The dilemma of physical activity questionnaires: Fitter people are less prone to over reporting

Kaja Meh; Vedrana Sember; Maroje Sorić; Henri Vähä-Ypyä; Paulo Rocha; Gregor Jurak

doi:10.1371/journal.pone.0285357

. 2023 Aug 30;18(8):e0285357. doi: 10.1371/journal.pone.0285357

The dilemma of physical activity questionnaires: Fitter people are less prone to over reporting

Kaja Meh ^1,^*, Vedrana Sember ¹, Maroje Sorić ^1,², Henri Vähä-Ypyä ³, Paulo Rocha ⁴, Gregor Jurak ¹

Editor: Giulia Squillacioti⁵

PMCID: PMC10468079 PMID: 37647304

Abstract

Physical activity questionnaires (PAQs) are a popular method of monitoring physical activity, although their validity is usually low. Descriptions of physical activity levels in questionnaires usually rely on physical responses to physical activity. Therefore, we hypothesised that the validity of PAQs would be higher in the more physically fit group of participants. To test this, we conducted a validation study with 179 adults whom we divided into three fitness groups based on their cardiovascular fitness and age. Participants were measured for one week using the UKK RM42 accelerometer and self-reported their physical activity using IPAQ-SF, GPAQ, and EHIS-PAQ. We analysed the differences between fitness groups in terms of validity for each PAQ using ANOVA. We also performed an equivalence testing to compare the data obtained with the PAQs and the accelerometers. The results showed a significant trend toward higher validity for moderate to vigorous physical activity from the low to high fitness group as assessed by GPAQ and IPAQ-SF (low, intermediate and high fitness group: 0.06–0.21; 0.26–0.29; 0.40, respectively). The equivalence testing showed that all fitness groups overestimated their physical activity and underestimated their sedentary behaviour, with the high fitness group overestimating their physical activity the least. However, EHIS-PAQ was found to agree best with accelerometer data in assessing moderate to vigorous physical activity, regardless of fitness group, and had a validity greater than 0.4 for all fitness groups. In conclusion, we confirmed that when using PAQs describing physical responses to physical activity, participants’ fitness should be considered in the interpretation, especially when comparing results internationally.

Introduction

Physical inactivity significantly impairs health [1–3] and is increasingly becoming a burden in developed countries [3, 4]. The COVID -19 pandemic fostered this trend [5, 6] because movement restrictions were put in place to contain the spread of the virus. However, the long-term consequences of isolation and social distancing on behavioural patterns are unknown [7]. Indeed, a combination of movement behaviours across the day is very important for health outcomes because it predicts health risk better than a single behaviour [8, 9]. Therefore, the 24-hour movement behaviour paradigm, which combines three behaviours (physical activity, sleep, and sedentary behaviour) within 24 hours, is a hot research topic. A recent meta-analysis showed that shifting from undesirable physical activity behaviours (sedentary behaviour) to physical activity (PA) is associated with several health benefits such as lower body mass index (BMI) and mortality [10].

All three movement behaviours can be assessed with more objective measurements, like accelerometers, and subjective measurements, like questionnaires or diaries. Accelerometers are considered to be more valid measures of PA than self-reports [11]. However, accelerometers measures depend on movement of certain parts of body (e.g., hip, wrist) and their metrics (movement counts, bodily position etc.) [12], which could not detect all habitual movements. Consequently, validity between different types of accelerometers varies [13]. Because of feasibility of performing large scale studies and the above-mentioned characteristics of accelerometers, PA questionnaires (PAQs) are still an important part of PA research. They provide individuals perception of PA and in combination with accelerometers and other measurement devices provide richer data, needed for understanding of human behaviour [14]. However, they should be validated to obtain reliable and valid results. Despite described weaknesses, accelerometers are the best instruments to assess their validity, since they can measure habitual movement behaviour with movement sensor. Moreover, comparison of PAQs with doubly labelled water as a golden standard for measuring PA showed low correlations between the two methods [15, 16] and systematic bias in underestimation of energy expenditure [17]. Oposite, comparison of accelerometers with doubly labelled water demonstrate high correlations [18, 19].

The most commonly used PAQs in population-based studies are the International physical activity questionnaire-short form (IPAQ-SF) [20], the Global physical activity questionnaire (GPAQ) [21], and the PAQ from the European health interview survey (EHIS-PAQ) [22]. All three questionnaires assess PA and sedentary behaviour, but not sleep. The descriptions are provided to better understand the questions included in the PAQs and with the intention of distinguishing between PA of different intensities. The descriptions in IPAQ-SF and GPAQ rely on physical responses to PA and use explanations of heavy breathing and increased heartbeat to distinguish between moderate (MPA) and vigorous PA (VPA). For example, the GPAQ uses the following description of VPA: "Do you do any vigorous-intensity sports, fitness, or recreational (leisure) activities that cause a large increase in breathing or heart rate like [running or football], for at least 10 minutes continuously?" and MPA: "Do you do any moderate-intensity sports, fitness, or recreational (leisure) activities that cause a small increase in breathing or heart rate, such as brisk walking, [cycling, swimming, volleyball] for at least 10 minutes continuously?" Both descriptions are highly subjective, as participants may perceive the physical signs of PA differently. In addition, less fit participants perceive heavy breathing or increased heart rate at a lower PA intensity than fitter individuals. Inexperienced and inactive participants may not know what a sharp increase in heart rate or breathing is and may interpret even the slightest changes as VPA. Even everyday activities, such as climbing stairs, may elicit different physical responses in less and very fit participants and consequently lead to different responses to the same PA question. All this could lead to associating the measurement error of the PA questionnaires with the fitness level of a respondent. On the other hand, EHIS-PAQ is not based on the description of physiological responses, but focuses on the description of activities, e.g.: "In a typical week, on how many days do you carry out sports, fitness or recreational (leisure) activities for at least 10 minutes continuously?".

A study by Fogelholm and colleagues [23] found differences in self-reported PA and cardiorespiratory fitness between inactive (divided into two groups) and active participants (divided into three groups). Cardiorespiratory fitness increased from the least active group to the more active groups. They also reported an unusual phenomenon. The most physically active group (based on the health enhancing PA from the IPAQ), was the ’overreporters’ group; older participants with low physical fitness and more abdominal obesity who overreported their PA in the IPAQ, but had similar fitness levels to the lowest 20% by IPAQ grouping. Considering the differences in PA self-report, the validity of PAQs might differ between different groups of participants. There are few studies that have compared the validity of PAQs in different fitness groups, and these generally showed differences in criterion validity between fitness groups. In the Active Australia Survey (ActiGraph GT3X accelerometer was used as an objective measure of PA), lower criterion validity was found for moderate to vigorous PA (MVPA; Spearman ρ = 0.165 and Spearman ρ = 0.192) in participants with overweight and obesity compared to the healthy weight group (Spearman ρ = 0.361) [24]. Comparison of fit and unfit participants in the Energy Balance Study performed by SenseWear accelerometers [25] found that both fit and unfit participants overestimated VPA, but unfit participants overestimated their VPA by more than 600%, whereas fit participants overestimated their PA by less than 300%. Both groups underestimated sitting time, while fit participants underestimated MPA.

Because previous studies showed some differences in self-reported PA between differently fit individuals, we aimed to analyse this problem by comparing the criterion validity of the most commonly used adult PAQs for adults between groups of individuals with different fitness levels. We hypothesised that the validity of all PAQs used would be higher in groups of participants with higher fitness.

Materials and methods

Study design and participants

Participants in the study were gathered through 9 Slovenian primary schools using snowball sampling. Parents, grandparents, and adult siblings of 12- to 14-year-old students were invited to participate in the study. Only participants whose PA was not affected by a health condition were included. A kinesiologist reviewed the participants’ health status and decided whether they could participate in the study. Permission to conduct the study was granted by the Ethical Committee of the Faculty of Sport in Ljubljana in accordance with the Declaration of Helsinki (No: 6:2020–274). The data of the present study were obtained within the European project EUPASMOS No. 590662-EPP-1-2017-1- PT-SPO-SCP.

A total of 399 participants volunteered for the study (41% male, mean age = 41, SD = 14, mean BMI = 25, SD = 4). We excluded 220 individuals due to an incomplete study protocol (invalid questionnaire and/or accelerometer data; only participants who completed all three PAQs were included in the study) or missing physical fitness data, leaving 179 participants (42% male, mean age = 47, SD = 10; mean BMI = 25, SD = 5) included in the analysis. While the gender distribution of our sample was similar to the initial sample, excluded participants were approximately 10 years younger than those included in the analysis. More importantly, there were no differences in BMI or accelerometer-measured MVPA between these two groups. Participants attended the study for a full week. At the first visit, all participants were provided with accelerometers and familiarized with their use. They were instructed to wear the accelerometers for seven consecutive days (24 hours/day), except during water activities (e.g., swimming, showering, sauna visits). After seven days, all participants returned for the second visit. We collected their accelerometers and participants continued with anthropometric measurements (height, weight, waist circumference) and physical fitness testing. Participants then completed the three selected PAQs in a random order.

Subjective measures of PA

Three adult PAQs most commonly used in the European Union were used to assess PA: IPAQ-SF, GPAQ, and EHIS-PAQ. IPAQ-SF and GPAQ are standardised instruments and have been used for many years in different cultural settings [26, 27]. Both assess moderate and vigorous PA, transport PA (walking in IPAQ-SF) and sedentary behaviour. In addition, EHIS-PAQ item MV Aerobic Recreational Activity with additional walking and cycling was used as MVPA because EHIS-PAQ was not designed to measure total PA or MVPA [22]. The GPAQ is more detailed and includes separate questions for work PA and leisure time PA. EHIS-PAQ is part of the European Health Interview Survey and is used in all European Union Member States. The EHIS-PAQ does not measure the intensity of PA, but measures PA in areas relevant to public health, such as work, transport, and leisure domain [22]. All three questionnaires measure duration of PA and sedentary behaviour in minutes; with IPAQ-SF participants report their PA in the last week, while GPAQ and EHIS-PAQ ask about PA in a regular week. On all three PAQs, participants self-reported number of days in each activity and daily time spent in the activity. From that we calculated the weekly PA of the participants using the original scoring protocol. Sedentary behaviour (minutes per day) and moderate to vigorous recreational activity from EHIS-PAQ (minutes per week) were already self-reported in the units presented in the paper. Participants in our study completed the Slovenian versions of PAQs, that were translated using the forward-backward translation method [28]. Two independent translators interpreted the PAQs from English into Slovenian and two other independent translators back into English. Then, the two English versions were compared, and we decided on the best translation. The participants completed online form of the selected PAQs, all during the same visit in a randomised order. The reliability and validity of all three PAQs have already been tested, but mostly on English versions; IPAQ-SF and GPAQ have already been validated in many EU countries [26, 27], while the measurement characteristics of EHIS-PAQ have been tested, but not in all European Union countries [29].

