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A Comparison of Questionnaire, Accelerometer, and Pedometer

Measures in Older People


Author Information
Medicine & Science in Sports & Exercise: July 2009 - Volume 41 - Issue 7 - p 1392-1402
doi: 10.1249/MSS.0b013e31819b3533
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Physical activity (PA) is vitally important for older peoples' health and well-being (4,17); however, there is debate about how best to measure PA levels in this age group (37), hence the value of studies comparing measurements. Doubly labelled water (DLW) is considered a gold standard for estimating free-living total energy expenditure (EE) but cannot describe PA patterns and is too expensive and reliant on technical expertise for widespread application (30). Self-reported PA questionnaires are cheap and easy to administer, but none are entirely satisfactory (16). Questionnaires can suffer from recall bias and floor effects, with the lowest response category too high for many respondents (34). The most predominant activity in this age group, walking, is unreliably assessed by questionnaires (34).

Motion sensors (pedometers and accelerometers) provide objective PA measurements, are sensitive to walking, and are unrestricted by floor values (34). Pedometers are cheap; they measure step count but not intensity and therefore cannot distinguish between walking speeds. Accelerometers measure the body's acceleration in one or more directions continuously for long periods. The output, activity counts per unit of time, calculated from the magnitude and the intensity of the acceleration, distinguishes between different walking speeds and intensity levels. Some accelerometers can record step counts (the frequency domain of the vertical acceleration), allowing comparison with pedometers. Accelerometers have been validated in older people using indirect calorimetry (9) and DLW (26).

Several studies have compared PA questionnaires with pedometers or accelerometers in older people, that is, examined convergent validity (the extent to which an instrument's output is associated with that of other instruments intended to measure the same exposure). Correlation coefficients have been modest: ranging from 0.35 (24) to 0.56 (31). However, studies have been limited to those with specific disorders (24,29,32), those which recruited volunteers without a clear population base (38) or cohort study survivors (31). The exception was a small population-based study that included 49 older people (23). To date, the only large population-based study of older people with objective and subjective PA measures has reported only on time spent in sedentary behavior (22).

Motion sensor comparisons under controlled conditions show that accelerometers exactly measure observed steps, whereas pedometers tend to underrecord at low speeds (20). Free-living comparisons showed strong correlations between the Yamax pedometer (Yamax Corp, Tokyo, Japan) and the Actigraph (GT1M; Manufacturing Technology Inc, Fort Walton Beach, FL), 0.84 (21) and 0.80 (33), respectively. Although highly correlated for relative values, the pedometer underestimated daily accumulated steps by approximately 10% relative to the Actigraph (20,33). However, these comparisons have been restricted to small, homogenous samples of young and middle-aged adults (20,33).

This article has two aims. First, to assess convergent validity by comparing accelerometer activity counts, accelerometer step counts, pedometer step counts, and Zutphen questionnaire (3) self-report PA in a population-based sample of community-dwelling older people. Second, to assess construct validity (the extent to which an instrument is associated with other measures of theoretically related parameters) by comparing the strength of association of known PA predictors in older people (age, sex, disability, health, body mass index [BMI], exercise self-efficacy, etc.) with objective (accelerometer activity counts) and subjective (Zutphen questionnaire) PA measures.


Participants, Sample Size, and Selection

The target population was community-dwelling older people ≥65 yr, able to walk outside, and registered with a primary care practice in Oxfordshire, UK. One thousand five hundred and twenty-nine patients ≥65 yr were registered. Two hundred and seventy-three (18%) were excluded for the following reasons: dementia, care home resident, terminally ill, housebound, and unstable angina/recent myocardial infarction/coronary intervention. This study is part of a randomized controlled trial to test the effect of two interventions (questionnaire inclusion with the study invitation and telephone contact) on recruitment. Random selection and randomization were performed at household level to avoid partners receiving different interventions. Participants were invited to take part in a study to assess their customary PA levels and were unaware that there was randomization of different recruitment methods. We invited 560 individuals to have sufficient power in the randomized comparisons. Full details of the interventions and their recruitment effects have been reported (13). Five hundred and sixty invitations were sent out over 20 wk from September 2006.

