Influence of smartphone game play on head flexion angle,muscle activity,and load at C7 among adolescents

Abstract

BACKGROUND:

Smartphones cause physiological problems due to inappropriate postures and extensive usage. India, being the second leading country with the highest number of smartphone users (492 million in 2021), has witnessed a significant rise in smartphone-related musculoskeletal disorders (MSD).

OBJECTIVE:

This study compared the effects of 60-min smartphone gameplay on head flexion angle, muscle activity, and loads at C7 on Indian adolescents.

METHODS:

A subjective assessment was conducted on 1659 participants, of which, 40 young male adults aged between 20–28 years performed the experimental trial. Muscle (Sternocleidomastoid) activity, head flexion angle, and load acting at the neck (C7) were analyzed through postural assessment, pre-and post-subjective analysis.

RESULTS:

Participants maintained an average of 28.46°±4.04° head flexion angle for more than 43 min (71%) in an hour while performing the task. The muscle activity increased to 23% (p < 0.001) of MVC at the end of the task compared to the beginning.

CONCLUSION:

The results indicated a significant increase in muscle activity (1.61 times), spinal loads (4.6 times) and subjective discomfort (2.9 times) after prolonged smartphone usage. It is evident that various aspects (duration, posture, content) play a vital role in smartphone-related MSD and there is a potential risk of cervical spine problems. The increased loads reduce muscle stiffness and increase intervertebral disc pressure.

Keywords

Electromyogram flexed head posture musculoskeletal disorders neck discomfort smartphone

1 Introduction

Smartphones are becoming an integral part of our daily life. Millions of users have reported musculoskeletal disorders (MSD) due to prolonged smartphone usage [1]. Approximately 3.5 billion individuals worldwide owned a smartphone in 2019 [2], 492 million in India alone [3]. However, with the COVID-19 pandemic, the utilization of smartphones rapidly increased by 45% globally in 2021 [3] and an increase of 39% in India with the usage of 6 hours 55 minutes per day [4]. Smartphone usage leads to problems such as shoulder pain, neck pain, vision defects, addiction, and fatigue [5 –10]. Fatigue is caused by demographic, environmental, physical, and habitual risk factors [11, 12]. A higher level of muscle fatigue leads to musculoskeletal imbalance or MSD [13]. During smartphone texting, people usually bend over their phones; significantly straining their spinal area and leads to neck imbalance, thus causing neck pain [5].

Many studies have attempted to identify the correlation between text neck syndrome and muscle activity [5–8 , 14–17]. To measure head flexion angle, sensors such as the inertial measurement unit [6 , 14], accelerometer, and gyroscope [5] have been used. Few studies have examined the association between the duration of smartphone usage with the head tilt angle during web browsing [6] and texting [7] under natural movement, and postures [5]. Studies have observed that mechanical load on the neck increased by 3–5 times while using smartphones compared to neutral posture. Neck, shoulder, wrist, lower back, and thumb muscles are majorly involved in smartphone-related MSD [8 –10]. Discomfort in the muscles was recorded through surface electromyography (SEMG) in the cervical electro spinae [7 , 18], trapezius pars descendens (TRP) [15 –17] and sternocleidomastoid (SCM) [15, 16] muscles. Muscle activity depends on the posture; an increased muscle activity increases the fatigue level [11, 12].

Recent studies have used 3D camera, a multi-object detection system, and photogrammetry [19] to estimate the flexion angles. Angle data were analyzed to examine the correlation of muscle activity with the flexion angle. For ergonomic evaluation/postural analysis, rapid upper limb assessment (RULA) is a convenient tool that assigns scores based on body posture while using a smartphone [20]. The load acting on the cervical spine increases with the head flexion angle. The calculated load acting on the cervical spine at 15°, 30°, 45°, and 60° was 12.24, 18.14, 22.22, 27.21 kgf respectively [21]. Some studies evaluated participants’ discomfort levels through subjective evaluation by considering their socio-demographic, usage patterns, and body posture maintained [22, 23]. Subjective data was evaluated through Q-factor analysis, Likert scale [24, 25] or the neck disability index [18] to assign a score to participants’ discomfort level.

The key findings from the past literature are (1) increased smartphone utilization increases the muscle fatigue induced, leading to MSD. (2) For head stabilization, TRP and SCM muscles play a significant impact on muscle forces acting on the cervical spine. (3) Researchers evaluated head flexion angle during smartphone usage using different techniques like motion sensors, image processing and chart-based methods, to correlate muscle activity with head flexion angle. (4) Many researchers collected questionnaire data from smartphone users, either pen-paper or web-based method to analyze the discomfort during smartphone use under different scenarios (postures, durations, age group and usage content).

The research gap from past developments state that, although many studies related to smartphones and their effects were conducted in different countries, India being the second-highest smartphone user, smartphone-related studies are still lacking in India. This study targets the effects of smartphones on Indian adolescents, as the anthropometry [26] and smartphone usage pattern [27] varies among different countries and significantly affect smartphone usage. Also, the previous studies [16 , 29] reported the effects due to smartphones for 3–5 minutes of usage (4% of MVC on average), which is significantly less compared to this era of smartphone dependency or only the subjective assessment [1, 10] without any experimental trails. Hence it is important to address the effects of prolonged smartphone usage. Therefore, a correlative study is conducted to examine the relationship between neck muscle activity and 1-h smartphone usage in Indian adults. Although fatigue induced due to prolonged smartphone usage primarily affects TRP and SCM (responsible for head flexion) muscles, the SCM muscle is not studied extensively. This study elucidates the effects of prolonged smartphone usage on the SCM muscle by analyzing the muscle activity, head flexion angle, and load at C7 of Indian individuals. This study proposed two hypotheses: based on [15], (a) the muscle activity reaches 15% of MVC after 1-hour smartphone usage and (b) the head flexion angle is static during continuous smartphone gaming.