Their reliability has been shown to be moderate to high (IPAQ-SF: Spearman’s ρ = 0.66 to 0.87 for PA and Spearman’s ρ = 0.50 to 0.95 for sedentary behaviour [27]; GPAQ: Spearman’s ρ = 0.67 to 0.73 for PA and Spearman’s ρ = 0.68 to 0.73 for sedentary behaviour [26]; EHIS-PAQ: ICC = 0.51 to 0.73 for PA [29]). Criterion validity of all three PAQs tested with the ActiGraph accelerometer was low for both PA (IPAQ-SF: Spearman’s ρ = 0.17 to 0.49 [30, 31]; GPAQ: Spearman’s ρ = 0.24 to 0.48 [32, 33]; EHIS-PAQ: Spearman’s ρ = 0.13 to 0.37 [29]) as well as for sedentary behaviour (IPAQ-SF: Pearson’s ρ = 0.16 to Spearman’s ρ = 0.28 [30, 34]; GPAQ: Spearman’s ρ = 0.19 to 0.42 [32, 33].

Objective measures of PA

PA was measured using an RM42 triaxial accelerometer (UKK Terveyspalvelut Oy, Tampere, Finland). The accelerometer was worn on the right hip during waking hours and on the nondominant wrist during sleeping hours. Acceleration data were acquired in a range of ± 16 G at a sampling rate of 100 Hz and stored on a hard disc for further analysis. The analysis of PA was based on the mean amplitude deviation (MAD) in six-second epochs [35]. MAD has been shown to be a valid indicator of incident oxygen consumption during locomotion [36]. For each epoch, MAD values were converted to METs (3.5 mL/kg/min oxygen consumption). The epoch-wise MET values were further smoothed by calculating an exponential moving average for each epoch time point [37]. The smoothed data were analysed in 6-s epochs and the PA cut points were set as follows: 3.0 METs ≤ MPA < 6.0 METs and VPA ≥ 6.0 METs.

Sedentary behaviour (sitting and lying) and standing were identified for epochs where in which the predicted MET value was less than 1.5. The orientation of the accelerometer with respect to the gravity vector was taken as the reference, and the angle for posture (APE) estimation was determined from the orientation of the accelerometer with respect to the reference vector [38]. Body posture was classified as standing if the angle for body posture was less than 11.6°, sitting if the angle for body posture was between 11.6° and 72.0°, and lying if the angle for body posture was greater than 72.0°. The epochal six-second values for posture were also smoothed by a one-minute exponential moving average.

A valid day was defined as one in which the monitor was worn for at least 600 minutes during awake time. Participants were required to wear the accelerometer for at least 4 valid days, one of which had to be a weekend day, to be included in the analyse.

Anthropometry and physical fitness

Height (to the nearest 0.1 cm) and weight (to the nearest 0.1 kg) were measured using a Seca 799 electronic scale (Seca Germany, Hamburg, Germany), waist circumference was measured with measuring tape to the nearest 0.1 cm midway between the lowest rib and the iliac crest. We calculated body mass index (BMI) from height and weight. Participants were barefoot and wore light sports clothing during measurements, they were asked to wear athletic footwear during the 6-minute walk test. Participants’ fitness level was determined using the 6-minute walk test [39], one of the most popular cardiorespiratory fitness tests for adults [40]. The test has been validated in healthy adult populations and can be used as valid test for assessing cardiorespiratory fitness [41, 42]. The test was performed in the school gym: A 30-m oval track was prepared for the participants. Cones were placed 5 meters apart to mark the track. Participants were familiarized with the test beforehand: they were first informed about the duration and aim of the test, and next the test protocol was demonstrated. Participants started the test at one of the cones and walked for 6 minutes. After each elapsed minute, they were informed how much time was left. After 6 minutes, they stopped, and the distance was measured to the nearest 1 m so that the number of full laps was counted and the remained distance from the starting point to finish was measured. Maximum of 4 participants performed the test at the same time. Each participant completed the test once.

Statistical analysis

Data analysis was performed using IBM SPSS 27 software (Armonk, NY: IBM Corp), Microsoft Excel, and Jamovi [43, 44]. Fitness groups were formed by first dividing female and male participants separately into 4 age groups (18–34.99, 35–49.99, 50–64.99, and > 65 years). Second, we divided participants in each age group into terciles based on their 6-minute walk test distance. The 6-minute walk distance in the low fitness group was 461–630 m for males and 360–640 m for females, in the intermediate fitness group 528–690 m for males and 525–690 m for females and in the high fitness group 660–870 m for males and 585–840 m for females. Normality of the data was tested with the Kolmogorov-Smirnov test. Differences between groups were calculated with the Kruskal-Wallis test for nonnormally distributed data and ANOVA for normally distributed data using the Bonferroni correction with the Kruskal-Wallis test. Criterion validity was assessed with Spearman correlation coefficients. Validity values were categorised as follows: ≤0.29 very low, 0.30–0.49 low, 0.50–0.69 moderate, 0.70–0.89 high, and above 0.90 very high validity [45]. In addition, equivalence testing was conducted to evaluate the agreement between each PAQ and the accelerometer in assessing the duration of MVPA and sedentary behaviour. The Confidence Interval Method [46, 47] was used to provide empirical evidence of equivalence between the selected measurements. Because the accelerometer data were used as a known reference value, we set bounds as raw values and defined them as ± 15% [46]. Therefore, equivalence bounds for sedentary behaviour were set at ± 78.5 min/day and ± 58 min/week for MVPA.

Results

Baseline characteristics of participants, stratified by fitness level, are shown in Table 1. There were no age differences among the three fitness groups, but the low fitness group had statistically higher mean BMI and waist circumference than the high fitness group. In addition, participants in the high fitness group reported more sedentary behaviour and less MPA and MVPA (except EHIS-PAQ moderate to vigorous recreational activity) compared to the other two groups. At the same time, UKK RM42 recorded the highest amount of MPA and MVPA in the high fitness group. Sedentary behaviour measured by accelerometer was similar in all fitness groups.

Table 1. Baseline characteristics of participants across three fitness groups (data shown are median and (interquartile range)).

	Fitness group
	Low fitness			Intermediate fitness			High fitness
	Male (N = 16)	Female (N = 36)	Total (N = 52)	Male (N = 24)	Female (N = 38)	Total (N = 62)	Male (N = 35)	Female (N = 30)	Total (N = 65)
Age (years)	43.0 (9.0)	44.0 (8.5)	43.0 (7.8)	44.5 (7.0)	44.0 (13.0)	44.0 (10.0)	45.0 (7.0)	43.0 (9.5)	44.0 (9.0)
BMI (kg/m²)	27.5 (5.7)	25.1 (10.0)	26.4 (9.0)	26.7 (4.8)	24.3 (5.8)	25.7 (6.0)	24.5 (4.7)	22.5 (3.3)	23.4 (5.3)
Waist circumference (cm)	103.0 (20.7)	89.5 (24.5)	93.1 (23.0)	97.0 (14.0)	84.0 (19.1)	89.5 (17.6)	90.0 (13.4)	80.5 (13.5)	85.0 (15.2)
6-minute walk test (distance in m)	547.5 (110.0)	577.5 (90.0)	570 (90.0)	660.0 (43.0)	660.0 (49.0)	660 (49.0)	750.0 (80.0)	727.5 (86.0)	750.0 (90.0)
Sedentary behaviour
RM42(min/day)	500.7 (137.8)	504.1 (179.0)	502.8 (168.6)	574.1 (207.7)	501.7 (141.5)	514.2 (145.9)	534.1 (152.8)	480.7 (150.8)	496.0 (150.2)
IPAQ-SF (min/day)	245.0 (142.5)	450.0 (318.8)	360.0 (281.3)	287.5 (450.0)	345.0 (270.0)	317.5 (312.5)	360.0 (240.0)	420.0 (270.0)	390.0 (240.0)
GPAQ (min/day)	305.0 (270.0)	480.0 (285.0)	335.0 (292.5)	240.0 (270.0)	285.0 (337.5)	270.0 (300.0)	480.0 (360.0)	360.0 (277.5)	420.0 (300.0)
EHIS-PAQ (min/day)	360.0 (330.0)	420.0 (330.0)	420.0 (330.0)	300.0 (360.0)	360.0 (360.0)	300.0 (360.0)	4200.0 (240.0)	360.0 (360.0)	420.0 (360.0)
Moderate physical activity
RM42 (min/week)	357.2 (349.6)	303.8 (190.5)	317.6 (199.5)	424.1 (296.6)	292.9 (281.3)	331.5 (295.6)	383.6 (215.5)	290.7 (185.7)	332.5 (217.4)
IPAQ-SF (min/week)	457.5 (1522.0)	545.0 (1301.3)	530.0 (1428.8)	350.0 (690.0)	420.0 (1022.5)	400.0 (780.0)	370.0 (890.0)	240.0 (415.0)	315.0 (515.0)
GPAQ (min/week)	765.0 (1603.3)	810.0 (978.8)	810.0 (1353.8)	770.0 (2004.8)	585.0 (1080.0)	600.0 (1307.5)	465.0 (485.0)	210.0 (765.0)	345.0 (660.0)
Vigorous physical activity
RM42 (min/week)	4.3 (41.0)	0.2 (12.6)	0.8 (23.6)	9.5 (41.2)	5.2 (34.9)	6.9 (36.1)	19.6 (37.2)	10.2 (37.9)	13.3 (38.1)
IPAQ-SF (min/week)	250.0 (277.5)	200.0 (390.0)	240.0 (322.5)	210.0 (446.3)	180.0 (310.0)	180.0 (330.0)	270.0 (342.3)	180.0 (210.0)	195.0 (300.0)
GPAQ (min/week)	360.0 (490.0)	240.0 (337.5)	270.0 (405.0)	225.0 (390.0)	242.5 (345.0)	240.0 (360.0)	270.0 (607.5	255.0 (247.5)	270.0 (382.5)
Moderate to vigorous physical activity
RM42 (min/week)	357.2 (378.8)	318.1 (232.1)	324.3 (243.1)	430.1 (306.9)	355.2 (291.8)	390.8 (317.6)	418.3 (269.4)	316.6 (232.3)	377.6 (240.6)
IPAQ-SF (min/week)	757.5 (1665.0)	795.0 (1413.8)	795.0 (1582.5)	485.0 (810.0)	600.0 (967.0)	560.0 (937.5)	510.0 (1330.0)	420.0 (647.5)	480.0 (907.5)
GPAQ (min/week)	1260.0 (2081.0)	930.0 (1462.5)	960.0 (1620.0)	882.5 (2515.0)	810.0 (960.0)	810.0 (1562.5)	607.5 (1093.8)	460.0 (987.5)	562.5 (877.5)
EHIS-PAQ(min/week)	310.0 (810.0)	297.5 (301.3)	297.5 (307.3)	280.0 (533.8)	220.0 (445.0)	280.0 (442.5)	355.0 (367.5)	310.0 (220.0)	327.5 (291.3)

Open in a new tab

RM42, accelerometer RM42; IPAQ-SF, International physical activity questionnaire-short form; GPAQ, Global physical activity questionnaire; EHIS-PAQ, European health interview survey–physical activity questionnaire.

