Lifestyle factors and determination of optimal cut-off values for forward head posture in young adults with neck pain: a cross-sectional analysis

Abstract

Background

Neck pain is a common condition across various populations, with a substantial impact on daily life and quality of life. Forward head posture is frequently observed in individuals with neck pain and is closely associated with lifestyle factors. This study aimed to examine the relationship between lifestyle factors and forward head posture in young adults with neck pain and determine the optimal cutoff value for assessing the risk of forward head posture.

Methods

In total, 200 men and women aged 35–44 years with persistent or recurrent neck pain with a numeric rating scale score of ≥ 3 in the previous week were included in the study. The participants’ sex, age, medical history, anthropometric parameters, posture- and activity-related lifestyle, pain, radiographs, and the craniovertebral angle were obtained. The associations between lifestyle factors and forward head posture were analyzed using logistic regression. The cutoff values for risk prediction were analyzed using receiver operating characteristic curves. The impact of lifestyle factors on changes in craniovertebral angle at the 6-month follow-up was analyzed using multiple linear regression and analysis of covariance.

Results

After adjusting for covariates, there were significant differences in lying time (odds ratio = 3.342, 95% confidence interval = 1.607–6.952) and physical activity level index (odds ratio = 0.404, 95% confidence interval = 0.210–0.775) between the forward and non-forward head posture groups. The cutoff values for detecting forward head posture were 6.50 h of lying time and a physical activity level score of 2.88. At the 6-month follow-up, the craniovertebral angle was closer to the diagnosis of forward head posture, with increasing lying time and lower physical activity level score; however, the association was not statistically significant.

Conclusions

The findings indicate that lying time and physical activity level scores are important lifestyle-related predictors of forward head posture. Thus, lying time and physical activity level should be addressed to predict and prevent forward head posture.

Background

Low back pain, neck pain, osteoarthritis, and other musculoskeletal disorders are prevalent across nearly all racial and age groups. Musculoskeletal disorders are associated with pain, discomfort, reduced mobility, anxiety, insomnia, healthcare utilization and cost, and disability rise with advancing age. This trend continues to rise globally owing to the aging population [1].

Among the several musculoskeletal disorders, neck pain is one of the most common types. The proportion of individuals experiencing neck pain at least once annually is 12.1–71.5% in the general population. Additionally, among workers, the prevalence of neck pain is 27.1–47.8%, with 11–14.1% of workers experiencing activity limitations owing to neck pain every year [2].

Forward head posture (FHP), often seen in patients with neck pain, is characterized by the anterior displacement of the head relative to the body’s vertical center of mass. This posture typically involves flexion of the lower cervical spine and extension of the upper cervical spine, and is predominantly observed in individuals with neck and shoulder pain [3–5]. It cannot be ruled out that FHP results from an antalgic posture intended to reduce pain [6]. There is a significant correlation between FHP and neck pain in adults, including elderly individuals [7]. Persistent FHP increases the load on supporting structures, leading to neck pain and musculoskeletal strain. Consequently, this may lead to tension-type headaches, cervicogenic headaches, or a combination of both, resulting from movement or a weakened cervical structure [8]. These symptoms are collectively known as posture-induced headaches (PHA), and prolonged exposure to chronic pain conditions such as PHA sensitizes the central nervous system, heightening pain sensitivity [8, 9]. To alleviate the abnormal posture associated with FHP, control over head position and movement is crucial; however, patients with PHA often have diminished awareness of the neutral head position, which complicates self-management [8]. Additionally, neck pain and FHP can adversely affect overall quality of life, including sleep and respiratory function, and can be detrimental to cognitive function in older adults [10–12].

The high prevalence of neck pain is attributed to multiple factors, including age, sex, genetic predisposition, and environmental influences [2]. Specifically, individuals often repeatedly engage in and maintain neck flexion, reduced head movement, forward head positioning, and extended arms to perform activities such as computer, laptop, and smartphone use. Such static postures induce changes in cervical and thoracic curvatures, contributing to headache-related neck pain and leading to upper extremity musculoskeletal disorders [3, 8, 13].

