1. Introduction
Physical activity (PA) has decreased across all age groups over recent decades, and most people no longer meet the recommended levels of daily activity (Strain et al., 2024; Sallis et al., 2016). This decline has been especially notable among children and adolescents (Strain et al., 2024). At the same time, obesity rates have increased markedly (Chooi, Ding and Magkos, 2019). Children and adolescents are affected at younger ages, leading to early health challenges. According to the World Health Organization (2023) the prevalence of childhood and adolescent obesity has substantially increased over the past four decades. It is now considered one of the most critical global public health challenges. Since 1990, the global prevalence of overweight and obesity among 5–19-year-olds has quadrupled, affecting boys and girls equally. In 2022, there were over 390 million overweight children and adolescents worldwide (World Health Organization, 2023). In Finland, similar trends have been observed; among young men aged 18–29, 47% have overweight and 17% have obesity based on body mass index (BMI) (Koponen et al., 2019). Childhood obesity often continues into adulthood and is associated with chronic diseases such as type 2 diabetes and hypertension (Simmonds et al., 2016).
Heart rate variability (HRV) is a non-invasive, straightforward indicator of autonomic nervous system regulation (Sammito, Thielmann and Böckelmann, 2024). It reflects the dynamic balance between sympathetic and parasympathetic activity, particularly the influence of the vagus nerve on cardiac function (Malik, 1996; Shaffer and Ginsberg, 2017). HRV is considered a sensitive marker of vagal tone, offering insights into parasympathetic modulation of the heart. Reduced HRV, often reflecting impaired parasympathetic (vagal) function, has been linked to cardiovascular and metabolic disorders (Thayer, Yamamoto and Brosschot, 2010). It is also associated with an increased risk of all-cause mortality (Jarczok et al., 2022).
Both PA (Tornberg et al., 2019; May et al., 2017; Henje Blom et al., 2009; Soares-Miranda et al., 2009; Oliveira et al., 2017) and obesity (Strüven et al., 2021; Felber Dietrich et al., 2008; Gutin et al., 2005; Rossi et al., 2015) are strongly linked to cardiac health and autonomic function, but in opposite directions: High level of PA is associated with increased HRV, while obesity is associated with impaired HRV. Among adolescents, both the amount (Tornberg et al., 2019; Farah et al., 2018) and intensity of PA (Oliveira et al., 2017; May et al., 2017) have been linked to HRV. Our earlier study with this same adolescent population showed a dose-response relationship between overall PA and vagal related HRV indices (Tornberg et al., 2019). Another study found that vigorous, but not moderate, activity significantly predicted higher HRV in young adults (May et al., 2017). Several other studies have confirmed positive associations between moderate to vigorous PA and HRV (Soares-Miranda et al., 2009; Henje Blom et al., 2009), although findings related to lower-intensity PA are less consistent (Oliveira et al., 2017).
Evidence also suggests that obesity and autonomic function are interconnected from early childhood. An inverse relationship between HRV and BMI has been observed even in preschoolers (Speer et al., 2021), and HRV imbalance has been linked to adolescent obesity (Gutin et al., 2000). Most studies have used BMI as a measure of obesity, but similar associations have been found using waist circumference and body fat percentage (Strüven et al., 2021). Obesity appears to particularly relate to reduced vagal activity, as observed in both adults and adolescents (Speer et al., 2021; Rossi et al., 2015; Gutin et al., 2000).
Although obesity (Gutin et al., 2000; Rossi et al., 2015) and low PA levels (Gutin et al., 2005; Oliveira et al., 2017; Soares-Miranda et al., 2012; Henje Blom et al., 2009) have been independently associated with lower vagal HRV, their potential interaction has been less studied, particularly among adolescents. Therefore, the aim of this study was to examine whether BMI modifies the association between PA and HRV in adolescent men. We hypothesized that regular PA would be associated with improved HRV, particularly among young men with overweight or obesity.