Ethical approval was granted by the Oxfordshire, UK REC A (reference no. 06/Q1604/94). All participants provided written consent.


A baseline questionnaire assessed measures associated with PA in older people: health (general health, disability, longstanding illness, chronic diseases, pain, and smoking status) and psychological measures (depression, exercise self-efficacy, exercise control, and exercise attitudes) (see Table 1). Weight was assessed using calibrated, sensitive scales, height with a wall-mounted tape measure, and waist circumference using a constant-tension tape.

Details of questionnaire measures and sources.

Self-Reported PA

The 17-item Zutphen Physical Activity Questionnaire (3) has been tested for its reliability and validity in older people (16) and includes questions on frequency and duration of walking, cycling, gardening, odd jobs, sporting activities, and hobbies, allowing calculation of estimated EE in kilocalories per kilogram per day. The recall time frames are past week, month, and usual activity, depending on activity. Each activity's EE is calculated by multiplying frequency, duration, and intensity using published MET values (e.g., walking = 3.5 and swimming = 5.0). Total EE for PA is then calculated by summation (3,19). The Zutphen Physical Activity Questionnaire has been criticized for excluding household activities, particularly important in older women (31); therefore, we added questions on light and heavy housework (intensity codes 2.3 and 3.3 METs) from the Physical Activity Scale for the Elderly (38). We were interested in dog walking as a specific source of walking, which is not separately identified by the Zutphen questionnaire. We therefore added a further question on dog walking, but this was not included in the EE calculations.

Objective PA


The Actigraph (GT1M; Manufacturing Technology Inc) measures vertical accelerations in magnitude from 0.05g to 2.0g, sampled at 30 Hz, then summed over a selected period (epoch); we used 5-s epochs. The pedometer function records steps for vertical accelerations ≥0.30g. Participants were asked to wear accelerometers over the hip on a belt, all day, only removing for bathing/swimming and to maintain and record usual activities in a log. Participants were seen ≥7 d later to allow seven full days of recording. Twenty participants wore the accelerometer for a further week, 6-8 wk later, to assess repeatability.


A random half of participants additionally wore a Yamax Digi-walker SW-200 pedometer (Yamax, Corp) over the other hip and recorded daily step counts on their logs. The reason why only a random half of participants wore pedometers was that the purpose of a previous article was to test whether pedometer step-count monitoring might increase PA, although participants were asked to maintain usual PA levels. Results showed only a small nonsignificant additional accelerometer step count or activity count in those wearing pedometers (12). Yamax pedometers measure vertical acceleration and record steps at thresholds ≥0.35g.

Outcome measures.

Average daily activity counts and step counts were calculated from accelerometer data. Average daily pedometer step counts were calculated from logs. Estimated EE in kilocalories per kilogram per day for PA was calculated from the Zutphen questionnaire (3), with additional household activities (38). (We also repeated some analyses using the traditional Zutphen score, without housework, to see if this affected the findings.) We compared Zutphen EE with accelerometer activity counts and step counts (raw data). We chose not to calculate EE for accelerometer data because there are no free-living EE equations available for estimating PAEE from accelerometry in older individuals.

Data management.

Accelerometer data were downloaded and checked using the Actigraph ActiLife Monitoring System alongside activity logs. Participants with <5 d of accelerometer or pedometer data were excluded. Accelerometer data were processed and analyzed by a custom-written program (MAHUFFE.exe, available from For Zutphen EE, all cases with no missing data on walking items were included. Analyses were repeated using a reduced data set, including only cases with complete data on all Zutphen items.

Statistical methods.