The causes and symptoms of neck pain-associated FHP are intricately linked to lifestyle. It is well established that lifestyle significantly impacts health. Approximately 60% of factors related to individual health and quality of life—including metabolic disorders, musculoskeletal disorders, cardiovascular diseases, and overweight—are correlated with lifestyle, often arising from unhealthy habits [14]. Key indicators include body mass index (BMI), exercise, sleep, sexual activity, substance use, modern technology use, leisure activities, and education [14]. Many studies have shown that some lifestyle habits such as physical activity are linked to musculoskeletal diseases. However, only a few studies have identified specific risk levels associated with these habits [15, 16]. Therefore, this study aimed to investigate the relationship between lifestyle and FHP and identify the optimal cutoff values for determining the risk of FHP in young adults with neck pain.

Methods

Participants

This cross-sectional study was conducted over a 6-month period from March 10, 2016 at the Kyung Hee University Korean Medicine Hospital, Seoul, Republic of Korea.

The inclusion criteria were as follows: age 35–44 years, persistent or recurrent neck pain for 4 weeks or longer, neck pain with a numeric rating scale (NRS) score of ≥ 3 in the previous week, and office workers who work on a computer for 20 h or more per week. The exclusion criteria were as follows: (1) diagnosis of traumatic or organic diseases, including neurological pain in the cervical area; (2) upper extremity weakness or paresthesia; (3) medical treatment for neck pain in the past month; (4) pregnancy, breastfeeding, or plans to become pregnant within 6 months; (5) anticipated changes in the work environment during the course of the clinical study; and (6) individuals deemed inappropriate for the study by the principal investigator.

Based on the eligibility criteria, 200 participants were selected. These participants received the informed consent form and underwent screening tests. A human chorionic gonadotropin urine test was performed to rule out potential pregnancy. All study parameters were measured on the first visit within a week following the screening test. The measured parameters were demographic survey, anthropometric parameters, facial photograph, radiographs (Harrison posterior tangent method), NRS score, McGill Pain Questionnaire Short Form (MPQSF), lifestyle survey, and medical history survey. Measurements of all study parameters, excluding anthropometric measurements and medical history survey, were repeated 6 months after the first visit. Consequently, ineligible participants, based on the eligibility criteria, were excluded in the first visit, resulting in a total of 173 participants. At the final visit at 6 months after the screening, 149 participants completed all the tests as scheduled (Fig. 1). The study was approved by the Institutional Review Board (IRB) prior to the first visit (IRB No. KOMCIRB150818HR035), and registered in the Clinical Research Information Service (No. KCT0001898, https://cris.nih.go.kr). Data from the Korean Medicine Data Center were used for this study.

Fig. 1.

Flowchart of the study

Data collection

General characteristics and lifestyle factors

Sex, age, and medical history were obtained using a survey. Height and body weight were measured using a scale, and BMI was calculated based on this information. Disease history was self-reported by participants and was restricted to chronic conditions diagnosed by a physician, including hypertension, diabetes mellitus, or hyperlipidemia. Posture-related lifestyle was assessed using a question about the predominant posture assumed during a 24-h period, and posture was divided into lying, sedentary, and standing time [17]. The original questionnaire used in the study has been previously shown to be reliable, with a correlation coefficient of 0.64 via test-retest [17]. Physical activity-related lifestyle was assessed using the Baecke Physical Activity Questionnaire [18], which has been previously shown to be reliable (Cronbach’s α: 0.88, correlation coefficient: 0.74 − 0.85 via test-retest) [18, 19]. This questionnaire assesses physical activity through three components: physical activity leisure (PAL), physical activity work (PAW), and physical activity sports (PAS) indexes. Each component has four to ten items, leading to a total of 22 items; these items are used to evaluate frequency and duration of habitual physical activities. Scores for each component are calculated using specific formulas, with higher scores indicating higher levels of activity. Neck pain was assessed using the NRS and MPQSF [20, 21]. The NRS is a subjective pain assessment tool using a scale of 1–10, where 0 indicates no pain and 10 indicates the worst pain possible. The MPQSF is a shortened version of the original McGill Pain Questionnaire, consisting of 15 selected descriptors divided into 11 sensory and four affective categories. Each scale ranges from 0 to 3, where 0 indicates no pain and 3 indicates severe pain. Participants were asked to indicate the average intensity of their pain during the previous week.