To compare criterion validity between fitness groups, we calculated Spearman’s correlation coefficients for each PAQ (Table 2). We found statistically significant correlations for sedentary behaviour in all three fitness groups and for all PAQs. Nevertheless, criterion validity was low to moderate for all PAQs and in all fitness groups. Validity results for the GPAQ were similar in all fitness groups, while the intermediate fitness group showed higher validity results for the IPAQ-SF and EHIS-PAQ.

Table 2. Criterion validity between RM42 accelerometer and IPAQ-SF, GPAQ and EHIS-PAQ for three fitness groups.

			SB			MPA		VPA		MVPA
			IPAQ-SF	GPAQ	EHIS-PAQ	IPAQ-SF	GPAQ	IPAQ-SF	GPAQ	IPAQ-SF	GPAQ	EHIS-PAQ
Accelerometer	SB	High fitness	.456^***	.410^***	.359^**
		Intermediate fitness	.524^***	.432^***	.601^***
		Low fitness	.404^**	.388^**	.317^*
		Total	.468^***	.414^***	.415^***
	MPA	High fitness				.340^*	.459^***
		Intermediate fitness				.253	.357^*
		Low fitness				.205	.080
		Total				.253^**	.292^***
	VPA	High fitness						.049	.059
		Intermediate fitness						-.094	.272
		Low fitness						.081	.192
		Total						.047	.167
	MVPA	High fitness								.396^***	.399^***	.407^***
		Intermediate fitness								.264^*	.289^*	.485^***
		Low fitness								.206	.063	.491^***
		Total								.267^***	.237^**	.446^***

Open in a new tab

* p ≤ 0.01

** ≤ 0.005

*** p ≤ 0.001

MPA, moderate physical activity; VPA, vigorous physical activity; MVPA, moderate to vigorous physical activity; SB, sedentary behaviour; IPAQ-SF, International physical activity questionnaire-short form; GPAQ, Global physical activity questionnaire; EHIS-PAQ, European health interview survey–physical activity questionnaire

For MVPA, validity was lower in the low fitness group for IPAQ-SF and especially for GPAQ. However, for EHIS-PAQ, validity was slightly lower in the high fitness group than in the intermediate and low fitness group. The validity of MPA showed similar patterns in all groups, while the validity of VPA was very low in all fitness groups and showed no statistically significant correlations.

To assess the agreement between self-reported and accelerometer-measured PA and sedentary behaviour in the three fitness groups, we created Bland-Altman plots for each PAQ and fitness group (Figs 1 and 2). There were differences in PA and sedentary behaviour duration between accelerometer and PAQs in all fitness groups. The duration of self-reported PA was longer compared to accelerometer and sedentary behaviour duration was shorter when using PAQs. The differences between the accelerometer and PAQs for sedentary behaviour were smallest for the high fitness group for IPAQ-SF and GPAQ, while for EHIS-PAQ the smallest differences were found in the low fitness group. The difference in duration of sedentary behaviour was largest for participants from intermediate fitness group for all three PAQs. The differences in MPA and MVPA duration between PAQs and accelerometer UKK RM42 were the lowest in high fitness group. These results were also influenced by outliers in all three groups, as shown in Fig 2. The limits of agreement differed between groups; the limits were tightest for the high fitness group for PA and sedentary behaviour, suggesting that the high fitness group’s results were most equivalent to the accelerometer. On the other hand, the limits of agreement were greatest in the intermediate fitness group, where the bias between the two measurements was also greatest, especially for the PA. There were quite a few outliers in the high and intermediate fitness group for IPAQ-SF and GPAQ. The outliers show a substantial difference between the accelerometer and PAQ in a few individuals.

Fig 1 — LF, Low fitness group; IF, Intermediate fitness group; HF, high fitness group; SB, sedentary behaviour.

Fig 2 — LF, Low fitness group, IF, Intermediate fitness group, HF, high fitness group, MVPA, moderate to vigorous physical activity.

The results of the equivalence testing are shown in Figs 3 and 4. In the equivalence testing Two One-Sided Tests were used; we performed two paired-samples T-tests: for sedentary behaviour and for MVPA. The differences between the accelerometer UKK RM 42 and the PAQs were statistically significant for sedentary behaviour (IPAQ-SF: t(171) = -12.6, p < 0.001; GPAQ: t(178) = -11.6, p < 0.001; EHIS-PAQ: t(177) = -10.0, p < 0.001) in all fitness groups at the p < 0.001 level. Results for MVPA were statistically significant for IPAQ-SF and GPAQ (IPAQ-SF: t(175) = 7.48, p < 0.001; GPAQ: t(162) = 7.54, p < 0.001; EHIS-PAQ: t(172) = 0.416, p = 0.678). Differences were significant at the p < 0.001 level for IPAQ-SF and GPAQ in all fitness groups. There were no statistically significant results for MVPA measured with EHIS-PAQ (high fitness group: p = 0.901, intermediate fitness group: p = 0.313, low fitness group: p = 0.109). MVPA measured by EHIS-PAQ was the only value where the results showed equivalence between accelerometer and PAQ, especially for the high fitness group. The results of IPAQ-SF and GPAQ were not within the equivalence bounds for PA (dotted line), but the results of the high fitness group were closest to the equivalence bounds.

Fig 3 — Black points represent all data for each PAQ, from bottom to top grey dots represent high, intermediate and low fitness group for each of the PAQs. LF, Low fitness group, IF, Intermediate fitness group, HF, high fitness group.

Fig 4 — Black points represent all data for each PAQ, from bottom to top grey dots represent high, intermediate and low fitness group for each of the PAQs. LF, Low fitness group, IF, Intermediate fitness group, HF, high fitness group.

Discussion

In the present study, we compared the criterion validity of the Slovenian versions of three PAQs popular in Europe (IPAQ-SF, GPAQ and EHIS-PAQ) between differently fit participants. Results showed that self-reported movement behaviour assessed with IPAQ-SF and GPAQ is more comparable to the accelerometer UKK RM42 results for PA and sedentary behaviour in fitter individuals. The same trend was found for EHIS-PAQ, where questions on PA are based on activity descriptions. However, the differences between fitness groups for PA were not significant. In addition, EHIS-PAQ proved to be the most equivalent to the accelerometer assessment of PA among all selected PAQs.

The self-reported PA in our sample was slightly higher compared with other studies in European countries [29, 48]. However, none of the participants included in the study exceeded the maximum daily or weekly value of PA [20, 49]. We hypothesise that Slovenian participants may be more active compared with some other European countries, but similar or higher PA values have been reported in some prior studies. In Hungary, participants reported similar VPA levels when using IPAQ-SF (180 min/week) [50]. In a Lithuanian study, more MPA and MVPA measured with IPAQ-SF was reported (MPA = 490 min/week; MVPA = 600 min/week) [34]. Riviere and colleagues [33] reported higher self-reported PA in the French sample for IPAQ (MPA = 750 min/week, VPA = 880 min/week) and GPAQ (MPA = 900 min/week, VPA = 900 min/week). Difference in self-reported PA between differently fit individuals was previously reported in adolescents, with low-fit participants also over-reporting PA more [51]. Over-reporting of PA and under-reporting of sedentary behaviour is typically present when using PAQs [52, 53], but only one previous study has shown how over-reporting may differ between different adult fitness groups [23]. This is in line with our findings-the high fitness group overreported MVPA and MPA the least, whereas the low fitness group overreported it the most, compared to the accelerometer results. At the same time, underreporting of sedentary behaviour was lowest in the high fitness group and highest in intermediate fitness group, except at EHIS-PAQ, where the low fitness group underreported the least, when comparing results to accelerometer.

Validity of sedentary behaviour was similar between fitness groups and highest in the intermediate fitness group (Spearman’s ρ = 0.432–0.601), although the differences in sedentary behaviour duration were greatest in this group (1111–1415 min/week). The exception to this is the results from EHIS-PAQ, where the data and analysis of this behaviour are moderately different because the data are not reported in exact hours and minutes (as in IPAQ-SF and GPAQ), but rather participants choose from the options offered (e.g., less than 4 hours, 4 to 6 hours, etc.). Overall results for sedentary behaviour validity were higher than European data from the recent meta-analysis of sedentary behaviour questions (weighted mean for criterion validity = 0.23) [54].

The main finding supporting our hypothesis is that the agreement between the PAQs and the accelerometer recordings of the self-reported MPA and MVPA values of IPAQ-SF and GPAQ decrease from the high to the low fitness group (IPAQ-SF MVPA: high fitness group = 0.40, intermediate fitness group = 0.26, low fitness group = 0.21; GPAQ MVPA: high fitness group = 0.40, intermediate fitness group = 0.29, low fitness group = 0.06). In addition, overreporting of MVPA and underreporting of sedentary behaviour compared to accelerometer results were lowest in the high fitness group. In contrast, Shook and colleagues [25] reported higher criterion validity (against Sense Wear Armband) of the IPAQ in unfit participants. Validity was higher for MPA (fit = 0.11, unfit = 0.26) and MVPA (fit = 0.16, unfit = 0.3). In addition, some difference in validity was found for MPA and MVPA compared with other studies. In our study, the validity coefficients were low to very low, however, the results in the high fitness group (MPA: IPAQ-SF = 0.34, GPAQ = 0.46; MVPA: IPAQ-SF = 0.4, GPAQ = 0.4) were among the highest compared to a recent meta-analysis [55]. On the other hand, validity results for the low fitness group were among the lowest reported validity results for IPAQ-SF and GPAQ. For all three PAQs, the agreement between accelerometer results and PAQs self-reported for VPA was very low in all fitness groups in the present study. There were no correlations between self-reported VPA and accelerometer-measured VPA, even in the high fitness group. Since the difference between accelerometer based VPA and self-reported VPA was the biggest in all groups (more than 80% of overreporting), this could influence the poor validity result. In addition, the validity results of VPA were among the lowest reported [55]. The low validity results between accelerometer UKK RM42 and PAQs could be a result of different constructs measured with each of the methods. Accelerometers tend to poorly measure some bipedal activities, such as cycling or skiing, but participants can self-report all those activities with PAQs [11]. Nonetheless, PAQs are subjective measures and depend primarily on individuals retrospective reporting of movement behaviours, specifically participants are least precise when self-reporting sedentary behaviour, where differences between subjective and objective measures are large [56, 57].

The better validity of the fitter participants when using IPAQ-SF and GPAQ can be explained by the assessment items used to determine PA different intensities in these two instruments. Indeed, the descriptions are based on physical responses to PA (e.g., heavy breathing, increase in heart rate), which are highly subjective and depend primarily on the cardiorespiratory fitness of the individual. Therefore, it is not surprising that more active participants have higher cardiorespiratory fitness and therefore more accurately estimate their PA. Thus, equivalence testing on these two instruments showed statistically significant differences between PAQs and accelerometer assessments for PA and sedentary behaviour. Nevertheless, the results again showed differences between fitness groups, as the high fitness group came closest to the equivalence bounds, but participants in all groups underreported time spent sitting. Similar was found for MVPA, where the difference for IPAQ-SF and GPAQ was significant, and the results were not within the equivalence bounds.