2. Methods
2.1. Participants and study protocol
This study was part of a large population-based research project (MOPO), conducted among Finnish adolescent men attending mandatory annual military call-ups in Oulu, Finland. These call-ups are organized for all men in the year they turn 18 (Ahola et al., 2013). The data were collected between 2009 and 2013 during the autumn call-up events and included a comprehensive questionnaire on health, lifestyle, and physical activity (PA), as well as physiological measurements. A total of 5,824 young men were invited to participate. The final analytical sample comprised 3,389 adolescent men (58% participation rate) with both valid HRV recordings and completed questionnaires. The participant recruitment and selection process is illustrated in Figure 1.

Figure 1
Participant flow diagram showing recruitment, exclusions, and the final analytical sample of adolescent men in the MOPO study conducted during military call-ups in Oulu, Finland (2009–2013).
The study followed the ethical principles of the Declaration of Helsinki and was approved by the Ethical Committee of the Northern Ostrobothnia Hospital District (ETTM123/2009). Participation was voluntary, and individuals could withdraw from the study at any time without consequences for their future military service. Written informed consent was obtained from all participants.
2.2. Measurements
2.2.1. HRV measurements
HRV recordings were conducted between 11 a.m. and 3 p.m. in a calm environment to minimize stress and external disturbances. Participants were instructed to lie down and relax for three minutes prior to measurement. A heart rate monitor (Polar RS800; Polar Electro Oy, Kempele, Finland) recorded RR intervals with a 1 ms resolution. The duration of each recording was five minutes, and participants were allowed to breathe spontaneously. Before the HRV measurement, perceived stress was assessed using a visual analogue scale (VAS) (The et al., 2020).
HRV data were analyzed using Kubios HRV software (Tarvainen et al., 2013). Artefacts and ectopic beats were identified and corrected prior to analysis. The built-in automatic artefact correction algorithm was applied using the “very low” to “medium” filter settings, depending on signal quality. All recordings were visually inspected to ensure appropriate correction. Recordings with excessive artefacts or poor signal quality were excluded from further analyses; in total, 149 recordings (4.1% of all HRV measurements) were rejected. The last four minutes of each recording, representing a stable measurement period, were used for HRV analysis. HRV was assessed with both time-domain and frequency-domain parameters according to the guidelines of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (Malik, 1996). Time-domain parameters included mean heart rate (HR), mean RR interval (RRi), standard deviation of RRi (SDNN), and root mean square of successive differences (rMSSD). Frequency-domain parameters included peak low-frequency (LF, 0.04–0.15 Hz) and high-frequency (HF, 0.15–0.4 Hz) power, and the LF/HF ratio.
2.2.2. Health questionnaire and self-reported physical activity
Participants completed a questionnaire (Ahola et al., 2013) covering health, health behaviors, amount of PA, and self-rated physical fitness. Alcohol consumption was assessed using a single item from the Alcohol Use Disorders Identification Test (AUDIT): “How often do you have six or more drinks on one occasion?”, with response options ranging from “Never” to “Daily or almost daily” (Saunders et al., 1993). In physical activity, they were asked to choose the option that best described their average level of PA during the past six months. The categories were (Borodulin et al., 2004):
Low: no regular PA, occasional walking (<0.5 h/week)
Moderate: regular recreational or moderate occupational PA (0.5–2 h/week)
High: regular vigorous PA (2–4 h/week)
Health data were also collected from the medical examination conducted as part of the military conscription process, during which the participants were examined by a conscription physician. During this examination, information on physical and mental health was recorded, including ICD-10 diagnoses. For analysis purposes, the diagnosed conditions were categorized into five main groups: diabetes, mental disorders, thyroid disorders, asthma and musculoskeletal diseases.
2.2.3. Body composition and aerobic fitness
Height and waist circumference were measured with a standard ruler. Body composition, including BMI, fat and muscle percentage, and weight, was assessed using bioelectrical impedance (InBody 720, Biospace Co., Ltd., 2005). This test was conducted simultaneously with the HRV recording, with participants in a supine position after five minutes of rest. Participants were categorized by BMI as follows: underweight (<18.5), normal weight (18.5–24.9), overweight (25–29.9), and obesity (≥30).
2.3. Statistical analyses
Statistical analyses were performed using SPSS/PASW version 21 (IBM Corp., Armonk, NY, USA). Descriptive statistics are presented as means and standard deviations for continuous variables, and as counts with percentages for categorical variables. Normality was tested using the Kolmogorov–Smirnov test. Due to skewed distribution, HRV values (rMSSD) were log-transformed (ln) for analyses across PA categories.