STATA 9 was used for analyses. Distributions of all four PA outcome measures were transformed and visualized using the gladder command. Chi-squared tests were used to identify the transformation closest to a normal distribution (the ladder command); this was the square root distribution for each outcome. Outcomes were therefore transformed to standard normal deviates after applying the square root transformation (each value being expressed as the number of standard deviations from the mean). Scatterplots were constructed, and Pearson correlation coefficients were calculated to examine the relationships between the different PA variables using both the raw data and the square root-transformed variables because the latter were normally distributed. Bland-Altman plots were used to examine the association between step counts recorded on the accelerometer and pedometer for the 121 individuals wearing both instruments; mean difference in step counts was calculated using paired t-tests. Because they were based on different measurement scales, Bland-Altman plots comparing accelerometer activity counts and Zutphen scores used the standard normal deviates.

All the objective PA measures (pedometer step count, accelerometer step count, and accelerometer activity count) were highly correlated (r = 0.82-0.95); therefore, one measure (activity count) was chosen for further regression analyses. Linear regression was used to compare the effect estimates for the associations between the exposures that potentially predict PA in older people (age, sex, health, and anthropometric and psychological variables) and the following outcomes (i) objectively measured PA and (ii) self-reported PA. For each outcome, linear regression was performed using the standard normal deviate transformed data; thus, effect estimates are directly comparable. Effect estimates for different exposures are given adjusted for important structural variables (age, sex, household clustering, and pedometer use). Linear regression was also used to examine associations between the self-reported PA measures not included in the Zutphen score (e.g., dog walking, number of long walks) and the objective and self-report PA outcome measures.

Repeatability for the 20 participants with two accelerometer readings was examined using Pearson correlation coefficients for step and activity counts and Bland-Altman plots. Mean differences were also analyzed using paired t-tests.


Participation and data completeness.

Two hundred and forty (43%) of 560 invited individuals participated. Two hundred and thirty-eight participants provided ≥5 d of accelerometer data. Fifty-one percent (122/240) wore pedometers, 121 participants provided ≥5 d of data. All 240 participants completed the Zutphen Physical Activity Questionnaire: 6 had missing walking data and 49 had missing data on ≥1 item. Analyses are based on N = 234 participants with complete accelerometer data and no missing data on walking items in the Zutphen questionnaire. Pedometer analyses are based on N = 121 with complete pedometer data.

Participant characteristics and summary PA measures (Table 2).

Participant characteristics and PA measures by gender, Oxfordshire, UK, 2006-2007.

Men had higher total Zutphen daily EE scores than women and higher scores for walking, cycling, gardening, odd jobs, sports, and hobbies. Women scored higher on light and heavy housework. Men had slightly higher activity and step counts than women, but these differences were not statistically significant (e.g., P=0.21, for accelerometer step counts).

Distributions of PA measures.

Only the Zutphen showed a floor effect, with some participants scoring zero. All distributions were skewed (Fig. 1).

Distributions of average daily EE from Zutphen self-report questionnaire (A); accelerometer average daily activity counts (B); accelerometer average daily step counts (C); and pedometer average daily step counts (D).

Convergent validity.

Self-reported EE from the modified Zutphen score was significantly and positively correlated with accelerometer activity count (R = 0.34, P<0.001). (The equivalent correlation for traditional Zutphen score without housework included was R = 0.35, P < 0.001.) Figure 2A demonstrates the association between self-reported EE and activity counts, suggesting a wide range of EE estimated for any given level of PA measured by accelerometry. Similarly, self-reported EE was significantly and positively correlated with accelerometer (R=0.35, P < 0.001) and pedometer (R = 0.36, P < 0.001) step counts. Pedometer step count was highly correlated with accelerometer step count (R = 0.86, P < 0.001) and activity count (R = 0.82, P < 0.001). (All correlations were either unchanged or very similar when square root-transformed data were used.)