EHIS-PAQ performed the best on the equivalence testing regardless of fitness group. However, although there were no significant differences in validity between the fitness groups, we can notice a trend. The results of the high fitness group were most equivalent to the UKK 42 accelerometer, while the intermediate fitness group tended to underreport and the low fitness group tended to overreport, but the main result was still within the equivalence bound. Since EHIS-PAQ does not measure total PA or PA by intensity, it still gave us the best validity results and the best equivalence compared to the accelerometer. Considering that EHIS-PAQ was developed as a part of the European Health Interview Survey, the design of the questionnaire is different than in other two used PAQs: the intensity of PA was intentionally excluded because participants had a difficulty distinguishing between different intensities of PA [22]. This could be the explanation why we did not find differences between fitness groups when using EHIS-PAQ. The questionnaire also includes recreational activities that are not included in other PAQs and are primarily health enhancing type of PA [58].

Strengths and limitations

This is one of the first studies to compare the differences between differently fit individuals in terms of their subjective and self-reported PA However, the study has some limitations. First, the study sample was not representative. Because we formed the fitness groups by dividing the participants into terciles, a possible bias in the fitness level of the participants (e.g., participants who were fitter than average) could affect the results of the study. Second, the accelerometer results are dependent on the body placement and metrics used; thus they have limitations assessing some movement behaviours (e.g., swimming, cycling, jumping on trampoline). This should be considered when interpreting results of our study, however validity of accelerometers is still much higher than in PAQs compered to doubly labelled water as a golden standard [13, 59]. Third, field fitness test, i.e., 6 minutes of walking, only assessing and not objectively measure cardiorespiratory fitness was used to determine fitness level. However, this is the popular test in patients and older adults [60] with several advantages: it is simple and can be performed indoors, no equipment is required, and it is not intimidating to participants [41]. Fourth, the MAD algorithm used to analyse accelerometer data in the present study has been validated for bipedal [36]. This could affect the intensity of activities, such as cycling, which may be underestimated, and consequently the volume of VPA measured by the accelerometer may be underestimated. However, similar problems with the measurement of VPA have been highlighted in other studies that used other algorithms for accelerometer data [61, 62].

Conclusions

The present study showed differences in self-reporting PA between differently fit individuals. The differences in validity of the PAQs among differently fit individuals highlight another dilemma of PAQs. It shows the importance of validating PAQs, not only between nations and cultures, but also between differently fit individuals. Even though self-report PA by intensity is common in PAQs, our results showed that this type of question is not the most appropriate for all fitness groups. EHIS-PAQ, which does not include PA intensities, performed the best in validity and equivalence testing regardless of fitness group and is therefore the most appropriate PAQ for measuring PA without knowing the fitness level of participants. We believe that future research is needed and would like to emphasise the importance of critically evaluating data collected with PAQs. One contextual piece of information for interpreting PAQ results that can be collected in epidemiological studies and surveillance could be body mass index as a proxy for participants’ physical fitness.

Supporting information

S1 Data. Accelerometer and PAQs dataset.

(SAV)

Click here for additional data file.^{(28KB, sav)}

Acknowledgments

The authors thank the UKK Institute for providing the RM42 accelerometers and for help with data processing. Special thanks to Dr. Saša Đurić for his assistance in data collection, to Antonio Martinko for his advice in the analysis of the equivalence testing analysis and to all school coordinators for their help in organizing the measurements.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This research was co-funded by the Erasmus+ Programme of the European Union within the project EUPASMOS No. 590662-EPP-1-2017-1-PT-SPO-SCP, and Slovenian Research Agency within the Research programme Bio-psycho-social context of kinesiology No. P5-0142. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.González K, Fuentes J, Márquez JL. Physical inactivity, sedentary behavior and chronic diseases. Korean J Fam Med. 2017;38: 111–115. doi: 10.4082/kjfm.2017.38.3.111 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Hamer M O’Donovan G Murphy M. Physical inactivity and the economic and health burdens due to cardiovascular disease: exercise as medicine. Exercise for Cardiovascular Disease Prevention and Treatment. Springer; 2017. pp. 3–18. [DOI] [PubMed] [Google Scholar]
3.Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet. 2012;380: 219–229. doi: 10.1016/S0140-6736(12)61031-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388: 1311–1324. doi: 10.1016/S0140-6736(16)30383-X [DOI] [PubMed] [Google Scholar]
5.Castañeda-Babarro A, Arbillaga-Etxarri A, Gutiérrez-Santamaría B, Coca A. Physical activity change during COVID-19 confinement. Int J Environ Res Public Health. 2020;17: 6878. doi: 10.3390/ijerph17186878 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Tison GH, Avram R, Kuhar P, Abreau S, Marcus GM, Pletcher MJ, et al. Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173: 767–770. doi: 10.7326/M20-2665 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Hall G, Laddu DR, Phillips SA, Lavie CJ, Arena R. A tale of two pandemics: How will COVID-19 and global trends in physical inactivity and sedentary behavior affect one another? Prog Cardiovasc Dis. 2020. doi: 10.1016/j.pcad.2020.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Prochaska JO. Multiple health behavior research represents the future of preventive medicine. Prev Med (Baltim). 2008;46: 281–285. doi: 10.1016/j.ypmed.2008.01.015 [DOI] [PubMed] [Google Scholar]
9.Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Heal Sci. 2020. doi: 10.1016/j.jshs.2020.07.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15: 1–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5: 56. doi: 10.1186/1479-5868-5-56 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Silfee VJ, Haughton CF, Jake-Schoffman DE, Lopez-Cepero A, May CN, Sreedhara M, et al. Objective measurement of physical activity outcomes in lifestyle interventions among adults: A systematic review. Prev Med reports. 2018;11: 74–80. doi: 10.1016/j.pmedr.2018.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. 2013;14: 451–462. doi: 10.1111/obr.12021 [DOI] [PubMed] [Google Scholar]
14.Sattler MC, Ainsworth BE, Andersen LB, Foster C, Hagströmer M, Jaunig J, et al. Physical activity self-reports: past or future? Br J Sports Med. 2021. doi: 10.1136/bjsports-2020-103595 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Neilson HK, Robson PJ, Friedenreich CM, Csizmadi I. Estimating activity energy expenditure: how valid are physical activity questionnaires? Am J Clin Nutr. 2008;87: 279–291. doi: 10.1093/ajcn/87.2.279 [DOI] [PubMed] [Google Scholar]
16.Sharifzadeh M, Bagheri M, Speakman JR, Djafarian K. Comparison of total and activity energy expenditure estimates from physical activity questionnaires and doubly labelled water: a systematic review and meta-analysis. Br J Nutr. 2020/07/28. 2021;125: 983–997. doi: 10.1017/S0007114520003049 [DOI] [PubMed] [Google Scholar]
17.Maddison R, Ni Mhurchu C, Jiang Y, Vander Hoorn S, Rodgers A, Lawes CMM, et al. International Physical Activity Questionnaire (IPAQ) and New Zealand Physical Activity Questionnaire (NZPAQ): A doubly labelled water validation. Int J Behav Nutr Phys Act. 2007;4: 62. doi: 10.1186/1479-5868-4-62 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Chomistek AK, Yuan C, Matthews CE, Troiano RP, Bowles HR, Rood J, et al. Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water. Med Sci Sport Exerc. 2017;49. Available: https://journals.lww.com/acsm-msse/Fulltext/2017/09000/Physical_Activity_Assessment_with_the_ActiGraph.21.aspx doi: 10.1249/MSS.0000000000001299 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity. 2007;15: 2371–2379. doi: 10.1038/oby.2007.281 [DOI] [PubMed] [Google Scholar]
20.IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. http//wwwipaqkise/scoringpdf. 2005.
21.Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). J Public Health (Bangkok). 2006;14: 66–70. doi: 10.1007/s10389-006-0024-x [DOI] [Google Scholar]
22.Finger JD, Tafforeau J, Gisle L, Oja L, Ziese T, Thelen J, et al. Development of the European health interview survey-physical activity questionnaire (EHIS-PAQ) to monitor physical activity in the European Union. Arch Public Heal. 2015;73: 59. doi: 10.1186/s13690-015-0110-z [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Fogelholm M, Malmberg J, Suni J, Santtila M, Kyrolainen H, Mantysaari M, et al. International physical activity questionnaire: validity against fitness. Med Sci Sports Exerc. 2006;38: 753. doi: 10.1249/01.mss.0000194075.16960.20 [DOI] [PubMed] [Google Scholar]
24.Vandelanotte C, Duncan MJ, Stanton R, Rosenkranz RR, Caperchione CM, Rebar AL, et al. Validity and responsiveness to change of the Active Australia Survey according to gender, age, BMI, education, and physical activity level and awareness. BMC Public Health. 2019;19: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Shook RP, Gribben NC, Hand GA, Paluch AE, Welk GJ, Jakicic JM, et al. Subjective estimation of physical activity using the international physical activity questionnaire varies by fitness level. J Phys Act Heal. 2016;13: 79–86. doi: 10.1123/jpah.2014-0543 [DOI] [PubMed] [Google Scholar]
26.Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Heal. 2009;6: 790–804. doi: 10.1123/jpah.6.6.790 [DOI] [PubMed] [Google Scholar]
27.Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exerc. 2003;35: 1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]
28.World Health Organization. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance. Geneva: World Health Organization; 2005. [Google Scholar]
29.Baumeister SE, Ricci C, Kohler S, Fischer B, Töpfer C, Finger JD, et al. Physical activity surveillance in the European Union: reliability and validity of the European health interview survey-physical activity questionnaire (EHIS-PAQ). Int J Behav Nutr Phys Act. 2016;13: 61. doi: 10.1186/s12966-016-0386-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006;9: 258–265. doi: 10.1079/phn2005840 [DOI] [PubMed] [Google Scholar]
31.Rodríguez-Muńoz S, Corella C, Abarca-Sos A, Zaragoza J. Validation of three short physical activity questionnaires with accelerometers among university students in Spain. J Sports Med Phys Fitness. 2017;57: 1660. doi: 10.23736/S0022-4707.17.06665-8 [DOI] [PubMed] [Google Scholar]
32.Cleland CL, Hunter RF, Kee F, Cupples ME, Sallis JF, Tully MA. Validity of the global physical activity questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health. 2014;14: 1255. doi: 10.1186/1471-2458-14-1255 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Rivière F, Widad FZ, Speyer E, Erpelding M-L, Escalon H, Vuillemin A. Reliability and validity of the French version of the global physical activity questionnaire. J Sport Heal Sci. 2018;7: 339–345. doi: 10.1016/j.jshs.2016.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Kalvenas A, Burlacu I, Abu-Omar K. Reliability and validity of the International Physical Activity Questionnaire in Lithuania. Balt J Heal Phys Act. 2016;8: 29–41. doi: 10.29359/BJHPA.08.2.03 [DOI] [Google Scholar]
35.Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila A-M, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7. doi: 10.1186/s13102-015-0010-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, et al. Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD). PLoS One. 2015;10: e0134813. doi: 10.1371/journal.pone.0134813 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Vähä-Ypyä H, Sievänen H, Husu P, Tokola K, Vasankari T. Intensity Paradox—Low-Fit People Are Physically Most Active in Terms of Their Fitness. Sensors. 2021;21: 2063. doi: 10.3390/s21062063 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Vähä‐Ypyä H, Husu P, Suni J, Vasankari T, Sievänen H. Reliable recognition of lying, sitting, and standing with a hip‐worn accelerometer. Scand J Med Sci Sports. 2018;28: 1092–1102. doi: 10.1111/sms.13017 [DOI] [PubMed] [Google Scholar]
39.Ross R, Blair SN, Arena R, Church TS, Després JP, Franklin BA, et al. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement from the American Heart Association. Circulation. 2016;134: 653–699. doi: 10.1161/CIR.0000000000000461 [DOI] [PubMed] [Google Scholar]
40.Lang JJ, Phillips EW, Orpana HM, Tremblay MS, Ross R, Ortega FB, et al. Field-based measurement of cardiorespiratory fitness to evaluate physical activity interventions. Bull World Health Organ. 2018;96: 794–796. doi: 10.2471/BLT.18.213728 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Burr JF, Bredin SSD, Faktor MD, Warburton DER. The 6-Minute Walk Test as a Predictor of Objectively Measured Aerobic Fitness in Healthy Working-Aged Adults. Phys Sportsmed. 2011;39: 133–139. doi: 10.3810/psm.2011.05.1904 [DOI] [PubMed] [Google Scholar]
42.Mänttäri A, Suni J, Sievänen H, Husu P, Vähä-Ypyä H, Valkeinen H, et al. Six-minute walk test: a tool for predicting maximal aerobic power (VO2 max) in healthy adults. Clin Physiol Funct Imaging. 2018;38: 1038–1045. 10.1111/cpf.12525 [DOI] [PubMed] [Google Scholar]
43.The jamovi project. jamovi. 2021. Available: https://www.jamovi.org/ [Google Scholar]
44.R Core Team. R: A Language and environment for statistical computing. 2021. Available: https://cran.r-project.org/ [Google Scholar]
45.Evans JD. Straightforward statistics for the behavioral sciences. Pacific Grove, CA: Thomson Brooks/Cole Publishing Co; 1996. [Google Scholar]
46.Dixon PM, Saint-Maurice PF, Kim Y, Hibbing P, Bai Y, Welk GJ. A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement. Med Sci Sport Exerc. 2018;50: 837–845. doi: 10.1249/MSS.0000000000001481 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Lakens D. Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Soc Psychol Personal Sci. 2017;8: 355–362. doi: 10.1177/1948550617697177 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Charles M, Thivel D, Verney J, Isacco L, Husu P, Vähä-Ypyä H, et al. Reliability and validity of the ONAPS physical activity questionnaire in assessing physical activity and sedentary behavior in French adults. Int J Environ Res Public Health. 2021;18: 5643. doi: 10.3390/ijerph18115643 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. Geneva: World Health Organisation; p. 23. [Google Scholar]
50.Ács P, Veress R, Rocha P, Dóczi T, Raposa BL, Baumann P, et al. Criterion validity and reliability of the International Physical Activity Questionnaire–Hungarian short form against the RM42 accelerometer. BMC Public Health. 2021;21: 1–10. doi: 10.1186/s12889-021-10372-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Premelč J, Meh K, Vähä-Ypyä H, Sember V, Jurak G. Do Fitter Children Better Assess Their Physical Activity with Questionnaire Than Less Fit Children? International Journal of Environmental Research and Public Health. 2022. doi: 10.3390/ijerph19031304 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int J Behav Nutr Phys Act. 2011;8: 115. doi: 10.1186/1479-5868-8-115 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Rzewnicki R, Vanden Auweele Y, De Bourdeaudhuij I. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutr. 2003;6: 299–305. doi: 10.1079/PHN2002427 [DOI] [PubMed] [Google Scholar]
54.Meh K, Jurak G, Sorić M, Rocha P, Sember V. Validity and Reliability of IPAQ-SF and GPAQ for Assessing Sedentary Behaviour in Adults in the European Union: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2021. doi: 10.3390/ijerph18094602 [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Sember V, Meh K, Sorić M, Starc G, Rocha P, Jurak G. Validity and Reliability of International Physical Activity Questionnaires for Adults across EU Countries: Systematic Review and Meta Analysis. Int J Environ Res Public Health. 2020;17: 7161. doi: 10.3390/ijerph17197161 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Prince SA, Cardilli L, Reed JL, Saunders TJ, Kite C, Douillette K, et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17. doi: doi.org/10.1186/s12966-020-00938-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Meh K, Sember V, Đurić S, Vähä-Ypyä H, Rocha P, Jurak G. Reliability and Validity of Slovenian Versions of IPAQ-SF, GPAQ and EHIS-PAQ for Assessing Physical Activity and Sedentarism of Adults. Int J Environ Res Public Health. 2022;19: 430. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Howley ET. Type of activity: resistance, aerobic and leisure versus occupational physical activity. Med Sci Sports Exerc. 2001;33: S364–9; discussion S419-20. doi: 10.1097/00005768-200106001-00005 [DOI] [PubMed] [Google Scholar]
59.Hills AP, Mokhtar N, Byrne NM. Assessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures. Frontiers in Nutrition. 2014. Available: https://www.frontiersin.org/10.3389/fnut.2014.00005 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Enright PL. The six-minute walk test. Respir Care. 2003;48: 783–785. [PubMed] [Google Scholar]
61.Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc. 2000;32: S442–9. doi: 10.1097/00005768-200009001-00002 [DOI] [PubMed] [Google Scholar]
62.Welk GJ, Blair SN, Wood K, Jones S, Thompson RW. A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc. 2000;32: S489–97. doi: 10.1097/00005768-200009001-00008 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0285357.r001