One-way ANOVA with Bonferroni correction was used to compare means between PA categories. Multivariable linear regression analyses were conducted to examine associations between PA, body composition, and HRV. Models were adjusted for relevant confounders identified in univariate analyses, including diseases (diabetes, mental disorders, thyroid conditions, musculoskeletal diseases, asthma), smoking, coffee consumption, and perceived stress within 15 minutes before HRV measurement. Statistical significance was set at p < 0.05.
3. Results
The characteristics of the study participants are presented in Table 1. The final analysis included data from 3,389 adolescent men, with a mean age of 18.0 years (SD 0.2). Of all participants, 68% (n = 2.309) were classified as normal weight, 23.9% (n = 598) with overweight, and 6.3% (n = 214) with obesity.
Table 1
Characteristics of study participants (n = 3.389) by body mass index (BMI) categories. Values are presented as means (SD) unless otherwise indicated.
| VARIABLE | BMI | ||||
|---|---|---|---|---|---|
| <18.5 N = 268 | 18.5 < 24.9 N = 2309 | 25 ≤ 29.9 N = 598 | ≥30 N = 214 | p VALUE | |
| Height, cm | 178.0 (6.5) | 177.9 (6.2) | 178.0 (6.6) | 177.4 (7.3) | 0.658 |
| Weight, kg | 55.7 (4.7)* | 68.7 (7.4) | 84.7 (7.7)* | 106.5 (14.0)* | <0.001 |
| BMI, kg/m2 | 17.5 (0.8)* | 21.6 (1.7) | 26.7 (1.3)* | 33.9 (3.6)* | <0.001 |
| Body fat, % | 9.3 (2.9)* | 13.6 (4.9) | 22.6 (6.1)* | 34.9 (6.5)* | <0.001 |
| Waist circumference, cm | 70.1 (3.4)* | 78.3 (5.0) | 90.0 (6.1)* | 106.7 (9.9)* | <0.001 |
| HR, beats/min | 75.9 (12.7)* | 72.9 (12.3) | 73.2 (11.9) | 76.7 (13.2)* | <0.001 |
| Daily smoking, n (%) | 56 (20.9) | 430 (18.2) | 102 (17.1) | 41 (19.2) | 0.626 |
| Daily snuffing, n (%) | 18 (6.7) | 306 (13.0) | 88 (14.7) | 18 (8.4) | <0.001 |
| Weekly alcohol consumption, n (%) | 32 (11.9) | 312 (13.2) | 99 (16.6)* | 29 (13.6) | 0.047 |
| Diabetes, n (%) | 3 (1.1) | 10 (0.4) | 4 (0.7) | 0 (0.0) | 0.283 |
| Mental disorders, n (%) | 23 (8.6) | 181 (7.7) | 45 (7.5) | 26 (12.1) | 0.130 |
| Thyroid disorders, n (%) | 2 (0.7) | 6 (0.3) | 0 (0.0) | 0 (0.0) | 0.173 |
| Asthma, n (%) | 19 (7.1) | 147 (6.2) | 52 (9.7) | 25 (11.7)* | 0.008 |
| Musculoskeletal diseases, n (%) | 35 (13.1) | 283 (12.0) | 76 (12.7) | 38 (17.8) | 0.111 |
[i] *Statistically significant difference (p < 0.05) compared to those with normal weight.
BMI = body mass index.
SD = standard deviation.
HR = heart rate.
The mean body fat percentage (%) increased progressively across BMI categories and was significantly higher among participants with overweight (22.6 vs 13.6, p < 0.001) and with obesity (34.9 vs 13.6, p < 0.001) compared to those with normal weight. The mean waist circumference (cm) increased according to BMI categories being significantly higher among those with overweight and obesity compared to those with normal weight (106.7 vs 90.0 vs 78.3, p < 0.001).
The mean resting heart rate (beats/min) was higher in both participants with underweight (75.9 vs 72.9, p < 0.001) and with obesity (76.7 vs 72.9, p < 0.001) when compared to those with normal weight. In addition, the prevalence (%) of asthma was notably higher among participants with obesity compared to those with normal weight (11.7 vs 6.2).