Scatterplot and Bland-Altman plots. A, Scatterplot of Zutphen PA score against accelerometer average daily activity count. B, Bland-Altman plot of accelerometer and pedometer mean step count against step-count difference. Correlation of mean and difference r = 0.07, mean difference = 44.2 (95% CI = −373 to 284). Dash lines (- - - - -) give 95% reference range for difference; dash-dot lines (-.-.-.-.) give regression line. C, Bland-Altman plot of mean against difference of standard normal deviates of accelerometer activity counts and Zutphen score. Correlation of mean and difference r = 0.00, mean difference = 0.014 (95% CI = −0.13 to 0.15). Dash lines (- - - - -) give 95% reference range for difference; dash-dot lines (-.-.-.-.) give regression line.

Not only were step counts measured by accelerometer and pedometer highly correlated, but both instruments actually provided very similar step counts (mean difference accelerometer minus pedometer = −44, 95% CI = −372 to 284). There was no evidence that the difference between methods varied by mean level (Fig. 2B). Figure 2C shows no evidence from Bland-Altman plots that level was related to difference for comparisons of accelerometer activity count and Zutphen score.

Construct validity (Table 3).

Table 3 shows the factors associated with objectively measured PA (activity count) and with self-report PA (Zutphen EE). Effect estimates for both outcomes are directly comparable because they are based on regression of the standard normal deviate analyses repeated using (i) an average daily accelerometer step count as an outcome and (ii) a reduced data set, including only cases with complete Zutphen data (n=191), produced very similar effect estimates, so neither are shown. Throughout, the exposure variables were more strongly related to objectively measured activity count than to self-reported EE. Increasing age was inversely associated with both outcomes but much more strongly with activity count. Smoking status was not associated with either outcome. The following health factors showed significant inverse associations with average daily activity count: disability, poor health, limiting longstanding illness, pain, medication use, chronic diseases, diabetes, falls, walking aid use, BMI, and waist circumference and depression score. All showed evidence of dose-response effects, with the intermediate categories having intermediate coefficients for accelerometer counts; for many, the coefficients for the intermediate categories were significantly different from both the top referent group and the bottom group (see Table 3). In contrast, although most factors showed a similar direction of association in relation to self-reported average daily EE, the only statistically significant associations observed were for disability, poor health, and depression score. In each case, the magnitude of association was smaller with limited evidence of dose-response effects. All of the psychological factors (exercise self-efficacy, attitudes toward exercise, and exercise control beliefs) were significantly positively related to both outcomes; but associations were stronger with activity count than with self-reported PA. There were also positive associations between average daily activity count and all the self-reported PA measures that were not included in the Zutphen score, for example, dog walking. When analyses were repeated using the original Zutphen score, without the addition of household activities, the results were very similar to those presented.

Factors associated with objective (accelerometer average daily activity count) and subjective (Zutphen questionnaire) PA measures in older people.

Accelerometer repeatability (Table 4).

Test-retest reliability of daily PA measurements obtained from an accelerometer during two 7-d testing periods 2 months apart for 20 participants.

Repeated measurements were highly correlated (R = 0.87 activity counts, R = 0.78 step counts), but daily activity levels increased significantly (e.g., steps by 882, SD = 1682, P=0.03). There was no evidence from the Bland-Altman plots (not shown) that level was related to change (R = 0.06 activity counts, R = 0.09 step counts).


Main study findings

The objective PA measures (Actigraph step count and activity count and pedometer step count) showed excellent convergent validity in a population sample of older people under free-living conditions. There was no significant difference between accumulated pedometer and accelerometer step counts. Convergent validity between these objective measures and the Zutphen questionnaire was weaker, but self-reported EE from the questionnaire appeared to rank individuals according to their overall PA levels measured by accelerometry.

Accelerometer activity count showed good construct validity, demonstrated by strong dose-response associations with a wide range of health and anthropometric and psychological measures known to predict PA in older people (18). Self-reported EE was less strongly related (many effects not being statistically significant) to health and anthropometric measures, although it was associated with psychological factors. The accelerometer therefore offers better value to researchers than the Zutphen questionnaire due to its demonstrated greater construct validity; however, the latter provided useful information about activity type and possible gender differences in activities, which the motion sensors could not provide.