Decision Letter 0

Giulia Squillacioti

22 Feb 2023

PONE-D-22-33819The dilemma of physical activity questionnaires: fitter people are less prone to over reportingPLOS ONE

Dear Dr. Meh,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

I recommend following the reviewers' comments that are concordat in suggesting major revisions. The manuscript is interesting and I warmly recommend providing a reviewed version for further consideration in PLOS ONE.

Please submit your revised manuscript by Apr 08 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Giulia Squillacioti

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Thank you for stating the following in the Competing Interests section:

“I have read the journal's policy and the authors of this manuscript have the following competing interests: Author Henri Vähä-Ypyä is employee of the UKK Institute which is the main owner of company UKK tervepalvelut that developed RM42 accelerometers.”

Please confirm that this does not alter your adherence to all PLOS ONE policies on sharing data and materials, by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests). If there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

3. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: OVERALL

This article reads well, the presentation is easy to follow, and the data analysis is excellent. Creating three fitness groups to compare PAQ results and accelerometer counts was novel and may help researchers understand PA and movement data by fitness levels.

This reviewer is concerned about how the authors compare the PAQs and the accelerometer results. The two measure different constructs, and it is incorrect to assume the accelerometer is the ‘gold standard’ for establishing movement levels. One cannot assume that the self-report of one’s PA on a PAQ is under-estimated or over-estimated based on accelerometer counts. Studies show the accelerometer depends on movement counts to identify changes in body positions. Many body positions and types of movement are not registered as counts on an accelerometer (e.g., bicycling, economical running, cross-country skiing, skating, skateboarding) but are performed vigorously or at moderate intensities. Perhaps it is better to rephrase the correlations in this article as showing agreement between the PAQ and accelerometer responses and not under-reporting or over-reporting the responses. There is less value judgment of the PAQ responses.

Also, an interesting question is how well the three PAQs correlate for the sedentary, moderate, vigorous, and MVPA responses. This information would inform researchers engaged in surveillance and research about the consistency of results between populations.

SPECIFIC COMMENTS

Line 41-100. The reader needs to understand that accelerometers measure objective movement intensity and PAQ measure subjective recall of PA intensities. The two measures are different constructs, and their differences have been discussed in the literature. Please note the differences in the introduction, so the reader interprets the data with differences between the measures in mind.

Line 102-110. Why did you target adults of 12-14-year-old adolescents to enroll in the study? Can the authors state why this sampling approach was used?

Line 114-116. The meaning of this sentence needs to be clarified.

Line 125. Please identify the language in the PAQ’s were administered. Are there metrics that specify the translation process?

125-135. Please describe the units the questionnaires ask for each item and how each questionnaire was scored in this section or as an appendix to the paper. The reader needs to know the units that the PA is presented and how items are scored. For example, the IPAQ-SF scoring instructions yield scores in MET-min, yet Table 1 presents the scores in min/week. Clarifications are needed to explain how the authors modified the PAQ’s scoring protocols.

Line 171-176. Determination of the participants’ fitness levels is important for this study’s internal validity. More information is needed.

1. Please identify the validity and reliability of the 6-minute walking test and the type of populations the validity and reliability were determined.

2. How was the test scored to identify the fitness groups? Terciles based on the distance covered?

3. What clothing did the participants wear while completing the fitness test?

4. How was the distance covered in the walking test measured?

5. How many participants were measured in the fitness test simultaneously? One at a time or more than one?

6. How many times did each participant complete the test? Once or more than once to familiarize themselves with the test?

Line 181. Please identify the cut points for the distance of the three fitness groups in a parenthesis.

Table 1. This is a very detailed table with much information. However, much of the data for the PAQs are easier to compare if the authors describe how each questionnaire is scored.

Table 2. The presentation for this table is very nice.

Discussion. The discussion is very long and seems repetitive. It should be shortened and focused on explaining plausible reasons for the results.

Line 224 to 232. Because of the different constructs of objective movement recordings vs. recall of PA, is it correct to assume the accelerometer is the ‘gold standard’ of PA? This question is difficult to answer, but one that pits the PAQ as inferior to the accelerometers in assessing PA. PAQs have been used for decades to establish the relationship between PA and health and mortality. At the same time, accelerometers have recently been used for various purposes. The assumption that an accelerometer-recorded movement is superior to the PAQ may not be valid. Perhaps the author can revise the wording in this section to not imply that the PA was not overreported compared to the accelerometer but that the low correlations may represent the measurement of different aspects of the movement. Take bicycling, economical running, cross-country skiing, and skating as MVPA or vigorous activities that register very low counts on an accelerometer. These activities will be classified as low intensity. Hence, one can’t assume the PA is overestimated on a PAQ when the type and performance of an activity fail to register acceleration counts on an accelerometer.

Line 272-277. Is it possible to restate the conclusion of the analyses to state that the measures were similar or not similar instead of underreporting and overreporting one’s PA on the PAQ? Again, such wording implies that an accelerometer is a superior movement-measuring device. This assumption is not the case for all forms of movement, as noted earlier.

Line 288-290, 295-302. How is it possible to state that there is under-reporting of sedentary behavior on a PAQ with the accelerometer that fails to differentiate between low-intensity PA and little-to-no movement?

Line 303. Wouldn’t this initial statement be more correct by stating that there is ‘agreement between the PAQ and the accelerometer recordings’ instead of validity? Such a statement does not imply the accelerometer is the gold standard for PA measurements.

Line 310-313. The low Spearman correlations may result from comparing two uniquely different constructs. Low correlations have been established previously between PAQs and accelerometers, as shown in the article from Craig et al. and others.

