
The PASE was developed in the USA in a general older adult population and aims to measure self-reported PA in older adults in the previous week (55).
The PASE contains 3 subdomains; leisure activities, household activities and occupational work.
12 items. The leisure activities domain contains 5 items (sub domains), the household activities domain contains 6 items, and the occupational work domain contains a single item.
PASE scores are calculated using both weights and frequency values for each of the 12 item activity types. Respondents report activities by a) providing categorical responses to the number of days per week and average hours per day (leisure activity domain) b) reporting if they have carried out an activity or not (items in the household activity domain) and, c) hours worked per week (occupational domain). Scale range is 0–400+ (higher scores indicate higher PA level).
In the last week.
There is a cost for the scoring manual and cost per use of the questionnaire (contact www.healthcore.com for more details).
Questionnaire and scoring protocol available from www.healthcore.com
Self-administered or via telephone interview (recommended).
Scoring involves totaling the scores from the three activity domains and rounding to the nearest integer. PASE scores are calculated using both weights (intensity) and frequency values for each of the 12 item activity types. However, each activity domain has a unique scoring method. The weighting of item activities was based on an algorithm derived using accelerometry, activity diary and global activity self-assessment (55, see scoring manual available from www.healthcore.com).
For leisure activities, individuals respond with categorical responses to the number of days per week (never, seldom, sometimes, often) and average hours per day of activity () of activities within each item subdomain. A “PASE activity time to hours per day conversion table” is then used to convert this categorical data into hours per day. Different item activities are assigned different weight scores which are then multiplied by the hours per day score for each item and totaled to give the domain sub-score. For household activities, individuals provide a binary response to whether they have carried out individual household activities in the last week which is then weighted by each sub domain item and totaled for the domain sub-score. Occupational hours worked is divided by 7 and given a weight score for the occupational domain sub-score.
Higher PASE scores indicate higher levels of physical activity. The PASE estimates PA, however, its scores are not directly interpretable in meaningful PA units.
Self-administered or interviewer-administered versions can be completed in 5–15 minutes.
Administering time is 5–15 minutes. Time taken to score depends on the use of computer coding and is not provided in the literature.
Originally developed in English in USA. Has been translated into Dutch (56), Norwegian (57), Japanese (58), Chinese (59), German (60), Malaysian (61), Turkish (62), Italian (63) and Persian (64). It has been adapted for Mexican origin Latinos in South Western USA using an adapted scoring algorithm (65). It has also been adapted for Dutch populations by adding bicycling for transportation to the question about time spent walking (56).
Not specifically reported in any studies.
Not reported in any studies.
ICC = 0.77 in patients with hip OA (40); 0.77 in men following TKR and 0.58 in women (66); 0.77 post THR (60); 0.68 in a hip, knee, foot and hand OA sample (37).
SEM of 31, SDC of 87 in patients with hip OA (67); SEM of 32% and 35%, SDC of 89% and 97% in men and women respectively following TKR (67); SEM of 23.0% post THR (60); SEM 46.7 and SDC of 129.6 in a hip, knee, foot and hand OA sample (37).
Not reported in any studies.
PASE scores significantly correlated in expected directions with performance in the 6-minute walk test, knee strength, knee pain frequency during transfer, and perceived difficulty with physical functioning in older adults with knee pain and physical disability (68).
Correlation of total PASE score with accelerometer-based activity counts in patients with hip OA: 0.30 (p=0.089) and ranged from 0.20–0.38 for the different PA categories (67), correlation with accelerometer 0.45 in men following TKA and 0.06 in women following TKR (66), correlation with accelerometer 0.27 in patients following THR (60).
Effect size −0.16, Standard Response Measurement of −0.21 and Response Ratio of 0.09 in a hip, knee, foot and hand OA sample (37).
Not reported in any studies.
Developed in a USA population but has undergone translation and validation in multiple countries (some of these have been in MSK populations) (40, 60, 66). One study (65), has questioned the generalisability (weighting of items) in Mexican elderly Americans.
The measure has been used in multiple RCTs in OA populations (e.g. 43, 67, 69–76).
Designed specifically for older adults. Relatively quick to complete (5–15 minutes). Translated into multiple languages. Often used in OA studies.
Mixed reliability results, large measurement error and poor responsiveness. Better suited to older adult populations. Weak correlations with objective measures of PA. Unable to discriminate between intensity of activity within individual subdomains (68). Cost associated with use.
Relatively quick and easy to administer but scoring maybe more time consuming and difficult in a consultation setting. Scores are not easily interpreted into meaningful units.
Quick and easy to administer, so could be used in large studies including trials and observational studies (77–80). Validated in older adult populations with joint pain (e.g. OA populations). However, large measurement error and poor responsiveness properties suggest it is not useful in measuring change in PA.
The SQUASH was developed in the Netherlands and aims to measure the habitual activities in a normal week over the “past months” (81).
The SQUASH contains 5 subdomains; a) commuting activities, b) activity at work or school, c) household activities, d) leisure time activities and d) sports.
Up to 14 items, although not all need to be completed as each subdomain has a ‘Not applicable’ option as an item. The domains of commuting, work / school and household each have 2 items, the leisure time domain has 4 items, and the sports domain can include up to 4 sports activities indicated by the respondent.
Individuals respond with the number of days per week and average time per day (hours and minutes) spent on each activity within each subdomain.
An average week over the past months
Questionnaire and scoring protocol published in Wendel-Vos et al (81).
Scoring is completed by taking a sum of the time spent active in each domain in total minutes and multiplying them by their intensity scores (81). Activities are divided into 3 intensity categories based on Ainsworth’s compendium of physical activities, 2 to
The SQUASH does not estimate energy expenditure but estimates habitual activity on an average week for individuals. Some studies have summed the number of days per week for moderate and vigorous activity lasting at least 30 minutes per week to evaluate adherence to American College of Sports Medicine and Dutch activity guidelines (85–86).