Study strengths.

The study used a randomly selected, population-based sample and was larger than previous studies comparing objective and subjective PA measures in older people. It had two objective and one subjective PA measures and a wide range of variables, including anthropometric measures, known to predict PA in older people.

Study weaknesses.

There was randomization of different recruitment methods for the study, but participants were unaware of this; therefore, it should not have had any effect on the generalizability of the study findings. Participants were registered with a single general (family) practice, and previous findings demonstrated they were more likely to be male and more physically active than nonparticipants (14). Therefore, our PA levels may overestimate actual levels in community-dwelling older people. Recruitment bias of more active participants is common to other PA studies of older people (10), and our study reports similar PA levels compared with previous studies (see below). The observed relationships between objectively measured and self-reported PA and their associations with health-related variables are unlikely to be biased, irrespective of our sample.

The Actigraph and the pedometer measured customary PA levels for 7 d. The Zutphen questionnaire was completed at baseline and used a mixture of different time frames to assess customary PA, which makes its comparison with objective monitors over 7 d more difficult. However, its assessment of the predominant types of PA in this sample (walking and housework in women, walking and gardening in men) is based on self-reported activity in the last 7 d (for walking) and usual weekly amounts (for housework and gardening). We did not assess self-reported activity over the same 7 d as monitoring was performed, as has been done previously in older people (32); but these authors found that a single survey question on customary PA was as strongly related to objective pedometer measures, as the 7-d self-reported PA measure (32). The Zutphen questionnaire assigns single MET values to walking, regardless of self-reported walking pace; this is probably reasonable given the difficulties that have been reported in participants' comprehension of survey questions about walking speed/pace and intensity (34). We included housework in our Zutphen score, and for women at least, this was a significant contribution to their estimated EE. However, its inclusion did not markedly change the correlation of the Zutphen score with accelerometer activity counts, nor did it much influence the analysis presented in Table 3. It seems likely that difficulty in assessing the intensity of housework and of accurately recalling the time spent doing housework may explain this lack of improvement. There was some evidence that EE from housework might differ in importance for men and women, but we lacked power for addressing this issue. We looked for associations between different PA assessment methods rather than making the accelerometer a gold standard. Although the accelerometer accurately measures ambulation, it underestimates upper body PA and does not record cycling or swimming (21).

Cycling was included in the Zutphen questionnaire, another reason for possible discrepancy between questionnaire and objective measures; however, Table 2 shows that it was an uncommon activity, accounting for only approximately 1% of the total EE for this sample.

The Zutphen instrument was completed contemporaneously with health, disability, and psychological questions, all on the same questionnaire, whereas the accelerometer PA measure was collected over the following week. This could lead to bias, with strengthened associations between self-reported EE and other questionnaire variables. The fact that the accelerometer results provided stronger associations with the other questionnaire measures is therefore likely to be valid.

Comparisons with other studies.

Self-reported average daily EE (9.1 kcal·kg−1·d−1) is equivalent to 683 kcal·d−1 in our sample. Although comparisons of estimated EE from different questionnaires is limited by their different assumptions, our findings appear consistent with other PA questionnaire findings in this age group, where means of 498 kcal·d−1 (11), 1160 kcal·d−1 (11), and 11.3 kcal·kg−1·d−1 (23) have been reported. Our average daily step counts are consistent with a review, suggesting 6000-8500 steps·d−1 forhealthy older adults and 3500-5550 steps·d−1 for those with chronic illness (35). Recent studies show Canadian (1) and Japanese (40) seniors reaching the higher levels, with American seniors averaging lower levels (39).

Although pedometer and accelerometer values can be converted into EE units and some studies with older people have done this (29,32), this approach has been questioned because the conversion equations validated using free-living activities are not available for all ages (21) and the conversion may add errors that do not exist in the raw data (5). We therefore used activity counts and step counts as our objective outcomes, similar to other studies (24,31).