Strength and Limitations. This section makes no mention of the limitations of comparing accelerometers and PAQs. This omission is a significant limitation of this article. Estimating fitness levels using the 6-min walk test is a limitation to the validity of the fitness measures.

Line 360-361. Can the author identify the population this sample differed?

Reviewer #2: This manuscript is of interest, especially as the need to quantify physical activity in varying populations grows. While several investigations have looked at the relationship between self-report PA and objective accelerometry, few have looked at the factors which may moderate these relationships. Overall, this study is sound, although limited by the use of the six-minute walk test, and there can be some improvements in the clarity of writing and conclusions made. In particular, the overuse of acronyms makes readability so difficult, it does impact my ability to judge the soundness of the research. I have some specific comments as follows:

Comment 1: Line 112 - the number of participants excluded from the analysis is very high (>50%). Can the authors provide some detail on the reasons for exclusion, particularly where it is due to invalid questionnaire/accelerometer data. Can the authors also clarify what made this data valid and if participants were required to have all three questionnaires completed.

Comment 2: for participants with less than 7 days data, how were the daily average values calculated (e.g. was there always 1 weekend counted)?

Comment 3: Can the authors consider improving the readability of the results. For example, from line 196, the excessive use of abbreviations means these 4 lines are extremely difficult to interpret. The same applies for table 1. I acknowledge that table 1 has a lot of information in it, but it is very hard to compare the measures, groups and intensities measured as they are physically far apart. How could this be improved for the reader?

Comment 4: Given the three surveys ask very similar information, can the authors make comment on the agreement between these measures?

Comment 5: Line 247 - what is a TOST? Please review all acronyms and consider only using when absolutely necessary.

Comment 6: Could the authors have consider using Linear regressions and test for interactions between the self-report tool and fitness for predicting the objective PA measure? The intercepts of the models can be used to determine the significance of any absolute differences.

Comment 7: Could the authors report the overall correlations between the self-report and accelerometers, ignoring fitness. I do note that depending on your response to comment 6 this may not be necessary.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Aug 30;18(8):e0285357. doi: 10.1371/journal.pone.0285357.r002

Author response to Decision Letter 0

17 Mar 2023

Academic editor, Plos One

Dear academic editor and reviewers,

On behalf of my co-authors, I would like to thank you for taking your time to read our manuscript and giving us the opportunity to revise and improve our paper. We are grateful for your suggestions and believe they increased the value of our manuscript. You can find our replies to your comments in Italics below.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Thank you for your comment, we checked the manuscript again and made necessary changes to meet the PLOS ONE’s style requirement:

- We changed the font in Headings 1 and 2,

- We revised the citing in the text,

- We renamed the files uploaded with the manuscript,

- We revised the heading of tables and figures,

- We revised the font and dimensions of figures.

2. Thank you for stating the following in the Competing Interests section:

Please include your updated Competing Interests statement in your cover letter; we will change the online submission form on your behalf.

There are no restrictions on data sharing, and we confirm it does not alter the adherence to PLOS ONE policies on sharing the data. Our updated Competing interested statement can be found below.

We added the captions at the end of the manuscript.

Reviewer #1: OVERALL

Thank you for taking your time and reading our manuscript. We find your comments valuable and tried to include them in the improved version of the manuscript. We added more information regarding accelerometer and PAQs measurement of movement behavior and tried to point out the differences between the two measures. We do have information about the correlation of movement behavior items between the used PAQs: it was published in a previous paper; however, we also have the information for the data included in present manuscript and can include it as a supporting information see response to comment 4 from Reviewer #2). We considered all your following comments and provided explanation in italics, under each comment.

Meh, K.; Sember, V.; Đurić, S.; Vähä-Ypyä, H.; Rocha, P.; Jurak, G. Reliability and Validity of Slovenian Versions of IPAQ-SF, GPAQ, and EHIS-PAQ for Assessing Physical Activity and Sedentarism of Adults. Int. J. Environ. Res. Public Health 2022, 19, 430. https://doi.org/10.3390/ijerph19010430

SPECIFIC COMMENTS

Thank you for your comment, we agree that measures differ and wanted to include that information in the introduction, but it was not clear enough. We have added additional information in the lines 52– 72.

Line 102-110. Why did you target adults of 12-14-year-old adolescents to enroll in the study? Can the authors state why this sampling approach was used?

We used a sampling method with which we wanted to ensure as impartial a sample as possible. By including primary schools and their students, we were able to invite different individuals from different backgrounds to the research.

Line 114-116. The meaning of this sentence needs to be clarified.

Thank you for your comment, we added some details and hopefully improved the meaning of the sentence (lines 128 – 129).

Line 125. Please identify the language in the PAQ’s were administered. Are there metrics that specify the translation process?

We provided a more detailed description of the translation process in lines 156 – 159. We used the forward-backward translation process, recommended by the World Health Organization and included independent translators to complete this process. During the translation we did not find any major discrepancies between the translators.

We added additional information about units from each of the questionnaires in the lines 139 - 145). All three questionnaires are collecting weekly physical activity data and daily sedentary behavior data. The MET values are later on calculated from the weekly self-reported physical activity. As MET values differ between PAQs and between accelerometer (e.g. walking in IPAQ-SF = 3.3 MET, transport in GPAQ = 4 MET, MPA in accelerometer = 3 MET, EHIS-PAQ does not include MET calculations). We do believe that there should be a more unified approach in MET calculation, which would enable comparison of MET values between different PAQs and different methods. Since this approach is not unified between the methods used in our paper, we decided to compare time spent in different movement behaviors (e.g. MPA, VPA, SB), as participants should be able to self-assess it similarly. In similar PAQ validation studies, researchers also compared times spent in different movement behaviors.

Line 171-176. Determination of the participants’ fitness levels is important for this study’s internal validity. More information is needed.

1. Please identify the validity and reliability of the 6-minute walking test and the type of populations the validity and reliability were determined.

2. How was the test scored to identify the fitness groups? Terciles based on the distance covered?

3. What clothing did the participants wear while completing the fitness test?

4. How was the distance covered in the walking test measured?

5. How many participants were measured in the fitness test simultaneously? One at a time or more than one?

6. How many times did each participant complete the test? Once or more than once to familiarize themselves with the test?

Thank you for the comments and additional questions that helped us improve this part of the methodology. In the lines 195-210 we improved the text and described the test and protocol in more detail. We are writing additional comments below:

1. Even though 6-minute walk test is usually used in clinical populations; a few studies tested its measurement characteristics in healthy participants. It is a suitable measure of cardiorespiratory fitness in children and youth

(Li, A. M., Yin, J., Yu, C. C. W., Tsang, T., So, H. K., Wong, E., ... & Sung, R. (2005). The six-minute walk test in healthy children: reliability and validity. European Respiratory Journal, 25(6), 1057-1060.,

Castro-Piñero J, Artero EG, España-Romero V, Ortega FB, Sjöström M, Suni J, Ruiz JR. Criterion-related validity of field-based fitness tests in youth: a systematic review. Br J Sports Med. 2010 Oct;44(13):934-43. doi: 10.1136/bjsm.2009.058321. Epub 2009 Apr 12. PMID: 19364756.)

and in adults

(Burr JF, Bredin SS, Faktor MD, Warburton DE. The 6-minute walk test as a predictor of objectively measured aerobic fitness in healthy working-aged adults. Phys Sportsmed. 2011 May;39(2):133-9. doi: 10.3810/psm.2011.05.1904. PMID: 21673494.,

Mänttäri, A., Suni, J., Sievänen, H., Husu, P., Vähä-Ypyä, H., Valkeinen, H., Tokola, K. and Vasankari, T. (2018), Six-minute walk test: a tool for predicting maximal aerobic power (VO2 max) in healthy adults. Clin Physiol Funct Imaging, 38: 1038-1045. https://doi.org/10.1111/cpf.12525)

2. The 6-minute walk distance was used as a score of 6-minute walk test. We cleared that up in line 208.

3. During the fitness testing participants were wearing a sport wear (T-shirt and shorts, leggings or tracksuit and athletic footwear). We added this information in lines 202 - 204.

4. We marked the track with cones 5 meters apart. The researcher counted the number of full laps each participant covered in 6 minutes (from the starting cone) and then added the remaining distance to the last cone they reached. We added this information in lines 207 - 209.

5. Maximum of 4 participants were measured at once, if there were enough researchers to keep track of the distance covered. We added this information in the lines 209 - 210.

6. Each participant completed the test once. If they would have completed it multiple times, tiredness could have influenced their performance and 6-minute walk distance. We added this information in the lines 209 - 210.

Line 181. Please identify the cut points for the distance of the three fitness groups in a parenthesis.

We added this information separated by gender in lines 215 - 218, it should be noted that distance was also calculated for the 4 age groups described in the same paragraph.

Table 1. This is a very detailed table with much information. However, much of the data for the PAQs are easier to compare if the authors describe how each questionnaire is scored.

We described the scoring of the questionnaires in the Methods section of the paper in lines 150 - 155. Furthermore, we made some changes to Table 1 to make it more readable.

Table 2. The presentation for this table is very nice.

Thank you for your comment.

Discussion. The discussion is very long and seems repetitive. It should be shortened and focused on explaining plausible reasons for the results.

Thank you for your valuable comment. We changed the wording in this part and did not use the words over and under estimation (lines 268 – 271 and 273 – 276). We changed the wording in other parts of the discussion in line with your comment and suggestion.

We changed this part of the text and explained in more detail differences in measurement of PA between accelerometer and PAQs in lines 319 – 323.

The accelerometer used in present study did not report difficulties in differentiating between low-intensity physical activity and sedentary behavior. The MAD method used with the UKK RM42 accelerometer can differentiate between different intensity levels of activity and sedentary behavior 1,2. Similar results were obtained in other studies, where the term under-reporting was also used 3,4,5. Nevertheless, we decided to change the wording to compared to accelerometer to make it clearer that the difference is only present when comparing PAQ results to the UKK RM42 accelerometer.

1 Vähä-Ypyä, H., Vasankari, T., Husu, P., Suni, J. and Sievänen, H. (2015), A universal, accurate intensity-based classification of different physical activities using raw data of accelerometer. Clin Physiol Funct Imaging, 35: 64-70. https://doi.org/10.1111/cpf.12127

2 Vähä-Ypyä, H, Husu, P, Suni, J, Vasankari, T, Sievänen, H. Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer. Scand J Med Sci Sports. 2018; 28: 1092- 1102. https://doi.org/10.1111/sms.13017

3 Bakker, E.A., Hartman, Y.A.W., Hopman, M.T.E. et al. Validity and reliability of subjective methods to assess sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 17, 75 (2020). https://doi.org/10.1186/s12966-020-00972-1

4 Healy GN, Clark BK, Winkler EA, Gardiner PA, Brown WJ, Matthews CE. Measurement of adults' sedentary time in population-based studies. Am J Prev Med. 2011 Aug;41(2):216-27. doi: 10.1016/j.amepre.2011.05.005. PMID: 21767730; PMCID: PMC3179387.