Measurement of FHP

To assess FHP, the angle formed by a line connecting the spinous processes of the second (C2) and seventh cervical vertebra (C7) on radiographs was measured using the Harrison posterior tangent method [22]. Facial photographs were also obtained using a camera mounted on a tripod. The camera was positioned 160 cm away from the participant, ensuring precise alignment and horizontality. The participant’s chin was positioned 1 cm below the vertical line of the camera. The lens axis was aligned with the participant’s eye level, and the camera was focused on the point below the ear. Participants maintained a natural seated posture without forced alignment, and their gaze was directed horizontally. FHP was measured based on the craniovertebral angle (CVA) [5, 23]. The CVA is calculated by measuring the angle between a line drawn from the tragus of the ear to the spinous process of the seventh cervical vertebra (C7) and a horizontal line from the ground [24]. This traditional and widely recognized method is objective and straightforward, where a smaller CVA indicates more progression of FHP. In this study, a CVA < 50° was designated as FHP, and a CVA ≥ 50º was defined as non-FHP (NFHP) [5, 23]. Anthropometric measurements were taken once during the initial visit. Radiographs and facial photographs were taken twice: once during the initial visit and again during a follow-up visit 6 months later.

Statistical method

The differences in general characteristics and lifestyle factors between the FHP and NFHP groups were analyzed using the independent t-test for continuous data and the chi-square test for categorical data. The associations between FHP and lifestyle factors were analyzed with odds ratio (OR) and 95% confidence interval (CI) using logistic regression. Model 1 was adjusted for sex, age, and BMI, and model 2 was additionally adjusted for posture- and activity-related lifestyle. Risk prediction was assessed using receiver operating characteristic (ROC) curves, specifically with area under the curve (AUC) (95% CI). The cutoff point was determined by selecting the coordinate on the curve where the difference between the true positive rate (TPR) and false positive rate (FPR) was the greatest. In addition, the impact of these factors on changes in the CVA at the 6-month follow-up was analyzed using multiple linear regression with all participants. Analysis of covariance (ANCOVA) was then conducted with the participants classified into three groups specifically for the 6-month follow-up. Data were analyzed using the SPSS Version 21.0 (SPSS Inc., Chicago, USA) software, and statistical significance was set at p < .05.

Results

Participants’ general characteristics

Of the total 173 study participants, 98 were assigned to the FHP group and 75 to the NFHP group based on the CVA. The mean CVA was 45.56 ± 3.61 in the FHP group and 54.52 ± 2.77 in the NFHP group. Regarding sex, there were more females than males in the FHP (n = 60, 61.2%) and NFHP groups (n = 48, 64.0%). The mean age of the FHP and NFHP groups was 39.67 ± 3.13 years and 39.96 ± 2.95 years, respectively. There were significant differences in the CVA, lying time, PAL index, and chronic conditions between the two groups but not in other variables (Table 1). However, as 17 out of 98 participants in the FHP group and three of 75 participants in the NFHP groups had a chronic condition or a history of one, the variable was deemed inappropriate for classification of groups and was thus excluded from the statistical comparison. Although the two groups did not significantly differ in sedentary time, standing time, PAW index score, and PAS index score, they did significantly differ in the related parameters, namely lying time and PAL index score. Hence, additional analysis was performed for these two variables.

Table 1.

