Latent profile analysis patterns of exercise, sitting and fitness in adults – Associations with metabolic risk factors, perceived health, and perceived symptoms

Elin Ekblom-Bak; Andreas Stenling; Jane Salier Eriksson; Erik Hemmingsson; Lena V Kallings; Gunnar Andersson; Peter Wallin; Örjan Ekblom; Björn Ekblom; Magnus Lindwall

doi:10.1371/journal.pone.0232210

. 2020 Apr 24;15(4):e0232210. doi: 10.1371/journal.pone.0232210

Latent profile analysis patterns of exercise, sitting and fitness in adults – Associations with metabolic risk factors, perceived health, and perceived symptoms

Elin Ekblom-Bak ^1,^*,^#, Andreas Stenling ^2,^#, Jane Salier Eriksson ^1,^‡, Erik Hemmingsson ^1,^‡, Lena V Kallings ^1,^‡, Gunnar Andersson ^3,^‡, Peter Wallin ^3,^‡, Örjan Ekblom ^1,^‡, Björn Ekblom ^1,^‡, Magnus Lindwall ^1,^4,^#

Editor: Yoshihiro Fukumoto⁵

PMCID: PMC7182226 PMID: 32330191

Abstract

Aim

To identify and describe the characteristics of naturally occurring patterns of exercise, sitting in leisure time and at work and cardiorespiratory fitness, and the association of such profiles with metabolic risk factors, perceived health, and perceived symptoms.

Methods

64,970 participants (42% women, 18–75 years) participating in an occupational health service screening in 2014–2018 were included. Exercise and sitting were self-reported. Cardiorespiratory fitness was estimated using a submaximal cycle test. Latent profile analysis was used to identify profiles. BMI and blood pressure were assessed through physical examination. Perceived back/neck pain, overall stress, global health, and sleeping problems were self-reported.

Results

Six profiles based on exercise, sitting in leisure time and at work and cardiorespiratory fitness were identified and labelled; Profile 1 “Inactive, low fit and average sitting in leisure, with less sitting at work”; Profile 2 “Inactive, low fit and sedentary”; Profile 3 “Active and average fit, with less sitting at work”; Profile 4 “Active, average fit and sedentary in leisure, with a sedentary work” (the most common profile, 35% of the population); Profile 5 “Active and fit, with a sedentary work”; Profile 6 “Active and fit, with less sitting at work”. Some pairwise similarities were found between profiles (1 and 2, 3 and 4, 5 and 6), mainly based on similar levels of exercise, leisure time sitting and fitness, which translated into similar dose-response associations with the outcomes. In general, profile 1 and 2 demonstrated most adverse metabolic and perceived health, profile 4 had a more beneficial health than profile 3, as did profile 6 compared to profile 5.

Conclusions

The present results implies a large variation in exercise, sitting, and fitness when studying naturally occurring patterns, and emphasize the possibility to target exercise, sitting time, and/or fitness in health enhancing promotion intervention and strategies.

Introduction

Exercise, sedentary time and cardiorespiratory fitness are independent predictors of metabolic health and cardiovascular disease risk [1–3]. Recent trend analyses indicate increased time spent sedentary, both at work and in leisure time [4, 5], sustained or decreased levels of moderate-to-vigorous physical activity [4, 6], and decreased levels in cardiorespiratory fitness [7] over the last decade, which may have induced a greater variability in daily physical activity patterns and physical performance between individuals. As these physical activity-related variables tend to cluster, analyses on the interaction and the interactive effect of various physical activity variables on health and disease risk are highly relevant. Traditionally, this has been investigated through variable-centered analyses where interaction effects typically were tested in regression models or stratified analyses. Although this strategy may provide valuable information, testing, and interpreting interactions consisting of more than two variables is challenging. Person-oriented approaches offer an alternative way to understand how different variables interact that may complement and extend variable-centered research [8, 9]. Although cluster-analysis has traditionally been the most commonly used choice of method in person-centered research, other types of analyses, such as latent profile analysis (LPA), are today generally recommended as they entail a number of benefits compared to cluster analyses [10, 11].

LPA identifies interactions of input variables (e.g., exercise, fitness, and sedentariness) to create naturally occurring profiles, or typical patterns, of combinations of different variables, in a heterogeneous population [10, 11]. This is particularly useful for complex within-person interactions, including more than two variables at the time. Previous studies in adults have mainly applied LPA to identify how physical activity and sedentary behaviour varies in different patterns over 7 days of the week, and how combinations of physical activity and sedentary behaviour are associated with metabolic health and longevity [12–14]. A few studies have used LPA to identify patterns of variations in physical activity, commuting, energy expenditure, and sedentary time [15, 16]. However, no previous study have used LPA to study the naturally occurring patterns of sedentary time at work and during leisure time, exercise (defined as physical activity that is planned, structured, repetitive, and that favours physical fitness maintenance), and cardiorespiratory fitness. Also, the way such patterns relate to health are not known. Such knowledge would expand the understanding how different physical activity-related variables naturally interacts and associate with health, which in turn could be used to target specific high-risk populations and for future intervention strategies.

Hence, in a large population of men and women of different ages, we aimed to identify and describe the characteristics of naturally occurring patterns/latent profiles of exercise, sitting in leisure time and at work, and fitness (VO₂max). Also, we aim to study the association of such profiles with metabolic risk factors, perceived health, and perceived symptoms.

Material and methods

This study has a cross-sectional study design. Data was derived from the HPI Health Profile Institute database, which comprises data from Health Profile Assessments (HPAs) carried out in Swedish health services all over Sweden. The test protocol, methods used and education of HPA coaches is standardized and has been the same since the start of HPA in the middle of the 1970s. The participant answers an extensive questionnaire including current lifestyle, physical activity habits, and perceived symptoms and health, followed by a dialogue with a HPA coach, who also collected data on height, body weight, and diastolic and systolic blood pressure (see below). Cardiorespiratory fitness was estimated using submaximal testing. After completion of the HPA, all data were subsequently recorded in the HPI database. Participation in HPA was offered to all employees working for a company or an organisation connected to occupational or other health services in Sweden, and free of charge for the employee.

In January 2014, questions regarding self-reported sitting time at work and during leisure time were added to the protocol. In April 2018, a withdrawal of all participants with one HPA registered in the HPI database since January 2014 and with valid data for the self-reported sitting questions was made for the present analyses (N = 84,937). Of these, n = 188 lacked data on self-reported exercise, n = 19,526 on valid fitness estimate, and n = 253 of the descriptive (smoking and diet) or outcome (BMI, systolic and diastolic blood pressure, symptom back/neck, overall stress, and perceived health) variables, resulting in n = 64,970 participants (42% women, aged 18–75 years) included in the latent profile analysis. The protocols used were approved by the institutional review boards of the institutions involved in this study and all participants provided informed consent prior to data collection. The study was approved by the Stockholm Ethics Review Board (Dnr 2015/1864-31/2 and 2016/9-32), and adhered to the Declaration of Helsinki.

