Advertisement

An Updated Systematic Review of Childhood Physical Activity Questionnaires

  • Lisan M. Hidding
  • Mai. J. M. Chinapaw
  • Mireille N. M. van Poppel
  • Lidwine B. Mokkink
  • Teatske M. Altenburg
Open Access
Systematic Review

Abstract

Background and Objective

This review is an update of a previous review published in 2010, and aims to summarize the available studies on the measurement properties of physical activity questionnaires for young people under the age of 18 years.

Methods

Systematic literature searches were carried out using the online PubMed, EMBASE, and SPORTDiscus databases up to 2018. Articles had to evaluate at least one of the measurement properties of a questionnaire measuring at least the duration or frequency of children’s physical activity, and be published in the English language. The standardized COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist was used for the quality assessment of the studies.

Results

This review yielded 87 articles on 89 different questionnaires. Within the 87 articles, 162 studies were conducted: 103 studies assessed construct validity, 50 assessed test–retest reliability, and nine assessed measurement error. Of these studies, 38% were of poor methodological quality and 49% of fair methodological quality. A questionnaire with acceptable validity was found only for adolescents, i.e., the Greek version of the 3-Day Physical Activity Record. Questionnaires with acceptable test–retest reliability were found in all age categories, i.e., preschoolers, children, and adolescents.

Conclusion

Unfortunately, no questionnaires were identified with conclusive evidence for both acceptable validity and reliability, partly due to the low methodological quality of the studies. This evidence is urgently needed, as current research and practice are using physical activity questionnaires of unknown validity and reliability. Therefore, recommendations for high-quality studies on measurement properties of physical activity questionnaires were formulated in the discussion.

PROSPERO Registration Number

CRD42016038695.

Key Points

No conclusive evidence was found for both the validity and reliability for any of the included physical activity questionnaires for youth.

High-quality studies on the measurement properties of the most promising physical activity questionnaires are urgently needed, e.g., by using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist.

More attention on the content validity of physical activity questionnaires is needed to confirm that questionnaires measure what they intend to measure.

1 Introduction

Numerous studies have demonstrated beneficial effects of physical activity, in particular of moderate to vigorous intensity, on metabolic syndrome, bone strength, physical fitness, and mental health in children and adolescents [1, 2]. In order to monitor trends in physical activity, examine associations between physical activity and health outcomes, and evaluate the effectiveness of physical activity-enhancing interventions, valid, reliable, responsive, and feasible measures of physical activity are needed.

Accelerometers are considered to provide valid and reliable measures of physical activity in children and adolescents [3]. However, accelerometers are not gold standard and underestimate activities such as cycling, swimming, weight lifting, and many household chores. Moreover, physical activity estimates vary depending on subjective decisions in data reduction such as the choice of cut-points for intensity levels, the minimum number of valid days, the minimum number of valid hours per day, and the definition of non-wear time [4]. Furthermore, accelerometers cannot provide information on the type and context of the behavior and are labor-intensive and costly, especially in large populations [5].

Self-report or proxy-report questionnaires are seen as a convenient and affordable way to assess physical activity that can provide information on the context and type of the activity [5, 6]. However, questionnaires have their limitations as well, such as the potential for social desirability and recall bias [6, 7]. Thus, for measuring physical activity a combination of the more objective measures such as accelerometers and self-report questionnaires seems most promising.

A great many questionnaires measuring physical activity in children and adolescents have been developed, with varying formats, recall periods, and types of physical activity recalled. To be able to select the most appropriate questionnaire, an overview of the measurement properties of the available physical activity questionnaires in children and adolescents is highly warranted. In 2010, Chinapaw et al. [8] reviewed the measurement properties of self-report and proxy-report measures of physical activity in children and adolescents. As many studies assessing measurement properties of physical activity questionnaires have been published since then, an update is timely.

Therefore, we aimed to summarize studies that assessed the measurement properties (e.g., responsiveness, reliability, measurement error, and validity) of self-report or proxy-report questionnaires in children and adolescents under the age of 18 years published since May 2009. Furthermore, we aimed to provide recommendations regarding the best available questionnaires, taking into account the best available questionnaires from the previous review.

2 Methods

This review is an update of the previously published review of Chinapaw et al. [8]. We followed the Preferred Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines and registered the review on PROSPERO (international prospective register of systematic reviews; registration number: CRD42016038695).

2.1 Literature Search

Systematic literature searches were carried out in PubMed, EMBASE, and SPORTDiscus (from January 2009 up until April 2018). In PubMed more overlap in time was maintained (search from May 2008), as our previous searches showed that the PubMed time filter can be inaccurate, e.g., due to incorrect labeling of publication dates. The full search strategy can be found in the Electronic Supplementary Material (Online Resource 1).

Search terms in PubMed were used in AND-combination, and related to physical activity (e.g., motor activity, exercise), children and adolescents (e.g., schoolchildren, adolescents), measurement properties (e.g., reliability, reproducibility, validity) [9], and self- or proxy-report measures (e.g., child-reported questionnaire). Medical Subject Heading (MESH), title and abstract (TIAB), and free-text search terms were used, and a variety of publication types (e.g., biography, comment, case reports, editorial) were excluded. In EMBASE, search terms related to physical activity, measurement properties [9], and self- or proxy-report measures were used in AND-combination. The search was limited to children and adolescents (e.g., child, adolescent), and EMBASE-only. EMBASE subject headings, TIAB, and free-text search terms were used. In SPORTDiscus, TIAB and free-text search terms were used in AND-combination, related to physical activity, children and adolescents, and self- or proxy-report measures.

2.2 Inclusion and Exclusion Criteria

Studies were eligible for inclusion when (1) the aim of the study was to evaluate at least one of the measurement properties of a self-report or proxy-report physical activity questionnaire, or a questionnaire containing physical activity items; (2) the questionnaire under study at least reported data on the duration or frequency of physical activity; (3) the mean age of the study population was < 18 years; and (4) the study was available in the English language. Studies were excluded in the following situations: (1) studies assessing physical activity using self-report measures administered by an interview (one-on-one assessment) or using a diary; (2) studies evaluating the measurement properties in a specific population (e.g., children who are affected by overweight or obesity); (3) studies examining structural validity and/or internal consistency for questionnaires that represent a formative measurement model; (4) construct validity studies examining the relationship between the questionnaire and a non-physical activity measure, e.g., body mass index (BMI) or percentage body fat; and (5) responsiveness studies that did not use a physical activity comparison measure, e.g., accelerometer, to assess a questionnaire’s ability to detect change.

2.3 Selection Procedures

Titles and abstracts were screened for eligible studies by two independent researchers [Lisan Hidding (LH) and either Mai Chinapaw (MC), Mireille van Poppel (MP), Teatske Altenburg (TA), or Lidwine Mokkink (LM)]. Subsequently, full texts were obtained and screened for eligibility by two independent researchers (LH and either TA or MP). A fourth researcher (MC) was consulted in the case of doubt.

2.4 Data Extraction

For all eligible studies, two independent reviewers (LH and either TA or MP) extracted data regarding the characteristics of studies and results of the assessed measurement properties, using a structured form. Extracted data regarding the methods and results of the assessed measurement properties included study population, questionnaire under study, studied measurement properties, comparison measures, time interval, statistical methods used, and results regarding the studied measurement properties. In the case of disagreement regarding data extraction, a fourth researcher (MC) was consulted.

2.5 Methodological Quality Assessment

Two independent reviewers (LH and either MC or LM) rated the methodological quality of the included studies using the standardized COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) checklist [10, 11, 12]. For each measurement property, the design requirements were rated using a 4-point scale (i.e., excellent, good, fair, or poor). The lowest score counts method was applied, e.g., the final methodological quality was scored as poor in the case of a poor score on one of the items. The lowest rated items that determined the final score for each study are shown in Electronic Supplementary Material Online Resource 2. The methodological quality of the content validity studies was not assessed as often little or no information on the development of the questionnaire or on the assessment of relevance, comprehensiveness, and comprehensibility of items was available. One minor adaption to the original COSMIN checklist, also described in a previous review [13], was applied: Percentage of Agreement (PoA) was removed from the reliability box and added to the measurement error box as an excellent statistical method [14]. To assess the methodological quality of test–retest reliability studies, standards previously described by Chinapaw et al. [8] regarding the time interval were applied: between > 1 day and < 3 months for questionnaires recalling a standard week; between > 1 day and < 2 weeks for questionnaires recalling the previous week; and between > 1 day and < 1 week for questionnaires recalling the previous day.

2.6 Questionnaire Quality Assessment

2.6.1 Reliability

Reliability is defined as “the degree to which a measurement instrument is free from measurement error” [15]. Test–retest reliability outcomes were considered acceptable under the following conditions: (1) intraclass correlation coefficients and kappa values ≥ 0.70 [16]; or (2) Pearson, Spearman, or unknown correlations ≥ 0.80 [17]. Measurement error is defined as “the systematic and random error of a score that is not attributed to true changes in the construct” [15]. Measurement error outcomes were considered acceptable when the smallest detectable change (SDC) was smaller than the minimal important change (MIC) [16].

The majority of the included studies reported multiple correlations per questionnaire for test–retest reliability, e.g., separate correlations for each questionnaire item. Therefore, an overall evidence rating was applied in order to obtain a final test–retest reliability rating, incorporating all correlations per questionnaire for each study. A positive (+) evidence rating was obtained if ≥ 80% of correlations were acceptable, a mixed (±) evidence rating was obtained when ≥ 50% and < 80% of correlations were acceptable, and a negative (–) evidence rating was obtained when < 50% of correlations were acceptable. For measurement error, no final evidence rating could be applied, as to our knowledge no information on the MIC is available for the included questionnaires. Furthermore, in the case of PoA, higher scores represent less measurement error.

2.6.2 Validity

For validity, three different measurement properties can be distinguished, i.e., content validity, construct validity, and criterion validity [15]. Content validity is defined as “the degree to which the content of a measurement instrument is an adequate reflection of the construct to be measured” [15]. Construct validity is “the degree to which the scores of a measurement instrument are consistent with (a priori drafted) hypotheses” [15]. Hypotheses can concern internal relationships, i.e., structural validity, or relationships with other instruments. Criterion validity is defined as “the degree to which the scores of an instrument are an adequate reflection of a gold standard” [15].

Content validity could not be assessed, as for most studies a justification of choices, e.g., comprehensibility findings based on input from the target population or experts in the field, were missing. A summary of the studies examining content validity has been added in the results section. Since a priori formulated hypotheses for construct validity were often lacking, in line with previous reviews [13, 18] we formulated criteria with regard to the relationships with other instruments; see Table 1 for criteria. The criteria were subdivided by level of evidence, level 1 indicating strong evidence, level 2 indicating moderate evidence, and level 3 indicating weak evidence. Table 1 also includes criteria for criterion validity, e.g., when doubly labeled water was used as a comparison measure for questionnaires aiming to assess physical activity energy expenditure.
Table 1

Constructs of physical activity measured by the questionnaires evaluating construct and/or criterion validity, subdivided by level of evidence, and criteria for acceptable correlations

Constructs of physical activity measured

Level 1

Level 2

Level 3

Physical activity, all constructs (i.e., at least including active transport, sports, physical education, recreational activities, and chores)

Direct observation ≥ 0.70

Accelerometer total or activity counts ≥ 0.60a

PAEE measured by doubly labeled water ≥ 0.60

Accelerometer vigorous counts, moderate counts, or moderate and vigorous counts ≥ 0.40

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.40

Physical activity, not all constructs or timeframes (e.g., excluding time spent at school or chores)

Direct observation ≥ 0.70

Accelerometer total or activity counts; corresponding timeframe ≥ 0.60

Accelerometer total or activity counts; total daytime ≥ 0.40

Accelerometer moderate and vigorous counts ≥ 0.50

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.40

Physical activity, single constructs (e.g., only unstructured free play, cycling, time spent outdoors)

 

Accelerometer total or activity counts ≥ 0.40

Accelerometer moderate and vigorous counts ≥ 0.50

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.40

Cycle computer ≥ 0.70b

Physical activity energy expenditure

PAEE measured by doubly labeled water ≥ 0.70

Accelerometer total or activity counts ≥ 0.50

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.40

Vigorous activity

Accelerometer vigorous counts ≥ 0.60

Accelerometer total or activity counts ≥ 0.40

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.60

Moderate and vigorous activity

Accelerometer moderate and vigorous counts ≥ 0.60

Accelerometer total or activity counts ≥ 0.40

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.60

Moderate activity

Accelerometer moderate counts ≥ 0.60

Accelerometer total or activity counts ≥ 0.40

Pedometer counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

VO2max ≥ 0.50

Walking

Pedometer, accelerometer walking counts ≥ 0.70

Accelerometer total or activity counts ≥ 0.40

Questionnaire, diary, or interview; corresponding constructs ≥ 0.70

PAEE physical activity energy expenditure, VO2max maximal oxygen uptake

aPreferably activity counts (i.e., light, moderate, and vigorous); however, as sedentary counts have a minimal contribution, total counts are also acceptable

bIf used as a comparison for cycling

Most construct validity studies examined relationships with other instruments, reporting separate correlations for each questionnaire item. As with reliability, an overall evidence rating was applied incorporating all available correlations for each questionnaire per study (i.e., a positive, mixed, or negative evidence rating was obtained). Since no hypotheses were available for mean differences and limits of agreement, only a description of these results is included in the Results section (Sect. 3).

2.7 Inclusion of Results from the Previous Review

To draw definite conclusions regarding the best available questionnaires, the most promising questionnaires based on the previous review [8], i.e., published before May 2009, were also taken into account. As the previous review combined the methodological quality assessment and the questionnaire quality (i.e., results regarding measurement properties) in one rating, we reassessed the methodological and questionnaire quality of these previously published studies. We included only the studies that received a positive rating in the previous review for each measurement property. However, in the previous review, no final rating for measurement error was applied; therefore, all measurement error studies were reassessed and included in the current review. In addition, for construct validity, no final rating was applied in the previous review, as the majority of studies did not formulate a priori hypotheses. We chose to reassess the two studies showing the highest correlations between the questionnaire and an accelerometer, for each age category. The studies below this ‘top 2’ showed such low correlations that they would receive a negative evidence rating using our criteria. Furthermore, we assessed three other studies that formulated a priori hypotheses, as these studies may score higher regarding methodological quality. The reassessed studies are included in Tables 2, 3, 4 in the Results section.
Table 2

Construct validity of physical activity questionnaires for youth sorted by age category, methodological quality, and level of evidence and evidence rating

Questionnaire

Study populationa

Comparison measure

Resultsb,c

Methodological qualityd

Level of evidence and evidence ratinge

Preschoolers (mean age < 6 years)

 Preschool-age Children’s Physical Activity Questionnaire (Pre-PAQ) (proxy) [58]

n = 67

Age: 3–5 years

Sex: 48% girls

Acc. (Actigraph)

(cut-points not reported)

Level 3 Pre-PAQ vs. LPA (Sirard): MD − 4.8, LoA [− 105.4; 96.0], r − 0.07

Level 4 Pre-PAQ vs. MPA (Sirard): MD 48.2, LoA [− 24.9; 121.3], r 0.13

Level 5 Pre-PAQ vs. VPA (Sirard): MD 1.9, LoA [− 37.5; 41.3], r 0.17

Level 4-5 Pre-PAQ vs. MVPA (Sirard): MD 50.1, LoA [− 42.9; 143.1], r 0.17

Level 3–5 Pre-PAQ vs. non-sedentary (Reilly): MD 20.9, LoA [− 121.9; 163.7], r 0.16

Level 3–5 Pre-PAQ vs. LMVPA (Sirard): MD 45.2, LoA [− 103.6; 194.1], r 0.05

Good

Level 1: –

 Modified Burdette proxy report (proxy) [59]

n = 107

Age: 3.4 ± 1.2 years

Sex: percentage girls unknown

Acc. (Actigraph)

(cut-points: LPA 38–419 counts/15 s.; MVPA ≥ 420 counts/15 s)

PA: vs. total PA min/day, PCC 0.30; vs. MVPA min/day, PCC 0.34

Fair

Level 1: –

 Modified Harro proxy report (proxy) [59]

n = 131

Age: 3.8 ± 1.3 years

Sex: percentage girls unknown

Acc. (Actigraph)

(cut-points: LPA 38–419 counts/15 s; MVPA ≥ 420 counts/15 s)

MVPA: vs. MVPA min/day, PCC 0.10; vs. total PA min/day, PCC 0.09

Fair

Level 1: –

 Physical activity questionnaire for parents of preschoolers in Mexico [40]

n = 35

Age: 4.4 ± 0.7 years [3–5]