General characteristics of participants (n = 173)

	FHP group (n=98)	NFHP group (n=75)	𝑥2 or t	p-value	Total
Sex (Male/Female)	38/60	27/48	0.140	.709b	65/108
Age (years)	39.67 ± 3.13a	39.96 ± 2.95	0.622	.535c	39.79 ± 3.05
Height (cm)	165.31 ± 8.26	165.47 ± 7.18	0.139	0.889	165.38 ± 7.78
Weight (kg)	65.29 ± 12.71	65.04 ± 11.77	− 0.132	0.895	65.18 ± 12.28
BMI	23.75 ± 3.46	23.65 ± 3.32	− 0.186	0.853	23.71 ± 3.39
CVA (°)	45.56 ± 3.61	54.52 ± 2.77	17.838	0.000*	49.44 ± 5.52
Radiograph (°)	16.78 ± 10.01	16.76 ± 10.61	− 0.010	0.992	16.77 ± 10.24
NRS (score)	5.99 ± 1.73	6.19 ± 1.75	0.738	0.461	6.08 ± 1.74
MPQSF (score)	10.42 ± 6.35	11.13 ± 6.11	0.746	0.456	10.73 ± 6.24
Lying time (hour)	7.71 ± 1.59	7.12 ± 1.34	-2.606	0.010*	7.46 ± 1.51
Sedentary time (hour)	10.19 ± 2.75	10.33 ± 3.00	0.318	0.751	10.25 ± 2.85
Standing time (hour)	6.02 ± 2.72	6.43 ± 3.04	0.925	0.356	6.20 ± 2.86
PAL index (score)	2.56 ± 0.65	2.82 ± 0.60	2.656	0.009*	2.67 ± 0.64
PAW index (score)	2.53 ± 0.71	2.50 ± 0.63	− 0.297	0.767	2.52 ± 0.68
PAS index (score)	2.25 ± 0.64	2.34 ± 0.76	0.877	0.382	2.29 ± 0.69
Chronic condition (-/+)	81/17	72/3	7.402	0.007*	153/20

^aMean ± SD, ^bChi-squared test, ^cIndependent t-test, Chronic condition: If there is any one of hypertension, diabetes mellitus, hyperlipidemia

FHP: forward head posture, NFHP: non-forward head posture, BMI: body mass index, CVA: craniovertebral angle, NRS: numeric rating scale, MPQSF: McGill pain questionnaire short form, PAL: physical activity leisure, PAW: physical activity work, PAS: physical activity sports

*p<.05

Association between posture- and activity-related lifestyle and FHP

To analyze the impact of posture- and activity-related lifestyle on FHP, each lifestyle factor was dichotomized based on its median value and CVA values were classified into binary categories of FHP and NFHP for logistic regression analysis. Significant associations were observed between the FHP group and lying time (Model 2: OR = 3.342, 95% CI = 1.607–6.952) and PAL (Model 2: OR = 0.404, 95% CI = 0.210–0.775) in the unadjusted base and adjusted models (controlling for sex, age, and other factors). However, sedentary time, standing time, PAW index, and PAS index were not significantly associated with FHP in any of the models (Table 2).

Table 2.

Effect of posture and activity-based lifestyle on FHP

Parameters		Crude				Model 1				Model 2
		OR (95% CI)		p-value		OR (95% CI)		p-value		OR (95% CI)		p-value
Posture-based lifestyle
Lying time		3.176 (1.581, 6.382)		0.001*		3.357 (1.642, 6.861)		0.001*		3.342 (1.607, 6.952)		0.001*
Sedentary time		0.888 (0.477, 1.655)		0.709		0.864 (0.456, 1.635)		0.653		0.928 (0.414, 2.079)		0.855
Standing time		0.889 (0.482, 1.638)		0.706		0.901 (0.486, 1.670)		0.740		1.005 (0.458, 2.204)		0.990
Activity-based lifestyle
PAL index		0.442 (0.237, 0.825)		0.010*		0.439 (0.235, 0.820)		0.010*		0.404 (0.210, 0.775)		0.006*
PAW index		1.133 (0.620, 2.070)		0.685		1.207 (0.638, 2.284)		0.562		1.215 (0.639, 2.312)		0.553
PAS index		0.989 (0.541, 1.807)		0.972		0.980 (0.532, 1.806)		0.948		0.955 (0.515, 1.772)		0.884

NFHP was employed as the reference in every analysis.