Latent profile analysis input variables

Current exercise habits were self-reported through the statement; I exercise for the purpose of maintaining/improving my physical fitness, health and well-being … with the alternatives 1 = Never, 2 = Sometimes, 3 = 1–2 times/week, 4 = 3–5 times/week or 5 = ≥6 times/week. Workplace and leisure time sitting were self-reported through the statements: I sit at work… and I sit in my leisure time…, with the alternatives 1 = Almost all of the time, 2 = 75% of the time, 3 = 50% of the time, 4 = 25% of the time or 5 = Almost none of the time. The exercise question has yet not been validated against objective measures. The sitting questions were derived from the question previously showed to have high predictive validity by Katzmarzyk et al. [17], and strong convergent validity with total and prolonged stationary time [18]. Cardiorespiratory fitness (hereby referred to as fitness) was estimated using the Åstrand submaximal cycle ergometer test [19]. All participants were requested to refrain from vigorous activity the day before the test, heavy meal three hours and smoking/snuff use one hour before the test, and avoiding stressing to the test. The participant cycled on a calibrated ergometer at an individually, chosen submaximal work rate for six minutes to achieve a steady-state pulse. Using the steady-state pulse, VO₂max was estimated from a sex-specific nomogram, with corresponding age-correction factors, expressed as absolute (L·min^-1) and relative (ml·min^-1·kg^-1) VO₂max. Criterion validity has been tested for the Åstrand test, showing no systematic bias and limited variation in mean difference between estimated and directly measured VO₂max, mean difference 0.01 L O₂ min^-1 (95% CI -0.10 to 0.11) [20, 21].

Anthropometrics and blood pressure

Body mass was assessed with a calibrated scale in light-weight clothing to the nearest 0.5 kg, and body height measured to the nearest 0.5 cm using a wall-mounted stadiometer. BMI (kg·m^-2) was subsequently calculated. Waist circumference was measured to the nearest 0.5 cm with a tape measure at the midpoint between the top of the iliac crest and the lower margin of the last palpable rib in the mid axillary line after normal exhalation. Systolic and diastolic BP (mmHg) was measured manually in the right arm using the standard auscultatory method after 20 minutes of seated resting.

Perceived health and symptoms

Perceived global health was assessed through the statement: I perceive my physical and mental health as … with the alternatives Very poor, Poor, Neither good or bad, Good, or Very good. Perceived back/neck pain, overall stress and sleeping problems were obtained through the statements: I have back/neck issues…, I perceive stress in my life, both personally and at work… and I perceive sleeping difficulties…, with the alternatives Very often, Often, Sometimes, Rarely or Never.

Other variables

Physical activity level prior to the age of 20 years was self-reported by selecting one of the following five given alternatives through the statement; Prior to the age of 20, I… Did not participate in physical education class, Participate only in physical education class, Participate in physical education class + 1–2 times/week of physical activity outside school hours, Participate in physical education class + 3–5 times/week of physical activity outside school hours or Participate in physical education class + At least 6 times/week of physical activity outside school hours. This question has previously shown predictive validity for exercise level, fitness and health later in life [22]. Dietary habits were obtained using the statement I consider my diet, regarding both meal frequency and nutritional content to be … with the alternatives Very poor, Poor, Neither good nor bad, Good or Very good. Smoking habits were obtained using the statement I smoke … with the alternatives At least 20 cig/day, 11–19 cig/day, 1–10 cig/day, Occasionally or Never.

In addition, data were also obtained from national quality control registers, including occupation, which was reported according to the Swedish Standard Classification of Occupations 1996 (SSYK96) until June 2014 and according to the SSYK 2012 after that. SSYK is a system for classifying and aggregating data about occupations in administrative registers or statistical surveys. Occupations reported according to both SSYK96 or SSYK 2012 can be further grouped into four broad skill levels defined by level of education for the particular occupation; Level 1 covers elementary education at primary school level, meaning no or a low formal education requirements, Level 2 covers education programs at upper secondary and tertiary level of no more than 2 years in length, Level 3 covers practical or vocational tertiary education programs of 2–3 years in length, and Level 4 covers theoretical or research-oriented tertiary education programs and third-cycle programs of at least 3 years, normally 4 years or longer in length.

Statistical analysis

Mplus version 8.3 was used to estimate the LPA and group individuals into profiles based on their individual patterns of exercise, sitting time, and fitness [23]. A sequence of nested models with an increasing number of profiles were compared to examine if more complex models (i.e., with more profiles) fit the data better than more parsimonious models (i.e., with less profiles). In the present study, models with 1 to 7 latent profiles were tested to identify the optimal number of profiles. Important criteria for determining the number of latent profiles in the data were substantive meaning and theoretical conformity [10] as well as statistical adequacy of the solution (e.g., absence of negative variance estimates and local solutions; e.g., [24–26]). For decisions about model retention we followed recommendations [27, 28] and relied on several indexes: the consistent Akaike Information Criterion, the Bayesian Information Criterion, and the sample-size adjusted Bayesian Information Criterion. Lower values for these indexes indicate better model fit. The entropy criterion was also examined, which indicates the precision of the classification of cases into the different profiles. However, the entropy should not be used when determining the optimal number of profiles [28, 29], but can be a useful tool to assess classification accuracy. Higher entropy values indicate fewer classification errors. When merging the total information regarding the most likely number of profiles in the data, subjective dimensions of choice beyond fit statistics were necessarily involved. More parsimonious models with fewer profiles were chosen over more complex profiles where this enhanced the interpretability of the profiles.

To support the interpretation of the best-fitting solution, z-scores of the observed variables were used. We are not aware of any agreed upon criteria for low and high values, but we interpreted the scores of the input variables in relative terms, compared to the group means. Following trends in previous studies using LPA [30], standardized scores between +0.5 and -0.5 were labelled as average, scores over 0.5 were labelled as (relatively) high, and scores lower than -0.5 were labelled as (relatively) low. The profiles retained in the final solution were characterized by sex, age, SSYK level, smoking, diet, and activity prior to 20 years of age (Table 1), and metabolic risk factors, perceived health, and perceived symptoms (Table 2). Statistically significant differences between the profiles for continuous data were tested for by general linear modelling (parametric) and Kruskal-Wallis ANOVA (non-parametric) with pairwise comparison adjusting for multiple comparisons, and for proportions by comparing proportions with the 99% confidence interval (CI) to compensate for multiple testing. Logistic regression modelling was used to assess the odds ratio (OR) and 95% CI associated with the different profiles (using profile 2 as reference) for dichotomized variables of BMI (> 30 vs. ≤ 30 kg·m^-2), high systolic (≥140 vs. <140 mmHg) or diastolic (≥90 vs. <90 mmHg) blood pressure, global health (Very poor/Poor vs. Neither good or bad/Good/Very good), back/neck pain (Very often/Often vs. Sometimes/Rarely/Never), sleeping problems (Very often/Often vs. Sometimes/Rarely/Never), and overall stress (Very often/Often vs. Sometimes/Rarely/Never). The ORs were adjusted for sex and age, and further adjusted for diet, smoking, and educational level (multi-adjusted). Comparisons between profiles were performed using IBM SPSS (version 24.0, SPSS Inc., Chicago IL) and Confidence Interval Analysis (version 2.2.0).

