Correlation between craniovertebral angle in the sagittal plane and angles and indices measured in the frontal plane at the moment of inducing forward head posture

Abstract

BACKGROUND:

Forward head posture (FHP) is the most common malposition in the head and neck area. With the growing use of digital devices, the prevalence of FHP may be expected to increase dramatically. Thus far, FHP has been evaluated only in the sagittal plane.

OBJECTIVE:

The objective of this study was to measure angles and indices from anatomical landmarks in the frontal plane and determine the possible correlations between these variables and craniovertebral angle (CVA) as an index of FHP in the sagittal plane.

METHODS:

Fifty eight healthy individuals (29 men, 29 women) between 18 and 40 years old participated in this cross-sectional study. Participants were evaluated with an 8-camera motion analysis system. After markers were placed on predetermined landmarks, the participants were asked to maintain their head and neck in the neutral position for 5 seconds. Then participants induced FHP by flexing and lowering their head. The correlation between CVA and a set of angles and indices was calculated at the moment of FHP induction.

RESULTS:

A moderate correlation was observed between 3-D CVA and the angle formed between the sternum and both tragi for the whole sample and separately in both sexes. A moderate negative correlation was observed between 3-D CVA and height, weight, and BMI in women. A moderate negative correlation was observed between 3-D CVA and height, weight, BMI, and hours on digital devices in men.

CONCLUSIONS:

Changes in CVA in the sagittal plane can be predicted from changes in the angle formed between the midpoint of the sternum and the left and right tragi in the frontal plane.

Keywords

Forward head posture digital device cantus tragus musculoskeletal

1 Introduction

Posture, defined as the relative alignment of body segments, is an important index in evaluating the health of the musculoskeletal system. Several factors such as genetics, age, sex, environment, emotion, physical activities, and individual habits can affect posture. Among these factors, physical activities and habits are under the control of the individual. Continuous compensatory postures can lead to imbalances and musculoskeletal pain [1 –3]. The most common deviation from ideal posture in the head and neck region is forward head posture (FHP), which is often associated with other postural defects such as sway back or round back, as well as with neck pain, cervical rib, obesity and respiratory problems. The prevalence of FHP is high among adolescents, housewives, employees, and students who spend prolonged periods using digital devices, and increases with age [4 –8].

With technological advances, the use of digital devices such as smartphones, tablets and notebooks has risen dramatically in comparison to traditional personal computers. In addition, the expansion of internet networks and their vast domains of applications has notably augmented the average time spent using these technologies. This has led to musculoskeletal impairments in the neck and shoulder regions. Because these devices are usually held below the user’s level of sight, users tend to flex and lower their heads [9, 10].

It has been reported that about 90%of smartphone users flex their neck while working with their instruments [11]. Maintaining this posture for prolonged periods may cause FHP [12 –15]. Because the head accounts for 6%of the total body weight, FHP displaces the head’s center of gravity from the spinal column axis, and can apparently affect the biomechanics of postural control and physical activities. This might lead to neuromuscular and musculoskeletal impairments [16, 17]. Regarding the high prevalence of FHP and its complications, precise methods of evaluation and preventive strategies are challenging to develop [18]. It could be assumed that postural changes in one plane would clearly be associated with alterations in other motion planes.

Currently, users of digital devices often hold devices in front of the body in the frontal plane. Few studies have evaluated neck posture during the use of digital devices [19 –23]. It is worth noting that most previous studies have focused on the sagittal plane and the craniovertebral angle (CVA); since smartphones and other digital devices are used mostly in frontal plane, it seems mandatory to correlate changes in sagittal planes with those in frontal plane. Kim et al. claimed that clinicians should consider frontal plane motion in addition to sagittal plane motion during evaluating and treating patients with FHP [24]. No studies to date were designed to evaluate induced FHP changes in other planes.

The aim of this study was to measure predefined angles and indices in the frontal plane at the moment of FHP induction in healthy individuals, and to search for possible correlations among these parameters and CVA evaluated in sagittal plane. These novel findings will add to our understanding of the impact of poor head and neck posture on postural variables in the frontal plane, to predict the moment of the occurrence of FHP from indices at frontal plane. These may provide a useful indicator of FHP in this plane for research and clinical purposes and also for preventive applications.

2 Materials and methods

This cross-sectional study was conducted between March 2018 and December 2018 in the Motion Laboratory of the School of Rehabilitation Sciences at Shiraz University of Medical Sciences. The participants were recruited through advertisements.

The included subjects were healthy people aged between 18 and 40 years. The exclusion criteria were the following: history of trauma or surgery in the tho-racic, cervical and shoulder regions asking the subject, spinal column deformities such as scoliosis, severe thoracic kyphosis, torticollis, temporo-mandibular joint dysfunction, balance impairment, cardiorespiratory problems, FHP and other musculoskeletal dysfunctions [14 , 25–27]. An expert physical therapist with more than 8 years of experience in musculoskeletal evaluation, investigated all the participants prior to be recruited in the study.

The study was approved by the Ethics Committee of Shiraz University of Medical Sciences in accordance with the standards of the Declaration of Helsinki (Ethics code: IR.SUMS.REC.1396.S992). An informed consent form was signed by all participants before the beginning of the study. Eighty individuals met the criteria for participation. After careful assessment, 22 were excluded due to FHP or other exclusion criteria and 58 persons (29 men and 29 women) were included in the study.

The participants were evaluated with an 8-camera motion analysis system (Proreflex, Qualisys Track Manager^®, Gothenburg, Sweden) at a sampling frequency of 60 Hz. The system was first calibrated using two T- and L- shaped frames for 10 seconds. Participants sat on an adjustable chair in a predetermined place. A 2-D (two-dimensional) camera was placed to their right at a distance of 1 m and a height of 1.5 m from the floor. To determine the 3-D (Three-dimensional) spatial position of the head and neck segments, ten retro-reflective spherical markers (19 mm diameter) were placed on the following anatomical landmarks: mid-forehead, bilateral canthi, bilateral tragi, middle of the chin, bilaterally on the acromions, midpoint between the suprasternal notch and xiphoid process, and spinous process of C7 (Fig. 1).

Fig. 1

Placement of markers on determined anatomical landmarks.

To synchronize the QTM cameras with a 2-D camera, a marker was placed on the dorsal surface of the right wrist at the start of the test. After the trunk was stabilized with a strap, the participant was asked to gaze forward while maintaining the head and neck in the neutral position. To achieve this, the participant was asked to perform maximal neck flexion or extension, then return gradually to the neutral position [27]. Then she/he was asked to maintain the neutral position for 5 seconds until the cameras recorded the static trial. After that, the participant induced FHP by flexing and lowering the head. After several practical trials to induce FHP, five acceptable trials were recorded for each participant. To educate FHP to the participants, the method of Silva and Johnson was used [28]. Patients were asked to 6° of anterior translation of head in sagittal plane. The participants were asked to maintain a horizontal gaze to avoid head flexion or extension. The predetermined angles and indices were defined as the angle formed between the midpoint of the sternum and left and right tragi, the angle formed between the midpoint of the sternum and the left and right canthi, the angle formed between the chin and the left and right acromions, the angle formed between the midpoint of the forehead and the left and right acromions, the distance between the midpoint of the forehead and the midpoint of the sternum, and CVA (Fig. 2). It is worth noting that these angles and distance were selected based on a pilot study on 10 subjects to select indices with the most changes in frontal plane an also to have clear visibility.

Fig. 2

A: Predetermined angles and indices including: 1- Angle between forehead and left and right acromion, 2- Angle between mid-sternum and left and right tragus, 3- Angle between mid-sternum and left and right cantus, 4- Angle between chin and left and right acromion, 5- Distance between forehead and mid-sternum. B: Craniovertebral angle.

Raw data were processed with QTM software. After each marker was defined in the software, angle information and the relative positions of selected markers were calculated. In accordance with the previous studies, normal CVA was considered 48–50 degrees, and lower values were used to induce FHP [6 , 29]. At the moment when CVA reached 48 degrees in the sagittal plane, all angles and indices were calculated in the frontal plane. In the video file of the sagittal plane, the moment of FHP induction was detected, then all parameters were calculated in a 3-D file. The average data from five trials performed successively was calculated and used for final analysis. Finally, the correlation between CVA and other defined angles and indices in the frontal plane was calculated.

The data were analyzed with SPSS software version 22 (SPSS Inc., Chicago, IL, USA). The level of significance was set at 95%(α< 0.05) Normal distribution of the data was confirmed with one-sample Kolmogorov-Smirnov test. To identify correlations between the variables, Pearson’s correlation coefficient was used depending on the distribution of the data. To refine the regression model, we first conducted curve fitting analysis. The selection was based on the greatest value of R-squared.

3 Results

The demographic data and characteristics of the participants are summarized in Table 1. After the data were extracted from the QTM software, correlations between the measured angles and indices were calculated as Pearson’s correlation coefficient. The results are shown in Table 2.

Table 1
Characteristics and demographic data for all participants and for men and women separately

Variable Sex Mean±SD Min Max

Age (years) Women 24.72±2.98 19 30

Men 25.51±6.00 18 40

Total 25.12±4.72 18 40

Height (m) Women 1.62±0.04 1.54 1.72

Men 1.78±0.06 1.68 1.96

Total 1.70±0.10 1.54 1.96

Weight (kg) Women 55.51±7.75 43 73

Men 80.00±15.87 60 126

Total 67.76±17.49 43 126

BMI (kg/m²) Women 20.89±2.21 16.46 25.04

Men 25.17±4.20 17.72 37.62

Total 23.03±3.97 16.46 37.62

Digital device use (hours/day) Women 5.13±2.70 1 12

Men 4.77±3.12 0.5 13

Total 4.96±2.90 0.5 13

2-D CVA (degrees) Women 51.13±2.62 48 56

Men 52.82±3.04 48 57

Total 52.07±2.92 48 57

3-D CVA (degrees) Women 37.68±2.85 33 43

Men 38.31±3.92 30 44

Total 38.00±3.41 30 44

Variable	Sex	Mean±SD	Min	Max
Age (years)	Women	24.72±2.98	19	30
	Men	25.51±6.00	18	40
	Total	25.12±4.72	18	40
Height (m)	Women	1.62±0.04	1.54	1.72
	Men	1.78±0.06	1.68	1.96
	Total	1.70±0.10	1.54	1.96
Weight (kg)	Women	55.51±7.75	43	73
	Men	80.00±15.87	60	126
	Total	67.76±17.49	43	126
BMI (kg/m²)	Women	20.89±2.21	16.46	25.04
	Men	25.17±4.20	17.72	37.62
	Total	23.03±3.97	16.46	37.62
Digital device use (hours/day)	Women	5.13±2.70	1	12
	Men	4.77±3.12	0.5	13
	Total	4.96±2.90	0.5	13
2-D CVA (degrees)	Women	51.13±2.62	48	56
	Men	52.82±3.04	48	57
	Total	52.07±2.92	48	57
3-D CVA (degrees)	Women	37.68±2.85	33	43
	Men	38.31±3.92	30	44
	Total	38.00±3.41	30	44

Table 2

Pearson’s correlation coefficients for3-D CVA and angles and indices measured in the frontal plane

Variables	3-D CVA
	Sex	Women		Men		Total
		Correlation	P-value	Correlation	P-value	Correlation	P-value
Frontal plane variables	Sternum-tragus angle	–0.39	0.03^*	–0.40	0.03^*	–0.40	0.002^*
	Sternum-canthus angle	–0.29	0.12	–0.15	0.44	–0.19	0.14
	Chin-acromion angle	–0.09	0.66	0.23	0.23	0.11	0.39
	Forehead-acromion angle	–0.32	0.09	0.11	0.57	–0.01	0.93
	Forehead-sternum distance	0.06	0.77	–0.04	0.83	0.001	0.99

^*Significant difference in mean values, P < 0.05.

According to these results, a moderate correlation was observed between 3-D CVA and the angle formed between the sternum and both tragi in the whole sample and separately in both sexes. To refine the regression model, curve fitting analysis was performed. The selection was based on the greatest value of R-squared. According to this analysis, the best-fit model to predict changes between the variables was the quadratic model. The regression formulas for the total sample and for each sex separately were as follows:

Total sample: 3-D CVA = 90.15 –2.84x + 0.03x²

Women: 3-D CVA = 15.61 + 1.24x –0.02x²

Men: 3-D CVA = 106.64 –3.85x + 0.05x²

The “x” represents the value of the angle formed between sternal marker and those on tragi.

3.1 Analysis of background variables

The correlations for 3-D CVA with demographic data and other background variables are summarized in Table 3. A moderate negative correlation was observed between 3-D CVA and height, weight, and body mass index (BMI) in women. A moderate negative correlation was observed between 3-D CVA and height, weight, BMI, and hours on digital devices in men. For the total sample, there was a weak to moderate negative correlation between 3-D CVA and weight, BMI, and hours on digital devices.

Table 3
Correlation of 3-D CVA with demographic data and background variables

Variables 3-D CVA

Sex Women Men Total

Correlation P-value Correlation P-value Correlation P-value

Background variables Age 0.33 0.08 –0.43 0.01^* –0.22 0.09

Height –0.39 0.03^* –0.13 0.49 –0.06 0.65

Weight –0.50 0.005^* –0.49 0.007^* –0.28 0.03^*

BMI –0.41 0.02^* –0.49 0.007^* –0.34 0.01^*

Hours on digital devices 0.09 0.65 –0.53 0.003^* –0.29 0.02^*

Variables	3-D CVA
Background variables	Age	0.33	0.08	–0.43	0.01^*	–0.22	0.09
	Height	–0.39	0.03^*	–0.13	0.49	–0.06	0.65
	Weight	–0.50	0.005^*	–0.49	0.007^*	–0.28	0.03^*
	BMI	–0.41	0.02^*	–0.49	0.007^*	–0.34	0.01^*
	Hours on digital devices	0.09	0.65	–0.53	0.003^*	–0.29	0.02^*

^*Significant difference in mean values, P < 0.05.

The correlation between 2-D CVA and 3-D CVA was positive and strong in women (r = 0.71, p < 0.001), positive and moderate in men (r = 0.62, P < 0.001), and positive and moderate in the total sample (r = 0.65, P < 0.001). Fitting a regression model to predict the correlation between changes in 2-D CVA and changes in 3-D CVA with curve fitting estimation showed that a cubic model adequately predicted these changes. The regression formulas for the total sample and each sex separately were as follows:

Total sample: 3-D CVA = 1.84×2-D CVA –1.06

Women: 3-D CVA = 2.94×2-D CVA –30.58

Men: 3-D CVA = 2.26×2-D CVA –9.62

3-D CVA and 2-D CVA presenting 3 dimensional and two-dimensional measured cerebro-vertebral angles.

Our study was not free from limitations. Due to lack of similar studies, we select some angles and indices that were supposed to show acceptable correlation with CVA. More reliable angles or indices might be determined in future studies. The other limitation was that we evaluated the predetermined angles and indices only in frontal plane. Future studies are warranted to evaluate correlated changes in transverse plane.

4 Discussion

The purpose of this study was to measure angles and indices at the moment of FHP induction in the frontal plane and determine whether they correlated with CVA in the sagittal plane. The angle between the mid-sternum point and both tragi correlated significantly with CVA. Because this study is the first attempt, to our knowledge, to investigate posture with three-dimensional imaging technology, we are aware of no similar studies with which to compare our results. Nonetheless, because the CVA and other angles were investigated here in three dimensions, each value was determined in all three planes of motion. The movement that occurs in three dimensions is much more complicated than two-dimensional movement, as the former is influenced by various factors.

There was a direct, significant correlation between 2-D and 3-D CVA. The observed correlation was strong in women, and moderate in men and the total sample. Although no previous studies have reported this finding, it was not unexpected.

Our results showed a significant negative correlation between CVA and weight, BMI and duration of digital device use. Richards et al. also found a correlation between weight and BMI. According to their results, individuals who are overweight habitually have greater neck and thoracic flexion while sitting. This increased flexion may be a consequence of increased load associated with higher BMI or comorbid muscle deconditioning, which may make it more difficult to maintain more erect postures [30]. Brink et al., who studied high school students, found similar results regarding the correlation between CVA with BMI and weight. They found that with increasing the weight, spinal posture was more flexed, and CVA was clearly smaller. However, they found no meaningful correlation between CVA and time spent working with a desktop computer monitor. They suggested that different findings across studies may be attributable to differences in the devices being used. They investigated only computer users, and other devices such as smartphones and tablets were not considered [31]. Shaghayegh Fard et al. also found a correlation between CVA and BMI [26]. Regarding the correlation between FHP and the use of digital devices, our findings are in line with previous studies. Straker et al. and Guan et al. investigated the correlation between exposure to digital devices and neck posture, and concluded that with increasing time spent using these devices, the neck flexion angle increased [21, 32].

In our sample of women, we found a significant negative correlation between CVA and height, weight and BMI. However, no other studies that we are aware of have investigated these correlations. Because they were significant only in women, we can find no rationale to suggest as a possible explanation.

Fig. 3

Alteration in craniovertebral angle in persons without and with subcutaneous fat. Angle 1: Normal CVA, angle 2: CVA in a person with subcutaneous fat.

For our entire sample, however, the correlation we found for BMI and weight variables with CVA has a number of possible explanations. The human head accounts for about 6%of the body weight, and head weight increases in parallel with body weight. The head may impart greater torque on the neck as the center of gravity of the head moves in front of the vertical body axis, and FHP may result [16, 17]. Another possible reason is the increased thickness of subcutaneous fat in individuals with higher body weight and BMI. These two factors cause the C7 spinous process, i.e. the vertex of the CVA, to be positioned away from its actual position; as a result CVA becomes smaller. As illustrated in Fig. 2, angle 2 is a smaller than angle 1 (angle 1 –angle 2 = angle a).

In our sample of men, we observed a significant negative correlation between CVA and age, weight, BMI and time spent on digital devices. Several earlier studies also reported a negative correlation between age and CVA. Our results are also in line with previous studies which showed that as age increases, CVA becomes smaller –a change that can increase the risk of FHP [14 , 34]. However, our results contradict those of Queck et al., and Shaghayegh Fard et al., who found no relationship between CVA and age [26, 35]. The participants in the study by Quek et al. were older people (60–78 years old), which probably accounts for the difference in findings given that the mean age of our participants was 25.12±4.72 years. In the study by Shaghayegh Fard et al., the age range of individuals was limited to between 19 and 30 years old, which may account for the absence of a significant correlation between CVA and age.

We found no significant correlations between CVA and demographic or other background variables in women, although it should be noted that in our population sample, the age range was more limited in women (19 to 30 years) than in men (18 to 40 years). The correlations of CVA with weight and BMI in men were similar to those in women. The same explanations offered in the preceding paragraph with regard to the findings in our sample of men probably also hold true for the similar findings in our sample of women.

However, we found a correlation between CVA and time spent on digital device only in men. In other words, men who use electronic devices for longer times per day have a smaller CVA and are more likely to be exposed to FHP. This is in line with results published by Guan et al, who found that head and neck position when looking at a mobile phone was significantly more flexed in men than in women [32]. Another study suggested that in general, even in a normal sitting position, flexion in the neck and thoracic region tends to be greater in men than women. The reasons for this difference are not clear, but maybe related to sex differences in anthropometric factors [30].

5 Conclusion

In conclusion, there was a significant negative correlation between CVA in the sagittal plane and the angle formed between the midpoint of the sternum and left and right tragi at the moment of FHP induction.

Footnotes

Acknowledgments

We thank all participants for their collaboration in this study. This work was supported by the Vice Chancellery of Shiraz University of Medical Sciences. We thank K. Shashok (AuthorAID in the Eastern Mediterranean) for improving the use of English in the manuscript.

Conflict of interest

None declared.

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