Parameters were divided by reference to the median value.

Crude: unadjusted, Model 1: adjusted for sex, age, BMI; Model 2: adjusted for sex, age, BMI, other related factors (in posture-based lifestyle: lying time, sedentary time, standing time; in activity-based lifestyle: PAL, PAW, PAS)

FHP: forward head posture, NFHP: non-forward head posture, PAL: physical activity leisure, PAW: physical activity work, PAS: physical activity sports, OR: odds ratio, CI: confidence interval

*p<.05

Prediction of FHP using posture- and activity-related lifestyle

The prediction of FHP based on significant lifestyle factors, specifically lying time and PAL index, was evaluated; the optimal cutoff values were determined using ROC curves (Fig. 2). The AUC for lying time was 0.617 (95% CI = 0.531–0.703), indicating that it is a significant predictor of FHP (p = .008). The optimal cutoff value for lying time was 6.50 h, with a sensitivity of 82.7% and specificity of 40.0%. When activity time was insufficient, the risk of FHP was assessed. The PAL score increased with activity time. Since we wanted to focus specifically on low activity levels, the Inverse PAL score was used instead (calculated by subtracting the PAL score from the maximum score of 5) to generate the ROC curve. The AUC for Inverse PAL was 0.614 (95% CI = 0.530–0.698), confirming that it is also a significant predictor of FHP (p = .010). The optimal cutoff value for Inverse PAL was 2.88, with a sensitivity of 53.1% and specificity of 66.7%.

Fig. 2.

Receiver operating characteristic (ROC) analysis of forward head posture (FHP) forecasts through lying time and inverse physical activity level (PAL) index

Results of the six-month observational study

The impact of lying time and PAL index, which were significant posture- and activity-related lifestyle factors for FHP, on the changes in the CVA over a 6-month period was analyzed using multiple linear regression and ANCOVA. Multiple linear regression revealed that lying time and PAL index did not significantly affect changes in the CVA over 6 months (Table 3). ANCOVA showed that although there were consistent trends in CVA change at 6 months in relation to lying time and PAL index, both trends were not statistically significant (Table 4).

Table 3.

Effect of lying time and PAL index on CVA change over 6 months (multiple linear regression analysis)

Outcome		B	β	95% CI	t	p-value
Δ CVA	Constant	-2.103
	Lying time	− 0.053	− 0.021	− 0.457, 0.351	− 0.259	0.796
	PAL index	0.457	0.072	− 0.582, 1.495	0.869	0.386

Δ CVA : Change between pre-CVA and post-CVA

CVA: craniovertebral angle, PAL: physical activity leisure, CI: confidence interval

Table 4.

Effect of lying time and PAL index on CVA change over 6 months (ANCOVA)

Groups		n		Post-CVAa		F		p-value
Lying time
Low (< 6 h)		42		51.90 ± 6.03		0.264		0.768
Middle (6–8 h)		86		50.62 ± 5.53
High (> 8 h)		21		49.78 ± 5.67
PAL index
Low (score < 2)		18		50.13 ± 6.40		0.027		0.974
Middle (score 2–3)		79		50.56 ± 5.56
High (score > 3)		52		51.57 ± 5.61

^aAdjusted by pre-CVA

CVA: craniovertebral angle, PAL: physical activity leisure

Discussion

This study examined the associations between lifestyle factors and FHP, and identified the cutoff values for the major predictors of FHP risk in young adults with neck pain. One key finding of the study is that lying time and PAL index score significantly differed between the FHP and NFHP groups, with a cutoff value of 6.50 h for lying time and a PAL index score of 2.88. The results of this study highlight the importance of reducing lying time outside work hours and increasing activity time during the day to prevent FHP. In addition, specific numerical recommendations for activity to be used in the management of FHP risk were presented.

In this study, the FHP group had a longer lying time and lower PAL score compared to the NFHP group. This can be attributed to a reduced ability to maintain an upright posture in the FHP group. Previous studies have shown that individuals with FHP have a diminished capacity to maintain an upright posture compared to those without FHP [25] and show increased activity of muscles involved in the upright posture [26]. Posture-related lifestyle was categorized into lying, sedentary, and standing time during a 24 h period. An increase in lying time typically leads to a decrease in sedentary and standing time, which in turn leads to lower PAL, PAW, and PAS index scores. However, there is a slight difference between the two factors. For instance, longer lying time entails shorter time available for activity. However, the type of activity performed within this limited time can have either a positive or negative impact on FHP.

In the crude and fully adjusted logistic regression analyses, FHP was three times more likely with longer lying time and 60% less likely with higher PAL index scores. Prolonged use of electronic devices, such as computers and smartphones, negatively impacts health, and the importance of physical activity and exercise is well recognized [27, 28]. Moreover, FHP can be improved with various corrective exercises, and workers who engage in general exercise and sports activities are more likely to experience improvements in neck pain [2, 29]. However, as shown in this study, the chances of achieving a positive impact on FHP are reduced in cases with increased lying time and consequently reduced sedentary and standing time, i.e., in situations with limited time available for physical activity. PAL scores are higher with higher frequency and duration of dynamic activities such as walking and cycling, and with shorter TV viewing time [18]. Therefore, a low PAL index score indicates less engagement in dynamic activities, such as walking and cycling, and longer periods engaged in static activities that contribute to FHP, such as using computers or smartphones and watching TV. Thus, young adults with neck pain should reduce lying time and engage in adequate physical activity.

Furthermore, the ROC curve results provide specific numerical recommendations for lying and activity durations, shedding light on the optimal cutoffs for determining FHP risk. In this study, we identified 6.5 h of lying time and 2.88 Inverse PAL score as the optimal cutoffs for FHP risk prediction, and these cutoffs showed moderate accuracy. This means that 6.50 h or more of lying time and a PAL index score of 2.125 (PAL = 5 – Inverse PAL) or lower significantly increase the risk for developing FHP. The recommended sleep duration for adults is 7–9 h [30], and deviations from this range (< 5 h or ≥ 9 h) are associated with increased neck and musculoskeletal pain [10]. The 6.50 h of lying time identified in this study is consistent with these findings. However, lying time and sleep duration are different concepts; thus, to prevent FHP, it is important for individuals to focus on sleeping and not be distracted by other activities while lying down. The PAL index score can reach values above 2.25 with occasional walking or cycling for 5–15 min, with no TV watching, or more frequent walking or cycling for 15–30 min with frequent TV watching. Previous research suggests that there is no clear link between physical activity and neck pain in adolescents, while for adult workers the relationship between the two remains unclear. This might be due to the fact that adolescents are generally more active than adults [31]. Additionally, engaging in light activities like walking or cycling for a total of 150 min per week has been reported to have a positive effect on neck pain [32]. Although FHP does not always result in neck pain, they are nevertheless associated. Thus, the total amount of PAL recommended in this study (105–210 min per week) aligns closely with the recommendations based on previous research. Interestingly, these findings suggest that preventing FHP does not require a large amount of time or high-intensity activity. Therefore, these results suggest that young adults with neck pain should spend 6.5 h per day lying down, ideally using this time entirely for sleep. Additionally, limiting the time dedicated to sedentary activities should be encouraged in adulthood, and light activities such as walking or cycling are recommended. The AUC values for lying time and Inverse PAL were approximately 0.60, which is slightly below the typically useful threshold of AUC > 0.7 and lower than the AUC value of 0.88 for CVA, a direct measure of FHP [33]. However, according to a previous study indicating that BMI and skeletal muscle mass index have AUC values of 0.623/0.596 (for men and women, respectively) and 0.608/0.579, lying time and Inverse PAL still hold some predictive value for FHP, although not as strong as that of the CVA [34].