Table 1. Characteristics of the different profiles (above) and mean with standard errors of the z-score for the latent profile variables (below).

	Profile 1	Profile 2	Profile 3	Profile 4	Profile 5	Profile 6
	(n = 9480)	(n = 11227)	(n = 14182)	(n = 23017)	(n = 4871)	(n = 2193)
Women	33% (3137)^a^,^b^,^c^,^d^,^e	39% (4400)^b^,^c^,^d	43% (6113)^c^,^d^,^e	45% (10438)^e	45% (2191)^e	37% (816)
Age (years)	43.2 (0.12)^a^,^b^,^c^,^d^,^e	44.2 (0.10)^d^,^e	44.1 (0.10)^d^,^e	44.4 (0.07)^d^,^e	37.0 (0.13)^e	34.6 (0.22)
SSYK 1–3	20% (1447/7337)	63% (4667/7382)	29% (3145/10762)	72% (10632/14842)	78% (2423/3112)	33% (542/1659)
Non-smokers	75% (7071)^a^,^b^,^c^,^d^,^e	83% (9311)^b^,^c^,^d	85% (12010)^c^,^d	89% (20574)^d^,^e	91% (4430)^e	84% (1851)
Very good/good diet	57% (5363)^b^,^c^,^d^,^e	58% (6493)^b^,^c^,^d^,^e	75% (10641)^d^,^e	74% (17061)^d^,^e	84% (4069)	82% (1786)
Leisure-time physically active in youth	79% (3355/4227)^a^,^b^,^c^,^d^,^e	82% (4016/4920)^b^,^c^,^d^,^e	86% (5766/6738)^c^,^d^,^e	87% (9003/10299)^d^,^e	93% (1971/2115)	92% (926/1010)
Profile variables
Exercise (1 = Never to 5 = ≥6 times/week)	2 (1–2)^b^,^c^,^d^,^e	2 (1–2)^b^,^c^,^d^,^e	4 (3–4)^c^,^d^,^e	3 (3–4)^d^,^e	4 (3–4)	4 (4–4)
Cardiorespiratory fitness (ml·min^-1·kg^-1)	31.4 (0.08)^b^,^c^,^d^,^e	31.3 (0.07)^b^,^c^,^d^,^e	34.2 (0.06)^c,^d^,^e	35.1 (0.05)^d^,^e	54.1 (0.08)	54.5 (0.13)
Leisure sitting (1 = All time to 5 = No time)	4 (3–4)^a^,^b^,^d^,^e	3 (3–4)^b^,^c^,^d^,^e	4 (3–4)^c^,^d^,^e	4 (3–4)^d^,^e	4 (3–4)^e	4 (4–5)
Workplace sitting (1 = All time to 5 = No time)	5 (4–5)^a^,^b^,^c^,^d	2 (2–3)^b^,^e	5 (4–5)^c^,^d	2 (2–3)^d^,^e	2 (1–3)^e	5 (4–5)
Profile variables in z-score
Exercise	-1.086 (0.010)	-1.064 (0.011)	0.603 (0.009)	0.424 (0.008)	0.740 (0.016)	0.819 (0.021)
Cardiorespiratory fitness	-0.473 (0.009)	-0.495 (0.011)	-0.135 (0.014)	-0.025 (0.013)	1.519 (0.032)	1.547 (0.044)
Leisure sitting	-0.108 (0.012)	-0.472 (0.015)	0.278 (0.009)	-0.037 (0.010)	0.272 (0.017)	0.516 (0.018)
Workplace sitting	0.984 (0.007)	-0.778 (0.009)	0.928 (0.007)	-0.721 (0.007)	-0.778 (0.014)	0.931 (0.015)

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Data is presented as mean (SE) (parametric), median (Q1-Q3) (non-parametric) and % (n). For variables with missing data, % (n/N) is presented.

^a significant different vs. Profile 2 after Bonferroni correction for multiple comparisons.

^b significant different vs. Profile 3 after Bonferroni correction for multiple comparisons.

^c significant different vs. Profile 4 after Bonferroni correction for multiple comparisons.

^d significant different vs Profile 5 after Bonferroni correction for multiple comparisons.

^e significant different vs Profile 6 after Bonferroni correction for multiple comparisons.

Table 2. Anthropometrics and blood pressure (above), and perceived symptoms and global health (below) in relation to the six profiles.

	Profile 1	Profile 2	Profile 3	Profile 4	Profile 5	Profile 6
	(n = 9480)	(n = 11227)	(n = 14182)	(n = 23017)	(n = 4871)	(n = 2193)
Body Mass Index (kg·m^-2)	26.9 (0.04)^b^,^c^,^d^,^e	26.9 (0.04)^b^,^c^,^d^,^e	26.2 (0.03)^c^,^d^,^e	25.8 (0.03)^d^,^e	23.4 (0.06)^e	23.7 (0.09)
BMI>30	22%	22%	16%	13%	1%	1%
Diastolic BP (mmHg)	79.7 (0.10)^a^,^b^,^c^,^d^,^e	79.2 (0.09)^b^,^c^,^d^,^e	78.4 (0.08)^c^,^d^,^e	78.0 (0.06)^d^,^e	74.8 (0.14)^e	75.5 (0.21)
Systolic BP (mmHg)	126.9 (0.14)^a^,^b^,^c^,^d^,^e	125.5 (0.13)^b^,^d^,^e	126.1 (0.10)^c^,^d^,^e	125.0 (0.09)^d^,^e	121.7 (0.73)^e	123.5 (0.29)
High diastolic or systolic BP^#	28%	25%	23%	21%	7%	9%
Symptoms, 1 = Very often to 5 = Never
Symptom Back/Neck	3.27 (0.01)^a^,^b^,^c^,^d^,^e	3.33 (0.01)^b^,^c^,^d^,^e	3.45 (0.01)^c^,^d^,^e	3.51 (0.01)^d^,^e	3.64 (0.02)	3.64 (0.02)
Very often/often	23%	23%	20%	18%	14%	13%
Sleeping problems	3.95 (0.01)^b^,^d^,^e	3.91 (0.01)^b^,^c^,^d^,^e	4.01 (0.01)^e	3.98 (0.01)^d^,^e	4.05 (0.02)	4.12 (0.03)
Very often/often	11%	11%	9%	9%	6%	6%
Overall stress	3.51 (0.01)^a^,^b^,^d^,^e	3.34 (0.01)^b^,^c^,^d^,^e	3.62 (0.01)^c^,^d	3.48 (0.01)^d^,^e	3.44 (0.01)^e	3.63 (0.02)
Very often/often	14%	18%	10%	12%	14%	10%
Global health, 1 = Very poor to 5 = Very good
Overall health	3.52 (0.01)^a^,^b^,^c^,^d^,^e	3.44 (0.01)^b^,^c^,^d^,^e	3.87 (0.01)^c^,^d^,^e	3.83 (0.01)^d^,^e	4.08 (0.01)^e	4.15 (0.02)
Very poor/poor	10%	12%	4%	4%	2%	1%

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All data is presented as mean (SE), and adjusted for sex and age (GLM).

BP, blood pressure.

^a significant different vs. Profile 2 after Bonferroni correction for multiple comparisons.

^b significant different vs. Profile 3 after Bonferroni correction for multiple comparisons.

^c significant different vs. Profile 4 after Bonferroni correction for multiple comparisons.

^d significant different vs Profile 5 after Bonferroni correction for multiple comparisons.

^e significant different vs Profile 6 after Bonferroni correction for multiple comparisons.

^# Diastolic BP ≥90 mmHg and/or Systolic BP ≥140 mmHg.

Results

The information criteria decreased for each additional profile, indicating a better model fit with more profiles (S1 Table). However, the model with seven profiles converged on a local solution (i.e., the best log likelihood value was not replicated despite that multiple start values were considered) rather than a global solution, which likely is an indication that too many profiles are being extracted [26, 31]. Hence, based on the statistical adequacy criteria [24], we did not estimate more than six profiles. We also compared the six-profile solution to the five-profile solution to examine the interpretability and meaningfulness of adding a sixth profile. Adding a sixth profile provided a theoretically interpretable and meaningful additional profile that differed not only in level but also in shape when compared to the profiles in the five-profile solution. Hence, based on the combined information from the statistical criteria and interpretability we retained the six-profile solution as our final model. The six profiles are presented in Fig 1. Positive z-scores indicates a more beneficial behaviour/level and relatively higher scoring (more exercise, less sitting in leisure and at work, and higher fitness), and negative z-scores indicates a less beneficial behaviour/level and relatively lower scoring (less exercise, more sitting in leisure and at work, and lower fitness,), compared to average. Z-scores around zero means average scoring/level.

Fig 1 — Percentages within parenthesis is in relation to the total sample.

The profiles and their characteristics

Profile 1 and 2 share similar patterns with low relative levels of exercise (median 2 =“Sometimes” for both profiles), low relative fitness (mean 31.4 and 31.3 ml·min^-1·kg^-1, respectively) and average/high relative levels of leisure time sitting (median 4 =“25% of the time” for profile 1 and 3 =“50% of the time”), but differs regarding sitting at work (median 5 =“Almost none of the time” for profile 1 and 2 =“75% of the time” for profile 2). Consequently, profile 1 may be labelled “Inactive, low fit and average sitting in leisure, with less sitting at work” and profile 2 “Inactive, low fit and sedentary”. Moreover, while profile 3 had high relative levels of exercise (4 =“3–5 times/week” for profile 3), profile 4 had average levels of exercise (3 =“1–2 times/week” for profile 4), but with similar moderate relative levels of fitness (mean 34.2 and 35.1 ml·min^-1·kg^-1, respectively). The profiles had similar low relative levels of leisure time sitting (4 =“25% of the time” for both profiles), but with low relative levels of sitting at work for profile 3 (median 5 =“Almost none of the time”) and high relative levels of sitting at work for profile 4 (median 2 =“75% of the time”). Consequently, profile 3 may be labelled “Active and average fit, with less sitting at work” and profile 4 “Active, average fit and sedentary in leisure, with a sedentary work”. Profiles 5 and 6 shared similar patterns with high relative levels of exercise (median 4 =“3–5 times/week” for both profiles), high relative fitness (mean 54.1 and 54.5 ml·min^-1·kg^-1, respectively) and low relative levels of leisure time sitting (4 =“25% of the time” for both profiles), but differs in terms of sitting at work (median 2 =“75% of the time” for profile 5 and 5 =“Almost none of the time” for profile 6). Consequently, profile 5 may be labelled “Active and fit, with a sedentary work” and profile 6 “Active and fit, with less sitting at work”.

The characteristics of the different profiles is presented in Table 1.

Associations with metabolic risk factors, perceived health and perceived symptoms

The six profiles were further associated to continuous (Table 2) and dichotomized (Fig 2) levels of metabolic risk factors, as well as perceived health and symptoms. For the continuous variables, Profile 1 and 2 demonstrated more adverse health, with specifically higher prevalence of obesity and very poor/poor perceived overall health, compared to the other profiles. Profile 4 had in general a more beneficial health and symptom profile than profile 3. Profile 5 had a more beneficial metabolic profile, but scored more overall stress and poorer overall health, compared to profile 6.

In Fig 2, profile 2 was set as reference because it had the least favourable profile based on the four input variables. In general, profile 1 and 2 had the highest OR for all outcomes, compared to the other profiles in multi-adjusted analyses. Profile 2 had highest OR for obesity, overall stress, and poor global health, while profile 1 had highest OR for high blood pressure. No differences were seen for back/neck pain and sleeping problems. Profile 3 and 4 had lower ORs for all outcomes compared to profile 1 and 2, with small differences between the two profiles. Further, profile 5 and 6 had lower ORs compared to profile 3 and 4 for obesity, high blood pressure, back/neck pain, and poor global health, but with similar OR as profile 3 and 4 for overall stress and sleeping problems. Differences between profile 5 and 6 were generally small.

A total of 19,892 participants lacked data on educational level and were hence excluded in the multi-adjusted analyses. A drop-out analysis was performed comparing core variables (including sex, age, profile, BMI, blood pressure, diet habits, smoking, and perceived stress, back/neck pain, global health, and sleeping problem) for participants included and excluded in the multi-adjusted analyses revealed significant but only marginal differences between the two groups (S2 Table).

Discussion

In a large sample including more than 64,000 men and women, we used LPA and identified six distinct profiles based on exercise, sitting in leisure time and at work, and cardiorespiratory fitness. The number of profiles identified, and the variation of the input variables between the profiles, confirm the large variation in the interaction of exercise, sitting, and fitness between individuals. However some similarities were found; profile 1 and 2, profile 3 and 4 and profile 5 and 6, respectively, shared similar patterns regarding exercise, fitness, and leisure time sitting in a dose-response manner (from least to most beneficial), but differed regarding workplace sitting. This shared pattern translated into similar dose-response associations with OR especially for obesity, high blood pressure, neck/back pain, and poor perceived global health. Regarding often perceived overall stress and sleeping problems, profile 3 to 6 had similar lower OR compared to profile 2 (reference) and profile 1. These results emphasize the possibility to target exercise, sitting time, and/or fitness in health enhancing promotion intervention and strategies.

The difference in workplace sitting was only associated with the metabolic risk factors, perceived health, and perceived symptoms for profile 1 and 2 (which both had low exercise and fitness level). The less sitting at work for profile 1 transduced into lower OR for obesity, overall stress, and poor global health. This is somewhat contradictory to the recently proposed physical activity paradox which suggests beneficial health outcomes associated with high level leisure time physical activity, but detrimental health consequences with those exposed to high level occupational physical activity [32] and lower mortality risk in those with highest sitting time at work [33]. Though, other results suggests an increase in obesity and type 2 diabetes with higher sitting time at work [34]. However, further comparisons of profile 3 vs. 4, and profile 5 vs. 6, which had similar differences in sitting at work between the profiles, show no similar discrimination of the health and symptom outcomes by level of sitting at work as when comparing profile 1 and 2. This is probably due to the higher relative levels of exercise, fitness and lower relative levels of leisure time sitting compared to profile 1 and 2 and may mirror how different characteristics may buffer lack in another.

Previous studies have used LPA to study day-to-day pattern of physical activity and sedentary time over the week, using one type of domain/measure. For example, Metzger et al. concluded that 78% of their U.S. population-based sample was classified into patterns of accelerometer-derived physical activity that average less than 25 min per day of moderate-to-vigorous physical activity, which is considered low compared to guidelines [14]. Accumulating the recommended amount of weekly physical activity was consistently associated with a more beneficial metabolic health profile [35]. However, the manner in which the physical activity was accumulated (spread over the week or in just a few days) had similar associations with metabolic risk. Similar results were reported for all-cause mortality risk, where time spent in more active classes (higher average physical activity intensity or percentages of time in moderate-to-vigorous intensity physical activity) reduced the risk and more time spent in sedentary bouts increased the risk, regardless of how physical activity and sedentary time was accumulated over the week [12]. This is somewhat similar to the present study, where the majority of the study population was classified into profiles with relative low/average exercise and relatively high/average sitting time. Moreover, we show a general association between the profiles accumulating more exercise and less sitting time, and lower OR for adverse metabolic health, perceived health, and perceived symptoms. A few studies have used LPA to combine physical activity related variables from different domains for identification of different profiles. For example Hansen et al. used dichotomized data for overall physical activity energy expenditure, active commuting, sitting time at work, and time in moderate-to-vigorous physical activity to identify and characterize patterns of physical activity in 392 participants (30–60 years) [15]. They identified three latent classes: “low-active occupational sitters”, “moderately-active commuters”, “active energy-spenders”. Although not fully comparable to the present profiles, the “low-active occupational sitters”-profile is similar to profile 2 in the present study, and the “active energy-spenders” are similar to profile 6. Comparing these profiles reveal similar characteristic patterns for age and fitness (higher age and lower fitness in the former profiles compared to the latter profiles), but not in sex distribution where the “low-active occupational sitters” were significantly more likely to be women in the Hansen-study with no differences in the present study. However, it should be emphasized that the Hansen-study assessed total physical activity, while the present study assessed exercise.

The present results have several clinical and societal relevant implications. First, the results open up for a different and alternative perspective on how different physical activity-related variables interact within individuals to create combinations that may carry additional information linked to health. For example, identifying and interpreting interactions between more than two variables at the same time can be difficult in traditional variable-centered analyses. In the present study, we identify patterns of naturally occurring interactions of exercise, sitting in leisure time and at work, and cardiorespiratory fitness, pointing to the value of using person-centered analytical approaches such as LPA. The profiles do not only differ in terms of levels (i.e., high vs. low in all variables), they also reveal complex combinations of profile level and shape. This mirrors a substantial heterogeneity of the interaction patterns found and support the importance of using analytical approaches that not only treat heterogeneity as noise/disturbance in models but as valuable information. Second, the main variation in associations between the different profiles and the health outcomes were due to variation in exercise, leisure time sitting, and fitness, which emphasize the need for these three to be targeted for health enhancing interventions. Third, workplace sitting seems to discriminate less for adverse health outcomes, except in those who were “worse off” otherwise (low fitness and low exercise level). This implies that workplace sitting might not be a primary target for health enhancing interventions for all, but for selected sub-groups.

Strengths of this study include the large sample size and the large heterogeneity displayed in the scores on the input variables, demographics, and the perceived health, symptoms, and metabolic measures. Moreover, fitness was estimated using exercise testing, which is considered a strength compared to self-reported fitness. Limitation is the self-report data for exercise, sitting in leisure and at work, as well as the lack of validation of the exercise question. Self-reported data is recognized to induce misclassification bias compared to actual exercise and sitting time, and mixing self-report and objective data for input variable may influence solutions in the latent profile analysis. Also, the cross-sectional design of the association analyses limits any conclusions of causality and temporal order.

Conclusions

LPA is particularly suited to capture heterogeneity in a sample and is useful when the aim is to examine complex interaction patterns between several variables and how these interaction patterns relate to various outcomes, which was the aim of the present study. We identified six profiles based on exercise, sitting in leisure time and at work, and cardiorespiratory fitness. There were large variations in the combination of the input variables between profiles, which implies a large variation in exercise, sitting time, and fitness between individuals. Some pairwise similarities were found between profiles (1 and 2, 3 and 4, 5 and 6), mainly based on similar exercise, leisure time sitting, and fitness levels for the pair of profiles. This translated into similar dose-response associations for the pairs with the outcomes. Smaller variations between the paired profiles and associations with the outcomes were explained by differences in sitting at work. In summary, these results emphasize the possibility to target exercise, time spent sitting, and/or fitness in health enhancing promotion intervention and strategies.

Supporting information

S1 Table. Fit indices of the estimated latent profile analysis.

(PDF)

Click here for additional data file.^{(198.7KB, pdf)}

S2 Table. Drop-out analysis comparing those excluded (n = 19.892) and excluded (n = 45.078) in multi-adjusted analyses in Fig 2.

(PDF)

Click here for additional data file.^{(192.1KB, pdf)}

Data Availability

The data underlying the findings in our study are not publicly available because the original approval by the regional ethic's board (Stockholm Ethics Review Board, Dnr 2015/1864-31/2 and 2016/9-32) and the informed consent from the subjects participating in the studies did not include such a direct, free access. If a reader wants access to the data underlying the present article, please contact the HPI Health Profile Institute at support@hpihealth.se.

Funding Statement

This work was supported by The Swedish Research Council for Health, Working Life and Welfare https://forte.se/en/ (Grant no 2018-00384, grant recieved by author EEB) and The Swedish Heart-Lung Foundation https://www.hjart-lungfonden.se/HLF/Om-Hjart-lungfonden/About-HLF/ (Grant no 20180636, grant recieved by author EEB). AS is supported by an international postdoc grant from the Swedish Research Council www.vr.se/english.html (Grant no2017-00273). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funder HPI Health Profile Institute provided support in the form of salaries for authors [GA, PW] and research materials, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

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PLoS One. doi: 10.1371/journal.pone.0232210.r001

Decision Letter 0

Anne Vuillemin

6 Jan 2020

PONE-D-19-27066

Latent profile analysis patterns of physical activity, fitness and sedentary behavior in adults – associations with metabolic risk factors, perceived health, and perceived symptoms

PLOS ONE

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PLoS One. 2020 Apr 24;15(4):e0232210. doi: 10.1371/journal.pone.0232210.r002

Author response to Decision Letter 0

19 Feb 2020

Authors: We are grateful for the many relevant and constructive comments, and we have replied to them point by point below. We sincerely believe that our revision, based on your comments, have improved our manuscript. All changes in the manuscript are highlighted in bold.

Reviewer 1: The purpose of this manuscript was to identify and describe the characteristics of naturally occurring patterns of sedentary time at work and in leisure time, exercise and cardiorespiratory fitness, and the association of such profiles with metabolic risk factors, perceived health and perceived symptoms.

Point 1: Title and Short Title: I suggest that the authors change the titles, replacing the word "physical activity" with "exercise". For in this study the authors evaluate the pattern of exercise and not of physical activity. Although such terms are still commonly used as synonyms, they have different meanings. Every exercise is a physical activity, but not every physical activity is an exercise.

Authors: Thank you for a relevant comment. We agree and have now changed the title accordingly.

Point 2: Conclusions: Lines 47-50: I think that the conclusion can be improved. The conclusion always has to focus on answering the study questions.

Authors: We have now revised the conclusion section of the manuscript

Point 3: Introduction: Lines 54-55: The authors report that “… sustained or decreased levels of exercise [6] …”. This reference refers to the study elaborated by Alan G. Knuth and Pedro C. Hallal entitled: Temporal Trends in Physical Activity: A Systematic Review. This study reported the trend regarding physical activity and not about exercise. Moreover, it seems to me that in the introduction and throughout the manuscript the authors used the terms "physical activity" and "exercise" as synonyms. I suggest that only the term "exercise" be used, as it was this domain that was evaluated in this study. It may be interesting to explain in the introduction the difference between the definitions of these terms: "physical activity" and "exercise".

Authors: Thank you for an important comment. We agree that “exercise” rather than “physical activity” describes what we have evaluated in the study. Accordingly, we have now:

- Included two other references (4 and 6) in the introduction section (page 3) that better highlights the trends in moderate-to-vigorous physical activity (and not overall physical activity), and defined that these references refers to trends in MVPA.

- Defined exercise in the introduction section (page 4).

- Revised throughout the manuscript, so when referring to the results in the study, the term “exercise” is used rather than physical activity.

Point 4: Introduction: Lines 56-57: Objective studies have identified that exercise represents a small portion of daily physical activity. Therefore, I think the following statement should be changed. “… resulting in greater variability in daily physical activity patterns and physical performance between individuals.”.

Authors: Thank you for this comment. We agree that more recent research implies that exercise represent only a small portion of overall physical activity for the general population. However, there are large intra-individual differences in daily or weekly exercise time (as well as sedentary time), that might, with the generally lower time spent in exercise and higher sedentary time, have been exaggerated the last decades and induced a larger variability in the physical activity pattern. This is what we tried to highlight in this sentence. We have now revised this sentence.

Point 5: Material and methods: I think the authors should inform in the session "Material and methods" the experimental design used in this study.

Authors: We have now stated this in the Material and methods section (page 4).

Point 6: Material and methods: Lines 92-93: The authors report that the participants answered an extensive questionnaire about physical activity habits. How reliable and reproducible is this questionnaire to identify daily physical activity and exercise?

Authors: The exercise question has yet not been validated against objective measures. The sedentary questions were derived from the question previously showed to have high predictive validity, and strong convergent validity with total and prolonged stationary time. We have now added this information in the Material and methods section (page 5).

Point 7: Other variables: Lines 151-57: The authors report that “… Physical activity level prior to the age of 20 years was self-reported by selecting …”. This variable has no reference. It would be interesting for the authors to indicate the reference for the elaboration of this variable. In this variable, is only considered the physical education classes held in the school environment? Another question I have is about the power of the physical education class to represent the level of physical activity prior to 20 years.

Authors: This question has yet not been validated (but work are undertaken to do that). However, in a previously publication, we showed predictive validity for this question regarding exercise level, fitness and health later in life (PMID: 29706117). Regarding what the question consider, the alternatives includes both PE at school and PA outside school hours (leisure time).

Point 8: Results: Line 222: What does “cf.” mean?

Authors: We have made a slight change to this sentence and deleted “cf.”

Point 9: Results: Lines 285-86: The authors report that “Profile 4 had in general a more beneficial health and symptom profile than profile 3, as did profile 6 compared to profile 5”. In table 2, the numbers show that profile 5 has lower results than profile 6. Therefore, from table 2, profile 5 is better (more beneficial) than 6. Please review this.

Authors: Thank you for this comment. We have now revised this sentence.

Point 10: Discussion: Line 336: The authors report: “physical activity patterns”. Although every exercise is a physical activity, not every physical activity is an exercise. What was subjectively assessed was exercise. So, I think it is more appropriate to use "exercise pattern".

Authors: We have revised this by including “sedentary behaviour, exercise and fitness” instead.

Point 11: Discussion: Lines 334-45: The authors report: “Rather than examining interactions between different types of domains or proxies of physical activity, as we do in the present study …”. I could not find in this paper the analysis of the interactions between the different types of domains or proxies of physical activity, since the present study only assessed exercise.

Authors: We have now deleted this part of the sentence.

Point 12: Discussion: Lines 350-52: The authors report: “Accumulating the recommended amount of weekly physical activity was consistently associated with a more beneficial metabolic health profile.”. Does this statement refer to data from the present study or citation data # 14 or # 29?

Authors: This is referring to the results in reference 31 (that was 29). We have clarified this.

Point 13: Discussion: Lines 352-53: The authors report: “The manner in which this physical activity was accumulated (spread over the week or in just a few days) was largely associated with metabolic risk [29].” I suggest revising this sentence, as the reference cited reports the opposite: “However, the manner in which this activity is accumulated, either spread over most days of the week or compressed into just a couple of days, may have similar associations with the risk factors for the MS. (Am J Health Promot 2010;24[3]:161–169.)”.

Authors: Thank you for pointing this out. This was incorrect reporting of the results, and we have now revised this sentence.

Point 14: Discussion: Line 354: The authors report “where time spent in more physical activity”. I think the authors should state what "more physical activity" means and what intensity of physical activity, because such information is very important.

Authors: We agree that this is important, and have now specified this.

Point 15: Discussion: Lines 355-56: The authors report: “regardless of how physical activity time was accumulated over the week [12].”. This sentence contradicts what is written in the sentence of lines 352-53.

Authors: This part of the sentence is referring to that it did not depend on if the time in MVPA and SED wer accumulated on one day or spread out during the week. We have now added also “sedentary time” to the sentence, as this was shown also for accumulation of sedentary time.

Point 16: Discussion: Lines 358 and 360: I suggest changing the term "physical activity" to "exercise".

Authors: We have now changed this.

Point 17: Discussion: Lines 359-60: The authors report: “… we show a general dose-response in the association between the profiles accumulating more physical activity and less sedentary time …”. I could not find in the text what would be the minimum necessary physical activity, exercise and sedentary time that should be adopted. In addition, I reinforce that the study evaluated exercise and not general physical activity. When considering dose response, we should take into account that the recommendations of physical activity, exercise and more recently sedentary behavior (at least for children and adolescents), the minimum amount of such behaviors for different age groups (children, adolescents, adults and the elderly) vary considerably. Thus, as the present study is composed of adults and the elderly, it might be more interesting to separate these groups.

Authors: Thank you for this comment. We have now deleted the “dose-response” part of this sentence.

Point 18: Discussion: Lines 369-70: As the authors previously reported: Hansen et al. [reference #15] identified and characterized patterns of physical activity. I think we should be very careful to say that: “Comparing these profiles reveal similar characteristic patterns for …”. Since the present study evaluated exercise and not total physical activity. I suggest reviewing the final part of this paragraph.

Authors: We have now added a sentence to emphasize this.

Point 19: Discussion: Lines 392-93: The authors report: “… we identify patterns of naturally occurring interactions of four different domains of physical activity …”. I could not identify in the manuscript the four different domains of physical activity.

Authors: We have now revised this sentence.

Point 20: Discussion: Lines 403-406: Although the authors have presented such limitations for this study, I believe there was a lack of warning about how these limitations may impact the interpretation of the results presented.

Authors: We have now added a sentence regarding this in the limitation section.

Point 21: Discussion: I would like to suggest that the authors address more the clinical relevance of these findings within the discussion.

Authors: Thank you for this comment. We have now elaborated more regarding the implications of the results.

Point 22: Conclusions: Line 413: I suggest changing the term "physical activity" to "exercise".

Authors: We have now changed this.

Point 23: Conclusions: Lines 414-15: The authors report: “and not only overall physical activity level …”. I disagree with the author's statement. For the public health message that must be propagated is that of stimulating the increase of physical activity levels, regardless of the type and intensity. I suggest reading the following papers:

Klenk J, Kerse N. Every step you take. BMJ. 2019;366:l5051. doi: 10.1136/bmj.l5051.

Ekelund U, Tarp J, Steene-Johannessen J, Hansen BH, Jefferis B, Fagerland MW, et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ. 2019;366:l4570. doi: 10.1136/bmj.l4570.

Authors: We have now revised the last sentence of the conclusion section.

Point 24: Conclusions: I suggest improving the wording of the conclusion and trying to respond to the objectives.

Authors: We have now revised the conclusion section.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(20.8KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0232210.r003

Decision Letter 1

Yoshihiro Fukumoto

30 Mar 2020

PONE-D-19-27066R1

Latent profile analysis patterns of exercise, sedentary behavior and fitness in adults

– associations with metabolic risk factors, perceived health, and perceived symptoms

PLOS ONE

Dear Mrs Ekblom-Bak,

We would appreciate receiving your revised manuscript by May 14 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

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

We look forward to receiving your revised manuscript.

Kind regards,

Yoshihiro Fukumoto

Academic Editor

PLOS ONE

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

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

Reviewer #1: Yes

Reviewer #2: Partly

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Reviewer 1: The purpose of this manuscript was to identify and describe the characteristics of naturally occurring patterns of sitting time at work and in leisure, exercise and cardiorespiratory fitness, and the association of such profiles with metabolic risk factors, perceived health and perceived symptoms.

Point 1: Title and lines 47, 322, 415:

Sitting time is one of the domains that make up the definition of sedentary behavior. Thus, in order to use a more appropriate definition, since the objective of the study was to assess sitting time, described in the methodology (line 117), I suggest changing the term "sedentary behavior" by "sitting time" in the following places: Title and lines 47, 322, 415.

In addition, I suggest reading these two articles below:

1) Tremblay, M. S., Aubert, S., Barnes, J. D., Saunders, T. J., Carson, V., … Chinapaw, M. J. M. (2017). Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. International Journal of Behavioral Nutrition and Physical Activity, 14(1). doi:10.1186/s12966-017-0525-8

2) Moura, B. P., Rufino, R. L., Faria, R. C., Sasaki, J. E., & Amorim, P. R. S. (2019). Can Replacing Sitting Time with Standing Time Improve Adolescents’ Cardiometabolic Health? International Journal of Environmental Research and Public Health, 16(17), 3115. doi:10.3390/ijerph16173115

The first presents the definitions most used today in the area of sedentary behavior and physical activity research. And the second, presents the benefits of replacing sitting time with standing time.

Point 2: Perceived health and symptoms (lines 143-149) and Other variables (lines 150-161):

The authors report that: This question has yet not been validated (but work are undertaken to do that). However, in a previously publication, we showed predictive validity for this question regarding exercise level, fitness and health later in life (PMID: 29706117). Regarding what the question consider, the alternatives includes both PE at school and PA outside school hours (leisure time).

Therefore, I suggest citing this study in the referred text. This makes it easier for the reader to search for a reference.

Ekblom-Bak, E., Ekblom, Ö., Andersson, G., Wallin, P., & Ekblom, B. (2018). Physical Education and Leisure-Time Physical Activity in Youth Are Both Important for Adulthood Activity, Physical Performance, and Health. Journal of Physical Activity and Health, 15(9), 661–670. doi:10.1123/jpah.2017-0083

Point 3: The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository.

Reviewer #2: This study evaluated the pattern of exercise, cardiorespiratory fitness, leisure fitness, and workplace sitting. The authors identified 6 profiles and shows the mean values in systolic blood pressure, diastolic pressure, BMI, symptom, and global health in workers. Then they assessed the association between obesity, hypertension, back pain, stress, global health, or sleeping stress and the profiles. First of all, I appreciate the authors for their meticulous efforts for the revision. However, I have several comments to the authors.

1. The participants are workers so essentially they are healthy enough to work. Can the authors provide the data on hypertension, diabetes, dyslipidemia and metabolic syndrome in the study sample? They also could evaluate the association between such lifestyle disease and the profiles.

2. Even though the authors identified the 6 profiles, the mean values in blood pressure and BMI are within normal ranges. The differences in absolute vales of pain and global health are small. Are these differences meaningful?

3. I think this study message is "these results emphasize the possibility to target exercise, sedentary behaviour, and/or fitness in health enhancing promotion intervention and strategies". I would suggest highlighting this point in abstract and the beginning in the discussion part.

**********

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

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

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

Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 Apr 24;15(4):e0232210. doi: 10.1371/journal.pone.0232210.r004

Author response to Decision Letter 1

3 Apr 2020

(Same text below as attached in the file "Response to reviewers"

Review Comments to the Author

Authors: We are grateful for the constructive and relevant comments from the reviewers. We have replied to them point by point below. All changes in the manuscript are highlighted by using “track changes”.

Point 1: Title and lines 47, 322, 415:

Authors: We agree, and have now made changes in the manuscript where suggested, as well as in lines 31, 120, 177 and 357.

In addition, I suggest reading these two articles below:

Authors: Thank you for these reading suggestions.

Point 2: Perceived health and symptoms (lines 143-149) and Other variables (lines 150-161):

Therefore, I suggest citing this study in the referred text. This makes it easier for the reader to search for a reference.

Authors: Thank you for this comment. We have now added this reference on line 157-158.

Authors: We are aware of that the PLOS Data policy requires the authors to make all data underlying the findings available. However, the data underlying these results are not publicly available because the original approval by the regional ethic's board and the informed consent from the subjects participating in the studies did not include such a direct, free access. However, if a reader wants to access the data, they may contact the holder of the database, HPI Health Profile Institute. We have stated this in the Data Availability Statement (see below).

"The data underlying the findings in our study are not publicly available because the original approval by the regional ethic's board (Stockholm Ethics Review Board, Dnr 2015/1864-31/2 and 2016/9-32) and the informed consent from the subjects participating in the studies did not include such a direct, free access. If a reader wants access to the data underlying the present article, please contact the HPI Health Profile Institute at support@hpihealth.se."

Authors: Thank you for a relevant comment. Unfortunately, we have no such data available for this population. However, we are linking the dataset to national register to be able to perform longitudinal analyses on CVD incidence, hypertension, mortality etc.

Authors: Thank you for an important comment. To highlight the absolute risk differences between the different profiles (adding to the relative risk differences presented in Figure 2), we are now presenting prevalences of the six dichotomized risk outcomes in relation to the profiles in Table 2.

Authors: Thank you for this comment, we have bow highlighted this in the abstract and the beginning of the discussion.

PLoS One. doi: 10.1371/journal.pone.0232210.r005

Decision Letter 2

Yoshihiro Fukumoto

10 Apr 2020

Latent profile analysis patterns of exercise, sitting and fitness in adults

– associations with metabolic risk factors, perceived health, and perceived symptoms

PONE-D-19-27066R2

Dear Dr. Ekblom-Bak,

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

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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

With kind regards,

Yoshihiro Fukumoto

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: No

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: In question 4 I marked "No", as there are only two possible answers (yes or no). But the authors reported the reason for not making the data publicly available.

Reviewer #2: (No Response)

**********

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

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

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

Reviewer #1: No

Reviewer #2: Yes: Kotaro Nochioka

PLoS One. doi: 10.1371/journal.pone.0232210.r006

Acceptance letter

Yoshihiro Fukumoto

14 Apr 2020

PONE-D-19-27066R2

Latent profile analysis patterns of exercise, sitting and fitness in adults – associations with metabolic risk factors, perceived health, and perceived symptoms

Dear Dr. Ekblom-Bak:

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

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

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Yoshihiro Fukumoto

Academic Editor

PLOS ONE

Associated Data

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

Supplementary Materials

S1 Table. Fit indices of the estimated latent profile analysis.

(PDF)

Click here for additional data file.^{(198.7KB, pdf)}

S2 Table. Drop-out analysis comparing those excluded (n = 19.892) and excluded (n = 45.078) in multi-adjusted analyses in Fig 2.

(PDF)

Click here for additional data file.^{(192.1KB, pdf)}

Attachment

Submitted filename: PONE-D-19-27066_Reviewer_Comments.docx

Click here for additional data file.^{(15.9KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file.^{(20.8KB, docx)}

Data Availability Statement

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Latent profile analysis patterns of exercise, sitting and fitness in adults – Associations with metabolic risk factors, perceived health, and perceived symptoms

Elin Ekblom-Bak

Andreas Stenling

Jane Salier Eriksson

Erik Hemmingsson

Lena V Kallings

Gunnar Andersson

Peter Wallin

Örjan Ekblom

Björn Ekblom

Magnus Lindwall

Roles

Abstract

Aim

Methods

Results

Conclusions

Introduction

Material and methods

Latent profile analysis input variables

Anthropometrics and blood pressure

Perceived health and symptoms

Other variables

Statistical analysis

Table 1. Characteristics of the different profiles (above) and mean with standard errors of the z-score for the latent profile variables (below).

Table 2. Anthropometrics and blood pressure (above), and perceived symptoms and global health (below) in relation to the six profiles.

Results

Fig 1. Illustration of z-score distribution of self-reported exercise, sitting in leisure time, sitting at work, and cardiorespiratory fitness, in the six profiles defined in the latent profile analysis.

The profiles and their characteristics

Associations with metabolic risk factors, perceived health and perceived symptoms

Fig 2. Sex- and age adjusted (dark bars) and multi-adjusted (lighter bars) OR (95% CI) for six dichotomized risk factors.

Discussion

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Anne Vuillemin

Roles

Author response to Decision Letter 0

Decision Letter 1

Yoshihiro Fukumoto

Roles

Author response to Decision Letter 1

Decision Letter 2

Yoshihiro Fukumoto

Roles

Acceptance letter

Yoshihiro Fukumoto

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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