Less than 5 minutes
Administering time is less than 5 minutes, but time taken to score could take relatively longer because intensity scores need to be assigned to activities, including open-ended sports questions.
Originally developed in Dutch (81). English version available but process for adaptation / translation not published. Has been translated into Turkish (87) and Japanese (88).
Not reported in any studies
Not reported in any studies.
ICC= 0.89 in patients with AS (32), Spearman’s correlation=0.57 in patients with THR (84).
Not reported in any studies.
Not reported in any studies.
Correlation with accelerometer-based activity counts in patients with AS: 0.35 (32), correlation to accelerometer parameters ranged= r=0.28–0.49 in patients with knee OA (89), correlations with accelerometer-based activity parameters= 0.20–0.67 in patients with THR (84).
Not reported in any studies
Not reported in any studies
Developed in Dutch population and limited use to date in other countries.
The measure has been used in RCTs investigating knee OA (aqua-cycling) (90) and RA (motivation and self-regulation for PA; combination therapies) (86, 91)
Scores can be related to time spent physically active, allowing individuals to be categorized in relation to recommended levels of PA. Takes less than 5 minutes to complete. Opportunity to report any sporting activities in open-ended questions.
Limited use among individuals with rheumatic and MSK conditions. Low correlations with objective measures of physical activity and mixed results on reliability. No evidence on sensitivity to changes/responsiveness and limited use in RCTs. Time intensive to score.
Quick and easy to administer but scoring maybe more time consuming and difficult in a consultation setting. Individual scores on weekly minutes of PA can be related to public health recommendations.
Quick and easy to administer, so could be used in large studies. However, some psychometric properties not well established. Use of the measure in MSK conditions has involved mostly observational studies, including measurement during daily activities in patients with knee OA (89) and multiple studies focused on establishing the psychometric properties of the scale in different patient groups (32, 84, 85, 87, 88). Use in RCTs has been limited, with studies among patients with knee OA (90) and RA (86, 91).
To our knowledge, no self-report measure of PA has been developed specifically for use in populations with MSK conditions. It is therefore important for clinicians and researchers to understand what options are available and how well commonly used measures reflect actual PA levels.
Four self-report PA measures were selected and reviewed based on their frequent use in MSK populations in the last 5 years and the identification of psychometric evidence properties in MSK populations. The authors acknowledge that other commonly used self-report measure such as the CHAMPS (92), the Minnesota Leisure Time Physical Activity Questionnaire (93) and the Yale Physical Activity Survey (94) could potentially be suitable for use in musculoskeletal populations, however they were not included in this review for two main reasons. Firstly, they are not commonly used in musculoskeletal research, and secondly, their psychometric evidence in musculoskeletal and older populations is lacking.
All of the measures included in this review were found to be quick and easy to complete. The majority of measures can also be scored relatively quickly (BPAQ, IPAQ forms, PASE). The SQUASH, however, features open response items to allow respondents to report any sports or physical activities that may not naturally fall into other subdomains, which can make scoring more difficult.
While it would be useful to use self-report PA measures to determine whether an individual or group are meeting current PA guidelines or allow clinically useful categorisation of PA level (e.g. inactive, low active, meeting recommendations), of all the measures included in this review, only the IPAQ and SQUASH allow for this. The impact of sedentary behaviour on MSK conditions has gained increasing interest in recent years (95–97), however of the measures included in this review, only the IPAQ forms assess sedentary time (sitting time). In addition, the SQUASH actually discounts low level activities (
The PASE and IPAQ-SF are currently the most commonly used measures in OA research and the BPAQ appears to be most frequently used in LBP research. Overall, psychometric evidence of all the measures identified is lacking in MSK populations (see table 2 ) and based on the evidence currently available, none of the measures demonstrated adequate measurement properties in terms of all components of reliability and validity. More studies have examined the measurement properties of the IPAQ-LF, yet the IPAQ-SF appears to be a more frequently used measure, possibly due to the reduced number of items and time to complete. There was a clear lack of evidence in terms of responsiveness for the measures in this review. The evidence for the PASE suggests poor responsiveness (37) and others have questioned its ability to detect change in PA levels (98).
In conclusion, as the measures included in this review lack evidence of their psychometric properties and responsiveness to change in MSK populations the authors suggest that caution should be taken when using self-reported PA measures. It is also important to note the wider limitations of all self-report measures i.e. potential for social desirability bias, recall bias, over and underestimation of activities/ misclassification of activities (99–100). Therefore, where possible, the use of objective measures of PA (e.g. accelerometry) should be considered. There is greater evidence of their validity and reliability (101) and they can objectively capture all dimensions of PA including time spent sedentary, which is known to detrimentally affect the general health and functional status of MSK populations (97). Further research is needed to investigate the measurement properties of commonly used self-report PA measures in MSK populations, to allow for informed recommendations and decisions on their use.
EH is partly funded by the National Institute for Health Research (NIHR) Applied Research Centre (ARC) West Midlands. JQ is partly funded by a NIHR Clinical Research Network West Midlands, Research Scholar Fellowship. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research or the Department of Health and Social Care. KDA is supported by the Center of Innovation for Health Services Research in Primary Care (CIN 13–410) at the Durham VA Health Care System and the National Institutes of Health-funded Core Center for Clinical Research (P30 AR072580). KB is supported by the National Health and Medical Research Council and Medibank Private for research into osteoarthritis.
CONFLICTS OF INTEREST
None of the authors report any conflicts of interest.
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