Additionally, we could not observe significant impact of lying time and PAL index on the CVA over the short-term follow-up of 6 months in this study. In one previous study, the absolute change in the CVA after 3–6 months of therapeutic exercise remained minimal despite being statistically significant in individuals with FHP [35]. Therefore, the limited duration of follow-up might have contributed to the lack of significant changes observed in this observational study that did not administer any interventions. However, individuals with longer lying times showed a tendency towards lower CVA (closer to FHP), whereas those with higher PAL index scores showed higher CVA (closer to NFHP); these trends should be considered in future studies.

Finally, other related factors such as sedentary time, standing time, PAW index score, and PAS index score did not significantly differ between the FHP and NFHP groups. The activation of muscles that affect FHP varies with posture and activity [26]. This suggests that sedentary time, standing time, PAW index score, and PAS index score might not have been specific enough to distinguish between FHP and NFHP in this study. Similarly, factors such as sex, age, height, weight, BMI, radiography results, NRS, and MPQSF did not significantly differ between the FHP and NFHP groups. Moreover, the results regarding these factors have been inconsistent in previous studies. The prevalence of FHP is higher among adults with neck pain compared to those without any symptoms, and age, sex, height, weight, and BMI may act as confounding factors [7]. However, the results for pain and disability level between adults with neck pain and adults without FHP symptoms were inconsistent in some studies [36]. Nevertheless, we performed additional analyses and adjusted for these factors in consideration that they might be mutually influential. Future studies should include a sufficient number of participants, considering sex and age, to provide more comprehensive insights. The Harrison posterior tangent method used in radiographic assessments is suitable for evaluating posture but may be more appropriate for assessing cervical lordosis rather than FHP, which might explain the lack of significant differences observed in this study [37].

Limitations

This study had some limitations. First, although structured tools were used to measure lifestyle factors, there may be recall bias because of the use of self-reported surveys. Future studies should use objective tools such as activity trackers or wearable devices. Second, the study focused on a specific age group (30–40 years); therefore, the findings have limited generalizability to other age groups. In the future, adequate sample size and diverse study sample should be ensured. Finally, this study could not establish causality owing to its cross-sectional nature. Therefore, longitudinal research with adequate follow-up periods is warranted to investigate the causal relationships between FHP and lifestyle factors.

Conclusions

This study revealed that lying time and PAL are significant posture- and activity-related lifestyle factors associated with FHP. Thus, these factors should be addressed to predict and prevent FHP. We hope that the findings of this study serve as a valuable foundation for future recommendations on optimal lying time and PAL levels to prevent FHP.

Abbreviations

FHP

Forward head posture

NRS

Numeric rating scale

CVA

Craniovertebral angle

PAL

Physical activity level

MPQSF

McGill Pain Questionnaire Short Form

IRB

Institutional Review Board

PAW

Physical activity work

PAS

Physical activity sports

NFHP

Non-FHP

TPR

True positive rate

FPR

False positive rate

ANCOVA

Analysis of covariance

Author contributions

JH: Formal analysis, Writing—review & editing; KS: Data curation, Writing – original draft; SJ: Methodology, Writing – original draft; SW: Writing—review & editing, Project administration; YH: Writing—review & editing, Conceptualization, Methodology; All authors have read and approved the final version of the manuscript.

Funding

This work was supported by a grant (No. KSN1732121) from the Korea Institute of Oriental Medicine.

Data availability

The datasets are not available owing to confidentiality and ethical concerns. Further inquiries can be directed to the corresponding author or Korea Medicine Data Center (https://kdc.kiom.re.kr/).

Declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board (IRB) prior to the first visit (IRB No. KOMCIRB150818HR035), and the study protocol was performed in accordance with the Declaration of Helsinki. Written informed consent was obtained from each participant before the beginning of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.