Figure 2 (alt-text: Line graph of HRV values by activity and BMI) illustrates the association between physical activity (PA) level and body mass index (BMI) category in relation to heart rate variability (HRV). Across all BMI categories, participants in the low PA group had the lowest HRV values (ln rMSSD), while HRV increased progressively with higher levels of physical activity.

Figure 2
Mean (SD) heart rate variability (ln rMSSD) values by physical activity level across body mass index categories in adolescent men (n = 3.389).
Statistically significant differences (p < 0.05) between PA (physical activity) groups: * vs. low PA, vs mod PA, # vs all other PA groups.
Low PA = no regular physical activity, occasional walking, < 0.5 h/week).
Moderate PA = regular recreational PA or moderate occupational PA, 0.5–2 h/week.
High PA = regular heavy physical exercise, 2–4 h/week or heavy physical exercise at least 5 times a week, >4 h/week.
Adolescent men who were both with obesity and reported low levels of physical activity exhibited the lowest HRV values of all BMI and PA groups combined. This suggests a cumulative negative effect of low physical activity and obesity on cardiac autonomic regulation.
Table 2 presents heart rate variability (HRV) indices across different physical activity (PA) levels within each body mass index (BMI) category. An overall increasing trend was observed for all vagally mediated HRV indices including rMSSD, ln rMSSD, SDNN, and pNN50 across PA levels, indicating better cardiac autonomic function with higher levels of physical activity.
Table 2
Mean (SD) values of heart rate variability indices by physical activity level across body mass index categories in adolescent men (n = 3.389).
| HRV VARIABLES | PA | ||
|---|---|---|---|
| LOW N = 815 | MODERATE N = 1229 | HIGH N = 1345 | |
| BMI < 18.5, n = 268 | n = 109 | n = 101 | n = 58 |
| RRi (ms) | 791 (124) | 824 (133) | 853 (176)* |
| rMSSD (ln, ms) | 3.68 (0.62) | 3.85 (0.65)* | 3.81 (0.71)* |
| rMSSD (ms) | 47.5 (28.0) | 56.4 (33.3) | 56.2 (35.4) |
| SDNN (ms) | 49.6 (24.7) | 56.5 (26.1) | 55.4 (29.2) |
| pNN50 (%) | 24.3 (20.4) | 29.9 (22.0) | 29.1 (23.1) |
| LFpower (ln) | 6.56 (1.07) | 6.85 (1.09) | 6.79 (1.18) |
| HFpower (ln) | 6.70 (1.26) | 6.87 (1.32) | 6.73 (1.37) |
| LF/HF | 1.00 (0.16) | 1.02 (0.17) | 1.03 (0.18) |
| BMI 18.5–24.9, n = 2309 | n = 479 | n = 819 | n = 1011 |
| RRi (ms) | 807 (126) | 839 (139)* | 882 (145)* |
| rMSSD (ln, ms) | 3.66 (0.64) | 3.79 (0.63)* | 3.88 (0.58)* |
| rMSSD (ms) | 46.0 (25.9) | 52.7 (30.3)* | 56.5 (30.4)* |
| SDNN (ms) | 47.8 (22.6.) | 52.7 (24.5)* | 56.0 (24.7)* |
| pNN50 (%) | 24.1 (20.1) | 28.2 (21.5)* | 31.0 (21.2)* |
| LFpower (ln) | 6.58 (1.00) | 6.73 (1.01)* | 6.93 (1.02)* |
| HFpower (ln) | 6.57 (1.24) | 6.80 (1.21)* | 6.91 (1.14)* |
| LF/HF | 1.02 (0.17) | 1.01 (0.16) | 1.02 (0.15) |
| BMI 25–29.9, n = 598 | n = 152 | n = 214 | n = 232 |
| RRi (ms) | 805 (127) | 829 (124) | 891 (150)* |
| rMSSD (ln, ms) | 3.65 (0.62) | 3.71 (0.59) | 3.86 (0.58)* |
| rMSSD (ms) | 46.2 (27.5) | 48.0 (26.7) | 55.2 (30.3)* |
| SDNN (ms) | 46.6 (22.2) | 50.1 (22.6) | 53.6 (23.1)* |
| pNN50 (%) | 22.8 (19.1) | 24.5 (19.9) | 29.9 (21.5)* |
| LFpower (ln) | 6.54 (0.92) | 6.75 (0.91) | 6.87 (0.91) |