5 Prince, S.A., Cardilli, L., Reed, J.L. et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 17, 31 (2020). https://doi.org/10.1186/s12966-020-00938-3

Thank you for your suggestion, we found it very helpful and changed the sentence in lines 352– 353.

Low correlations are usually reported when comparing physical activity questionnaires and accelerometers and our study did not differ in that aspect. We do agree that low correlations could be a result of different constructs and included that in the manuscript.

We mentioned the use of 6-minute walk test as a limitation in lines 440 – 445. We have added the limitation of comparing accelerometer with PAQs in lines 434 – 440.

Line 360-361. Can the author identify the population this sample differed?

We wanted to include a sample of healthy adults from Slovenia, but the sample included in the study is not representative of this population, as age distribution and BMI distribution differ from the populations.

Thank you for taking your time and reading our manuscript. We find your comments valuable and tried to include them in the improved version of the manuscript. We tried to improve the readability of the manuscript and lowered the number of abbreviations used.

Most of the participants were excluded due to the missing fitness data. As in all studies, participation was voluntary and some participants did not compete this part of the study, due to different reasons (illness, did not bring their sportswear, did not want to perform the test). The valid accelerometer data is described in the Objective measures section. Valid questionnaires data was completed each questionnaire, so there were no missing data and included participants completed all three questionnaires. We added this additional information in lines 128 - 129.

Comment 2: for participants with less than 7 days data, how were the daily average values calculated (e.g. was there always 1 weekend counted)?

As the inclusion criteria dictated, only participants with 4 valid days of data were included in the study. But as our data shows, participants included in the study only had one missing day or less. Therefore, there was only one weekend or weekday missing. For these participants average values were calculated for the 6 days of reported data.

We have changed Table 1 accordingly: we grouped together the intensities of physical activity and deleted some of the abbreviations. Hopefully it makes it easier to read and compare results.

Comment 4: Given the three surveys ask very similar information, can the authors make comment on the agreement between these measures?

There was a moderate agreement between questionnaires, the best results were for sedentary behavior and for moderate to vigorous physical activity (we added the table with results below). We did not include the results of the agreement between questionnaires in the paper, because we decided to include criterion validity in the present study and other measures (equivalence testing and Bland-Altman plots). Therefore, agreement between the questionnaires would take up too much space in the paper and was excluded from the manuscript. If you find this information crucial and interesting, we could add it as Supporting information.

Table 1. Agreement between IPAQ-SF, GPAQ and EHIS-PAQ.

IPAQ-SF EHIS-PAQ

SB MPA VPA Walk MVPA SB Walk Cycle MV Aerobic

Recreational

Activity

GPAQ SB 1 .890*** .828***

2 .765*** .720***

3 .788*** .706***

Work MPA 1 .489**

2 .409*

3 .601**

Work VPA 1 .908***

2 .700*

3 .483

Leisure MPA 1 .244

2 .499**

3 .189

Leisure VPA 1 .651***

2 .042

3 .502**

Transport 1 .486** .373* .315

2 .415* .708*** .508**

3 .671*** .689*** .311

MPA 1 .375*

2 .605***

3 .468**

VPA 1 .801***

2 .202

3 .597***

MVPA 1 .665*** .434***

2 .576** .472***

3 .597*** .363**

EHIS-PAQ SB 1 .707***

2 .841***

3 .721***

Walk 1 .487***

2 .368**

3 .540***

MV Aerobic

Recreational

Activity 1 .258

2 .452**

3 .375*

* p ≤ 0.05; ** ≤ 0.01; p ≤ 0.001

Notes: 3 = low fitness group; 2 = intermediate fitness group; 1 = high fitness group; VPA = vigorous physical activity; MPA = moderate physical activity; MVPA = moderate to vigorous physical activity; SB = sedentary behaviour; IPAQ = International physical activity; GPAQ = Global physical activity; EHIS-PAQ = European health interview survey – physical activity questionnaire.

Comment 5: Line 247 - what is a TOST? Please review all acronyms and consider only using when absolutely necessary.

TOST is a test of equivalence, that we used in present study. The TOST stands for two one-sided tests. We changed the abbreviation in the text in line 293 - 294.

This is a very valuable comment and a great idea for future research. In present study we wanted to explore the differences in self-reporting of the physical activity and sedentary behavior, therefore we did not use the linear regression. But we find your comment and idea a great opportunity for future work in the field of physical activity measurement.

We added the overall correlation coefficients in Table 2 for sedentary behavior, MPA, VPA and MVPA.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0285357.r003

Decision Letter 1

Giulia Squillacioti

24 Apr 2023

The dilemma of physical activity questionnaires: fitter people are less prone to over reporting

PONE-D-22-33819R1

Dear Dr. Meh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Giulia Squillacioti

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for editing your paper based on the reviewer’s comments. You did a good job. Two more things will be helpful to the reader. First, it would be helpful to have the abbreviations defined for the questionnaires in the abstract. Second, in the strengths and limitations section, addition of a comment that accelerometers and PAQs measure different constructs, hence, lower correlations between the two measures deemed significant are expected.

Reviewer #2: Thank you for addressing my comments, particularly the work in improving the clarity of the writing and use of accronyms. All the best.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0285357.r004

Acceptance letter

Giulia Squillacioti

12 May 2023

PONE-D-22-33819R1

The dilemma of physical activity questionnaires: fitter people are less prone to over reporting

Dear Dr. Meh:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Giulia Squillacioti

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. Accelerometer and PAQs dataset.

(SAV)

Click here for additional data file.^{(28KB, sav)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(29.5KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

[pone.0285357.ref001] 1.González K, Fuentes J, Márquez JL. Physical inactivity, sedentary behavior and chronic diseases. Korean J Fam Med. 2017;38: 111–115. doi: 10.4082/kjfm.2017.38.3.111 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref002] 2.Hamer M O’Donovan G Murphy M. Physical inactivity and the economic and health burdens due to cardiovascular disease: exercise as medicine. Exercise for Cardiovascular Disease Prevention and Treatment. Springer; 2017. pp. 3–18. [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref003] 3.Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: An analysis of burden of disease and life expectancy. Lancet. 2012;380: 219–229. doi: 10.1016/S0140-6736(12)61031-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref004] 4.Ding D, Lawson KD, Kolbe-Alexander TL, Finkelstein EA, Katzmarzyk PT, van Mechelen W, et al. The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. 2016;388: 1311–1324. doi: 10.1016/S0140-6736(16)30383-X [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref005] 5.Castañeda-Babarro A, Arbillaga-Etxarri A, Gutiérrez-Santamaría B, Coca A. Physical activity change during COVID-19 confinement. Int J Environ Res Public Health. 2020;17: 6878. doi: 10.3390/ijerph17186878 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref006] 6.Tison GH, Avram R, Kuhar P, Abreau S, Marcus GM, Pletcher MJ, et al. Worldwide effect of COVID-19 on physical activity: a descriptive study. Ann Intern Med. 2020;173: 767–770. doi: 10.7326/M20-2665 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref007] 7.Hall G, Laddu DR, Phillips SA, Lavie CJ, Arena R. A tale of two pandemics: How will COVID-19 and global trends in physical inactivity and sedentary behavior affect one another? Prog Cardiovasc Dis. 2020. doi: 10.1016/j.pcad.2020.04.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref008] 8.Prochaska JO. Multiple health behavior research represents the future of preventive medicine. Prev Med (Baltim). 2008;46: 281–285. doi: 10.1016/j.ypmed.2008.01.015 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref009] 9.Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Heal Sci. 2020. doi: 10.1016/j.jshs.2020.07.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref010] 10.Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, et al. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15: 1–68. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref011] 11.Prince SA, Adamo KB, Hamel ME, Hardt J, Gorber SC, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act. 2008;5: 56. doi: 10.1186/1479-5868-5-56 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref012] 12.Silfee VJ, Haughton CF, Jake-Schoffman DE, Lopez-Cepero A, May CN, Sreedhara M, et al. Objective measurement of physical activity outcomes in lifestyle interventions among adults: A systematic review. Prev Med reports. 2018;11: 74–80. doi: 10.1016/j.pmedr.2018.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref013] 13.Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. 2013;14: 451–462. doi: 10.1111/obr.12021 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref014] 14.Sattler MC, Ainsworth BE, Andersen LB, Foster C, Hagströmer M, Jaunig J, et al. Physical activity self-reports: past or future? Br J Sports Med. 2021. doi: 10.1136/bjsports-2020-103595 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref015] 15.Neilson HK, Robson PJ, Friedenreich CM, Csizmadi I. Estimating activity energy expenditure: how valid are physical activity questionnaires? Am J Clin Nutr. 2008;87: 279–291. doi: 10.1093/ajcn/87.2.279 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref016] 16.Sharifzadeh M, Bagheri M, Speakman JR, Djafarian K. Comparison of total and activity energy expenditure estimates from physical activity questionnaires and doubly labelled water: a systematic review and meta-analysis. Br J Nutr. 2020/07/28. 2021;125: 983–997. doi: 10.1017/S0007114520003049 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref017] 17.Maddison R, Ni Mhurchu C, Jiang Y, Vander Hoorn S, Rodgers A, Lawes CMM, et al. International Physical Activity Questionnaire (IPAQ) and New Zealand Physical Activity Questionnaire (NZPAQ): A doubly labelled water validation. Int J Behav Nutr Phys Act. 2007;4: 62. doi: 10.1186/1479-5868-4-62 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref018] 18.Chomistek AK, Yuan C, Matthews CE, Troiano RP, Bowles HR, Rood J, et al. Physical Activity Assessment with the ActiGraph GT3X and Doubly Labeled Water. Med Sci Sport Exerc. 2017;49. Available: https://journals.lww.com/acsm-msse/Fulltext/2017/09000/Physical_Activity_Assessment_with_the_ActiGraph.21.aspx doi: 10.1249/MSS.0000000000001299 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref019] 19.Plasqui G, Westerterp KR. Physical activity assessment with accelerometers: an evaluation against doubly labeled water. Obesity. 2007;15: 2371–2379. doi: 10.1038/oby.2007.281 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref020] 20.IPAQ Research Committee. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. http//wwwipaqkise/scoringpdf. 2005.