Sex: 51% girls

Acc. (Actigraph)

(age-specific cut-points used)

MPA vs.  % of time in MPA: Sirard SCC − 0.23, Pate SCC − 0.07

VPA vs.  % of time in VPA: Sirard SCC 0.53, Pate SCC 0.41

MVPA vs.  % of time in MVPA: Sirard SCC 0.49, Pate SCC 0.34

Poor

Level 1: –

 Children’s Physical Activity Questionnaire (CPAQ) (proxy) [60] f

n = 27

Age: 4.9 ± 0.7 years [4, 5]

Sex: 38% girls

DLW

Acc. (Actigraph)

(cut-points: MVPA ≥ 3000 or ≥ 1952 cpm)

MVPA: vs. acc. cut-point 3000 cpm SCC 0.42, MD (SD) 235.9 (362.0); vs. acc. cut-point 1952 cpm MD (SD) − 76.5 (361.6)                                                                           

PAEE vs. DLW: SCC 0.22, MD (SD) − 14.4 (52.4)

Poor (all comparison measures)

Level 1: –

 Physical activity and sedentary behavior proxy questionnaire (based on Canadian Health Measures Survey [CHMS]) (proxy) [61]

n = 87

Age: 4–70 months

Sex: 54% girls

Acc. (Actical)

(cut-points: LPA 100–1149 cpm; MVPA ≥ 1150 cpm; total PA ≥ 100 cpm)

Total PA vs. total PA min/day: MDg 131 min/day, LoA [–80; 290]h, SROC 0.39 (95% CI 0.19-0.56)

Outdoor unstructured free play aside from school daycare setting vs. total PA min/day: SROC 0.30 (95% CI 0.09–0.49)

Unstructured play in school/daycare setting vs. total PA min/day: SROC 0.42 (95% CI 0.23–0.58)

Structured PA vs. total PA min/day: SROC 0.26 (95% CI 0.05–0.46)

Poor

Level 1: –

Level 2: –

Children (mean age ≥ 6 to < 12 years)

 Out-of-school Physical Activity questionnaire [62]

n = 126

Age: 11 years

Sex: 60% girls (in total sample n = 155)

Acc. (Actigraph)

(cut-point: MVPA ≥ 2296 cpm)

MVPA duration vs. MVPA min/day: SCC 0.25, MDg − 6.3 min

MVPA frequency vs. MVPA min/day: SCC 0.25

Fair

Level 1: –

 Children’s Leisure Activities Study Survey Chinese-version questionnaire (CLASS-C) [50]

n = 139

Age: [9–12 years]

Sex: 65% girls

Acc. (Actigraph)

(age-specific cut-points used)

MPA vs. MPA min/week: boys weekdays SROC 0.21, weekends SROC 0.32, 1 week SROC 0.33, girls weekdays SROC 0.19, weekends SROC 0.22, 1 week SROC 0.29, total sample MD − 18.9 min, LoA [–89.3; 51.5]

VPA vs. VPA min/week: boys weekdays SROC 0.35, weekends SROC 0.33, 1 week SROC 0.29, girls weekdays SROC 0.48, weekends SROC 0.19, 1 week SROC 0.43, total sample MD 12.6 min, LoA [–34.8; 60.0]

Bland–Altman plot depicts a positive magnitude biasi

MVPA vs. MVPA min/week: boys weekdays SROC 0.21, weekends SROC 0.13, 1 week SROC 0.27, girls weekdays SROC 0.44, weekends SROC 0.19, 1 week SROC 0.48, total sample MD − 6.2 min, LoA [–101.5; 89.1]

Bland–Altman plot depicts a small positive magnitude biasj

Fair

Level 1: –

 Physical Activity Questionnaire for Older Children (PAQ-C) [27] f

n = from 73 (Caltrac) to 97 (activity rating and Godin 1)

Age: 11.3 ± 1.4 years [9–14]

Sex: 58% girls

Acc. (Caltrac)

(no cut-points used)

7-day PA recall by interview (PAR)

Activity rating

Godin 1 and 2 (leisure time exercise questionnaires)

CHFT

PAQ-C: vs. accumulated counts r 0.39; vs. PAR r 0.46; vs. PAR h r 0.43; vs. activity rating r 0.57; vs. Godin 1 r 0.41; vs. Godin 2 r − 0.57; vs. CHFT r 0.28

3 of 6 hypotheses correct

Fair (all comparison measures)

Level 1: –

 Previous Day Physical Activity Recall (PDPAR) [30]

n = 37

Age: 10.8 ± 0.1 years (in total sample n = 38)

Sex: 51% girls

Acc. (CSA activity monitor)

(cut-point not reported)

Mean METs: vs. total counts SROC 0.39; vs. MVPA min SROC 0.43

PA ≥ 3 METs: vs. total counts SROC 0.23; vs. MVPA min SROC 0.19

PA ≥ 6 METs: vs. total counts SROC 0.35; vs. MVPA min SROC 0.38

Fair

Level 2: –

Level 1: –

 Physical Activity Questionnaire for older Children (PAQ-C) (Spanish version) [52]

n = 78

Age: 11.0 ± 1.2 years (in total sample n = 83)

Sex: 45% girls (in total sample n = 83)

Acc. (Actigraph)

(cut-points: SB 0–100 cpm; LPA 101–2295 cpm; MPA 2296–4011 cpm; VPA ≥ 4012 cpm)

Total score vs. total PA: SROC 0.28, MD z value 0.10, LoA z values [–1.82; 2.02]k

Activity checklist: vs. total PA SROC 0.08, vs. MVPA SROC 0.04

PE vs. MVPA: SROC 0.04

Recess: vs. total PA SROC 0.14, vs. MVPA SROC 0.19

Lunch: vs. total PA SROC 0.07, vs. MVPA SROC 0.00

After school: vs. total PA SROC 0.15, vs. MVPA SROC 0.15

Afternoon: vs. total PA SROC 0.29, vs. MVPA SROC 0.28

Weekend: vs. total PA SROC 0.12, vs. MVPA SROC 0.08

Intensity last week: vs. total PA SROC 0.24, vs. MVPA SROC 0.21

Week summary: vs. total PA SROC 0.30, vs. MVPA SROC 0.31

Fair

Level 1: –

Level 2: –

 Godin Leisure-Time Exercise Questionnaire [63]

n = 31

Age: 10.6 ± 0.2 years

Sex: 45% girls

Acc. (Caltrac)

(no cut-points used)

Average total leisure activity score: PCC 0.50 (0.86 when two outliers were removed)

Fair

Level 2: + 

 Multimedia Activity Recall for Children and Adolescents (MARCA) [64] f

n = 66

Age: 11.6 ± 0.8 years

Sex: 50% girls

Acc. (Actigraph)

(no cut-points used)

PAL vs. cpm: r 0.45

MVPA vs. total counts: r 0.35

Min. locomotion vs. total counts: r 0.37

5 of 5 hypotheses correct

Fair

Level 2: –

 Chinese version of the Physical Activity Questionnaire for Older Children (PAQ-C) [43]

n = 358

Age: 10.5 ± 1.1 years [8–13] (in total sample n = 742)

Sex: 46% girls

Acc. (Actigraph)

(cut-points: MPA 2296–4011 cpm; VPA ≥ 4012 cpm)

PAQ-C: vs. MPA min/day SCC 0.24; vs. VPA min/day SCC 0.36; vs. MVPA min/day SCC 0.33

Fair

Level 2: –

 Youth Activity Profile (YAP) [38]

n = 291

Age: 9.7 ± 1.0 years (n = 135), 11.7 ± 0.8 years (n = 67), 15.7 ± 1.2 years (n = 89)

Sex: 56% girls

Sense Wear Armband (SWA)

(cut-point not reported)

School activity vs. MVPA min/week.: MD − 15.6 ± 6.2 min, LoA [− 25.8; − 5.3], r 0.58

Out-of-school activity weekday vs. MVPA min/week: MD 3.4 ± 16.6 min, LoA [− 24.2; 31.0], r 0.19

Out-of-school activity weekend vs. MVPA min/weekend: MD − 21.7 ± 13.2 min, LoA [− 43.7; 0.3], r 0.22

Fair

Level 2: –

 Food, Health, and Choices questionnaire (FHC-Q) [37]

n = 66

Age: < 9 to > 12 years

Sex: 50% girls

PAQ-C

Frequency of both medium and heavy activity vs. PAQ-C: PCC 0.52

Frequency of medium activity vs. PAQ-C medium activity: PCC 0.42

Frequency of heavy activity vs. PAQ-C heavy activity: PCC 0.46

Fair

Level 3: –

 Self-administered questionnaire to assess physical activity and sedentary behaviors [65]

n = 86

Age: 10.2 ± 1.1 years

Sex: 54% girls

Acc. (Actigraph)

(cut-points not reported)

MVPA vs. MVPA acc.: ICC 0.06, MD − 117.6 min. LoA [− 864.3; 629.0]g,l

Poor

Level 1: –

 The South American Youth/Child Cardiovascular and Environment Study (SAYCARE) Physical Activity (PA) questionnaire (proxy) [66]

n = 82

Age: 3–10 years

Sex: 54% girls

Acc. (Actigraph)

(cut-points: LPA 26–573 cpm; MPA 574–1002 cpm; VPA ≥ 1003 cpm)

MPA vs. acc. MPA: SCC 0.61, bias − 13.6 min/day, LoA − [–15.2; 41.4]

VPA vs. acc. VPA: SCC 0.27, bias − 35.3 min/day, LoA [− 36.8; 56.1]

Weekly total MVPA vs. acc. total MVPA: 0.44, bias − 22.9 min/day, LoA [− 24.6; 19.9]

 % of agreement with PA guidelines ≥ 60 min/day: κ − 0.40

Poor

Level 1: –

 Canadian Health Measures Survey (CHMS) [67]

n = 878

Age: 8.7 years (95% CI 8.5–8.9) [6–11]

Sex: 49% girls

Acc. (Actical)

(cut-point: MVPA ≥ 1500 cpm)

MVPA vs. MVPA min/day: PCC 0.29

Poor

Level 1: –

Many Rivers Physical Activity Recall Questionnaire (MRPARQ) (modified version of the APARQ) [68]

n = 86

Age: 11.1 ± 0.7 years

Sex: 59% girls

Acc. (Actigraph)

(cut-point not reported)

MVPA vs. mean weekday MVPA min/day: PCC 0.37, ICC 0.25

Bland–Altman plot depicts a positive magnitude biasm

Poor

Level 1: –

 Patient Assessment and Council for Exercise (PACE) [69]

n = 18

Age: 11.9 ± 2.0 years

Sex: 59% girls

(Age and sex total sample n = 22)

Acc. (sensewear SP3 PRO)

Acc. (Actigraph)

(cut-points not reported)

Diary (SRI and SRA)

Active days/week: vs. Actigraph (≥ 60 MVPA min/day) PCC 0.27; vs. SP3 (≥ 60 MVPA min/day) PCC 0.17; vs. SRI PCC 0.25; vs. SRA PCC 0.34

Meeting guideline (1 h MVPA/day): vs. Actigraph PoA 56%, sens 28%, spec 100%, kappa 0.22; vs. SP3 PoA 33%, sens 20%, spec 100%, kappa 0.07

Poor (all comparison measures)

Level 1: –

 Self-Administered Physical Activity Checklist (SAPAC) (Greek version) [49]

n = 90

Age: 11.4 ± 0.6 years (boys), 11.3 ± 0.6 years (girls)

Sex: 57% girls

Acc. (RT3 Research Tracker)

(cut-points not reported)

Total-MET vs. total METs: Kendall’s tau-b r 0.31, MD − 600, LoA [− 1800; 400]n

MET-LPA vs. LPA METs: Kendall’s tau-b r 0.03, MD − 750, LoA [− 1250; − 200]n

Bland–Altman plot depicts a negative magnitude biaso

MET-MVPA vs. MVPA METs: Kendall’s tau-b r 0.37, MD 0, LoA [− 900; 900]n

Poor

Level 1: –

 Assessment of Young Children’s Activity using Video Technology (ACTIVITY) [70] f

n = 47

Age: 7.7 ± 0.5 years

Sex: 40% girls

Acc. (Caltrac)

(no cut-points used)

HR monitor (Polar)

ACTIVITY total score: vs. cpm r 0.40; vs. HR average activity 0.17, vs. 50% HR reserve 0.51

Poor (all comparison measures)

Level 1: –

 Synchronised Nutrition and Activity Program (SNAP) [71] f

n = 121

Age: 10.7 ± 2.2 years [7–15]

Sex: 60% girls

Acc. (Actigraph)

(cut-point not reported)

MVPA vs. total MVPA min.: MD − 9 min (90% CI − 23 to 5)

Proportion complying to MVPA guideline: MD 0.02 (90% CI − 0.08 to 0.12)

Poor

Level 1:?

 PA questionnaire for parents and teachers [72] f

n = 62

Age: 7.0 ± 0.7 years [4–8]

Sex: 52% girls

Acc. (Caltrac)

(no cut-points used)

HR monitor (Polar)

MVPA vs. total Caltrac score: r 0.53; vs. HR: ≥ 140 and ≥ 150 bpm r 0.40

Poor (all comparison measures)

Level 2: +

 Physical Activity Questionnaire for older Children (PAQ-C) [51]

n = 58

Age: 7–9 years

Sex: 48% girls

Pedometer (Omron)

PAQ-C score: vs. average steps/day SROC 0.49; vs. total no. of steps weekdays SROC 0.53

Poor

Level 2: +

 The Modified Godin Leisure-Time Exercise Questionnaire [45]

n = 139

Age: 11.1 ± 0.4 years

Sex: 52% girls

Acc. (Actigraph)

(cut-points not reported)

Godin-Child Questionnaire total no. of min of activity/week. vs. acc. MVPA: r 0.22 (fall/autumn), r 0.24 (spring)

Poor

Level 2: –

 Parent proxy-report of physical activity and sedentary activities (proxy) [73]

n = 167 (validity vs. acc.), n = 125 (validity vs. diary)

Age: 6–10 years, 13–14 years

Sex: 51% girls (in total sample n = 189)

Acc. (Actigraph)

(cut-points not reported)

Time activity diary (PA record)

vs. acc. (adjusted for school grade, age, sex, and maternal education):

Active behavior score vs. MVPA min/day: SCC 0.21

Time spent outdoors vs. MVPA min/day: SCC 0.10

Playing vigorously active indoors vs. MVPA min/day: SCC 0.08

Playing vigorously active outdoors vs. MVPA min/day: SCC 0.19

Cycling vs. MVPA min/day: SCC 0.11

Time spent breathing hard and sweating vs. MVPA min/day: SCC 0.07

Attending sports training (outside school) vs. MVPA min/day: SCC 0.11

vs. diary:

Tended to overestimate actively playing indoors and cycling, active play outside was comparable across both measures

Poor (all comparison measures)

Level 2: –

 Diet and lifestyle questionnaire [74]

n = 446

Age: 9.0–11.9 years (in total sample n = 563)

Sex: 53% girls (in total sample n = 563)

Acc. (ActiGraph)

(cut-point: MVPA ≥ 3000 cpm)

No./days child was active > 60 min: vs. mean MVPA min/day SCC 0.04; vs.  % that MET MVPA guidelines SCC 0.07

Poor

Level 2: –

 Active Transportation to school and work in Norway (ATN) questionnaire [75]

n = 58

Age: 11.4 ± 0.5 years

Sex: 54% girls

Cycle computer

Acc.

(Actigraph)

(no cut-points used)

No. of trips walking vs. total cpm: SROC 0.12

No. of trips cycling vs. cycling km/week: SROC 0.60

Poor (all comparison measures)

Level 2: –

Level 3: –

 The ENERGY-child questionnaire [48]

n = 96

Age: [11.4 ± 0.6 to 12.0 ± 0.6 years]

Sex: [31–67% girls]

Cognitive interview

Walking to school (no./days): ICC 0.84, PoA 75%, (amount of time), ICC 0.59, PoA 74%

Transport today to school: ICC 0.67, PoA 74%

Activity during breaks: ICC 0.65, PoA 81%

Sport (h): (first sport) ICC 0.61, PoA 50%, (second sport) ICC 1.00, PoA 36%, (yesterday) ICC 0.22, PoA 50%

Bike to school (no./days): ICC 0.81, PoA 73%, (amount of time), ICC 0.66, PoA 75%

Poor

Level 3: –

 A physical activity questionnaire [76]

n = 4254

Age: 11.3 years

Sex: 51% girls (in total sample n = 4452)

Reported PA level of the adolescent by the mother and the adolescent

PA: vs. mothers perception kappa 0.13, PoA 64.7%; vs. adolescents perception kappa 0.11, PoA 64.8%

Poor

Level 3: –

 Instrument to assess children’s outdoor active play in various locations (proxy) [77]

n = 46

Age: 9.2 years [7.9–11.7]

Sex: 50% girls

Diary (parent-report)

Weekday: yard at home kappa 0.48, PoA 63.0% friend’s/neighbor’s yard kappa 0.40, PoA 65.2%, own street/court/footpath kappa 0.51, PoA 67.4%, nearby streets/court/footpath kappa 0.60, PoA 80.4%, park/playground kappa 0.39, PoA 73.9%, facilities or sport ovals kappa 0.35, PoA 67.4%, school grounds for free play outside school hours PoA 67.4%, other places PoA 86.9%

Weekend day: yard at home kappa 0.44, PoA 71.7%, friend’s/neighbor’s yard kappa 0.50, PoA 76.1%, own street/court/footpath kappa 0.43, PoA 67.4%, nearby streets/court/footpath kappa 0.44, PoA 78.3%, park/playground kappa 0.37, PoA 71.7%, facilities or sport ovals kappa 0.37, PoA 71.7%, school grounds for free play outside school hours PoA 100.0%, other places kappa 0.22, PoA 76.1%

Poor

Level 3: –

 Questions from the National Longitudinal Survey of Children and Youth [78]

n = 3940 (organized sports question)

n = 3958 (leisure sports question)

Age: 5th graders

Sex: percentage girls unknown

Parent-reported questions from the National Longitudinal Survey of Children and Youth

Organized sports: kappa 0.41 (95% CI 0.39–0.44)

Leisure sports: kappa 0.11 (95% CI 0.08–0.14)

Poor

Level 3: –

 Physical Activity Questionnaire for Older Children (PAQ-C) (minor modifications) [44]

n = 132

Age: 10.3 ± 0.6 years [9–11]

Sex: 48% girls

Cardiovascular fitness (½ mile walk run test)

PAQ-C summary score: PCC − 0.38

In-school factor: PCC − 0.27

Outside-of-school: PCC − 0.37

Poor

Level 3: –

Older children and adolescents (mean age ≥ 12 years)

 A physical activity questionnaire of the Estonian Children Personality Behavior and Health Study (ECPBHS) [79]

n = 224

Age: 12.2 ± 0.8 years

Sex: 0% girls

Acc. (Actigraph)

(cut-point: MVPA ≥ 2000 cpm)

Parent-reported child PA (same questionnaire)

Child MVPA index: vs. acc. MVPA min/day r 0.28 (95% CI 0.16–0.40); vs. parent r 0.54 (95% CI 0.44–0.62), MD 0.33 min, LoA [–14.8; 15.4]

Good (all comparison measures)

Level 1: –

 A physical activity questionnaire of the Estonian Children Personality Behavior and Health Study (ECPBHS) (proxy) [79]

n = 224

Age: 12.2 ± 0.8 years

Sex: 0% girls

Acc. (Actigraph)

(cut-point: MVPA ≥ 2000 cpm)

Child-reported child PA (same questionnaire)

Parent MVPA index: vs. acc. MVPA min/day r 0.30 (95% CI 0.18–0.42); vs. child r 0.54 (95% CI 0.44–0.62), MDp 0.33 min, LoA [–14.8; 15.4]

Good (all comparison measures)

Level 1: –

 3-Day Physical Activity Record (3DPARecord) (Greek version) [33]

n = 33

Age: 13.7 ± 0.8 years

Sex: 43% girls (age and sex total sample n = 40)

Acc. (MTI/CSA)

(no cut-points used)

3DPAR average scores vs. cpm: PCC 0.63

Fair

Level 1: +

Seven-Day Physical Activity Recall (7 Day-PAR) (Spanish version) [80]

n = 123

Age: 14.9 ± 0.9 years [13–17]

Sex: 59% girls

Acc. (Actigraph)

(cut-points: SB 0–100 cpm; LPA 101–2295 cpm; MPA 2296–4011 cpm; VPA ≥ 4012 cpm)

Aerobic fitness (20 m shuttle run)

LPA vs. LPA acc.: r − 0.22

MPA: vs. MPA acc. r 0.25, vs. fitness r − 0.17

Hard PA: vs. VPA acc. r 0.18, fitness r 0.07

Very hard: PA vs. VPA acc. r 0.38, fitness r 0.42

Fair (all comparison measures)

Level 1: –

 Youth Physical Activity Questionnaire (YPAQ) [81]

n = 44

Age: 12.7 years [12–13]

Sex: 61% girls

Acc. (Actigraph)

(cut-points: MVPA ≥ 2295 cpm)

MVPA vs. acc. MVPA: PCC 0.47, SROC 0.39, MD 25.7 min, LoA [− 72.7; 124.0]q

Fair

Level 1: –

 International Physical Activity Questionnaire – Short Form (IPAQ-SF) [82]

n = 191

Age: 14.0 ± 0.7 years

Sex: 0% girls

Acc. (Actigraph)

(cut-points: SB < 100 cpm; LPA > 100 cpm; MPA > 2000 cpm; VPA > 4000 cpm)

MPA min/day vs. acc. MPA min/day: PCC 0.11

VPA min/day vs. acc. VPA min/day: PCC 0.24

MVPA min/day vs. acc. MVPA min/day: PCC 0.31, MD 13.4 min/day, LoA [− 54.2; 80.8]g,r

Walking min/day: vs. acc. steps PCC 0.32, vs. acc. LPA min/day PCC 0.07, MD − 146.1 min/day

Fair

Level 1: –

 Tartu Physical Activity Questionnaire (TPAQ) [82]

n = 191

Age: 14.0 ± 0.7 years

Sex: 0% girls

Acc. (Actigraph)

(cut-points: SB < 100 cpm; LPA > 100 cpm; MPA > 2000 cpm; VPA > 4000 cpm)

MVPA min/day vs. acc. MVPA min/day: PCC 0.35, MD − 3.40 min/day, LoA [–49.6; 42.8]g,s

Walking/cycling min/day: vs. acc. steps PCC 0.19, vs. MVPA PCC 0.21, vs. LPA PCC − 0.02, MD − 125.1 min/day

Fair

Level 1: –

 Physical Activity and Lifestyle Questionnaire (PALQ) (Greek version) [33]

n = 33

Age: 13.7 ± 0.8 years

Sex: 43% girls (age and sex total sample n = 40)

Acc. (MTI/CSA)

(no cut-points used)

PALQ average scores vs. cpm: PCC 0.53

Fair

Level 1: –

 Moderate and vigorous physical activity items of the Youth Risk Behavior Survey (YRBS) [83]

n = 125

Age: 12.2 ± 0.6 years

Sex: 53% girls (age and sex total sample n = 139)

Acc. (Actigraph)

(age-specific cut-points used [Freedson])

Meeting MPA recommendations (≥ 30 min/day for ≥ 5 days/week) vs. accumulated MPA min.: ≥ 5 days PoA 20.8%, < 5 days PoA 8.8%, sens 0.23, spec 0.92, kappa across four acc. measures ranged from − 0.05 to 0.03

Meeting VPA recommendations (≥ 20 min/day for ≥ 3 days/week) vs. accumulated VPA min: ≥ 3 days PoA 19.2, < 3 days PoA 20.0, sens 0.86, spec 0.26; kappa across four acc. measures ranged from − 0.002 to 0.06

Fair

Level 1: –

 3-Day Physical Activity Recall (3DPARecall) instrument [20]

n = 70

Age: 14.0 ± 0.9 years [13–16]

Sex: 100% girls

Acc. (CSA activity monitor)

(cut-points not reported)

Total METs/day: vs. 7 days counts/day PCC 0.51; vs. 3 days counts/day PCC 0.46

MVPA blocks/day: vs. 7 days MVPA min/day PCC 0.35; vs. 3 days MVPA min/day PCC 0.27

VPA blocks/day: vs. 7 days VPA min/day PCC 0.45; vs. 3 days VPA min/day PCC 0.41

Fair

Level 1: –

 International Physical Activity Questionnaire - Short Form (IPAQ - SF) [84]

n = 1021

Age: 14.3 ± 1.6 years [12–18]

Sex: 47% girls

Acc. (ActiGraph)

(cut-points: LPA 101–2799 cpm; MPA 2800–3999 cpm; VPA ≥ 4000 cpm)

Total activities vs. cpm: SCC 0.31

MPA and walking vs. MPA min/day: SCC 0.20

VPA vs. VPA min/day: SCC 0.22

MVPA and walking vs. MVPA min/day: SCC 0.22

Fair

Level 1: –

 PACE + questionnaire [85]

n = 235

Age: 14.7 ± 3.1 years

Sex: 59% girls

Acc. (Actigraph)

(cut-point not reported)

PA (days/week ≥ 60 min MVPA): vs. MVPA min/day ≥ 5 valid days SCC 0.34; vs. MVPA min/day 7 valid SCC 0.27; vs. cpm ≥ 5 valid days SCC 0.33; vs. cpm 7 valid SCC 0.30

Agreement meeting PA guideline, average method: ≥ 5 valid days PoA 78.7%, 7 valid days PoA 77.9%

Agreement meeting PA guideline, all day method: ≥ 5 valid days PoA 90.2%, 7 valid days PoA 90.2%

Fair

Level 1: –

 3-Day Physical Activity Recall (3DPARecall) (modified for Australian youth) [86]

n = 155

Age: 12.3 ± 0.9 years

Sex: 50% girls

Activity monitor (CSA)

(cut-points not reported)

MPA: vs. 3 days counts/day SCC 0.16; vs. 6 days counts/day SCC 0.15; vs. 3 days MPA min/day SCC 0.15; vs. 6 days MPA min/day SCC 0.14; vs. 3 days MVPA min/day SCC 0.14; vs. 6 days MVPA min/day SCC 0.12

MET: vs. 3 days counts/day SCC 0.31; vs. 6 days counts/day SCC 0.31; vs. 3 days MPA min/day SCC 0.28; vs. 6 days MPA min/day SCC 0.26; vs. 3 days MVPA min/day SCC 0.29; vs. 6 days MVPA min/day SCC 0.27

MVPA: vs. 3 days counts/day SCC 0.27; vs. 6 days counts/day SCC 0.26; vs. 3 days MPA min/day SCC 0.24; vs. 6 days MPA min/day SCC 0.24; vs. 3 days MVPA min/day SCC 0.23; vs. 6 days MVPA min/day SCC 0.25

VPA: vs. 3 days VPA min/day males SCC 0.19, females SCC 0.33; vs. 6 days VPA min/day males SCC 0.16, females SCC 0.30

Fair

Level 1: –

 Single-item activity measure [23]

n = 96 (acc. wear time 480 min/day)

Age: 14.7 ± 0.5 years

Sex: 38% girls (total sample)

(Age and sex total sample n = 123)

n = 72 (acc. wear time 600 min/day)

Age: 14.7 ± 0.5 years

Sex: 38% girls

(Age and sex total sample n = 123)

Acc. (Actigraph)

(cut-point not reported)

No. of days being physically active ≥ 60 min: vs. time spent in MVPA (480 min/day wear time) PCC 0.46 (95% CI 0.24–0.63); vs. time spent in MVPA (600 min/day wear time) PCC 0.44 (95% CI 0.24–0.63)

Fair

Level 1: –

 Oxford Physical Activity Questionnaire (OPAQ) [23]

n = 96 (acc. wear time 480 min/day)

Age: 14.7 ± 0.5 years

Sex: 38% girls (total sample)

(Age and sex total sample n = 123)

n = 72 (acc. wear time 600 min/day)

Age: 14.7 ± 0.5 years

Sex: 38% girls

(Age and sex total sample n = 123)

Acc. (Actigraph)

(cut-point not reported)

MVPA: vs. time spent in MVPA (480 min/day wear time) PCC 0.43 (95% CI 0.23–0.62); vs. time spent in MVPA (600 min/day wear time) PCC 0.50 (95% CI 0.30–0.65)

Fair

Level 1: –

 MVPA self-report questionnaire [87]

n = 203 (5 valid acc. days)

Age: 15.8 ± 0.7 years

Sex: 61% girls

n = 103 (7 valid acc. days)

Age: 15.8 ± 0.7 (total sample n = 203)

Sex: 67% girls

Acc. (Actigraph)

(cut-points not reported)

MVPA: vs. MVPA min/day (5 valid days) SROC 0.40 (95% CI 0.28–0.51); vs. MVPA min/day (7 valid days) SROC 0.49 (95% CI 0.32–0.62); vs. total cpm/day (5 valid days) SROC 0.42 (95% CI 0.30–0.5); vs. total cpm/day (7 valid days) SROC 0.49 (95% CI 0.33–0.63)

Meeting PA recommendations (≥ 60 MVPA min/day): vs. average method (average of 60 MVPA min/valid day) (5 valid days) PoA 71.9%, sens 45.5%, spec 73.4%; vs. average method (7 valid days) PoA 88.2%, sens 16.7%, spec 92.7%; vs. all-day method (60 MVPA min on ≥ 5 days) (5 valid days) PoA 71.9%, sens 0%, spec 72.3%; vs. all-day method (60 MVPA min on ≥ 7 days) (7 valid days) PoA 69.6%, spec 69.6%

Fair

Level 1: –

 Activity Questionnaire for Adults and Adolescents (AQuAA) [21]

n = 42

Age: 13.4 ± 1.0 years

Sex: 50% girls

Acc. (Actigraph)

(cut-points: LPA 700–4478 cpm; MPA 4479–8252 cpm; VPA; ≥ 8253 cpm)

Light activities vs. LPA min/week: SCC 0.11

Moderate activities vs. MPA min/week: SCC − 0.21

Vigorous activities vs. VPA min/week: SCC 0.21

Moderate to vigorous activities vs. MVPA min/week: SCC − 0.23

AQuAA score vs. PA cpm: SCC 0.13

Fair

Level 1: –

 Physical Activity Questionnaire for Adolescents (PAQ-A) [88] f

n = ranging from 48 (Caltrac) to 85 (Activity rating, Godin 1 and 2)

Age: 16.3 ± 1.5 years

Sex: 52% girls

Acc. (Caltrac)

(cut-points not reported)

7-day recall interview (PAR)

Activity rating

Godin 1 and 2 (leisure time exercise questionnaires)

PAQ-A: vs. acc. activity counts/day r 0.33; vs. PAR 0.59; vs. PAR hours r 0.51; vs. activity rating r 0.73; vs. Godin 1 r 0.57; vs. Godin 2 r − 0.62

3 of 5 hypotheses correct

Fair (all comparison measures)

Level 1: –

 Modified Physical Activity Questionnaire for Adolescents (PAQ-A) [34]

n = 88

Age: 14.5 ± 1.7 years

Sex: 42% girls

(Age and sex total sample n = 169)

Acc. (Actigraph)

(cut-points not reported)

IFIS (Fitness)

PAQ-A total score: vs. daily MVPA min/day SCC 0.39; vs. daily PA min/day SCC 0.42

Sport and activity list: vs. daily MVPA min/day SCC 0.12; vs. daily PA min/day SCC 0.21

Before school activity: vs. daily MVPA min/day SCC 0.02; vs. daily PA min/day SCC 0.14

To school active travel: vs. daily MVPA min/day SCC 0.32; vs. daily PA min/day SCC 0.33

PE: vs. daily MVPA min/day SCC 0.25; vs. daily PA min/day SCC 0.12

After-school activity: vs. daily MVPA min/day SCC 0.26; vs. daily PA min/day SCC 0.26

From school active travel: vs. daily MVPA min/day SCC 0.30; daily PA min/day SCC 0.22

Evening activity: vs. daily MVPA min/day SCC 0.23; vs. daily PA min/day SCC 0.23

Weekend activity: vs. daily MVPA min/day SCC 0.10; vs. daily PA min/day SCC 0.28

Statement: vs. daily MVPA min/day SCC 0.38; vs. daily PA min/day SCC 0.33

Weekly activity: vs. daily MVPA min/day SCC 0.34; vs. daily PA min/day SCC 0.29

PAQ-A total score: vs. IFIS scores SCC 0.35

Fair (all comparison measures)

Level 1: –

Level 2: –

 An adapted version of the Assessment of Physical Activity Levels Questionnaire (APALQ) [53]

n = 77

Age: 13.6 ± 1.1 years

Sex: 35% girls

Acc. (CSA)

(cut-points: MPA 3000–5399 cpm; VPA > 5400 cpm)

PA index: vs. acc. MVPA min/day PCC 0.53, vs. steps/day PCC 0.47

Fair

Level 2: +

 3-Day Physical Activity Recall (3DPARecall) instrument (Singaporean version) [42]

n = 219

Age: 14.5 ± 1.1 years [13–16]

Sex: 53% girls (age and sex total sample n = 221)

Pedometer (Digiwalker)

3-day average mean METs vs. step counts: SCC 0.40

3-day average VPA blocks vs. step counts: SCC 0.34

3-day average MVPA blocks vs. step counts: SCC 0.32

Fair

Level 2: –

 Web-based physical Activity Questionnaire for Older Children (PAQ-C) [28]

n = 342 (pedometer), 391 (shuttlerun)

Age: 12.8 years

Sex: 51% girls

(Age and sex total sample n = 459)

Pedometer (Digiwalker)

20mSRT

PAQ-C: vs. 3 days pedometer record PCC 0.28, vs. 20mSRT PCC 0.28

Fair (all comparison measures)

Level 2: –

 Physical activity questionnaire of the Arab Teen Lifestyle Study [89]

n = 75

Age: 16.1 ± 1.1 years

Sex: 48% girls

Pedometer (Digi-walker SW 701)

All activities vs. step counts/day: PCC 0.37

MPA vs. step counts/day: PCC 0.27

VPA vs. step counts/day: PCC 0.34

Specific activities vs. step counts/day: walking PCC 0.35, jogging PCC 0.38, swimming PCC 0.14, household activities PCC 0.14, bicycling PCC 0.12, martial arts PCC 0.10, weight training PCC 0.04

Fair

Level 2: –

 Previous Day Physical Activity Recall (PDPAR) [31]

ACTIVITYGRAM

n = 147

Age:12.4 ± 0.4 years

Sex: 44% girls

Biotrainer (first sample)

n = 28 [25–28]

Age: 12.4 ± 0.5 years

Sex: 50% girls

Biotrainer (second sample)

n = 128

Age: unknown

Sex: 36% girls

Activity monitor (Biotrainer Pro)

(no cut-points used)

ACTIVITYGRAM self-report assessment

PDPAR1 (compute no. of time intervals > 4 METs): vs. Biotrainer activity counts afternoon/evening r 0.65 (95% CI 0.36–0.94) (first sample), r 0.50 (second sample); vs. ACTIVITYGRAM r 0.40 (95% CI 0.25–0.55)

PDPAR2 (SRI level was used instead of METs) vs. Biotrainer activity counts afternoon/evening r 0.56 (95% CI 0.24–0.88) (first sample), r 0.52 (second sample); vs. ACTIVITYGRAM r 0.50 (95% CI 0.36–0.64)

Poor vs. Biotrainer

Fair vs. questionnaire

Level 1: ± (PDPAR1)

Level 1: – (PDPAR2)

 Activitygram self-report assessment [31]

PDPAR

n = 147

Age:12.4 ± 0.4 years

Sex: 44% girls

Biotrainer

n = 28 [25–28]

Age: 12.4 ± 0.5 years

Sex: 50% girls

Activity monitor (Biotrainer Pro)

(no cut-points used)

PDPAR

ACTIVITYGRAM: vs. PDPAR 1 (compute no. of time intervals > 4 METs) r 0.40 (95% CI 0.25–0.55); vs. PDPAR 2 (SRI level scoring was used instead of METs) r 0.50 (95% CI 0.36–0.64); vs. Biotrainer activity counts r 0.50 (95% CI 0.17–0.83)

Poor vs. Biotrainer

Fair vs. questionnaire

Level 1: –

 MVPA scores of the International Physical Activity Questionnaire Short form (IPAQ-SF) [90]

n = 76 (vs. acc.)

Age: 12.7 ± 1.4 years (total sample n = 998)

Sex: 53% girls

n = 998 (vs. questionnaire)

Age: 12.7 ± 1.4 years

Sex: 50% girls

Acc. (Actigraph)

(cut-point MVPA ≥ 3581 cpm), MVPA scores of the HBSC Research Protocol

MVPA IPAQ-SF T0: vs. MVPA acc. T0 girls r 0.08, boys r 0.10; vs. MVPA HBSC T0 girls r 0.55, boys r 0.62

MVPA IPAQ-SF T1: vs. MVPA acc. T1 girls r 0.38, boys r − 0.05; vs. MVPA HBSC T1 girls r 0.76, boys r 0.70

Fair vs. acc.

Poor vs. questionnaire

Level 1: –

 MVPA scores of the Health Behavior in School-aged Children (HBSC) Research Protocol [90]

n = 76 (vs. acc.)

Age: 12.7 ± 1.4 years (total sample n = 998)

Sex: 53% girls

n = 998 (vs. questionnaire)

Age: 12.7 ± 1.4 years

Sex: 50% girls

Acc. (Actigraph)

(cut-point MVPA ≥ 3581 cpm), MVPA scores of the IPAQ-SF

MVPA HBSC T0: vs. MVPA acc. T0 girls r 0.10, boys r 0.35; vs. MVPA IPAQ-SF T0 girls r 0.55, boys r 0.62

MVPA HBSC T1: vs. MVPA acc. T1 girls r 0.37, boys r 0.04; vs. MVPA IPAQ-SF T1 girls r 0.76, boys r 0.70

Fair vs. acc.

Poor vs. questionnaire

Level 1: –

 The South American Youth/Child Cardiovascular and Environment Study (SAYCARE) Physical Activity (PA) questionnaire [66]

n = 60

Age: 11–18 years

Sex: 56% girls

Acc. (Actigraph)

(cut-points: LPA 101–1999 cpm; MPA 2000–4999 cpm; VPA ≥ 4000 cpm)

MPA vs. acc. MPA: SCC 0.11, bias − 19.5 min/day, LoA [–41.6; 58.9]

VPA vs. acc. VPA: SCC 0.65, bias 18.3 min/day, LoA [–92.6; 56.0]

Weekly total MVPA vs. acc. total MVPA: 0.88, bias 16.0 min/day, LoA [–14.2; 17.4]

 % of agreement with PA guidelines ≥ 60 min/day: κ0.51

Poor

Level 1: ±

 Pelotas Birth cohort physical activity questionnaire [91]

n = 25

Age: 13.0 ± 0.3 years

Sex: 64% girls

DLW

PA: vs. total energy expenditure SROC 0.41; vs. PAEE SROC 0.30

Poor

Level 1: –

 3-Day Physical Activity Recall (3DPARecall) questionnaire (modified) [92]

n = 20

Age: 13.3 ± 0.9 years

Sex: 100% girls

Acc. (CSA)

(cut-points not reported)

Total METs/day: vs. 7 days counts/day PCC 0.36; vs. 3 days counts/day PCC 0.63

MPA blocks/day: vs. 7 days MPA min/day PCC 0.25; vs. 3 days MPA min/day PCC 0.29

VPA blocks/day: vs. 7 days VPA min/day PCC 0.57; vs. 3 days VPA min/day PCC 0.49

Poor

Level 1: –

 Short Questionnaire to ASsess Health-enhancing (SQUASH) physical activity in adolescents [93]

n = 17

Age: 17.5 ± 0.6 years

Sex: 53% girls

DLW

PAEE: MDt 126 kcal/day, 95% LoA [–1207; 1459], SROC 0.50

Poor

Level 1: –

 International Physical Activity Questionnaire for Adolescents (adapted version of the IPAQ) [94]

n = 2018

Age: [12.5–17.5 years]

Sex: 54% girls

Acc. (Actigraph)

(cut-points: MPA 2000–3999 cpm; VPA ≥ 4000 cpm)

VO2max

MPA: vs. MPA acc. min/day SROC 0.15, MD 31.6 min/day LoA [− 74.0; 137.2]; vs. VO2max SROC 0.08

MVPA: vs. acc. MVPA min/day SROC 0.21; vs. VO2max SROC 0.21

VPA: vs. acc. VPA min/day SROC 0.25, MD 13.2 min/day LoA [–65.0; 91.4]; vs. VO2max SROC 0.35

Bland–Altman plots depict a positive magnitude biasu

Poor (all comparison measures)

Level 1: –

 Recess Physical Activity Recall (RPAR) [95]

n = 49 (pedometer)

Age: 13.3 ± 0.5 years

Sex: 65% girls

n = 32 (Biotrainer)

Age: 12.9 ± 0.8 years

Sex: 31% girls

n = 32 (Actigraph)

Age: 12.7 ± 0.8 years

Sex: 38% girls

Acc. (Actigraph)

(cut-points not reported)

Acc. (Biotrainer)

(cut-points not reported)

Pedometer (Yamax digiwalker)

Total PA: vs. pedometer steps PCC 0.35; vs. Biotrainer total counts PCC 0.40, counts adjusted for movement time PCC 0.54; vs. Actigraph total counts PCC 0.42

MPA vs. MPA min: PCC 0.47

VPA vs. VPA min: PCC 0.31

MVPA vs. MVPA min: PCC 0.52, MDg 2.15 ± 3.67 min, LoA [–5.04; 9.34], syst. bias r = − 0.51

Bland–Altman plot depicts a positive magnitude biasv

Total PA tertiles classification agreement (low, medium, high): vs. pedometer steps PoA 46.9% kappa 0.21; vs. Biotrainer total PA counts PoA 59.3% kappa 0.39, counts adjusted for movement time PoA 43.8% kappa 0.16; vs. Actigraph total counts 43.8%, kappa 0.16

MVPA tertiles classification agreement (low, medium, high) vs. Actigraph MVPA min: PoA 62.5%, kappa 0.44

Poor (all comparison measures)

Level 1: –

 Swedish Adolescent Physical Activity Questionnaire (SAPAQ) [96] f

n = 50

Age: 16.9 ± 0.4 years

Sex: 62% girls

Acc. (MTI)

(cut-points: LPA 500–1999 cpm; MPA 2000–5500 cpm; VPA ≥ 5500)

Total PA: vs. time spent in PA r 0.51; vs. counts/day r 0.49; vs. cpm r 0.45

Poor

Level 1: –

Activity Questionnaire for Adults and Adolescents (AQuAA) [22]

n = 236

Age: 15.0 ± 1.0 years

Sex: 60% girls

Acc. (PAM)

(cut-points not reported)

MPA vs. MPA min/week: MD 600 min/week, LoA [− 600; 1800]n

VPA vs. VPA min/week: MD 200 min/week, LoA [− 500; 900]n

MVPA vs. MVPA min/week: MD 800 min/week, LoA [− 700; 2100]n

MVPA (-cycling) vs. MVPA min/week: MD 500 min/week, LoA [− 800; 1800]n

Agreement between self-report and acc. differed by gender

Bland–Altman plots depict a positive magnitude biasw

Poor

Level 1:?

 Computer assisted interview based on National Health and Nutrition Examination Survey (NHANES) survey [97]

n = 2761

Age: 12–19 years

Sex: 48% girls

Acc. (Actigraph)

(cut-point: MVPA ≥ 3000 cpm)

MVPA vs. MVPA min/day: median difference 27.4 min/day

Bland–Altman plot depicts a negative magnitude biasx

Poor

Level 1:?

 Previous Day Physical Activity Recall (PDPAR-24) self-report instrument [32]

n = 122

Age: 13.8 ± 1.2 years

Sex: 53% girls

Pedometer (Digiwalker)

Mean METs vs. step counts: SCC 0.34

30 min blocks VPA vs. step counts: SCC 0.30

30 min blocks MVPA vs. step counts: SCC 0.29

Poor

Level 2: –

 Dutch Physical Activity Checklist for Adolescents (PAQ-A) [35]

n = 44

Age: 14.2 ± 1.8 years

Sex: 41% girls

Cardiopulmonary exercise test (CPET)

Spare-time activity—sports: SCC − 0.01

Activity during PE: SCC 0.44

Lunchtime activity: SCC 0.01

After-school activity: SCC 0.05

Evening activity: SCC 0.55

Weekend activity: SCC 0.61

Activity frequency during last 7 days: SCC 0.43

Activity frequency during each day last week: SCC 0.41

Total PA: SCC 0.52

Poor

Level 3: ±

 Godin-Shephard Survey [98]

n = 102

Age: 11.2 ± 0.7 years (n = 36), 13.6 ± 0.5 years (n = 36), 16.4 ± 0.8 years (n = 30)

Sex: 51% girls

Activity rating

Seven-day Physical Activity Recall (PAR)

Godin-Shephard survey: vs. PAR total kcal of expenditure and kcal per kg body weight (KKD) r 0.39; vs. activity rating r 0.32

Poor

Level 3: –

 Children’s Leisure Activities Study Survey (CLASS) questionnaire (modified version) [99]

n = 108

Age: 12 years

Sex: 58.3% girls

Eurofit test battery: aerobic fitness

Total PA: SROC 0.43

MPA: SROC 0.13

VPA: SROC 0.20

Poor

Level 3: –

20mSRT 20 m shuttle run test, acc. accelerometer, bpm beats per min, CHFT Canadian Home Fitness Test, CI confidence interval, COSMIN COnsensus-based Standards for the selection of health Measurement Instruments, cpm counts per min, DLW doubly labeled water, HR heart rate, ICC intraclass correlation coefficient, LMVPA light, moderate, and vigorous physical activity, LoA limits of agreement, LPA light physical activity, MD mean difference, MET metabolic equivalent, MPA moderate physical activity, MVPA moderate to vigorous physical activity, PA physical activity, PAEE physical activity energy expenditure, PCC Pearson correlation coefficient, PE physical education, PoA percentage of agreement, r correlation coefficient without specific information on the kind of correlation, SCC Spearman correlation coefficient, SD standard deviation, sens sensitivity, spec specificity, SRA self-reported activity, SRI self-reported intensity, SROC Spearman rank order correlation, VO2max maximal oxygen uptake, VPA vigorous physical activity

aAge presented as mean age ± SD [range]

bMD represents mean questionnaire value – mean comparison measure value, unless stated otherwise

cData are presented in the following order: (i) construct measured by questionnaire; (ii) versus construct measured by comparison measure; and (iii) statistical method(s) and outcome(s). Terms used in the original papers to clarify the cutpoints used are provided in parentheses

dBased on the COSMIN checklist

eBased on Table 1 and best available comparison measure: + indicates ≥ 80% acceptable correlations; ± indicates ≥ 50% to < 80% acceptable correlations; – indicates < 50% acceptable correlations

fStudy from previous review

gMean accelerometer value − mean questionnaire value

hLoA extracted from figure in article

iBland–Altman plot indicates larger overestimation by questionnaire with increasing mean VPA time (no statistical analysis applied)

jBland–Altman plot indicates larger overestimation by questionnaire with increasing mean MVPA time (no statistical analysis applied)

kBland–Altman plot indicates underestimation by questionnaire with decreasing mean MVPA time and overestimation with increasing mean MVPA time (no statistical analysis applied)

lBland–Altman plot indicates underestimation by questionnaire with decreasing mean MVPA time and overestimation with increasing mean MVPA time (no statistical analysis applied)

mBland–Altman plot indicates underestimation by questionnaire with decreasing mean MVPA time and overestimation with increasing mean MVPA time (no statistical analysis applied)

nLoA and MD extracted from figure in article

oBland–Altman plot indicates larger underestimation by questionnaire with increasing mean LPA time (no statistical analysis applied)

pChild report mean value − parent report mean value

qBland–Altman plot indicates underestimation by questionnaire with decreasing mean MVPA time and overestimation with increasing mean MVPA time (no statistical analysis applied)

rBland–Altman plot indicates smaller underestimation by questionnaire with increasing mean MVPA time (r = 0.14, p < 0.05)

sBland–Altman plot indicates overestimation by questionnaire with decreasing mean MVPA time and underestimation with increasing mean MVPA time (r = 0.78, p < 0.0001)

tDLW mean value − questionnaire mean value

uFor both MPA and VPA the Bland–Altman plot indicates overestimation by questionnaire with increasing mean MPA and VPA time (no statistical analysis applied)

vBland–Altman plot indicates underestimation by questionnaire with decreasing time spent in PA and overestimation with increasing time spent in PA (no statistical analysis applied)

wFor MPA, MVPA, MVPA (-cycling) and VPA the Bland–Altman plot indicates larger overestimation by questionnaire with increasing mean activity min/week (no statistical analysis applied)

xBland–Altman plot indicates overestimation by questionnaire with decreasing mean MVPA time and underestimation with increasing mean MVPA time (no statistical analysis applied)

Table 3

Reliability of physical activity questionnaires for youth sorted by age category, methodological quality, and evidence rating

Questionnaire

Study populationa

Time interval

Results

Methodological qualityb

Evidence rating

Preschoolers (mean age < 6 years)

 Preschool-age Children’s Physical Activity Questionnaire (Pre-PAQ) [58]

n = 103

Age: 3.8 ± 0.74 years

Sex: 48% girls

2 weeks

Pre-PAQ level 3: ICC 0.53

Pre-PAQ level 4: ICC 0.44

Pre-PAQ level 5: ICC 0.64

Time spent in fast-paced activities: ICC 0.64

Time spent in organized activities: ICC ranged from 0.96 to 0.99

Good

 Energy Balance Related Behaviors (ERBs) self-administered primary caregivers questionnaire (PCQ), from the ToyBox-study (proxy) [46]

n = 93 preschoolers

2 weeks

Sports: time per week ICC 0.93 (95% CI 0.85–0.97), type of sport 0.71 (95% CI 0.46–0.86)

Active/passive transport: travel forth ICC 0.91 (95% CI 0.87–0.94), time 0.82 (95% CI 0.73–0.88), travel home 0.88 (95% CI 0.82–0.92), time 0.89 (95% CI 0.83–0.93)

Fair

+

 Children’s Leisure Activities Study Survey (CLASS) (proxy) [100] c

n = 58

Age: 5.3 ± 0.5 years [5–6]

Sex: 37% girls

At least 14 days

MPA: ICC frequency 0.74, duration 0.49

VPA: ICC frequency 0.87, duration 0.81

Total PA: ICC frequency 0.83, duration 0.76

List of activities: ICC frequency ranging from − 0.03 to 0.94, duration ranging from − 0.04 to 0.91

Fair

 Physical activity questionnaire for parents of preschoolers in Mexico [40]

n = 21

Age: 3–5 years

Sex: percentage girls unknown

1 week

Duration moderate activity: r 0.79

Duration vigorous activity: r 0.94

Overall activity: r 0.97

Poor

±

Kid Active Q (Web-based)(proxy) [101]

n = 20

Age: 4.2 ± 1.3 years [2–6]

Sex: 50% girls

3 weeks

Overall PA level: ICC 0.66 (95% CI 0.41–0.91)

Time spent outdoors: ICC 0.60 (95% CI 0.31–0.88)

Poor

Children (mean age ≥ 6 to < 12 years)

 Chinese version of the Physical Activity Questionnaire for Older Children (PAQ-C) [43]

n = 92

Age: 8–13 years

Sex: 45% girls

7–10 days

PAQ-C: ICC 0.82

Good

+

 Active Transportation to school and work in Norway (ATN) questionnaire [41]

n = 87

Age: 11–12 years

Sex: percentage girls unknown

2 weeks

Walking: SROC 0.92

Cycling: SROC 0.92

Classification in major mode of commuting: kappa 0.93

Good

+

 Children’s Leisure Activities Study Survey Chinese-version questionnaire (CLASS-C)

[50]

n = 214

Age: 10.9 ± 0.9 years [9–12] 

Sex: 62% girls

Approx. 1 week

Weekly MPA (min): ICC 0.61 (95% CI 0.49–0.70)

Weekly VPA (min): ICC 0.73 (95% CI 0.64–0.79)

Weekly MVPA (min): ICC 0.71 (95% CI 0.61–0.77)

Good

±

 Out-of-school Physical Activity questionnaire [62]

n = 151

Age: 11 years

Sex: 60% girls (in total sample n = 155)

Approx. 30 days

MVPA duration: ICC 0.65

MVPA frequency: ICC 0.64

Good

 The Energy-child questionnaire [48]

n = 730

Age: [11.3 ± 0.5 to 12.5 ± 0.6 years]

Sex: [47–58% girls]

1 week

Walking to school: (no./days) ICC 0.91; (amount of time) ICC 0.70

Transport today to school: ICC 0.79

Activity during breaks: ICC 0.80

Sport hours: (first sport) ICC 0.74, (second sport) ICC 1.00, (yesterday) ICC 0.22

Bike to school: (no./days) ICC 0.94, (amount of time) ICC 0.81

Fair

+

 Self-Administered Physical Activity Checklist (SAPAC) (Greek version) [49]

n = 72

Age: 11.5 ± 0.5 years

Sex: 49% girls

2 weeks

Total-MET: ICC 0.87 (95% CI 0.85–0.88)

MET-LPA: ICC 0.85 (95% CI 0.82–0.88)

MET-MVPA: ICC 0.88 (95% CI 0.86–0.90)

Fair

+

 Physical Activity Questionnaire for Older Children (PAQ-C) [29] c

n = 84

Age: 9–14 years

Sex: 49% girls

1 week

ICC boys 0.75, girls 0.82

Fair

+

 Girls health Enrichment Multisite Study Activity Questionnaire (GAQ) [102] c

n = 68

Age: 9.0 ± 0.6 years

Sex: 100% girls

4 days

28 activities: yesterday ICC 0.78, usual 0.82

18 activities: yesterday ICC 0.70, usual 0.79

Fair

+

 Food, Health, and Choices questionnaire (FHC-Q) [37]

n = 82 (digital vs. paper)

Age: < 9 to > 12 years

Sex: 51% girls

n = 73 (digital vs. digital)

Age: < 9 to > 12 years

Sex: 45% girls

2 weeks

PA digital vs. paper: ICC 0.73

PA digital vs. digital: ICC 0.66

Fair (both groups)

±

 The South American Youth/Child Cardiovascular and Environment Study (SAYCARE) Physical Activity (PA) questionnaire (proxy) [66]

n = 161

Age: 3–10 years

Sex: 50% girls

15 days

Active commuting: SCC 0.28

PA at school: SCC 0.31

PA at leisure time: SCC 0.33

MPA: SCC 0.37

VPA: SCC 0.89

Weekly total MVPA: SCC 0.56

 % of agreement with current PA guidelines ≥ 60 min/day: κ 0.32

Fair

 Dutch Physical Activity Checklist for Children (PAQ-C) [35]

n = 192

Age: 8.9 ± 1.7 years [5–12]

Sex: 53% girls

NA: inter-rater (parent vs. child)

Spare-time activity—sports: kappa 0.50 (95% CI 0.41–0.60)

Activity during PE classes: 0.48 (95% CI 0.37–0.59)

Break-time activity: 0.64 (95% CI 0.55–0.73)

Lunchtime activity: 0.68 (95% CI 0.60–0.77)

After-school activity: 0.63 (95% CI 0.54–0.71)

Evening activity: 0.69 (95% CI 0.62–0.77)

Weekend activity: 0.56 (95% CI 0.46–0.67)

Activity frequency last 7 days: 0.65 (95% CI 0.56–0.74)

Activity frequency during each day: 0.64 (95% CI 0.55–0.72)

Total PA: 0.60 (95% CI 0.52–0.67)

Fair

 Instrument to assess children’s outdoor active play in various locations (proxy) [77]

n = 53

Age: 9.5 ± 0.7 years [8.3–12.3]

Sex: 42% girls

2 weeks

Weekday ICC: yard at home 0.80, friend’s/neighbor’s yard 0.70, own street/court/footpath 0.82, nearby streets/court/footpath 0.40, park/playground 0.63, facilities or sport ovals 0.48, school grounds for free play outside school hours 0.51, other places 0.47

Weekend day ICC: yard at home 0.58, friend’s/neighbor’s yard 0.77, own street/court/footpath 0.76, nearby streets/court/footpath 0.33, park/playground 0.64, facilities or sport ovals 0.63, school grounds for free play outside school hours 0.18, other places 0.62

Fair

 Parent proxy-report of physical activity and sedentary activities (proxy) [73]

n = 147

Age: 6–10 years, 13–14 years

Sex: 51% girls (in total sample n = 189)

2 months

6 months

After 2 months:

Playing vigorously indoors: ICC 0.41, MD − 8.7 (min/day) (− 17.6 to 0.1)

Playing vigorously outdoors: ICC 0.43, MD − 10.0 (− 19.2 to − 0.8)

Cycling: ICC 0.64 MD − 1.4 (− 7.2 to 4.5)

After 6 months:

Playing vigorously indoors: ICC 0.67, MD − 8.3 (− 14.2 to − 2.4)

Playing vigorously outdoors: ICC 0.60, MD − 3.1 (− 11.3 to 5.1)

Cycling: ICC 0.45, MD 2.6 (− 4.4 to 9.7)

2 months’ time interval: fair

6 months’ time interval: poor

 Physical Activity Questionnaire for older Children (PAQ-C) (Spanish version) [52]

n = 83

Age: 11.0 ± 1.2 years

Sex: 45% girls

6 h

Total score: ICC 0.96

Activity checklist: ICC 0.96

PE: ICC 0.95

Recess: ICC 0.79

Lunch: ICC 0.87

After school: ICC 0.82

Afternoon: ICC 0.77

Weekend: ICC 0.63

Intensity last week: ICC 0.90

Week summary: ICC 0.95

Poor

+

 Godin Leisure-Time Exercise Questionnaire [63]

n = 31

Age: 10.6 ± 0.2 years

Sex: 45% girls

Same day (beginning and end of the school day)

Mild exercise: PCC 0.25

Moderate exercise: PCC 0.38

Strenuous exercise: PCC 0.69

Total leisure activity score: PCC 0.62, MD − 33.4, LoA [− 239; 172.2]

Poor

 The Modified Godin Leisure-Time Exercise Questionnaire [45]

n = 139

Age: 11.1 ± 0.4 years

Sex: 52% girls

Fall (autumn) and spring (6 months)

Total min of exercise: PCC 0.68

Poor

Older children and adolescents (mean age ≥ 12 years)

 Single-item activity measure [23]

n = 107

Age: 14.7 ± 0.5 years

Sex: 38% girls

(Age and sex total sample n = 123)

2 weeks

ICC 0.75 (95% CI 0.64–0.83), MD 0.08 (95% CI − 0.12 to 0.26)

Good

+

 Web-based and paper-based Physical Activity Questionnaire for Older Children (PAQ-C) [28]

n = 323

Age 12.8 years

Sex: 51% girls

(Age and sex total sample n = 459)

Approx. 8 days

Web-based vs. web-based: ICC 0.79 (95% CI 0.74–0.82), PCC 0.79, MD 0.11 (95% CI 0.06–0.15)

Web-based vs. paper-based: ICC 0.70 (95% CI 0.65–0.75), PCC 0.70, MD − 0.02 (95% CI − 0.06 to 0.03)

Good

+

 An adapted version of the Assessment of Physical Activity Levels Questionnaire (APALQ) [53]

n = 150

Age: 13.6 ± 1.1 years

Sex: 52% girls

7 days

PA index: ICC 0.76

Organized sport participation outside school: ICC 0.86

Non-organized sport participation outside school: ICC 0.58

PE: ICC 0.61

Hours per week out of school PA intensity: ICC 0.82

Participation in competitive sport: ICC 0.93

Good

±

 International Physical Activity Questionnaire - Short Form (IPAQ-SF) [84]

n = 92

Age: 15.9 ± 1.4 years [12–18]

Sex: 53% girls

1 week

VPA: ICC 0.79 (95% CI 0.70–0.86)

MPA: ICC 0.53 (95% CI 0.36–0.66)

Walking: ICC 0.66 (95% CI 0.53–0.76)

Total PA: ICC 0.74 (95% CI 0.63–0.82)

Good

±

 Child and Adolescent Physical Activity and Nutrition survey (CAPANS-PA) recall questionnaire [103]

n = 77

Age: 12 ± 0.8 years [11–14]

Sex: 51% girls

1 week

Frequency MVPA: ICC Monday–Friday 0.77 (95% CI 0.67–0.85), Saturday 0.73 (95% CI 0.57–0.84), Sunday 0.19 (95% CI − 0.16 to 0.50), Monday–Sunday 0.86 (95% CI 0.79–0.91)

Duration MVPA: ICC Monday–Friday 0.74 (95% CI 0.62–0.83), Saturday 0.70 (95% CI 0.51–0.82), Sunday 0.36 (95% CI 0.01–0.63), Monday–Sunday 0.78 (95% CI 0.66–0.85)

Frequency active in PE: kappa 0.51 (95% CI 0.34–0.67)

Frequency PA right after school: 0.48 (95% CI 0.37–0.66)

Frequency PA evenings: 0.50 (95% CI 0.37–0.66)

Frequency PA last weekend: 0.49 (95% CI 0.34–0.64)

Participation in 32 PAs: kappa ranging from − 0.04 to 0.82

Good

 Activity Questionnaire for Adults and Adolescents (AQuAA) [21]

n = 53

Age: 14.1 ± 1.4 years

Sex: 43% girls

2 weeks

AQuAA score (MET × min/week): ICC 0.44 (95% CI 0.16–0.65)

Light activities (min/week): ICC 0.30 (95% CI 0.04–0.52)

Moderate activities (min/week): ICC 0.50 (95% CI 0.27–0.68)

Moderate to vigorous activities: ICC 0.54 (95% CI 0.32–0.70)

Vigorous activities (min/week): ICC 0.59 (95% CI 0.38–0.75)

Good

 Godin-Shephard Survey [98]

n = 102

Age: 11.2 ± 0.7 years (n = 36), 13.6 ± 0.5 years (n = 36), 16.4 ± 0.8 years (n = 30)

Sex: 51% girls

2 weeks

Godin-Shephard Survey: r 0.81

Fair

+

 VISA-TEEN questionnaire [104]

n = 228

Age: 15.4 ± 1.6 years

Sex: 46% girls

(Age and sex total sample n = 396)

15 days

MVPA: (days/week) ICC 0.77 (95% CI 0.71–0.82), (h/week) 0.86 (95% CI 0.81–0.89)

VPA: (h/week) ICC 0.80 (95% CI 0.75–0.85)

Fair

+

 Children’s Leisure Activities Study Survey (CLASS) questionnaire (modified version) [99]

n = 108

Age: 12 years

Sex: 58.3% girls

3 weeks

MPA: ICC 0.95

VPA: ICC 0.83

Total PA: ICC 0.93

Fair

+

 Oxford Physical Activity Questionnaire (OPAQ) [23]

n = 104

Age: 14.7 ± 0.5 years

Sex: 38% girls

(Age and sex total sample n = 123)

2 weeks

ICC 0.79 (95% CI 0.69–0.86), MD − 0.17 (95% CI − 0.43 to 0.10)

Fair

+

 Quantification de l’activité physique en altitude chez les enfants (QAPACE) [105] c

n = 121

Age: 8–16 years

Sex: 54% girls

90 days

Toilet: ICC 0.90 (95% CI 0.87–0.93)

Transportation: ICC 0.84 (95% CI 0.78–0.89)

Mandatory PE: ICC 0.95 (95% CI 0.93–0.97)

Other activities in school: ICC 0.94 (95% CI 0.92–0.96)

Personal artistic activities: ICC 0.98 (95% CI 0.97–0.99)

Sport competition: ICC 0.98 (95% CI 0.97–0.99)

Home activities: ICC 0.89 (95% CI 0.85–0.92)

Daily EE: LoA [–515.5; 532.5 kJ/d]

Fair

+

 Oxford Physical Activity Questionnaire (OPAQ) [24] c

n = 87

Age: 13.1 ± 0.9 years

Sex: 45% girls

1 week

MPA: ICC 0.76 (95% CI 0.63–0.84)

VPA: ICC 0.80 (95% CI 0.70–0.87)

MVPA: ICC 0.91 (95% CI 0.87–0.95)

Fair

+

World Health Organization Health Behavior in Schoolchildren questionnaire (WHO HBSC) [106] c

n = 71

Age: 14.9 ± 1.6 years [13–18]

Sex: 56% girls

8–12 days

Frequency: ICC 0.73 (95% CI 0.60–0.82)

Duration: ICC 0.71 (95% CI 0.57–0.81)

Fair

+

 Selected indicators from the Health Behaviour in School-aged Children (HBSC) questionnaire (Chinese version) [107]

n = 95 (11 years [n = 44], 15 years [n = 51])

Age: [11.7 ± 0.4 to 15.8 ± 0.3 years]

Sex: 46% girls

3 weeks

MVPA: last 7 days ICC 0.82 (95% CI 0.74–0.88), usual week 0.74 (95% CI 0.64–0.82)

VPA: frequency 0.68 (95% CI 0.55–0.77), times per week 0.57 (95% CI 0.42–0.66)

Fair

±

 Selected physical activity items of the international Health Behavior in School-aged Children (HBSC) questionnaire (Czech version) [108]

n = 693

Age: 11.1 ± 0.5 and 15.1 ± 0.5 years

Sex: 49.1% girls

4 weeks (n = 580)

1 week (n = 113)

4-week time interval:

MVPA: ICC 0.52 (95% CI 0.46–0.58), kappa 0.44

VPA: ICC 0.55 (95% CI 0.49–0.61), kappa 0.41

1-week time interval:

MVPA: ICC 0.98 (95% CI 0.97–0.99)

VPA: ICC 0.90 (95% CI 0.86–0.93)

Fair

±

 Measures of in-school and out-of-school physical activity, and travel behaviors of the international Healthy Environments and active living in teenagers – Hong Kong [iHealt(H)] study [47]

n = 68

Age: 15.4 years

Sex: 47% girls

13 days (range: 8–16 days)

PE min/class: ICC 0.89, min/week 0.84

No. of sport teams or after school PA in school: ICC 0.74

No. of sport teams or after school PA out-of-school: ICC 0.89

Leisure time PA: past 7 days ICC 0.70, usual week ICC 0.79, average ICC 0.76

Walking or cycling to/from destinations: Indoor or exercise facility 0.61, friend’s or relative’s house 0.48, outdoor recreation place 0.47, food store or restaurant/cafe 0.82, other retail stores 0.51, non-school social or educational activities 0.51, public transportation stop 0.71, total score walking or cycling times/week 0.59

Walk to school: ICC 0.89

Walk from school: ICC 0.76

Fair

±

 Physical Activity and Lifestyle Questionnaire (PALQ) (Greek version) [33]

n = 21

Age: 13.7 ± 0.8 years

Sex: 43% girls (age and sex total sample n = 40)

2 weeks

PALQ: ICC 0.52, typical error 2.39, MD (LoA) − 1.88 ± 6.82

Fair

The South American Youth/Child Cardiovascular and Environment Study (SAYCARE) Physical Activity (PA) questionnaire [66]

n = 177

Age: 11–18 years

Sex: 58% girls

15 days

Active commuting: SCC 0.51

PA at school: SCC 0.63

PA at leisure time: SCC 0.68

MPA: SCC 0.36

VPA: SCC 0.93

Weekly total MVPA: SCC 0.60

 % of agreement with current PA guidelines ≥ 60 min/day: κ 0.56

Fair

 Self-administered questionnaire on children’s travel to school [39]

n = 61 (study 1), n = 68 (study 2)

Age: 11–14 years

Sex: percentage of girls unknown

1 week

After school exercise no. of days: study 1, kappa 0.07; study 2, kappa 0.01

After school exercise no. of hours: study 1, kappa NA; study 2, kappa 0.01

Physical training: study 1, kappa 0.07; study 2, kappa − 0.01

Fair

 Dutch Physical Activity Checklist for Adolescents (PAQ-A)

[35]

n = 94

Age: 13.6 ± 1.4 years [12–17]

Sex: 55% girls

NA: inter-rater (parent vs. child)

Spare-time activity—sports: kappa 0.67 (95% CI 0.54–0.81)

Activity during PE classes: 0.53 (95% CI 0.33–0.72)

Lunchtime activity: 0.60 (95% CI 0.46–0.73)

After-school activity: 0.61 (95% CI 0.47–0.76)

Evening activity: 0.68 (95% CI 0.53–0.79)

Weekend activity: 0.51 (95% CI 0.38–0.65)

Activity frequency last 7 days: 0.63 (95% CI 0.51–0.76)

Activity frequency during each day: 0.51 (95% CI 0.38–0.64)

Total PA: 0.64 (95% CI 0.51–0.77)

Fair

 3-Day Physical Activity Recall (3DPARecall) instrument (Singaporean version) [42]

n = 106

Age: 14.5 ± 1.1 years [13–16]

Sex: 53% girls

(Age and sex total sample n = 221)

6–8 h

3-day average MET level: ICC 0.88 (95% CI 0.83–0.92)

Poor

+

 3-Day Physical Activity Record (3DPARecord) (Greek version) [33]

n = 21

Age: 13.7 ± 0.8 years

Sex: 43% girls

(Age and sex total sample n = 40)

2 weeks

All days: ICC 0.97, typical error 382.51, LoA [–375.3; 1092.7] Weekend: ICC 0.88, typical error 276.4, LoA [–230.6; 789.5]

Weekdays: ICC 0.97, day 1 typical error 119.8, LoA [–66.12; 342.19], day 2 typical error 131.5, MD (LoA) − 78.6 ± 375.6

Poor

+

 Recess Physical Activity Recall (RPAR) [95]

n = 113

Age: 13.1 ± 0.7 years

Sex: 48% girls

1 h

Total PA: ICC 0.87

MVPA: ICC 0.88

Poor

+

 Refined 60-min MVPA screening measure [109] c

n = 138

Age: 12.1 ± 0.9 years

Sex: 65% girls

Same day up to 1 month

ICC: total sample 0.77, same day 0.88 (n = 42), up to 1 month 0.53 (n = 31)

Kappa: total sample 61%, same day 84%, up to 1 month 36%

Poor

+

 MVPA scores of the Health Behavior in School-aged Children (HBSC) Research Protocol [90]

n = 998

Age: 12.7 ± 1.4 years

Sex: 50% girls

1 year

MVPA girls r 0.43, boys r 0.50

Poor

 MVPA scores of the International Physical Activity Questionnaire Short form (IPAQ-SF) [90]

n = 998

Age: 12.7 ± 1.4 years

Sex: 50% girls

1 year

MVPA girls r 0.45, boys r 0.44

Poor

 Moderate and vigorous physical activity items of the Youth Risk Behavior Survey (YRBS)

[83]

n = 128

Age: 12.2 ± 0.6 years (in total sample n = 139)

Sex: 53% girls

Ranged from 1 to 40 days (n = 92 [≤ 15 days] and n = 36 [> 15 days])

MPA: ICC ≤ 15 days 0.57, > 15 days 0.35, total sample 0.51

VPA: ICC ≤ 15 days 0.47, > 15 days 0.34, total sample 0.46

Poor

approx. approximately, CI confidence interval, COSMIN COnsensus-based Standards for the selection of health Measurement Instruments, ICC intraclass correlation coefficient, LoA limits of agreement, LPA light physical activity, MD mean difference, MET metabolic equivalent, MPA moderate physical activity, MVPA moderate to vigorous physical activity, NA not applicable, PA physical activity, PCC Pearson correlation coefficient, PE physical education, SD standard deviation, SROC Spearman rank order correlation, VPA vigorous physical activity; + indicates ≥ 80% acceptable correlations, ± indicates ≥ 50% to < 80% acceptable correlations, – indicates < 50% acceptable correlations

aAge presented as mean age ± SD [range]

bBased on the COSMIN checklist

cStudy from previous review

Table 4

Measurement error of physical activity questionnaires for youth sorted by age category and methodological quality

Questionnaire

Study populationa

Time interval

Results

Methodological qualityb

Preschoolers (mean age < 6 years)

 Preschool-age Children’s Physical Activity Questionnaire (Pre-PAQ) [58]

n = 103

Age: 3.8 ± 0.74 years

Sex: 48% girls

2 weeks

Time spent in organized activities: ME ranged from 1.0 to 1.1 min

Good

Children (mean age ≥ 6 to < 12 years)

 The ENERGY-child questionnaire [48]

n = 730

Age: [11.3 ± 0.5 to 12.5 ± 0.6 years]

Sex: [47–58% girls]

1 week

Walking to school: (no./days) PoA 81%, (amount of time) 76%

Transport today to school: PoA 83%

Activity during breaks: PoA 86%

Sport hours: (first sport) PoA 55%; (second sport) 43%; (yesterday) 28%

Bike to school (no./days): PoA 88%, (amount of time) 85%

Fair

 Dutch Physical Activity Checklist for Children (PAQ-C) [35]

n = 192

Age: 8.9 ± 1.7 years [5–12]

Sex: 53% girls

NA: inter-rater (parent vs. child)

Spare-time activity—sports: PoA 59.9%

Activity during PE classes: 71.4%

Break-time activity: 74.0%

Lunchtime activity: 71.9%

After-school activity: 67.7%

Evening activity: 71.9%

Weekend activity: 69.8%

Activity frequency last 7 days: 72.4%

Activity frequency during each day: 65.6%

Total PA: 65.6%

Fair

 Children’s Leisure Activities Study Survey (CLASS) [100] c

n = 109

Age: 10.6 ± 0.8 years [10–12] (in total sample n = 111)

Sex: 63% girls

NA: inter-rater (parent vs. child)

Total VPA: PoA 58.6%

Total MPA: PoA 84.7%

Total PA: PoA 89.2%

Individual activities: PoA ranges from 8.0% to 97.8%

Fair

Older children and adolescents (mean age ≥ 12 years)

 Active Transportation to school and work in Norway (ATN) questionnaire (days/week type of transportation) [41]

n = 87

Age: 11–12 years

Sex: percentage girls unknown

2 weeks

Classification in major mode of commuting: PoA 97%

Good

 3-Day Physical Activity Recall (3DPARecall) [19] c

n = 65

Age: 12.5 ± 1.1 years

Sex: 64% girls

(Age and sex in total sample n = 320)

1 day

List of activities: PoA boys ranges from 0% to 75%, mean (SD) 51% (29); girls from 18% to 75%, mean (SD) 47% (18)

Good

 Self-Administered Physical Activity Checklist (SAPAC) (modified) [19] c

n = 84

Age: 12.5 ± 1.1 years

Sex: 64% girls

(Age and sex in total sample n = 320)

1 day

List of activities: PoA boys ranges from 7% to 70%, mean (SD) 34% (20); girls from 26% to 75%, mean (SD) 42% (15)

Good

 Measures of in-school and out-of-school physical activity, and travel behaviors of the international Healthy Environments and active living in teenagers – Hong Kong [iHealt(H)] study [47]

n = 68

Age: 15.4 years

Sex: 47% girls

13 days (range: 8–16 days)

PE days/week: PoA 98%

No. of sport teams or after school PA in school: PoA 79%

No. of sport teams or after school PA out-of-school: PoA 90%

Leisure-time PA: past 7 days PoA 76%, usual week PoA 65%

Walking or cycling to/from destinations: indoor or exercise facility 76%, friend’s or relative’s house 57%, outdoor recreation place 62%, food store or restaurant/cafe 80%, other retail stores 62%, non-school social or educational activities 68%, public transportation stop 69%, work 100%, other 100%

Transportation to school: walk PoA 90%, bicycle 100%

Transportation from school: walk PoA 79%, bicycle 100%

Fair

 Dutch Physical Activity Checklist for Adolescents (PAQ-A) [35]

n = 94

Age: 13.6 ± 1.4 years [12, 13, 14, 15, 16, 17]

Sex: 55% girls

NA: inter-rater (parent vs. child)

Spare-time activity—sports: PoA 77.7%

Activity during PE classes: 73.4%

Lunchtime activity: 64.9%

After-school activity: 69.2%

Evening activity: 71.0%

Weekend activity: 57.5%

Activity frequency last 7 days: 70.2%

Activity frequency during each day: 51.0%

Total PA: 70.2%

Fair

COSMIN COnsensus-based Standards for the selection of health Measurement Instruments, ME measurement error, MPA moderate physical activity, NA not applicable, PA physical activity, PE physical education, PoA percentage of agreement, SD standard deviation, VPA vigorous physical activity

aAge presented as mean age ± SD [range]

bBased on the COSMIN checklist

cStudy from previous review

2.8 Best Evidence

We chose to divide the included studies in three age categories, i.e., preschoolers, children, and adolescents, and draw conclusions on the best available questionnaire(s) for each age category. A questionnaire was considered of interest when at least a fair methodological quality and a positive evidence rating were achieved. Additionally, for construct validity, the level of evidence (see Table 1) was taken into account, so questionnaires with a higher level of evidence comparison measure were considered more valuable. Because no evidence ratings were available for measurement error, these measurement properties were not taken into account when drawing conclusions about the best available questionnaire.

3 Results

Systematic literature searches using the PubMed, EMBASE, and SPORTDiscus databases yielded 15,220 articles after removal of duplicates. After title and abstract screening, 110 eligible articles remained. Another 21 articles were found through cross-reference searches. Therefore, 131 full-text articles were screened, which resulted in the inclusion of 71 articles examining 76 (versions of) questionnaires. After additionally including 16 articles from the previous review, this resulted in 87 articles examining 89 (versions) of questionnaires. See Fig. 1 for the full selection process. Within the 87 articles, 162 studies were conducted, with 103 assessing construct validity, 50 test–retest reliability, and nine measurement error. Four of the included questionnaires were assessed by two of the included studies, i.e., the 3-Day Physical Activity Recall (3DPARecall) [19, 20], the Activity Questionnaire for Adults and Adolescents (AQuAA) [21, 22], the Oxford Physical Activity Questionnaire (OPAQ) [23, 24], and a physical activity, sedentary behavior, and strength questionnaire [25, 26]. Furthermore, two of the questionnaires were assessed by three of the included studies, i.e., the Physical Activity Questionnaire for Older Children (PAQ-C) [27, 28, 29], and the Previous Day Physical Activity Recall (PDPAR) [30, 31, 32]. In addition, various modified versions of questionnaires were assessed by the included studies.
Fig. 1

Preferred Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram of study inclusion

3.1 Construct Validity

The construct validity results are summarized in Table 2. Of the 72 questionnaires that were assessed on construct validity, eight were from the previous review. Fifteen of the questionnaires were assessed by two studies, two were assessed by three studies, one by four, one by five, and one by six studies. Six questionnaires were assessed in preschoolers, 29 in children, and 38 in adolescents (one questionnaire was assessed in both children and adolescents). The methodological quality rating of the construct validity studies ranged from poor to good: 49 studies received a poor, 49 a fair, and five a good rating. The low methodological scores were predominantly due to comparison measures with unacceptable or unknown measurement properties, and a lack of a priori formulated hypotheses. No definite conclusion could be drawn regarding the best available questionnaires for preschoolers, as studies on construct validity within this age category were of low methodological quality or received negative evidence ratings. For children, the best available questionnaire was found to be the Godin Leisure-Time Exercise Questionnaire [63] (fair methodological quality and positive level 2 evidence). Although the moderate level 2 evidence hampered our ability to draw conclusions on the validity, it is worthwhile to investigate further. We concluded that the most valid questionnaire in adolescents was the Greek version of the 3-Day Physical Activity Record (3DPARecord) [33] (fair methodological quality and positive level 1 evidence rating). Note that the 3DPARecord uses a different format (i.e., different time segments and categories) than the frequently used 3DPARecall.

3.2 Content Validity

Six of the included questionnaires were qualitatively assessed on content validity, one of which was assessed by two studies [25, 26, 34, 35, 36, 37]. Studies used cognitive interviews, semi-structured interviews, and focus groups with children and adolescents and/or experts (e.g., researchers in the field of sports medicine, pediatrics, and measurement) to assess the comprehensibility, relevance of items, and comprehensiveness of the questionnaires. Due to a lack of details on the methods used regarding testing or developing these questionnaires, the methodological quality of these studies and the quality of the questionnaires could not be assessed. Ten of the included questionnaires were pilot-tested with children and/or parents on, for example, comprehensiveness and time to complete [33, 38, 39, 40, 41, 42, 43, 44, 45]. However, again, the study quality could not be assessed due to the minimal amount of information provided. Lastly, 15 of the questionnaires were translated versions [33, 35, 39, 40, 43, 46, 47, 48, 49, 50, 51, 52, 53]; the majority of these studies provided little information on the translation processes. These studies did not assess the cross-cultural validity, and thus no definite conclusion about the content validity of the translated questionnaires could be drawn.

3.3 Test–Retest Reliability

The test–retest reliability results are summarized in Table 3. Of the 46 questionnaires assessed on test–retest reliability, five were from the previous review. Four of the questionnaires were assessed by two studies. Five questionnaires were assessed in preschoolers, 16 in children, and 26 in adolescents (one questionnaire was assessed in both children and adolescents). The methodological quality of the studies was rated as follows: 13 scored poor, 26 fair, and 11 good. The majority of poor and fair scores were due to the lack of a description about how missing items were treated and inappropriate time intervals between test and retest. The most reliable questionnaire in preschoolers was the Energy Balance Related Behaviors (ERBs) self-administered primary caregivers questionnaire (PCQ) [46] (fair methodological quality and positive evidence rating). In children, the most reliable questionnaires were the Chinese version of the PAQ-C [43], and the Active Transportation to school and work in Norway (ATN) questionnaire [41] (both good methodological quality and positive evidence rating). The most reliable questionnaires in adolescents were a single-item activity measure [23], and the Web-based and paper-based PAQ-C [28] (both good methodological quality and positive evidence rating).

3.4 Measurement Error

Table 4 summarizes the measurement error outcomes. Of the nine questionnaires assessed on measurement error, two were from the previous review. One questionnaire was assessed in preschoolers, three in children, and five in adolescents. Four of the studies received a good methodological quality rating, and five received a fair one. Fair scores were predominantly due to the lack of a description about how missing items were treated.

4 Discussion

This review summarizes studies that assessed the measurement properties of physical activity questionnaires for children and adolescents under the age of 18 years. Questionnaires varied in (sub)constructs measured, recall periods, number of questions and format, and different measurement properties that were assessed, e.g., construct validity, test–retest reliability, or measurement error. Unfortunately, most studies had low methodological quality scores and low evidence ratings, especially for construct validity. Additionally, no questionnaire was identified with both high methodological quality and positive evidence ratings for reliability and validity. Furthermore, for the majority of questionnaires there was a lack of data on both reliability and validity. Consequently, no definite conclusion regarding the most promising questionnaire can be drawn.

4.1 Construct Validity

For adolescents, one valid questionnaire was found, i.e., the Greek version of the 3DPARecord [33]. The 3DPARecord is a questionnaire using a segmented day structure that divides the previous 3 days (1 weekend day) into timeframes of 15 min each, with the adolescents reporting their activity using nine categories ranging from 1 (sleep) to 9 (vigorous physical activity and sport) for each of the timeframes [33].

Due to the predominantly low methodological study quality and negative evidence ratings for study results in children and preschoolers, no valid questionnaires were identified. The low methodological quality of the studies was predominantly due to a lack of a priori formulated hypotheses and the use of comparison measures with unknown or unacceptable measurement properties. Moreover, in some studies comparisons between non-corresponding constructs were made, e.g., moderate to vigorous physical activity (MVPA) measured by a questionnaire compared with total accelerometer counts.

4.2 Test–Retest Reliability and Measurement Error

For preschoolers, one reliable questionnaire was identified: the ERBs self-administered PCQ [46]; two reliable questionnaires were identified for children: the Chinese version of the PAQ-C [43] and the ATN questionnaire [41]; and two for adolescents: a single-item activity measure [23] and the web- and paper-based PAQ-C [28].

Many questionnaires received a positive evidence rating but due to the low methodological quality of the studies no definite conclusions regarding their reliability could be drawn. The low methodological quality was mainly due to inappropriate time intervals between test and retest, and the lack of a description about how missing items were handled. Unfortunately, no final evidence rating for measurement error could be computed as none of the studies provided information on the MIC.

4.3 Strengths and Limitations

A strength of this review is the separate assessment of the questionnaire quality (i.e., results for measurement properties) and the methodological quality of the study in which the questionnaire was assessed. This provides transparency in the conclusion regarding the best available questionnaires. Furthermore, data extraction and assessment of methodological quality were carried out by at least two independent researchers, minimizing the chance of bias. In addition, cross-reference searches were carried out, thereby increasing the likelihood of finding all relevant studies. However, we only included English-language studies, disregarding relevant studies published in other languages.

4.4 Recommendations for Future Research

Due to the methodological limitations of existing studies, we cannot draw definite conclusions on the measurement properties of physical activity questionnaires. This hampers the identification of the most suitable questionnaires for assessing physical activity in children. To improve future research we recommend the following:
  • Using standardized tools for the evaluation of measurement properties such as COSMIN, to improve the quality of studies examining measurement properties [11, 54];

  • Using appropriate translation methods [17];

  • Using the mode of administration in a validation study that is intended in the field;

  • Defining the context of use and the measurement model of the questionnaire to determine which measurement properties are relevant to examine;

  • Conducting more studies assessing content validity to ensure questionnaires are comprehensive and an adequate reflection of the construct to be measured [13, 55];

  • For construct validity, choosing a comparison measure that measures a similar construct and formulating hypotheses a priori;

  • For reliability studies, test and retest should concern the same day/week when recalling a previous day/week;

  • More research on the responsiveness of valid and reliable questionnaires;

  • Building on or improving the most promising existing questionnaires rather than developing new questionnaires;

  • Providing open access to the examined questionnaire; and

  • Editors of journals to request reviewers and authors to use a standardized tool such as COSMIN for studies on measurement properties.

5 Conclusions

Unfortunately, conclusive evidence for both validity and reliability was not found for any of the identified physical activity questionnaires. The lack of high-quality studies examining both the reliability and the validity of a questionnaire hampered the ability to draw definite conclusions about the best available physical activity questionnaire for children and adolescents. Thus, high-quality methodological studies examining all relevant measurement properties are highly warranted. We strongly recommend researchers adopt standardized tools, e.g., the COSMIN methodology [11, 56, 57], for the design and report of future studies. Current studies using physical activity questionnaires should keep in mind that their results may not adequately reflect children’s and adolescents’ physical activity levels, as most questionnaires lack appropriate validity and/or reliability.

Notes

Compliance with Ethical Standards

Funding

The contribution of Lisan Hidding was funded by the municipality of Amsterdam, Amsterdam Healthy Weight Programme.

Conflict of interest

Lisan Hidding, Mai Chinapaw, Mireille van Poppel, and Teatske Altenburg declare that they have no conflicts of interest. The institute of which Lidwine Mokkink is a part receives royalties for one of the references cited in this review (de Vet HCW, Terwee CB, Mokkink LB, Knol DL. Measurement in medicine: a practical guide. 1st ed. Cambridge: Cambridge University Press; 2011).

Supplementary material

40279_2018_987_MOESM1_ESM.pdf (51 kb)
Supplementary material 1 (PDF 51 kb)
40279_2018_987_MOESM2_ESM.pdf (178 kb)
Supplementary material 2 (PDF 178 kb)

References

  1. 1.
    Bangsbo J, Krustrup P, Duda J, Hillman C, Andersen LB, Weiss M, et al. The Copenhagen Consensus Conference 2016: children, youth, and physical activity in schools and during leisure time. Br J Sports Med. 2016;50:1177–8.PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Janssen I, Leblanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys Act. 2010;7:40.PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37:531–43.CrossRefGoogle Scholar
  4. 4.
    Toftager M, Kristensen PL, Oliver M, Duncan S, Christiansen L, Boyle E, et al. Accelerometer data reduction in adolescents: effects on sample retention and bias. Int J Behav Nutr Phys Act. 2013;10:140.PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Welk GJ, Corbin CB, Dale D. Measurement issues in the assessment of physical activity in children. Res Q Exerc Sport. 2000;71:59–73.PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Sallis JF. Self-report measures of children’s physical activity. J Sch Health. 1991;61:215–9.PubMedCrossRefPubMedCentralGoogle Scholar
  7. 7.
    Kohl HW, Fulton JE, Caspersen CJ. Assessment of physical activity among children and adolescents: a review and synthesis. Prev Med. 2000;31:S54–76.CrossRefGoogle Scholar
  8. 8.
    Chinapaw MJM, Mokkink LB, van Poppel MNM, van Mechelen W, Terwee CB. Physical activity questionnaires for youth: a systematic review of measurement properties. Sports Med. 2010;40:539–63.PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Terwee CB, Jansma EP, Riphagen II, de Vet HCW. Development of a methodological PubMed search filter for finding studies on measurement properties of measurement instruments. Qual Life Res. 2009;18:1115–23.PubMedPubMedCentralCrossRefGoogle Scholar
  10. 10.
    Terwee CB, Mokkink LB, Knol DL, Ostelo RWJG, Bouter LM, de Vet HCW. Rating the methodological quality in systematic reviews of studies on measurement properties: a scoring system for the COSMIN checklist. Qual Life Res. 2012;21:651–7.PubMedCrossRefPubMedCentralGoogle Scholar
  11. 11.
    Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res. 2010;19:539–49.PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    Terwee CB. COSMIN checklist with 4-point scale. 2011. https://www.cosmin.nl. Accessed 1 Apr 2016.
  13. 13.
    Hidding LM, Altenburg TM, Mokkink LB, Terwee CB, Chinapaw MJM. Systematic review of childhood sedentary behavior questionnaires: what do we know and what is next? Sports Med. 2017;47:677–99.PubMedCrossRefPubMedCentralGoogle Scholar
  14. 14.
    de Vet HCW, Mokkink LB, Terwee CB, Hoekstra OS, Knol DL. Clinicians are right not to like Cohen’s κ. BMJ. 2013;346:f2125.PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol. 2010;63:737–45.PubMedCrossRefPubMedCentralGoogle Scholar
  16. 16.
    Terwee CB, Bot SDM, de Boer MR, van der Windt DAWM, Knol DL, Dekker J, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60:34–42.PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    de Vet HCW, Terwee CB, Mokkink LB, Knol DL. Measurement in medicine: a practical guide. 1st ed. Cambridge: Cambridge University Press; 2011.CrossRefGoogle Scholar
  18. 18.
    van Poppel MNM, Chinapaw MJM, Mokkink LB, van Mechelen W, Terwee CB. Physical activity questionnaires for adults. Sports Med. 2010;40:565–600.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    McMurray RG, Ring KB, Treuth MS, Gregory J, Pate RR, Schmitz KH, et al. Comparison of two approaches to structured physical activity surveys for adolescents. Med Sci Sports Exerc. 2008;36:2135–43.Google Scholar
  20. 20.
    Pate RR, Ross R, Dowda M, Trost SG, Sirard JR. Validation of a 3-day physical activity recall instrument in female youth recall. Pediatr Exerc Sci. 2003;15:257–65.CrossRefGoogle Scholar
  21. 21.
    Chinapaw MJM, Slootmaker SM, Schuit AJ, van Zuidam M, van Mechelen W. Reliability and validity of the Activity Questionnaire for Adults and Adolescents (AQuAA). BMC Med Res Methodol. 2009;9:58.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Slootmaker SM, Schuit AJ, Chinapaw MJM, Seidell JC, van Mechelen W, Sallis J, et al. Disagreement in physical activity assessed by accelerometer and self-report in subgroups of age, gender, education and weight status. Int J Behav Nutr Phys Act. 2009;6:17.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Scott JJ, Morgan PJ, Plotnikoff RC, Lubans DR. Reliability and validity of a single-item physical activity measure for adolescents. J Pediatr Child Health. 2015;51:787–93.CrossRefGoogle Scholar
  24. 24.
    Lubans DR, Sylva K, Osborn Z. Convergent validity and test–retest reliability of the Oxford Physical Activity Questionnaire for secondary school students. Behav Change. 2008;25:23–34.CrossRefGoogle Scholar
  25. 25.
    Tucker CA, Bevans KB, Teneralli RE, Smith AW, Bowles HR, Forrest CB. Self-reported pediatric measures of physical activity, sedentary behavior, and strength impact for PROMIS: conceptual framework. Pediatr Phys Ther. 2014;26:376–84.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Tucker CA, Bevans KB, Teneralli RE, Smith AW, Bowles HR, Forrest CB. Self-reported pediatric measures of physical activity, sedentary behavior, and strength impact for PROMIS: item development. Pediatr Phys Ther. 2014;26:385–92.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Kowalski KC, Crocker PRE, Faulkner RA. Validation of the physical activity questionnaire for older children. Pediatr Exerc Sci. 1997;9:174–86.CrossRefGoogle Scholar
  28. 28.
    Storey KE, McCargar LJ. Reliability and validity of Web-SPAN, a web-based method for assessing weight status, diet and physical activity in youth. J Hum Nutr Diet. 2012;25:59–68.PubMedCrossRefPubMedCentralGoogle Scholar
  29. 29.
    Crocker PR, Bailey DA, Faulkner RA, Kowalski KC, McGrath R. Measuring general levels of physical activity: preliminary evidence for the physical activity questionnaire for older children. Med Sci Sports Exerc. 1997;29:1344–9.PubMedCrossRefPubMedCentralGoogle Scholar
  30. 30.
    Trost SG, Ward DS, Mcgraw B, Pate RR. Validity of the Previous Day Physical Activity Recall (PDPAR) in fifth-grade children: validity of the previous day physical activity. Pediatr Exerc Sci. 1999;11:341–8.CrossRefGoogle Scholar
  31. 31.
    Welk GJ, Dzewaltowski DA, Hill JL. Comparison of the computerized ACTIVITYGRAM Instrument and the previous day physical activity recall for assessing physical activity in children. Res Q Exerc Sport. 2004;75:370–80.PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Trost SG, Marshall AL, Miller R, Hurley JT, Hunt JA. Validation of a 24-h physical activity recall in indigenous and non-indigenous Australian adolescents. J Sci Med Sport. 2007;10:428–35.PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Argiropoulou EC, Michalopoulou M, Aggeloussis N, Avgerinos A. Validity and reliability of physical activity measures in Greek high school age children. J Sports Sci Med. 2004;3:147–59.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Aggio D, Fairclough S, Knowles Z, Graves L. Validity and reliability of a modified english version of the physical activity questionnaire for adolescents. Arch Public Health. 2016;74:3.PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    Bervoets L, Van Noten C, Van Roosbroeck S, Hansen D, Van Hoorenbeeck K, Verheyen E, et al. Reliability and validity of the Dutch Physical Activity Questionnaires for Children (PAQ-C) and Adolescents (PAQ-A). Arch Public Health. 2014;72:47.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    DiStefano C, Pate R, McIver K, Dowda M, Beets M, Murrie D. Creating a physical activity self-report form for youth using Rasch methodology. J Appl Meas. 2016;17:125–41.PubMedPubMedCentralGoogle Scholar
  37. 37.
    Gray HL, Koch PA, Contento IR, Bandelli LN, Ang I, Di Noia J. Validity and reliability of behavior and theory-based psychosocial determinants measures, using audience response system technology in urban upper-elementary schoolchildren. J Nutr Educ Behav. 2016;48:437–52.PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Saint-Maurice PF, Welk GJ. Validity and calibration of the youth activity profile. PLoS One. 2015;10:e0143949.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Tetali S, Edwards P, Murthy GVS, Roberts I. Development and validation of a self-administered questionnaire to estimate the distance and mode of children’s travel to school in urban India. BMC Med Res Methodol. 2015;15:92.PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Bacardi-Gascón M, Reveles-Rojas C, Woodward-Lopez G, Crawford P, Jiménez-Cruz A. Assessing the validity of a physical activity questionnaire developed for parents of preschool children in Mexico. J Health Popul Nutr. 2012;30:439–46.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Bere E, Bjørkelund LA. Test-retest reliability of a new self reported comprehensive adolescents commuting to school and their parents commuting to work—the ATN questionnaire. Int J Behav Nutr Phys Act. 2009;6:68.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Lee KS, Trost SG. Validity and reliability of the 3-day physical activity recall in Singaporean adolescents. Res Q Exerc Sport. 2005;76:101–6.PubMedCrossRefPubMedCentralGoogle Scholar
  43. 43.
    Wang JJ, Baranowski T, Lau WP, Chen TA, Pitkethly AJ. Validation of the Physical Activity Questionnaire for Older Children (PAQ-C) among Chinese children. Biomed Environ Sci. 2016;29:177–86.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Thomas EL, Upton D. Psychometric properties of the physical activity questionnaire for older children (PAQ-C) in the UK. Psychol Sport Exerc. 2014;15:280–7.CrossRefGoogle Scholar
  45. 45.
    Zelener J, Schneider M. Adolescents and self-reported physical activity: an evaluation of the Modified Godin Leisure-Time Exercise Questionnaire. Int J Exerc Sci. 2016;9:587–98.PubMedPubMedCentralGoogle Scholar
  46. 46.
    González-Gil EM, Mouratidou T, Cardon G, Androutsos O, De Bourdeaudhuij I, Góźdź M, et al. Reliability of primary caregivers reports on lifestyle behaviours of European pre-school children: the ToyBox-study. Obes Rev. 2014;15:61–6.PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Cerin E, Sit CHP, Huang Y-J, Barnett A, Macfarlane DJ, Wong SSH. Repeatability of self-report measures of physical activity, sedentary and travel behaviour in Hong Kong adolescents for the iHealt(H) and IPEN—adolescent studies. BMC Pediatr. 2014;14:142.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Singh AS, Vik FN, Chinapaw MJM, Uijtdewilligen L, Verloigne M, Fernández-Alvira JM, et al. Test-retest reliability and construct validity of the ENERGY-child questionnaire on energy balance-related behaviours and their potential determinants: the ENERGY-project. Int J Behav Nutr Phys Act. 2011;8:136.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Gioxari A, Kavouras SA, Tambalis KD, Maraki M, Kollia M, Sidossis LS. Reliability and criterion validity of the self-administered physical activity checklist in Greek children. Eur J Sport Sci. 2013;1:105–11.CrossRefGoogle Scholar
  50. 50.
    Huang YJ, Wong SHS, Salmon J. Reliability and validity of the modified Chinese version of the Children’s Leisure Activities Study Survey (CLASS) questionnaire in assessing physical activity among Hong Kong children. Pediatr Exerc Sci. 2009;21:339–53.PubMedCrossRefPubMedCentralGoogle Scholar
  51. 51.
    Malan GF, Nolte K. Measuring physical activity in South African grade 2 and 3 learners: a self-report questionnaire versus pedometer testing. S Afr J Res Sport Phys Educ Recreation. 2017;39:79–91.Google Scholar
  52. 52.
    Benítez-porres J, López-Fernández I, Raya JF, Álvarez Carnero S, Alvero-Cruz JR, Álvarez Carnero E. Reliability and validity of the PAQ-C questionnaire to assess physical activity in children. J Sch Health. 2016;86:677–85.PubMedCrossRefPubMedCentralGoogle Scholar
  53. 53.
    Zaragoza Casterad J, Generelo E, Aznar S, Abarca-Sos A, Julián JA, Mota J. Validation of a short physical activity recall questionnaire completed by Spanish adolescents. Eur J Sport Sci. 2012;12:283–91.CrossRefGoogle Scholar
  54. 54.
    Terwee CB, Mokkink LB, Hidding LM, Altenburg TM, van Poppel MN, Chinapaw MJM, et al. Comment on “Should we reframe how we think about physical activity and sedentary behavior measurement? Validity and reliability reconsidered”. Int J Behav Nutr Phys Act. 2016;13:66.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Terwee CB, Prinsen CAC, Chiarotto A, Westerman MJ, Patrick DL, Alonso J, et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res. 2018;27:1159–70.PubMedPubMedCentralCrossRefGoogle Scholar
  56. 56.
    Prinsen CAC, Mokkink LB, Bouter LM, Alonso J, Patrick DL, de Vet HCW, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res. 2018;27:1147.  https://doi.org/10.1007/s11136-018-1798-3 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Mokkink LB, de Vet HCW, Prinsen CAC, Patrick DL, Alonso J, Bouter LM, et al. COSMIN Risk of Bias checklist for systematic reviews of Patient-Reported Outcome Measures. Qual Life Res. 2018;27:1171.  https://doi.org/10.1007/s11136-017-1765-4 CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Dwyer GM, Hardy LL, Peat JK, Baur LA. The validity and reliability of a home environment preschool-age physical activity questionnaire (Pre-PAQ). Int J Behav Nutr Phys Act. 2011;8:86.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Rice KR, Joschtel B, Trost SG. Validity of family child care providers’ proxy reports on children’s physical activity. Child Obes. 2013;9:393–8.PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Corder K, Van Sluijs EMF, Wright A, Whincup P, Wareham NJ, Ekelund U. Is it possible to assess free-living physical activity and energy expenditure in young people by self-report? Am J Clin Nutr. 2009;89:862–70.PubMedCrossRefPubMedCentralGoogle Scholar
  61. 61.
    Sarker H, Anderson LN, Borkhoff CM, Abreo K, Tremblay MS, Lebovic G, et al. Validation of parent-reported physical and sedentary activity by accelerometry in young children. BMC Res Notes. 2015;8:735.PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Määttä S, Nuutinen T, Ray C, Eriksson JG, Weiderpass E, Roos E. Validity of self-reported out-of-school physical activity among Finnish 11-year-old children. Arch Public Health. 2016;74:11.PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Eisenmann JC, Milburn N, Jacobsen L, Moore SJ. Reliability and convergent validity of the godin leisure-time exercise questionnaire in rural 5th-grade school-children. J Hum Movement Stud. 2002;43:135–49.Google Scholar
  64. 64.
    Ridley K, Olds TS, Hill A. The Multimedia activity recall for children and adolescents (MARCA): development and evaluation. Int J Behav Nutr Phys Act. 2006;3:10.PubMedPubMedCentralCrossRefGoogle Scholar
  65. 65.
    Ayala-Guzmán CI, Ramos-Ibáñez N, Ortiz-Hernández L. Accelerometry does not match with self-reported physical activity and sedentary behaviors in Mexican children. Bol Med Hosp Infant Mex. 2017;74:272–81.PubMedPubMedCentralGoogle Scholar
  66. 66.
    Nascimento-Ferreira MV, De Moraes ACF, Toazza-Oliveira PV, Forjaz CLM, Aristizabal JC, Santaliesra-Pasías AM, et al. Reliability and validity of a questionnaire for physical activity assessment in South American children and adolescents: the SAYCARE study. Obesity. 2018;26:S23–30.PubMedCrossRefPubMedCentralGoogle Scholar
  67. 67.
    Colley RC, Wong SL, Garriguet D, Janssen I, Gober SC, Tremblay MS. Physical activity, sedentary behaviour and sleep in canadian children: parent-report versus direct measures and relative associations with health risk. Health Rep. 2012;23:45–52.PubMedPubMedCentralGoogle Scholar
  68. 68.
    Gwynn JD, Hardy LL, Wiggers JH, Smith WT, D’Este CA, Turner N, et al. The validation of a self-report measure and physical activity of Australian Aboriginal and Torres Strait Islander and non-Indigenous rural children. Aust N Z J Public Health. 2010;34:57–65.CrossRefGoogle Scholar
  69. 69.
    Van Hoye A, Nicaise V, Sarrazin P. Self-reported and objective physical activity measurement by active youth. Sci Sports. 2014;29:78–87.CrossRefGoogle Scholar
  70. 70.
    Tremblay MS, Inman JW, Willms JD. Preliminary evaluation of a video questionnaire to assess activity levels of children. Med Sci Sports Exerc. 2001;33:2139–44.PubMedCrossRefPubMedCentralGoogle Scholar
  71. 71.
    Moore HJ, Ells LJ, McLure SA, Crooks S, Cumbor D, Summerbell CD, et al. The development and evaluation of a novel computer program to assess previous-day dietary and physical activity behaviours in school children: the Synchronised Nutrition and Activity Program (SNAP). Br J Nutr. 2008;99:1266–74.PubMedCrossRefPubMedCentralGoogle Scholar
  72. 72.
    Harro M. Validation of a questionnaire to assess physical activity of children ages 4-8 years. Res Q Exerc Sport. 1997;68:259–68.PubMedCrossRefPubMedCentralGoogle Scholar
  73. 73.
    Bringolf-Isler B, Mäder U, Ruch N, Kriemler S, Grize L, Braun-Fahrländer C. Measuring and validating physical activity and sedentary behavior comparing a parental questionnaire to accelerometer data and diaries. Pediatr Exerc Sci. 2012;24:229–45.PubMedCrossRefPubMedCentralGoogle Scholar
  74. 74.
    Muthuri SK, Wachira LJM, Onywera VO, Tremblay MS. Direct and self-reported measures of physical activity and sedentary behaviours by weight status in school-aged children: results from ISCOLE-Kenya. Ann Hum Biol. 2015;42:239–47.CrossRefGoogle Scholar
  75. 75.
    Børrestad L, Østergaard L, Andersen LB, Bere E. Associations between active commuting to school and objectively measured physical activity. J Phys Act Health. 2012;10:826–32.PubMedCrossRefPubMedCentralGoogle Scholar
  76. 76.
    Reichert FF, Menezes AMB, Araujo CL, Hallal PC. Self-reporting versus parental reporting of physical activity in adolescents: the 11-year follow-up of the 1993 Pelotas (Brazil) birth cohort study. Cad Saude Publica. 2010;26:1921–7.PubMedCrossRefPubMedCentralGoogle Scholar
  77. 77.
    Veitch J, Salmon J, Ball K. The validity and reliability of an instrument to assess children’s outdoor play in various locations. J Sci Med Sport. 2009;12:579–82.PubMedCrossRefPubMedCentralGoogle Scholar
  78. 78.
    Sithole F, Veugelers PJ. Parent and child reports of children’s activity. Health Rep. 2008;19:19–24.PubMedPubMedCentralGoogle Scholar
  79. 79.
    Rääsk T, Lätt E, Jürimäe T, Mäestu J, Jürimäe J, Konstabel K. Association of subjective ratings to objectively assessed physical activity in pubertal boys with differing BMI. Percept Mot Skills. 2015;121:245–59.PubMedCrossRefPubMedCentralGoogle Scholar
  80. 80.
    Beltrán-Carrillo VJ, González-Cutre D, Sierra AC, Jiménez-Loaisa A, Ferrández-Asencio MÁ, Cervelló E. Concurrent and criterion validity of the 7 Day-PAR in Spanish adolescents. Eur J Hum Mov. 2016;36:88–103.Google Scholar
  81. 81.
    McCrorie PRW, Perez A, Ellaway A. The validity of the Youth Physical Activity Questionnaire in 12–13-year-old Scottish adolescents. BMJ Open Sport Exerc Med. 2016;2:e000163.PubMedCrossRefPubMedCentralGoogle Scholar
  82. 82.
    Rääsk T, Maëstu J, Lätt E, Jürimäe J, Jürimäe T, Vainik U, et al. Comparison of IPAQ-SF and two other physical activity questionnaires with accelerometer in adolescent boys. PLoS One. 2017;12:e0169527.PubMedPubMedCentralCrossRefGoogle Scholar
  83. 83.
    Troped PJ, Wiecha JL, Fragala MS, Matthews CE, Finkelstein DM, Kim J, et al. Reliability and validity of YRBS physical activity items among middle school students. Med Sci Sports Exerc. 2007;39:416–25.PubMedCrossRefPubMedCentralGoogle Scholar
  84. 84.
    Wang C, Chen P, Zhuang J. Validity and reliability of International Physical Activity Questionnaire-Short Form in Chinese youth. Res Q Exerc Sport. 2013;84:S80–6.PubMedCrossRefPubMedCentralGoogle Scholar
  85. 85.
    Murphy MH, Rowe DA, Belton S, Woods CB. Validity of a two-item physical activity questionnaire for assessing attainment of physical activity guidelines in youth. BMC Public Health. 2015;15:1080.CrossRefGoogle Scholar
  86. 86.
    Dollman J, Stanley R, Wilson A. The concurrent validity of the 3-Day Physical Activity Recall in Australian youth. Pediatr Exerc Sci. 2015;27:262–7.PubMedCrossRefPubMedCentralGoogle Scholar
  87. 87.
    Ridgers ND, Timperio A, Crawford D, Salmon J. Validity of a brief self-report instrument for assessing compliance with physical activity guidelines amongst adolescents. J Sci Med Sport. 2012;15:136–41.PubMedCrossRefPubMedCentralGoogle Scholar
  88. 88.
    Kowalski KC, Crocker PRE, Kowalski NP. Convergent validity of the physical activity questionnaire for adolescents. Pediatr Exerc Sci. 1997;9:342–52.CrossRefGoogle Scholar
  89. 89.
    Al-Hazzaa HM, Al-Sobayel HI, Musaiger AO. Convergent validity of the Arab teens lifestyle study (ATLS) physical activity questionnaire. Int J Environ Res Public Health. 2011;8:3810–20.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Gråsten A, Watt A. A comparison of self-report scales and accelerometer-determined moderate to vigorous physical activity scores of Finnish school students. Meas Phys Educ Exerc Sci. 2016;20:220–9.CrossRefGoogle Scholar
  91. 91.
    Hallal PC, Reichert FF, Clark VL, Cordeira KL, Menezes AMB, Eaton S, et al. Energy expenditure compared to physical activity measured by accelerometry and self-report in adolescents: a validation study. PLoS One. 2013;8:e77036.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Stanley R, Boshoff K, Dollman J. The concurrent validity of the 3-day Physical Activity Recall questionnaire administered to female adolescents aged 12–14 years. Aust Occup Ther J. 2007;54:294–302.Google Scholar
  93. 93.
    Campbell N, Gaston A, Gray C, Rush E, Maddison R, Prapavessis H. The Short QUestionnaire to ASsess Health-enhancing (SQUASH) physical activity in adolescents: a validation study using doubly labeled water. J Phys Act Health. 2016;13:154–8.PubMedCrossRefPubMedCentralGoogle Scholar
  94. 94.
    Ottevaere C, Huybrechts I, De Bourdeaudhuij I, Sjöström M, Ruiz JR, Ortega FB, et al. Comparison of the IPAQ-A and Actigraph in relation to VO2max among European adolescents: the HELENA study. J Sci Med Sport. 2011;14:317–24.PubMedCrossRefPubMedCentralGoogle Scholar
  95. 95.
    Martínez-Gómez D, Calabro MA, Welk GJ, Marcos A, Veiga OL. Reliability and validity of a school recess physical activity recall in Spanish youth. Pediatr Exerc Sci. 2010;22:218–30.PubMedCrossRefPubMedCentralGoogle Scholar
  96. 96.
    Ekelund U, Neovius M, Linne Y, Rossner S. The criterion validity of a last 7-day physical activity questionnaire (SAPAQ) for use in adolescents with a wide variation in body fat: the Stockholm Weight Development Study. Int J Obes. 2006;30:1019–21.CrossRefGoogle Scholar
  97. 97.
    LeBlanc AGW, Janssen I. Difference between self-reported and accelerometer measured moderate-to-vigorous physical activity in youth. Pediatr Exerc Sci. 2010;22:523–34.PubMedCrossRefPubMedCentralGoogle Scholar
  98. 98.
    Sallis JF, Buono MJ, Roby JJ, Micale FG, Nelson JA. Seven-day recall and other physical activity self-reports in children and adolescents. Med Sci Sports Exerc. 1993;25:99–108.PubMedCrossRefPubMedCentralGoogle Scholar
  99. 99.
    Tian H, Du Toit D, Toriola AL. Validation of the Children’s Leisure Activities Study Survey Questionnaire for 12-year old South African children. Afr J Phys Health Educ Recreat Dance. 2014;20:1572–86.Google Scholar
  100. 100.
    Telford A, Salmon J, Jolley D, Crawford D. Reliability and validity of physical activity questionnaires for children: the Children’s Leisure Activities Study Survey (CLASS). Pediatr Exerc Sci. 2004;16:64–78.CrossRefGoogle Scholar
  101. 101.
    Bonn SE, Surkan PJ, Trolle Lagerros Y, Bälter K. Feasibility of a novel web-based physical activity questionnaire for young children. Pediatr Rep. 2012;4:127–9.CrossRefGoogle Scholar
  102. 102.
    Treuth MS, Sherwood NE, Butte NF, McClanahan B, Obarzanek E, Zhou A, et al. Validity and reliability of activity measures in African–American Girls for GEMS. Med Sci Sports Exerc. 2003;35:532–9.PubMedCrossRefPubMedCentralGoogle Scholar
  103. 103.
    Strugnell C, Renzaho A, Ridley K, Burns C. Reliability of the modified child and adolescent physical activity and nutrition survey, physical activity (CAPANS-PA) questionnaire among Chinese–Australian youth. BMC Med Res Methodol. 2011;11:122.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Costa-Tutusaus L, Guerra-Balic M. Development and psychometric validation of a scoring questionnaire to assess healthy lifestyles among adolescents in Catalonia. BMC Public Health. 2015;16:89.CrossRefGoogle Scholar
  105. 105.
    Barbosa N, Sanchez CE, Vera JA, Perez W, Thalabard J-C, Rieu M. A physical activity questionnaire: reproducibility and validity. J Sports Sci Med. 2007;6:505–18.PubMedPubMedCentralGoogle Scholar
  106. 106.
    Rangul V, Holmen TL, Kurtze N, Cuypers K, Midthjell K, Biddle S, et al. Reliability and validity of two frequently used self-administered physical activity questionnaires in adolescents. BMC Med Res Methodol. 2008;8:47.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    Liu Y, Wang M, Tynjälä J, Lv Y, Villberg J, Zhang Z, et al. Test-retest reliability of selected items of Health Behaviour in School-aged Children (HBSC) survey questionnaire in Beijing, China. BMC Med Res Methodol. 2010;10:73.PubMedPubMedCentralCrossRefGoogle Scholar
  108. 108.
    Bobakova D, Hamrik Z, Badura P, Sigmundova D, Nalecz H, Kalman M. Test–retest reliability of selected physical activity and sedentary behaviour HBSC items in the Czech Republic, Slovakia and Poland. Int J Public Health. 2014;60:59–67.PubMedCrossRefPubMedCentralGoogle Scholar
  109. 109.
    Prochaska JJ, Sallis JF, Long B. A physical activity screening measure for use with adolescents in primary care. Arch Pediatr Adolesc Med. 2001;155:554–9.PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© The Author(s) 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Public and Occupational HealthAmsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health research instituteAmsterdamThe Netherlands
  2. 2.Institute of Sport ScienceUniversity of GrazGrazAustria
  3. 3.Department of Epidemiology and BiostatisticsAmsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Public Health research instituteAmsterdamThe Netherlands

Personalised recommendations