| HFpower (ln) | 6.48 (1.23) | 6.63 (1.19) | 6.77 (1.13) |
| LF/HF | 1.03 (0.15) | 1.04 (0.16) | 1.03 (0.14) |
| BMI ≥ 30, n = 214 | n = 75 | n = 95 | n = 44 |
| RRi (ms) | 774 (139) | 817 (114) | 856 (154)* |
| rMSSD (ln, ms) | 3.39 (0.74) | 3.69 (0.63)* | 3.78 (0.73)* |
| rMSSD (ms) | 36.8 (22.0) | 48.6 (33.3)* | 54.3 (32.2)* |
| SDNN (ms) | 39.0 (19.5) | 48.8 (25.8)* | 52.4 (26.7)* |
| pNN50 (%) | 17.3 (16.9) | 24.6 (22.5) | 29.1 (21.8)* |
| LFpower (ln) | 6.27 (0.99) | 6.67 (0.96) | 6.86 (1.09)* |
| HFpower (ln) | 6.02 (1.47) | 6.55 (1.23) | 6.68 (1.38)* |
| LF/HF | 1.09 (0.22) | 1.03 (0.14) | 1.05 (0.18) |
[i] *Statistically significant difference (p < 0.05) compared with low physical activity group.
Low = no regular PA, occasional walking, < 0.5 h/week).
Moderate = regular recreational PA or moderate occupational PA, 0.5–2 h/week.
High = regular heavy physical exercise, 2–4 h/week or heavy physical exercise at least 5 times a week, >4 h/week.
HRV = heart rate variability.
BMI = body mass index.
RRi = R-R interval.
ms = millisecond.
rMSSD = Root Mean Square of Successive Differences.
ln = logarithmus naturalis.
SDNN = Standard Deviation of NN intervals.
pNN50 (%) = Percentage of NN50.
LFpower (ln) = Low Frequency Power.
HFpower (ln) = High Frequency Power.
LF/HF = Low Frequency to High Frequency Ratio.
SD = standard deviation.
Determinants of HRV according to multivariable linear regression analyses.
This trend was consistent across BMI categories, further supporting the positive association between physical activity and autonomic regulation of the heart.
Table 3 presents the results of multivariable linear regression analyses identifying statistically significant determinants of heart rate variability (HRV), measured as ln rMSSD, among adolescent men classified as having underweight, normal weight, overweight, or obesity.
Table 3
Factors associated with heart rate variability (ln rMSSD) in adolescent men with underweight, normal weight, overweight, and obesity (n = 3.389), based on multivariable linear regression analyses.
| VARIABLE | REGRESSION COEFFICIENT (95% CI) | SE | p VALUE |
|---|---|---|---|
| Underweight (BMI < 18.5) | |||
| ln rMSSD: model R2 = 0.038, standard error of the estimate = 0.64, p = 0.015 | |||
| Waist circumference | –0.151 | 0.013 | p = 0.026 |
| Constant | 5.919 | 0.911 | p < 0.001 |
| Normal weight (18.5 < BMI < 25) | |||
| ln rMSSD: model R2 = 0.042, standard error of the estimate = 0.60, p < 0.001 | |||
| Self-reported physical activity | 0.137 | 0.015 | p < 0.001 |
| Constant | 3.526 | 0.099 | p < 0.001 |
| Overweight (25 ≤ BMI < 30) | |||
| ln rMSSD: model R2 = 0.059, standard error of the estimate = 0.58, p = 0.001 | |||
| Self-reported physical activity | 0.104 | 0.029 | p = 0.030 |
| Waist circumference | –0.097 | 0.005 | p = 0.045 |
| Constant | 4.602 | 0.454 | p < 0.001 |
| Obesity (BMI ≥30) | |||
| ln rMSSD: model R2 = 0.074, standard error of the estimate = 0.68, p = 0.001 | |||
| Self-reported physical activity | 0.172 | 0.065 | p = 0.024 |
| Waist circumference | –0.168 | 0.005 | p = 0.028 |
| Constant | 4.600 | 0.624 | p < 0.001 |
[i] Models were adjusted for other significant variables in univariate analysis (stress, smoking and snuffing (15 min before), coffee (15 min before)).