[pone.0285357.ref021] 21.Armstrong T, Bull F. Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). J Public Health (Bangkok). 2006;14: 66–70. doi: 10.1007/s10389-006-0024-x [DOI] [Google Scholar]

[pone.0285357.ref022] 22.Finger JD, Tafforeau J, Gisle L, Oja L, Ziese T, Thelen J, et al. Development of the European health interview survey-physical activity questionnaire (EHIS-PAQ) to monitor physical activity in the European Union. Arch Public Heal. 2015;73: 59. doi: 10.1186/s13690-015-0110-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref023] 23.Fogelholm M, Malmberg J, Suni J, Santtila M, Kyrolainen H, Mantysaari M, et al. International physical activity questionnaire: validity against fitness. Med Sci Sports Exerc. 2006;38: 753. doi: 10.1249/01.mss.0000194075.16960.20 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref024] 24.Vandelanotte C, Duncan MJ, Stanton R, Rosenkranz RR, Caperchione CM, Rebar AL, et al. Validity and responsiveness to change of the Active Australia Survey according to gender, age, BMI, education, and physical activity level and awareness. BMC Public Health. 2019;19: 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref025] 25.Shook RP, Gribben NC, Hand GA, Paluch AE, Welk GJ, Jakicic JM, et al. Subjective estimation of physical activity using the international physical activity questionnaire varies by fitness level. J Phys Act Heal. 2016;13: 79–86. doi: 10.1123/jpah.2014-0543 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref026] 26.Bull FC, Maslin TS, Armstrong T. Global physical activity questionnaire (GPAQ): nine country reliability and validity study. J Phys Act Heal. 2009;6: 790–804. doi: 10.1123/jpah.6.6.790 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref027] 27.Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sport Exerc. 2003;35: 1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref028] 28.World Health Organization. WHO STEPS surveillance manual: the WHO STEPwise approach to chronic disease risk factor surveillance. Geneva: World Health Organization; 2005. [Google Scholar]

[pone.0285357.ref029] 29.Baumeister SE, Ricci C, Kohler S, Fischer B, Töpfer C, Finger JD, et al. Physical activity surveillance in the European Union: reliability and validity of the European health interview survey-physical activity questionnaire (EHIS-PAQ). Int J Behav Nutr Phys Act. 2016;13: 61. doi: 10.1186/s12966-016-0386-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref030] 30.Ekelund U, Sepp H, Brage S, Becker W, Jakes R, Hennings M, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutr. 2006;9: 258–265. doi: 10.1079/phn2005840 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref031] 31.Rodríguez-Muńoz S, Corella C, Abarca-Sos A, Zaragoza J. Validation of three short physical activity questionnaires with accelerometers among university students in Spain. J Sports Med Phys Fitness. 2017;57: 1660. doi: 10.23736/S0022-4707.17.06665-8 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref032] 32.Cleland CL, Hunter RF, Kee F, Cupples ME, Sallis JF, Tully MA. Validity of the global physical activity questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health. 2014;14: 1255. doi: 10.1186/1471-2458-14-1255 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref033] 33.Rivière F, Widad FZ, Speyer E, Erpelding M-L, Escalon H, Vuillemin A. Reliability and validity of the French version of the global physical activity questionnaire. J Sport Heal Sci. 2018;7: 339–345. doi: 10.1016/j.jshs.2016.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref034] 34.Kalvenas A, Burlacu I, Abu-Omar K. Reliability and validity of the International Physical Activity Questionnaire in Lithuania. Balt J Heal Phys Act. 2016;8: 29–41. doi: 10.29359/BJHPA.08.2.03 [DOI] [Google Scholar]

[pone.0285357.ref035] 35.Aittasalo M, Vähä-Ypyä H, Vasankari T, Husu P, Jussila A-M, Sievänen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents’ physical activity irrespective of accelerometer brand. BMC Sports Sci Med Rehabil. 2015;7. doi: 10.1186/s13102-015-0010-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref036] 36.Vähä-Ypyä H, Vasankari T, Husu P, Mänttäri A, Vuorimaa T, Suni J, et al. Validation of cut-points for evaluating the intensity of physical activity with accelerometry-based mean amplitude deviation (MAD). PLoS One. 2015;10: e0134813. doi: 10.1371/journal.pone.0134813 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref037] 37.Vähä-Ypyä H, Sievänen H, Husu P, Tokola K, Vasankari T. Intensity Paradox—Low-Fit People Are Physically Most Active in Terms of Their Fitness. Sensors. 2021;21: 2063. doi: 10.3390/s21062063 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref038] 38.Vähä‐Ypyä H, Husu P, Suni J, Vasankari T, Sievänen H. Reliable recognition of lying, sitting, and standing with a hip‐worn accelerometer. Scand J Med Sci Sports. 2018;28: 1092–1102. doi: 10.1111/sms.13017 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref039] 39.Ross R, Blair SN, Arena R, Church TS, Després JP, Franklin BA, et al. Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement from the American Heart Association. Circulation. 2016;134: 653–699. doi: 10.1161/CIR.0000000000000461 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref040] 40.Lang JJ, Phillips EW, Orpana HM, Tremblay MS, Ross R, Ortega FB, et al. Field-based measurement of cardiorespiratory fitness to evaluate physical activity interventions. Bull World Health Organ. 2018;96: 794–796. doi: 10.2471/BLT.18.213728 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref041] 41.Burr JF, Bredin SSD, Faktor MD, Warburton DER. The 6-Minute Walk Test as a Predictor of Objectively Measured Aerobic Fitness in Healthy Working-Aged Adults. Phys Sportsmed. 2011;39: 133–139. doi: 10.3810/psm.2011.05.1904 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref042] 42.Mänttäri A, Suni J, Sievänen H, Husu P, Vähä-Ypyä H, Valkeinen H, et al. Six-minute walk test: a tool for predicting maximal aerobic power (VO2 max) in healthy adults. Clin Physiol Funct Imaging. 2018;38: 1038–1045. 10.1111/cpf.12525 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref043] 43.The jamovi project. jamovi. 2021. Available: https://www.jamovi.org/ [Google Scholar]

[pone.0285357.ref044] 44.R Core Team. R: A Language and environment for statistical computing. 2021. Available: https://cran.r-project.org/ [Google Scholar]

[pone.0285357.ref045] 45.Evans JD. Straightforward statistics for the behavioral sciences. Pacific Grove, CA: Thomson Brooks/Cole Publishing Co; 1996. [Google Scholar]

[pone.0285357.ref046] 46.Dixon PM, Saint-Maurice PF, Kim Y, Hibbing P, Bai Y, Welk GJ. A Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement. Med Sci Sport Exerc. 2018;50: 837–845. doi: 10.1249/MSS.0000000000001481 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref047] 47.Lakens D. Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Soc Psychol Personal Sci. 2017;8: 355–362. doi: 10.1177/1948550617697177 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref048] 48.Charles M, Thivel D, Verney J, Isacco L, Husu P, Vähä-Ypyä H, et al. Reliability and validity of the ONAPS physical activity questionnaire in assessing physical activity and sedentary behavior in French adults. Int J Environ Res Public Health. 2021;18: 5643. doi: 10.3390/ijerph18115643 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref049] 49.World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. Geneva: World Health Organisation; p. 23. [Google Scholar]

[pone.0285357.ref050] 50.Ács P, Veress R, Rocha P, Dóczi T, Raposa BL, Baumann P, et al. Criterion validity and reliability of the International Physical Activity Questionnaire–Hungarian short form against the RM42 accelerometer. BMC Public Health. 2021;21: 1–10. doi: 10.1186/s12889-021-10372-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref051] 51.Premelč J, Meh K, Vähä-Ypyä H, Sember V, Jurak G. Do Fitter Children Better Assess Their Physical Activity with Questionnaire Than Less Fit Children? International Journal of Environmental Research and Public Health. 2022. doi: 10.3390/ijerph19031304 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref052] 52.Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. Int J Behav Nutr Phys Act. 2011;8: 115. doi: 10.1186/1479-5868-8-115 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref053] 53.Rzewnicki R, Vanden Auweele Y, De Bourdeaudhuij I. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutr. 2003;6: 299–305. doi: 10.1079/PHN2002427 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref054] 54.Meh K, Jurak G, Sorić M, Rocha P, Sember V. Validity and Reliability of IPAQ-SF and GPAQ for Assessing Sedentary Behaviour in Adults in the European Union: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2021. doi: 10.3390/ijerph18094602 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref055] 55.Sember V, Meh K, Sorić M, Starc G, Rocha P, Jurak G. Validity and Reliability of International Physical Activity Questionnaires for Adults across EU Countries: Systematic Review and Meta Analysis. Int J Environ Res Public Health. 2020;17: 7161. doi: 10.3390/ijerph17197161 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref056] 56.Prince SA, Cardilli L, Reed JL, Saunders TJ, Kite C, Douillette K, et al. A comparison of self-reported and device measured sedentary behaviour in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2020;17. doi: doi.org/10.1186/s12966-020-00938-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref057] 57.Meh K, Sember V, Đurić S, Vähä-Ypyä H, Rocha P, Jurak G. Reliability and Validity of Slovenian Versions of IPAQ-SF, GPAQ and EHIS-PAQ for Assessing Physical Activity and Sedentarism of Adults. Int J Environ Res Public Health. 2022;19: 430. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref058] 58.Howley ET. Type of activity: resistance, aerobic and leisure versus occupational physical activity. Med Sci Sports Exerc. 2001;33: S364–9; discussion S419-20. doi: 10.1097/00005768-200106001-00005 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref059] 59.Hills AP, Mokhtar N, Byrne NM. Assessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures. Frontiers in Nutrition. 2014. Available: https://www.frontiersin.org/10.3389/fnut.2014.00005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0285357.ref060] 60.Enright PL. The six-minute walk test. Respir Care. 2003;48: 783–785. [PubMed] [Google Scholar]

[pone.0285357.ref061] 61.Hendelman D, Miller K, Baggett C, Debold E, Freedson P. Validity of accelerometry for the assessment of moderate intensity physical activity in the field. Med Sci Sports Exerc. 2000;32: S442–9. doi: 10.1097/00005768-200009001-00002 [DOI] [PubMed] [Google Scholar]

[pone.0285357.ref062] 62.Welk GJ, Blair SN, Wood K, Jones S, Thompson RW. A comparative evaluation of three accelerometry-based physical activity monitors. Med Sci Sports Exerc. 2000;32: S489–97. doi: 10.1097/00005768-200009001-00008 [DOI] [PubMed] [Google Scholar]

PERMALINK

The dilemma of physical activity questionnaires: Fitter people are less prone to over reporting

Kaja Meh

Vedrana Sember

Maroje Sorić

Henri Vähä-Ypyä

Paulo Rocha

Gregor Jurak

Roles

Abstract

Introduction

Materials and methods

Study design and participants

Subjective measures of PA

Objective measures of PA

Anthropometry and physical fitness

Statistical analysis

Results

Table 1. Baseline characteristics of participants across three fitness groups (data shown are median and (interquartile range)).

Table 2. Criterion validity between RM42 accelerometer and IPAQ-SF, GPAQ and EHIS-PAQ for three fitness groups.

Fig 1. Bland-Altman plots for the PAQs and UKK RM42 accelerometer for sedentary behaviour (min/day) with 95% limit of agreement.

Fig 2. Bland-Altman plots for the PAQs and UKK RM42 accelerometer for to MVPA (min/week) with 95% limit of agreement.

Fig 3. Observed difference in minutes between UKK RM42 and PAQs for sedentary behaviour in minutes.

Fig 4. Observed difference in minutes between UKK RM42 and PAQs for MVPA in minutes.

Discussion

Strengths and limitations

Conclusions

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Giulia Squillacioti

Roles

Author response to Decision Letter 0

Decision Letter 1

Giulia Squillacioti

Roles

Acceptance letter

Giulia Squillacioti

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases