Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study.

BACKGROUND: The Saltin-Grimby Physical Activity Level Scale (SGPALS) is commonly used to measure physical activity (PA) in population studies, but its validity in adolescents is unknown. This study aimed to assess the criterion validity of the SGPALS against accelerometry in a large sample of adolescents. A secondary aim was to examine the validity across strata of sex, body mass index (BMI), parental educational level, study program and self-reported health.
METHODS: The study is based on data from 572 adolescents aged 15-17 years who participated in the Fit Futures Study 2010-11 in Northern Norway. The participants were invited to wear an accelerometer (GT3X) attached to their hip for seven consecutive days. We used Spearman's rho and linear regression models to assess the validity of the SGPALS against the following accelerometry estimates of PA; mean counts/minute (CPM), steps/day, and minutes/day of moderate-to-vigorous physical activity (MVPA).
RESULTS: The SGPALS correlated with mean CPM (ρ = 0.40, p<0.01), steps/day (ρ = 0.35, p<0.01) and MVPA min/day (ρ = 0.35, p<0.01). We observed no differences between correlations within demographic strata (all p>0.001). Higher scores on SGPALS were associated with a higher CPM, higher number of steps per day and more minutes of MVPA per day, with the following mean differences in PA measurements between the SGPALS ranks: CPM increased by 53 counts (95% CI: 44 to 62), steps/day increased by 925 steps (95% CI: 731 to 1118), and MVPA by 8.4 min/day (95% CI: 6.7 to 10.0). Mean difference between the highest and lowest SGPALS category was 2947 steps/day (6509 vs. 9456 steps/day) and 26.4 min/day MVPA (35.2 minutes vs 61.6 minutes).
CONCLUSION: We found satisfactory ranking validity of SGPALS measured against accelerometry in adolescents, which was fairly stable across strata of sex, BMI, and education. However, the validity of SGPALS in providing information on absolute physical activity levels seem limited.

Introduction

Low levels of physical activity (PA) in adolescence are associated with an increased risk of obesity and non-communicable diseases in adulthood [1, 2]. PA levels in childhood and adolescence seem to decline with increasing age [3] and tend to track into adulthood [4]. Consequently, surveillance of PA in childhood and adolescence is vital to inform public health policies aimed at increasing or maintaining PA levels in childhood and adolescence [4].

In population-based studies, questionnaires are the most common measure of PA, being inexpensive, practical and a quick and scalable method for collecting data. However, self-reported PA is likely influenced by recall and social desirability bias, which may introduce misclassification and influence the validity of self-reported PA [5-8]. Validation of self-reported PA instruments is therefore crucial for interpreting prevalence estimates of PA and associations between PA and health outcomes [9].

One of the most frequently used physical activity questionnaires (PAQs) in Scandinavia is the Saltin-Grimby Physical Activity Level Scale (SGPALS), introduced by Saltin and Grimby in 1968 [10, 11]. The SGPALS includes four hierarchical ranks of PA [10] and is included in numerous population studies [Scand J Med Sci Sports. 2015 ">11-14]. The SGPALS is predominantly used in adult cohorts but is also included in some adolescent cohort studies [15, 16]. In adults, higher SGPALS ranks has been shown to represent higher criterion measure estimates, such as accelerometry and cardiorespiratory fitness [11]. In children and adolescents, differences between self-reported and device measured PA has been reported [17, 18]. A modified Motric Module PAQ underestimated LPA and MVPA during school hours, but overestimated leisure-time activity, compared to accelerometry [17]. To our knowledge, the SGPALS has not been compared to such criterion measures in adolescents.

In adults, previous research indicates that the agreement between self-reported and device measured PA may differ within strata, showing a higher discordance among individuals with low education [19-21]. Moreover, men are found to report higher PA than women despite accumulating similar device measured PA [20] but not consistently [19, 22, 23].

In contrast to adults, higher education groups show higher differences between self-reported and device measured PA than lower education groups in adolescent samples [24]. Although not consistent [25], sex differences between self-reported and device measured PA also seem evident in adolescents [18, 26, 27]. Moreover, there are inconsistent findings in validity of self-reported PA by BMI groups in adolescents, where some studies found no differences by BMI groups [18], while others report BMI to influence the discrepancies between self-reported and device measured PA [28]. As several factors may influence the validity of self-reported PA in adolescents, further exploration on demographic factors that can influence discrepancies between self-reported, especially SGPALS, and device measured PA is warranted.

Thus, the aim of this study was to explore validity of the SGPALS in a sample of Norwegian adolescents. A secondary aim was to examine the validity by strata of sex, BMI, parental educational level, self-reported health status and school program.

Materials and methods

Design and participants

The Fit Futures Study (FF) is a population-based cohort study of adolescents in Northern Norway [15, 16]. We used data from the first survey of the FF (FF1), collected between September 2010 and April 2011. All first-year high school students (n = 1117) from one urban (Tromsø) and one rural (Balsfjord) municipality in Northern Norway were invited to participate, of which 1038 (92.7%) attended. The participants attended a half-day visit at the Clinical Research Department at the University Hospital of Northern Norway, Tromsø, and all procedures were performed by trained research technicians. The data collection included electronic questionnaires, clinical examinations and accelerometer measurements. The accelerometers (ActiGraph GT3X, ActiGraph, Pensacola, FL, United States) were handed out to the participants with instructions to wear the device on their right hip for seven consecutive days. In the present study we excluded participants with accelerometer wear time <10 hours for at least 4 days (n = 427) and those aged ≥ 18 years (n = 38). The final sample included 572 participants with valid accelerometer wear time and complete data on the SGPALS questionnaire. A larger proportion of girls than boys (68% vs 52%, p < 0.001) and those studying general studies (70% vs 58% of both vocational and sports, p < 0.001) provided valid accelerometry data while distribution of parental level of education (p = 0.33), BMI (p = 0.41) and self-reported health (p = 0.81) did not differ between those with and without valid accelerometry data (S1 Table).

Participants aged 16 years or above signed a written informed consent. Participants under 16 years signed with written permission from their legal guardians. The Regional Committee for Medical and Health Ethics approved the study (2012/1663/REK Nord).

Socio-demographic variables

Weight and height were measured on a Jenix DS-102 stadiometer (Dong Sahn Jenix co Ltd, Seoul, Korea), an automatic electronic scale. Weight was measured in kilograms (kg) with a precision of 0.1 kg and height in meters (m) to the nearest 0.1 cm. BMI was calculated as kg divided by the square height (kg/m2). According to the International Obesity Task Force (iso-BMI), at the age of 16 the cut-off for overweight is 23.9 kg/m2 for boys and 24.37 kg/m2 for girls [29]. As iso-BMI and adult cut-offs for BMI become more similar by increasing age, BMI was calculated according to adults’ cut-offs and categorized as normal weight (< 25 kg/m2), and overweight and obese (≥ 25 kg/m2). Socioeconomic status was determined by questionnaire data on the parent with the highest level of education, categorized as either; 1) Do not know, 2) Primary/high school, 3) University < 4 years, and 4) University 4 ≥ years. The participants rated their self-perceived health according to the question: «How do you in general consider your own health to be?”, with five alternatives: 1) Very poor, 2) Poor, 3) Neither good nor poor, 4) Good, or 5) Excellent. Only four participants rated their health as very poor, thus we categorized 1) Very poor and 2) Poor into “1) Very poor/poor”. Information on study program (vocational, general studies or sports) [30] was retrieved from the schools’ student database.

In Norway, first year of upper secondary school means the 11th year of Norwegian school attendance, where the students can choose between different study programs. About 38% choose general studies, 6% choose sports specialization, and the remaining students choose between 11 different vocational studies such as health programs, technical programs, maritime programs, creative schools and economic and administrative programs [31].

The Saltin-Grimby Physical Activity Level Scale—SGPALS

Participants answered the SGPALS by stating their PA level according to four hierarchical levels [10, 11]. Compared with the original wording by Saltin and Grimby in 1968 designed for adults [10], the participants in this study answered a slightly modified version with examples of activities suited for adolescents (Table 1), and with a duration requirement also for level 3 (in addition to level 2). This has later been the version recommended by Grimby and colleagues [11, 32].

Table 1

Saltin-Grimby Physical Activity Level Scale (SGPALS).

	Leisure Time Physical Activity Level
Question	Which description fits best regarding your physical activity level in leisure time during the last year?
Answering alternative 1	Almost completely inactive:
	“Sitting by the PC/TV, reading or other sedentary activity”
Answering alternative 2	Moderately active:
	“Walking, cycling, or other forms of exercise at least 4 hours per week (here, you should also consider transport to/from school, shopping, Sunday strolls etc.)”
Answering alternative 3	Highly active:
	“Participation in recreational sports, heavy outside activity, shoveling snow etc. at least 4 hours per week”
Answering alternative 4	Vigorously active:
	“Participation in hard training or sports competitions regularly several times a week”.

Accelerometer data collection and processing

The ActiGraph GT3X records accelerations in three axes (axial, coronal and sagittal). The devices were initialized using the manufacturer’s software (ActiLife, LLC, Pensacola, FL, USA) with 30 Hz sampling frequency, and set to record data from when the ActiGraph was attached to the hip and until 23:59 on day 8. The ActiLife software was used to download the accelerometer data using the normal (default) filter to aggregate raw acceleration data into 10-seconds epochs using a proprietary algorithm designed by the manufacturer. The data were further analyzed using the Quality Control & Analysis Tool (QCAT), a custom-made software developed in Matlab (The MathWorks, Inc, Natick, MA, USA). The 10-second epochs were summed to 60 seconds, and the first day of measurements was excluded from further analyses to reduce reactivity [33].

Wear time was calculated from triaxial vector magnitude (the square root of the sum of squared activity) counts per minute (CPM) as described by Hecht et al. [34], based on the following three criteria; 1) A vector magnitude value (VMU) in counts per minute (CPM) > 5; 2) Of the following 20 minutes, at least 2 minutes have VMU CPM values > 5; and 3) Of the preceding 20 minutes, at least 2 minutes have VMU CPM values > 5. If at least 2 of the criteria were positive, the 1-minute epoch was considered as wear-time. All other minutes were defined as non-wear time.

We expressed volume estimates of PA as mean uniaxial CPM per day, number of steps per day and moderate-to-vigorous physical activity (MVPA). The step count was derived from the vertical axis using a proprietary algorithm from the manufacturer. MVPA was defined as a CPM ≥ 1952 [35], measured in minutes per day (min/day).

Statistical analyses

Participants who did not meet our wear time criterion of at least four days with ≥ 10 hours of activity [36] were excluded from the analysis. All accelerometer estimates (CPM, steps, and MVPA) were considered normally distributed by visual inspection of histograms and QQ-plots. We used independent t-tests to assess differences in accelerometry wear time between boys and girls, and between under- and normal weight and overweight and obese participants. Differences in accelerometer wear time between study programs, parental education and self-reported health status were assessed by univariate analyses of variance (ANOVA). We also used ANOVAs to assess the association between indices of device-measured PA (CPM, steps, and MVPA) and the SGPALS. We used Spearman’s rho (ρ) to assess the ranked correlation between the SGPALS and accelerometer estimates of PA (mean CPM, mean steps/day and min/day MVPA) in total and in strata of sex, BMI, parental level of education, self-reported health, and study program. We visually inspected scatter plots following our correlation analyses to identify outliers. We used Fisher´s ρ to z transformation to compare rho correlations within demographic strata, as previously done by others [37]. To decrease false discovery rates, we adjusted the p-values from Spearman´s rho, and for comparison between rho´s, according to the Benjamin-Hochberg method [38] with 25% false discovery rate. A coefficient (ρ) of 0.00 to 0.10, 0.10 to 0.39, 0.40 to 0.69 and ≥ 0.70 was considered a negligible, weak, moderate and strong correlation, respectively [39]. Alpha was set to p < 0.05. All data are presented as mean ± standard deviation (SD), mean with 95% confidence interval (CI) or as frequency (percentage). All analyses were performed using the Statistical Package for Social Science (SPSS Version 25, International Business Machines Corporation, USA).

Results

The descriptive characteristics of participants are presented in Table 2. Among the 253 boys and 319 girls in this study, mean BMI was 22.4 kg/m2 (both sexes) and the mean age was 16.0 and 16.1 years, respectively. Among the 572 participants, 98 (17.1%) classified themselves in the first category of the SGPALS, 197 (34.4%) in the second category, 164 (28.7%) in the third and 113 (19.8%) in the last category. Girls were more likely to report lower self-reported health status than boys (p = 0.26). There were differences in wear time per valid day between sexes (p = 0.01), but not between BMI categories (p = 0.83), study program (p = 0.35), parental education (p = 0.23) and self-reported health status (p = 0.38) (S2 Table). Mean MVPA was 44.8 (SD 21.7) minutes per day, mean CPM 340.8 (SD 123.0) and mean number of steps per day was 7875 (SD 2508).

Table 2

Characteristics of boys and girls, the Fit Futures Study 2010–2011.

	All	Girls	Boys	SGPALS
	(n = 572)	(n = 319)	(n = 253)
				1	2	3	4
Age (years)	16.1±0.4	16.1±0.4	16.0±0.4	16.1±0.4	16.1±0.4	16.1±0.4	16.1±0.3
Height (cm)	170.4±8.8	165.2±6.4	177.0±6.8	170.9±8.8	169.2±8.5	170.1±8.6	172.6±9.3
Weight (kg)	65.3±13.6	61.2±11.3	70.4±14.4	65.6±15.1	65.5±13.5	65.0±14.6	65.0±10.7
BMI (kg/m2)	22.4±4.0	22.4±3.8	22.4±4.2	22.4±4.5	22.8±4.1	22.4±4.4	21.7±2.4
BMI category n (%)	570 (100)	317 (100)	253 (100)	1	2	3	4
Underweight or normal weight *	457 (80.2)	260 (82.0)	197 (77.9)	71 (15.5)	148 (32.4)	139 (30.4)	99 (21.7)
Overweight or obese	113 (19.8)	57 (18.0)	56 (22.1)	26 (23.0)	48 (42.5)	25 (22.1)	14 (12.4)
Study specialization n (%)	572 (100)	319 (100)	253 (100)	1	2	3	4
Vocational	238 (41.7)	108 (33.8)	130 (51.4)	57 (23.9)	107 (45.0)	53 (22.3)	21 (8.8)
General	273 (47.6)	184 (57.7)	89 (35.2)	41 (15.0)	87 (31.9)	99 (36.3)	46 (16.8)
Sports	61 (10.7)	27 (8.5)	34 (13.4)	0	3 (4.9)	12 (19.7)	46 (75.4)
Parents’ education n (%)	570 (100)	318 (100)	252 (100)	1	2	3	4
Do not know	113 (19.8)	52 (16.4)	61 (24.2)	24 (21.2)	39 (34.5)	30 (26.5)	20 (17.7)
Primary/high school	167 (29.3)	89 (28.0)	78 (31.0)	34 (20.4)	65 (38.9)	46 (27.5)	22 (13.2)
University <4 years	115 (20.2)	72 (22.6)	43 (17.0)	11 (9.6)	41 (35.7)	29 (25.2)	34 (29.6)
University ≥4 years	175 (30.7)	105 (33.0)	70 (27.8)	28 (16.0)	51 (29.1)	59 (33.7)	37 (21.1)
Self-perceived health n (%)	569 (100)	317 (100)	252 (100)	1	2	3	4
Very poor/poor	30 (5.3)	18 (5.7)	12 (4.8)	12 (40.0)	11 (36.7)	5 (16.7)	2 (6.7)
Neither good nor poor	119 (20.9)	61 (19.2)	58 (23.0)	36 (30.3)	55 (46.2)	18 (15.1)	10 (8.4)
Good	276 (48.5)	170 (53.6)	106 (42.1)	40 (14.5)	98 (35.5)	91 (33.0)	47 (17.0)
Excellent	144 (25.3)	68 (21.5)	76 (30.1)	10 (6.9)	31 (21.5)	50 (34.7)	53 (36.8)

Data are mean ± standard deviation. SGPALS = Saltin-Grimby Physical Activity Level Scale (Which description fits best regarding your physical activity level in leisure time during the last year? 1 Almost completely inactive “Sitting by the PC/TV, reading, or other sedentary activity”. 2 Moderately active “Walking, cycling or other forms of exercise at least 4 hours per week (here you should also consider transport to/from school, shopping, Sunday strolls etc.”. 3 Highly active “Participation in recreational sports, heavy outside activity, shoveling snow etc. at least 4 hours per week”. 4 Vigorously active “Participation in hard training or sports competitions regularly several times a week”. BMI = Body mass index.

*Cut-off value<25.

The distribution of CPM, steps and MVPA is illustrated by box plots in Fig 1. We observed statistically significant increases in all indices of accelerometer measured PA by increasing SGPALS levels (all p < 0.001). Mean difference between the lowest and highest SGPALS categories was 163 CPM (278 vs. 441 mean CPM), 2947 steps/day (6509 vs. 9456 steps/day) and 27 min/day MVPA (35 minutes vs 62 minutes) (Table 3).

Fig 1

Box plot with median, interquartile range, maximum and minimum with outliers of CPM, steps and MVPA per day by SGPALS ranks.

The Fit Futures Study 2010–2011.

Table 3

Accelerometer measured physical activity according to the Saltin-Grimby Physical Activity Level Scale (SGPALS).

The Fit Futures Study 2010–2011.

SGPALS (n = 572)
	Inactive	Moderately active	Highly active	Vigorously active
	(n = 98, 17.1%)	(n = 197, 34.4%)	(n = 164, 28.7%)	(n = 113, 19.8%)
Mean CPM per day* (95% CI)	277.9	307.1	348.6	441.4
	(256.1–299.7)	(291.7–322.4)	(331.8–365.5)	(421.1–461.7)
Steps per day* (95% CI)	6509	7481	8060	9456
	(6046–6971)	(7153–7808)	(7703–8418)	(9023–9889)
MVPA (min/day)* (95% CI)	35.2	39.8	44.7	61.6
	(31.3–39.1)	(37.1–42.6)	(41.7–47.8)	(57.9–65.2)
N (%)	98 (100)	197 (100)	164 (100)	113 (100)
Meeting PA guidelines	5 (5.1)	22 (11.2)	34 (20.7)	57 (50.4)

*Statistically significant difference between categories (between-subject difference): p<0.001. Data are unadjusted mean and 95%CI. CPM = counts per minute, Steps = steps per day, MVPA = moderate-to-vigorous physical activity, CI = confidence interval.

The SGPALS was positively correlated with steps/day (ρ = 0.35, p<0.01), min/day MVPA (ρ = 0.35, p<0.01), and mean CPM (ρ = 0.40, p<0.01) (Table 4). We observed no differences in correlations between socio-demographic strata (all p>0.001).

Table 4

Spearman rank correlations between SGPALS ranks and accelerometer-measured physical activity.

The Fit Futures Study 2010–2011.

	Mean CPM	Steps	MVPA
All (n = 572)	0.40 *	0.35 *	0.35 *
Sex
Boys (n = 253)	0.40 *	0.37 *	0.34 *
Girls (n = 319)	0.41 *	0.33 *	0.38 *
BMI category
Underweight or normal weight (n = 457)	0.43 *	0.35 *	0.38 *
Overweight or obese (n = 113)	0.27 *	0.32 *	0.20
Study specialization
Vocational (n = 238)	0.30 *	0.31 *	0.26 *
General (n = 273)	0.33 *	0.23 *	0.23 *
Sports (n = 61)	0.25	0.20	0.23
Parents’ education
Do not know (n = 113)	0.37 *	0.40 *	0.35 *
Primary/high school (n = 167)	0.26 *	0.25 *	0.24 *
University <4 years (n = 115)	0.47 *	0.38 *	0.41 *
University ≥4 years (n = 175)	0.48 *	0.38 *	0.41 *
Self-perceived health
Very poor/poor (n = 30)	0.40	0.39	0.35
Neither good nor poor (n = 119)	0.30 *	0.28 *	0.24 *
Good (n = 276)	0.31 *	0.25 *	0.28 *
Excellent (n = 144)	0.47 *	0.42 *	0.43 *

SGPALS = Saltin-Grimby Physical Activity Level Scale. BMI = body mass index, CPM = counts per minute, Steps = steps per day, MVPA = moderate-to-vigorous physical activity, bold numbers indicate significant Spearman’s rho at p<0.05

*Significant Spearman’s rho at p<0.01.

Discussion

In this population-based validation study among Norwegian adolescents, we found positive associations between self-reported PA measured by the SGPALS and accelerometer-measured PA. Although correlations between the SGPALS and accelerometer measured PA in general were weak, the SGPALS was able to correctly rank accelerometer-measured PA, as we observed a notable and gradual increase in accelerometry measures for each increase in SGPALS levels.

The SGPALS correlated moderately with accelerometer-measured mean CPM, steps/day and min/day of MVPA. These observations are consistent with previous studies in adults [40-43], where the ranking ability of the SGPALS has been demonstrated against accelerometry [41-43] and cardiorespiratory fitness measures in adults [40-44]. In our study of adolescents, the SGPALS demonstrated similar ranking ability of PA levels. For example, for every increase in SGPALS level, steps per day increased with ~1000 steps and MVPA with ~ 8 minutes per day. This sums up to ~7000 steps and ~60 minutes of MVPA extra per week if individuals increase their PA by one SGPALS level. Such increases would have relevant impact on public health and thus highlights the SGPALS´ ranking ability at the population level. Similar increases in step count by higher SGPALS ranks are found in adults, while increases in MVPA seem to be lower (~2 min by increasing SGPALS rank) [42].

In general, the correlations in our study and that of others [40-43] are modest, which highlight the imprecision associated with self-reported PA [45] and shows that the SGPALS is unable to precisely reflect accelerometry estimates of PA. Nevertheless, 95% of those reporting to be inactive (rank 1) in the SGPALS were also physically inactive in accelerometry estimates (<60 minutes of MVPA), indicating that the SGPALS is fairly good at classifying inactive individuals (Table 3). Although the proportion of individuals classified as active by the accelerometer increases by increasing rank, it seems that in the higher ranks, the precision in classifying active vs. inactive individuals decreases (Table 3). Although the accuracy of PA volume and intensity is limited when using the SGPALS, crude ranking of self-reported PA at population level is valuable [45]. The SGPALS is shorter than most recall-PAQs, which could make it more appealing to researchers, especially when planning population studies.

The SGPALS is sometimes labelled a “global questionnaire”, aiming to provide a prompt overview of the level of PA. Another common type of questionnaire is the “short recall PAQ”, providing a quick assessment of the total volume of PA, often classified by intensity level (often moderate and vigorous PA) or by domains (work related PA, leisure time PA, or transportation). Examples are “The School Health Action, Planning and Evaluation System (SHAPES) [28, 46], International Physical Activity Questionnaire (IPAQ) [22, 47, 48] and WHO Health Behavior in School-aged Children (HBSC) [49]. These recall questionnaires yield more information than global questionnaires, however, this also introduces a risk of lower precision. For example, more questions and exceeding details may hamper participants’ ability to recall all details associated with participation in physical activity. Moreover, there may be difficulties related to the comprehension of the concepts of “moderate” and “vigorous” PA and in recalling normal activities such as walking or sitting, and calculating total duration [50]. The SGPALS showed correlations with accelerometer measures that are comparable with other PAQs validated in adolescents [51, 52]. Consequently, although the accuracy of PA volume and intensity is limited when using the SGPALS, crude ranking of self-reported PA at population level is valuable [45], and presents SGPALS as a viable option when choosing PAQs as it is relatively easy to answer and obtain fairly accurate PA estimates.

The ranking ability of the SGPALS was similar across various socio-demographic strata. This contrasts with some previous studies comparing other PA questionnaires in adolescents against accelerometry measured PA by sex [18, 26, 27], parental education [24], and categories of BMI [18], although some have reported no differences in ranking ability between sex and BMI groups [17, 18].

Inconsistent findings may be explained by differences in the distribution of socio-demographic variables or by measurement properties in the PA questionnaires included. Most PA questionnaires ask participants to report minutes in different intensities [17–21, 24–28], while the SGPALS include four crude groups representing PA in the last year. Considering inconsistent findings between socio-demographic strata in previous studies [17–21, 24–28], multiple item PAQs may inherently influence measurement precision due to adolescents´ recall abilities. Our findings of stable correlations across strata suggest the SGPALS to be fairly robust in ranking PA levels without much influence from socio-demographical characteristics in adolescents.

Strengths and limitations

To our knowledge, this is the first study to assess the validity of the leisure time SGPALS in adolescents, as few other studies have used accelerometry to measure PA in larger samples in this particular age group. Moreover, Fit Futures had a high participation proportion (93%), although missing accelerometer data resulted in a considerably large proportion that did not provide valid accelerometer wear time; thus, our results may be influenced by selection bias. Consequently, one should be cautious when interpreting the results. However, in a recent publication based on the same population from Fit Futures, missing accelerometer data were imputed and a sensitivity analysis showed that the participants with missing accelerometer data did not differ significantly from the participants with valid data [53].

Further, the accelerometer assessment over seven days was not time-aligned with the SGPALS [10, 11, 49]; the SGPALS addresses habitual PA (over the last year) and participants completed the instrument at the start of the accelerometer wear period. However, PA instruments are in general designed to capture the habitual PA level [54], with the SGPALS [55, 56] showing acceptable reliability (moderate Kappa ~0.5–0.6), as does four days of ≥ 10 hours of accelerometer assessment (intraclass correlation: 0.8) [49, 54]. As the SGPALS was filled out immediately before wearing the accelerometer, this may have introduced reactivity [33]. In an attempt to overcome the potential for reactivity, we excluded the first day of accelerometry recording.

Furthermore, this study validated the leisure time PA part of the SGPALS, including modes of transportation to/from school, while the accelerometer assessment is not limited to leisure time. The occupational time SGPALS [10] was not included in this study of adolescents as it is not relevant for this age group attending high school.

Conclusion

Our study adds to building evidence for satisfactory ranking validity of SGPALS measured against accelerometry in adolescents, and the validity is fairly stable across strata of sex, BMI, and education. However, the validity of SGPALS in providing information on absolute physical activity levels is limited.

Implications for public health and future research

In a public health perspective, increasing PA is more important among those who are inactive, as changing PA levels from low levels seem to yield more health effects than increasing from higher levels of PA [57]. The current study provides evidence to support the use of SGPALS as a low cost and time efficient tool to identify the least active adolescents. Future research may involve exploring the stability of the SGPALS stability, compared to other self-report measurements. Another potential research area could be comparing the validity of SGPALS among youth to that among adults to elicit if age does influence the validity of the instrument.

Distribution of valid and invalid accelerometry wear time.

(DOCX)

Click here for additional data file.

Accelerometry wear time by BMI, study specialization, parental education and self-perceived health.

(DOCX)

Click here for additional data file.

19 May 2021

PONE-D-21-01038

Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study.

PLOS ONE

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Reviewer #1: PONE-D-21-01038_Rview Comments

This study aimed to assess the criterion validity of the Saltin-Grimby Physical Activity Level Scale (SGPALS) against accelerometry in a large sample of adolescents. In addition, the validity in strata of sex, body mass index (BMI), parental educational level, study program and self-reported health has also been examined.

1. I highly suggest authors to consider further revise the introduction section by including more detailed background information, rationales for why validating the SGPALS, the difference between the SGPALS, and the Godin Leisure-Time Exercise Questionnaire, and the International Physical Activity Questionnaires (IPAQ). The length of the current introduction is brief and lacks supporting and transition sentences. For example, there is a little bit logic gap from the first paragraph to the second paragraph of the introduction section. Even within the first paragraph, there are only three sentences the three sentences did not link to each other in a correct way. There is also a lack of a section focused on the aims of the study with hypotheses and so on. Given there is a second aim of the study, background information on justifying the necessary to conduct difference analyses on these variables are needed. Overall, one page for the introduction is not sufficient, suggest increasing the length to three pages.

2. I also have a concern regarding the standard for classifying participants as minors and adults using 16 as the cutoff criteria. The question is that why not using 18 as the cutoff point but choosing 18.

3. It seems that this is a secondary data analysis of an existing project called “The Fit Futures Study (FF)”. I think this needs to be specific across the whole study.

4. Page 5, the subheading is titled as “Covariates”. I am not sure whether this is correct as they were not controlled in regression models or SEM but more of a key social demographic variable that that been used to compare the difference for the SGPALS scores.

5. For the time frame of the first question in the scale “Which description fits best regarding your physical activity level in leisure time during the last year?” The whole year might be exceptionally long that leads to memory bias and inaccuracy in recall. I doubt this time frame.

6. For discussion, four brief paragraphs are not enough. Findings of the current study should be further explained with support from previous studies. In addition, findings of the current study should be compared with previous studies for consistency and inconsistency.

7. When talking about the “Strengths and limitations” of the current study, it is better that future studies should be mentioned. For example, the last point at Lines 268-270.

8. The last section of the study is not written in a well-summarized and clear way. Needs to be further revised to make it clear.

Reviewer #2: I have attached my full review in pdf document and now I am writing until I get to the minimum number of characters for this box on the website. The limit is 200 characters. Thank you for submitting and allowing me to read your work. I hope my comments help.

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Submitted filename: Review_PONE-D-21-01038.pdf

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18 Nov 2021

Response to reviewers

All line-references are to the line number in the revised tracked changes document.

Reviewer 1:

Reviewer comment 1: I highly suggest authors to consider further revise the introduction section by including more detailed background information, rationales for why validating the SGPALS, the difference between the SGPALS, and the Godin Leisure-Time Exercise Questionnaire, and the International Physical Activity Questionnaires (IPAQ). The length of the current introduction is brief and lacks supporting and transition sentences. For example, there is a little bit logic gap from the first paragraph to the second paragraph of the introduction section. Even within the first paragraph, there are only three sentences the three sentences did not link to each other in a correct way. There is also a lack of a section focused on the aims of the study with hypotheses and so on. Given there is a second aim of the study, background information on justifying the necessary to conduct difference analyses on these variables are needed. Overall, one page for the introduction is not sufficient; suggest increasing the length to three pages.

Response: Thank you.

Comparing PA questionnaires: Although the Godin Leisure-time Exercise Q and the IPAQ are commonly used questionnaires in many studies, these questionnaires are not included in the Fit-Futures study. Thus, providing background information on these questionnaires seems beyond the scope of this study, as it does not directly relate to our study or our aims.

Coherence and transition: We have made an effort to revise the text, with proper transitions between paragraphs.

Rationale for aims and length of introduction: We have now changed our introduction to more explicitly highlight the reason for validating the SGPALS in adolescents, line 77-90. Additionally, we have provided a new paragraph justifying the choice of stratification variables in our study. Please see line 92-106. This has added substantially to the length of the introduction.

Reviewer comment 2: I also have a concern regarding the standard for classifying participants as minors and adults using 16 as the cutoff criteria. The question is that why not using 18 as the cutoff point but choosing 18.

Response: According to the Norwegian Patient and User Rights Act, a person is of a legal age in relation to health service rights when they turn 16 years (Pasient- og brukerrettighetsloven,

LOV-1999-07-02-63, Retrieved from Lovdata.no; https://lovdata.no/dokument/NL/lov/1999-07-02-63.

Reviewer comment 3: It seems that this is a secondary data analysis of an existing project called “The Fit Futures Study (FF)”. I think this needs to be specific across the whole study.

Response: The Fit Futures Study (FF) is an ongoing longitudinal population-based cohort study, with survey waves conducted in 2010-11, 2012-13 and a third is ongoing (2021). The analyses in this manuscript are performed on the first survey performed in 2010-11 (FF1). Please see line 118-120.

Reviewer comment 4: Page 5, the subheading is titled as “Covariates”. I am not sure whether this is correct as they were not controlled in regression models or SEM but more of a key social demographic variable that that been used to compare the difference for the SGPALS scores.

Response: Thank you. We have accordingly changed the subheading to socio-demographic variables.

Reviewer comment 5: For the time frame of the first question in the scale “Which description fits best regarding your physical activity level in leisure time during the last year?” The whole year might be exceptionally long that leads to memory bias and inaccuracy in recall. I doubt this time frame.

Response: The SGPALS is designed to capture a person´s general PA level in the last year. Please see our reference nr 10: Saltin & Grimby, 1968, Circulation. The lack of knowledge on the validity of this question used in adolescents is part of the justification for our study.

Reviewer comment 6: For discussion, four brief paragraphs are not enough. Findings of the current study should be further explained with support from previous studies. In addition, findings of the current study should be compared with previous studies for consistency and inconsistency.

Response: Thank you. We have expanded our discussion. Please see line 290-344.

Reviewer comment 7: When talking about the “Strengths and limitations” of the current study, it is better that future studies should be mentioned. For example, the last point at Lines 268-270.

Response: We have added a section called “implications for public health and future studies”, see line 396-400.

Reviewer comment 8: The last section of the study is not written in a well-summarized and clear way. Needs to be further revised to make it clear.

Response: This section has been rewritten (line 388-391).

Reviewer 2:

Reviewer comment 1: With validity and reliability, we are generally building evidence that supports the use of a measure for a set of people, in a given situations, with reference to specific outcomes from the measure. My preference is that we talk about “building evidence for the validity of XX” and not “we are validating XX”. I understand that this is more of an individual preference, but single studies with specific groups and only 1 outcome are often used to support using measures in other groups with a different outcome from that measure. Part of this is because we often talk in our results and discussions about “validating XX”, which implies the instrument is good, not that it has support for use in similar contexts.

Response: Thank you. We have accordingly changed the phrasing in the paper.

Reviewer comment 2: For the models in table 3 and table 4 (correlation and regression) we need some indication of the distributions of the 4 SGPAL categories in those groups. Similar to how you show us girls vs boys. You could do this with an expansion of table 2. Just add 4 columns to right with each SGPAL category. The reason we want to know this is to understand the differences better. If the Overweight BMI category has 90% of people in categories 1 and 2 then the differences, we see in Table 4 (8.97 vs 5.33) per category need to be interpreted in this context.

Response: Thank you. We have added the suggested columns to table 2.

Reviewer comment 3: You might need to look at all the scatter plots and visually inspect for outliers driving correlation up or down. If you did this, mention it in the methods.

Response: Thank you. We performed such scatter plots to confirm not having extreme outliers. This is now mentioned in the methods section; see line 216-217.

Reviewer comment 4: Not needed but you might consider adding the relationship between SGPAL and a “meeting recommendation” outcome computed with Accelerometer MVPA or steps. You could show % in each SGPAL group getting more or less than 60 minutes of MVPA per day. Just more support for using SGPAL in the context of public health recommendations.

Response: Thank you. We have included this in Table 3 (previous submission Table 5), line 259-260.

Reviewer comment 5: Also not need but you could add to results (and evidence) by looking at accelerometer and SGPLAY score differences by group (sex, BMI, edu…). To see if SGPLAY identifies the same difference as accelerometer outcomes. o Example: Do we see the same difference in MVPA and SGPLAY score between girls and boys? �  Significance and percent difference �  Maybe it would look like this • Boys have 43 minutes of MVPA and average SGPLAY score of 3.1 • Girls have 37 minutes of MVPA and average SGPLAY score of 2.8 • Boys have 16% higher MVPA minutes and 11% higher SGplay scores both are statistically significant. •Supplement table 4 with figures to show group differences. This would be 15 plots in a supplement so we can see the change with each SGPAL level by group. Like figures I added below, but you would have each group (girls/boys) plotted, with different figures for each grouping (sex, BMI, education) and PA outcome (steps, CPM, MVPA)

Response: We choose to not include such plots at this time, as this paper is part of a PhD thesis which in its present form meet the objectives and rationale for the thesis. Although a good idea, this requires treating the SGPALS as a continuous variable using the mean for each strata, which we believe may introduce a false accuracy level.

Reviewer comment 6: You did some very nice work with the evidence and results for different groups (Sex, BMI, Health), but the discussion of this (line 229 to 233) is lacking. There is literature in adults and children about how some demographic factors may impact validity and reliability evidence for a given questionnaire. Dig in a little more in this area and really contribute to this area of research.

Response: Thank you! We have performed a new literature search to find evidence on questionnaire responses by different demographic variables. We have now included this in our introduction to provide a more thorough rationale for investigating our secondary aim. Please see line 92-106. We have also included a paragraph of this in our discussion. Please see line 325-344.

Specific Comments:

Line number / Priority / Comment

Reviewer comment 7: 39-40 moderate Correlations of 0.40 and 0.35 are fairly close for data like this. Consider removing “moderately” and “weakly” to describe magnitude. This will apply through the manuscript (discussion also).

Response: Thank you. We have removed this accordingly.

Reviewer comment 8: 70-74 Low I would prefer talking about the strength of the validity evidence rather than “is thoroughly validated”. As an example… your aim is to build validity evidence for using the SGPALS in Norwegian adolescents.

Response: Thank you. We have accordingly changed our phrasing.

Reviewer comment 9: 94 High Did excluded participants (n=427) differ from included (n=572)? Demographics and SGPALS ratings. You mention this in the discussion (line 241-247), but this information should be in the methods.

Response: Thank you. We included this under methods. Please see line 131-134.

Reviewer comment 10: 116 Low May need another few words or sentence with more info about “study program”. What does this mean for high schoolers in Norway?

Response: Thank you. We have provided more information in school programs in Norway. Please see line 158-162.

Reviewer comment 11: 150 High Either in methods or start of results/table 2 it would be good to report average wear times (hours per day and Days per week). You should also confirm that none of the comparison groups (Sex, BMI, others in table 3) have differences in wear hours per day. Wear time at the day level is likely correlated with minutes of MVPA and Steps at ~0.25-0.40 level. If wear time is different this would impact the values, you are using to assess validity evidence in this group.

Response: Thank you. We have performed analysis on wear time by different demographic variables. This is now included in supplementary Table 1. Additionally, please see line 208-212 for statistical outputs.

Reviewer comment 12: Table2 or 175 Moderate Need to have % participants at each SGPAL level. Numbers are in table 5, but should likely be presented at start of results. Same for Mean (SD) for all PA outcomes (MVPA, CPM, Steps)

Response: Thank you. To address this, and particularly your comment nr 17 (see below), we have changed Table 5 (ANOVA) to Table 3, and removed Table 4 (linear regressions). Table 3 (correlation analyses) is now labelled Table 4.

Reviewer comment 13: Table 2/175 Moderate Because a lot of your evidence is built around difference for various demographic variables, it would be beneficial to comment on how much those groups overlap. For example, Do BMI categories and Parent education overlap. Participants in lower education group tend to have higher BMI values.

Response: Thank you. There were no differences in distribution of BMI categories between parents´ education groups (p=0.20) and between sexes (p=0.22). Those being overweight and obese were more likely to report lower self-reported health status (p<0.001), while there were no differences in self-reported health between parental education groups (p=0.28). This can be added to the manuscript if desired.

Reviewer comment 14: 178 Low I realize you have a range for describing the strength of the correlations, but 0.35 and 0.40 are very close in this context. Jumping from “weak” to “moderate” is over statement.

Response: Thank you. We have removed our phrasing regarding magnitude in the correlations.

Reviewer comment 15: Table3 Low Rather than “**”for almost all correlations with indicators for p-value, it would be better for your story to only highlight the differences that are important (what you focus on in text). For example

BMI CPM Steps MVPA

Under 0.43 0.35 0.38

Over 0.27 0.32* 0.20*

You could pick two indicators. One for group difference for same PA outcome (bold) and one for difference across PA outcome within a group (*)

Response: Thank you. This is accordingly changed per your recommendation. Please see Table 4 (previous submission Table 3).

Reviewer comment 16: 187/table 4 Moderate Is this information from a regression model, different than the ANOVA model for results reported in table 5? May need to explain this model in stats section.

Response: Thank you. Please see our response to reviewer comment 18.

Reviewer comment 17: Table 4 Moderate What about intercepts, this tells us if the rate of change differed, but not the average starting value. This would be important for group differences

Response: Thank you. Please see our response to reviewer comment 18.

Reviewer comment 18: Table 4 High My guess is they are not all linear. I think you need to provide the 15 plots in a supplement so we can see the change with each SGPAL level by group. Like figure below, but you would have each group (girls/boys) plotted, with different figures for each grouping (sex, BMI, education) and PA outcome (steps, CPM, MVPA)

Response: Thank you. As you show with the included figures below, these outputs are not linear, and it may be misleading to present unstandardized beta coefficients from linear regressions. Therefore, we have removed this table from this study, while keeping the mean differences by SGPALS groups (ANOVA).

Reviewer comment 19: 188/189 Moderate This model may only be linear for steps (see plots below). For MVPA there is definitely a bigger jump moving from grp3 to grp4

Table5 Low I like this, but I think three box plots (one for each PA outcome) would have more impact for the reader when emphasizing the differences in activity across the 4 SGPAL ratings.

Response: Thank you. Please see our response to reviewer comment 18. Additionally, we have created Box plots of CPM, steps and MVPA for the four different groups. Please see line 252.

Reviewer comment 20: 207 Low Similar to rest of literature comparing SR to Objective measures

Response: Thank you – we agree.

Reviewer comment 21: 215 Low Not sure you mean bias

Response: Thank you. We have removed the sentence about PA volume and intensity, as we do not have specific data on these measures. Thereby, the sentence on biases is deleted.

Reviewer comment 22: 216 While it may not be a very accurate estimate of volume, table 5 suggests that you might get pretty good estimates of volume, especially if you also accounted for sex, BMI, edu, and rating of health. You could check the precision of these estimates by looking at the measured accelerometer outcomes versus those predicted from the regression model. This would give you average errors and a way to quantify bias and average prediction error. For example, MVPA from SGPALS rating vs MVPa from accelerometer, MVPA from SGPALS rating and Demographics vs MVPA from accelerometer

Response: Thank you. Please see our response to reviewer comment 18.

Reviewer comment 23: 221/222 This point is important, but would be more impactful is you gave us some of the values from the adult data (26,28) for comparison. Example… If in adults the difference in steps across the 4 groups is similar (1000 steps) this would be nice to see. If adults show a similar pattern but magnitude is different (15 min of MVPA vs 8) this would also be interesting and important.

Response: Thank you. We have provided comparison between our study and adult studies. Please see line 321-323.

Reviewer comment 24: 232 Why? Do we see this in what you present in tables

Response: Thank you. We have removed this statement.

Reviewer comment 25: 249 - 256 I don’t think you need this. No one will need you to justify using acceleometers as gold standard. You also used three estimates from accelerometer which quantify different but overlapping parts of the PA puzzle. This is strength!

Response: Thank you, we removed this paragraph.

Reviewer comment 26: 263 Would be better here, or in methods, for you to present the ICC for your data. ICC for all three outcomes over all days measured.

Response: We chose to use Spearman’s rho which is a measure of rank correlation. ICC typically assesses agreement between data in terms of absolute validity or reliability. We appreciate the advantage of ICC to assess agreement between for example multiple days; however, we do not currently have access to such data (please see comment 27).

Reviewer comment 27: 265/266 Did you look at the levels over days (1 vs 2,3,4,5,6,7) ? You could look at this an see if you think there was reactivity.

Response: Unfortunately, due to technical limitations we do not currently have access to day-by-day data.

Reviewer comment 28: 274-275 I think you need to provide the error levels and bias in this prediction at the individual and group levels before saying that volume and intensity should not be estimated.

Response: Thank you. In Table 3, we have now included mean and 95%CI, and also provided box plots for assessing error level at the individual level. We agree that we do not provide results to substantiate any conclusion on volume and intensity and have changed the conclusion accordingly.

Submitted filename: Response to reviewers.docx

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13 Jan 2022

8 Jul 2022

Ru Zhang

Academic Editor

PLOS ONE

We hereby submit the revised version of our manuscript entitled “Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study.” We wish to thank the reviewer for relevant and insightful comments and suggestions. A point-by-point response to the reviewers has been submitted together with the revised manuscript.

We apologize for the delay and look forward to continue the process with our study together with PLOS ONE.

Yours sincerely,

Sigurd Beldo

Submitted filename: Response to reviewer.docx

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10 Aug 2022

Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study.

PONE-D-21-01038R2

Dear Dr. Beldo ,

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23 Aug 2022

PONE-D-21-01038R2

Criterion validity of the Saltin-Grimby Physical Activity Level Scale in adolescents. The Fit Futures Study

Dear Dr. Beldo:

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