Low PA = no regular physical activity, occasional walking, < 0.5 h/week).
Moderate PA = regular recreational PA or moderate occupational PA, 0.5–2 h/week.
High PA = regular heavy physical exercise, 2–4 h/week or heavy physical exercise at least 5 times a week, >4 h/week.
HRV = heart rate variability.
rMSSD = root mean square of successive differences.
BMI = body mass index.
SE = standard error of the estimate.
Physical activity (PA) was positively associated with HRV in participants with normal weight, overweight, and obesity. The strongest associations were observed in the obesity group. Among participants with underweight (BMI < 18.5), however, only waist circumference was significantly associated with HRV.
Waist circumference was consistently and negatively associated with HRV in participants classified as having underweight, overweight, or obesity, but this association was not observed in those with normal weight.
4. Discussion
In this population-based study, the level of physical activity (PA) showed the strongest association with vagal heart rate variability (HRV) among adolescent men with normal weight, overweight, and obesity. As we hypothesized, the positive association between physical activity and HRV was strongest in those with obesity. Our results suggest that regular physical activity may be strongly associated with favorable cardiac autonomic function even at a young age, and physical activity may attenuate the negative association between obesity and HRV. Although PA showed the strongest association with HRV in our models, the proportion of explained variance (R²) remained relatively modest. This indicates that HRV is influenced by multiple physiological, behavioral, and environmental factors beyond PA alone. Nevertheless, even modest associations may be meaningful at the population level, particularly when considering modifiable lifestyle factors.
It has been shown that regular training (Tulppo et al., 2003; Hautala et al., 2004) as well as PA (Henje Blom et al., 2009; Oliveira et al., 2017; Tornberg et al., 2019) shifts HRV towards increased vagal dominance in all age groups. We also showed earlier that in adolescent men, PA was positively associated with HRV, and there was a dose-response relationship between overall PA and vagal HRV. Additionally, regular PA was the most important determinant of positive HRV responses (Tornberg et al., 2019). It seems that the effects of PA on HRV depend on both the intensity and the amount of PA (Oliveira et al., 2017; May et al., 2017), and the effects can also vary depending on the individual’s body composition (Farah et al., 2018). A previous review (Oliveira et al., 2017) showed that moderate to vigorous PA was positively associated with HRV in children and adolescents, but the association between other PA intensities and HRV remained less clear. However, the results of our previous population-based study suggest that even light PA is beneficial for the heart health of young men, regardless of their BMI (Tornberg et al., 2019). When compared with current international recommendations, the PA levels in the “Moderate” group (0.5–2 h/week) remained below the minimum recommended threshold of 150 minutes of moderate-intensity activity per week. Nevertheless, our findings indicate that positive associations between PA and HRV were observed even at activity levels below these recommendations. This suggests that improvements in cardiac autonomic regulation may occur along a continuum rather than only after a specific threshold is reached. From a public health perspective, even modest increases in physical activity among low-active individuals may therefore be relevant. Physical activity in the present study was categorized based on self-reported time spent in activity rather than energy expenditure. Therefore, direct comparison with studies using MET-based cutoffs (e.g., Borodulin et al., 2004) is limited. However, the applied categorization reflects habitual activity levels and is consistent with approaches commonly used in large population-based studies. This should be considered when comparing the present findings with studies using MET-based classifications.
Studies have shown that obesity can lead to autonomic imbalance at a very young age (Speer et al., 2021; Rossi et al., 2015). This is partially explained by the obesity-related increase in sympathetic cardiac activity and, especially, reduced cardiac parasympathetic activity, which is reflected as lowered vagal HRV (Speer et al., 2021; Rossi et al., 2015). One possible mechanism behind the association between obesity and lower HRV is that individuals with higher levels of obesity have higher serum leptin levels, which have been shown to activate neural pathways and lead to reduced parasympathetic activity (Brydon et al., 2008).
Regular physical exercise has been shown to reduce the detrimental effect of obesity on HRV (Felber Dietrich et al., 2008), a clinically relevant predictor of cardiovascular morbidity and mortality (Jarczok et al., 2022). As we hypothesized, the positive association between PA and HRV was strongest among adolescent men with obesity, and even small differences in PA levels were associated with more favorable HRV values. These results are consistent with a previous study among conscripts, suggesting that an increase in regular exercise during military training exerts the most beneficial impact on body composition and physical fitness among young men with overweight and obesity (Mikkola et al., 2012). Another study (Henje Blom et al., 2009) involving adolescents aged 15 to 17 years showed that reported PA was positively related to HF and SDNN, reflecting increased vagal activity and improved HRV. On the other hand, Farah et al. (Farah et al., 2018) showed that higher leisure-time PA but not overall or commuting PA levels, was associated with improved HRV in adolescents with abdominal obesity, suggesting that in adolescents with obesity, physical activity should be vigorous enough to reflect health benefits. However, our study showed that in young men with obesity, even relatively small differences in PA levels were associated with HRV values comparable to those with normal weight. Although there is limited research on this in children and adolescents, studies in middle-aged or older participants (≥50 years) have shown that regular physical exercise has strong beneficial effects on cardiac autonomic function and appears to offset the negative effect of obesity on HRV (Felber Dietrich et al., 2008). In contrast to the other BMI categories, PA was not significantly associated with HRV among participants with underweight. This may partly reflect the smaller sample size in this subgroup, which limits statistical power. In addition, underweight status may represent distinct physiological or metabolic characteristics that influence autonomic regulation differently compared to individuals with normal weight or overweight.
Today, obesity affects the lives of an increasing number of people. It is also known that a child with obesity is more than five times more likely to continue with obesity as an adult compared to a child with normal weight (Simmonds et al., 2016). Given that obesity is associated with reduced HRV, an independent predictor of cardiovascular disease risk (Jarczok et al., 2022), the findings may be relevant when considering health interventions targeting young individuals. A recent study (Palatini et al., 2024) showed that young individuals with metabolically healthy overweight have a lower risk of cardiovascular disease. The results of our study emphasize the importance of regular physical activity for young people, especially those with overweight or obesity.
A major strength of this study is the large, population-based sample of adolescent men of a specific age. In our study, the total participation rate was high, considering that, although the call-ups were mandatory for all men, participation in the study was voluntary. An additional strength is the strictly controlled conditions under which the HRV assessments were performed.
A limitation of this study is that physical activity was assessed using self-reported data. Although questionnaires are feasible and widely used in large epidemiological studies, self-reported PA is subject to measurement error. The six-month recall period may have introduced recall bias, as participants may not accurately remember the frequency, duration, or intensity of their activity. Previous research has shown that self-reported PA often differs substantially from objectively measured activity, with both overestimation and underestimation occurring (Prince et al., 2008).
In addition, categorizing PA into broad intensity-based groups increases the risk of misclassification, as individuals may interpret activity intensity differently. Participants may also rely on estimation rather than precise recall, leading to rounding or digit preference bias (Prince et al., 2008). These sources of measurement error may have influenced the observed dose–response patterns and should be considered when interpreting the results.
Moreover, reporting bias may vary across BMI categories, potentially affecting comparisons between weight groups. The absence of objective measures such as accelerometry limits the ability to verify reported activity levels. Finally, as participants were assessed during the conscription examination day, pre-measurement conditions could not be standardized, although adjustments for recent coffee consumption, stress, smoking, and snuffing were applied to minimize potential confounding.
5. Conclusions
Regular physical activity is positively associated with cardiac vagal regulation among adolescent men, especially those with obesity. Higher levels of physical activity were associated with more favorable HRV values, suggesting that physical activity may attenuate the negative association between obesity and cardiac autonomic function. However, due to the cross-sectional design, causal inferences cannot be made.
Data Accessibility Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Acknowledgements
The authors wish to acknowledge the City of Oulu, the Virpiniemi Sport Institute, and the Finnish Defence Forces for their support.
Author Contributions
JT conceived the study and performed the analyses. JT drafted the manuscript. All authors contributed to the study design, interpretation of the data, and critically revised the manuscript. All authors approved the final version of the manuscript.