The correlation found between the Actigraph and the Yamax pedometer (0.86 for step counts) is very similar to other studies (21,33). However, we also showed very similar mean step counts between instruments, contrary to smaller studies where the pedometer underestimated daily steps by approximately 10% relative to the Actigraph (20,33). In one study, pedometers were attached to clothing (33), which can tilt them, reducing accuracy. Both monitors in our study were worn on a belt, which may have reduced recording differences. Although treadmill studies suggest pedometers underestimate steps at slow speeds, this is typically approximately 54 m·min−1 (20); the strong agreement between our instruments may be due to a generally higher walking speed in our participants, as suggested by a study where pedometers accurately recorded community-dwelling older people, but not nursing home residents (6).

The correlations found between the self-reported EE and the objective measures are similar to those observed in some comparative studies in older people (24,32) but slightly lower than in some other studies (23,31). One interpretation of these slightly weaker correlations is that the accelerometer measures PA over a single week, whereas the Zutphen questionnaire measures average PA levels over a longer period. Interestingly, the study using an accelerometer and a 7-d recall over the same period showed similar correlations to ours (32), whereas none of the studies reporting stronger correlations assessed the same period with questionnaire and accelerometer (23,31). Another more likely interpretation of the weaker correlation is that the Zutphen questionnaire is subjective and people assess themselves on it very differently, whereas the accelerometer objectively measures PA without user interpretation and therefore more accurately estimates PA levels.

Although limited in sample size, the accelerometer repeatability findings emphasize that individuals who are more active during one period are likely to be more active during another. Initial and repeated activity counts were highly correlated (R = 0.83), consistent with other studies in older people over 7 d (0.84-0.93) (28,29). The accelerometer's strong repeatability correlations were observed despite its sensitivity to real change in PA levels over time. In most cases, the repeated average counts were higher, initial measures were collected from January to February 2007 (winter), whereas the repeated measures were collected March to April 2007 (spring). Others have shown similar seasonal effects in older people (28).

Despite being validated for older people, self-reported EE from the Zutphen questionnaire appeared to have limited construct validity as it was unrelated to most health or anthropometric variables. Other self-report PA instruments for older people have shown no association with BMI (11), confirming the need for more objective measures (16).

Implications for future work.

Our findings suggest that accelerometers and pedometers assess step count similarly in community-dwelling older people. Accelerometers have advantages in capturing intensity of activity and quantifying time spent at different PA levels (not presented in this article) but are expensive and require computer analysis. If simple metrics such as step count only are required, our study provides reassurance that pedometers provide a valid measure of this, which can be used more easily in large community-based studies in this age group. The seasonal effect discussed above raises important design issues for surveys using activity monitors.

Although the objective PA measures capture ambulatory activity well, they do not capture swimming and cycling, and they underestimate activities with upper body or arm movement. Heart rate measurement using combined heart rate and movement sensors (2) could overcome this, as they can be worn for swimming and cycling and measure response to exercise as well as activity. However, they are not yet fully validated and are expensive. No objective measures provide information on the types of PA being performed. Future studies should investigate innovative ways to combine PA questionnaires and activity monitors to try and capture all aspects of PA.


Convergent validity was strong between accelerometers and pedometers but weaker between these and self-report Zutphen questionnaire. The accelerometer had better construct validity being more strongly associated with established PA determinants than the self-report measure, but the latter provided useful detail on activity type. A combined approach to PA assessment, with both objective and self-report measures, is therefore likely to be preferable to allow fuller characterization of PA data.

The study was funded by the Thames Valley Primary Care Research Partnership (WCRM03). The sponsors played no role in the design, execution, analysis, and interpretation of data or writing of the study. We are grateful to all the partners, the staff, and the patients of Sonning Common Health Centre, Oxfordshire, for their support with this study. The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Conflict of interest: None of the authors have any conflict of interest.


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