2 Materials and methods

The participant selection process, experimental tools and techniques, data collection, and processing procedures are outlined in this section.

2.1 Subjects’ selection

Forty right-handed men having prior experience in smartphone gaming were recruited through advertisements, and their demographic data are listed in Table 1. Participants with MSD, especially neck, hand, and arm complaints, were excluded. Also, the participants were instructed not to perform any high muscle tension tasks on the day of experimentation.

Table 1
Subject’s demographic characteristics (n = 40)

Parameters Participant information (M±SD)

Age (years) 23.59±3.93

Height (cm) 168.5±12.43

Weight (kg) 69.9±11.27

Wear glasses (proportion) Yes = 46% No = 54%

Smartphone experience (years) >2 years

Usage duration (hours/day) 3.8±1.66

Phone input method Thumb finger of both hands

Parameters	Participant information (M±SD)
Age (years)	23.59±3.93
Height (cm)	168.5±12.43
Weight (kg)	69.9±11.27
Wear glasses (proportion)	Yes = 46% No = 54%
Smartphone experience (years)	>2 years
Usage duration (hours/day)	3.8±1.66
Phone input method	Thumb finger of both hands

M – Mean, SD – Standard Deviation.

The study was reviewed and approved by the Institutional Ethics Committee (Human Studies) of SRM Medical College Hospital and Research Centre under ethical clearance number: 1980/IEC/2020. The institute follows the Indian Council of Medical Research and Biomedical Research guidelines on human participants in accordance with the Declaration of Helsinki.

2.2 Instrumentation

A Myoware-EMG sensor (AT–04–001 muscle sensor) [13, 30] was used to calculate SCM muscle activity. The SCM (neck) muscles are exhibited to considerable fatigue induced due to prolonged smartphone usage [8]. This sensor is a safe and wearable research device approved by Conformite Europeenne and applicable directives. The sensor detects a raw signal and an EMG envelope (rectified signal) [13]. An average error of 2.82% was noticed during calibration and the same was corrected in the final readings. A 42-mm-diameter and 1-mm-thick medico electrode (Msglt-8 G) coated with polymer Ag–AgCl gel were used. A webcam was utilized to capture the head tilt angle using OpenCV-Python algorithms. A gyroscope sensor (MPU-6050) with an accuracy of 0.1° was used to validate the flexion angle received through the webcam. A gyroscope is a secondary evaluation sensor for estimating the load acting on the C7 based on the head flexion angle. A smartphone weighing 170 g with a screen size of 5.9^′^′ (720×1440-pixel ratio, and 40–50% brightness) and a nonadjustable office chair with a forearm and back support were used.

2.3 Subjective assessment

A pre-subjective assessment consisting of 26 questions was developed using a standard questionnaire [22]. The questionnaire included questions on sociodemographic data, smartphone usage (duration, pattern, and posture), and knowledge regarding the effects of excessive smartphone utilization as listed in Table 2. A total of 1659 responses were collected, and the experiment was conducted on 40 participants. In the post-subjective assessment, another questionnaire was filled out by the participants’ related to discomfort level in their neck, shoulder, back, and wrists/fingers on a scale of 1 (very comfortable) to 5 (very uncomfortable) for every 15 min [25].

Table 2
Pre-subjective questionnaire (26)

Q. no Questions Responses (N = 1659)

n %

1 Age

Below 17 years 62 4

18–30 years 1542 93

31–44 years 45 3

45 years and above 10 0

2 Gender

Male 1247 75

Female 412 25

Other 0 0

3 Dominant hand

Right 1444 87

Left 215 13

4 Visual acuity / eyesight

Very good- zero eyesight 530 32

Fairly good less than 1 power eyesight 462 28

Moderate vision - more than 1 power eyesight 419 25

Poor vision - more than 3 power eyesight 248 15

5 Do you have any neck or shoulder problems in your history?

Yes 232 14

No 1457 86

6 Education level

12th class or below 25 2

Graduation 1457 88

Post-graduation or above 83 5

Others 92 6

7 Occupation

Student 1487 90

Involving computers or smartphones 95 6

Not involving computers or smartphones 53 3

Others 24 1

8 From how many years you are using smartphone?

≤2 years 77 5

≥2 and ≤4 years 702 42

≥4 and ≤8 years 785 47

≥8 years 95 6

9 Smartphone screen size (inch)

Less than 5 inches 14 1

5–6 inches 755 46

More than 6 inches 795 54

10 How often you use smartphone for your work practices (including studies)?

≤1 hour 16 1

1 – 2 hours 403 24

2 – 3 hours 557 34

3 – 4 hours 516 31

4 – 5 hours 108 7

Above 5 hours 59 4

11 Number of messages sent daily including messengers

<25 per day 54 3

26–50 per day 323 19

50–100 per day 275 17

51–100 per day 310 19

101–200 per day 331 20

>200 per day 366 22

12 Average time spent on smartphone (per day) including work and entertainment

≤1 hour 0 0

1 – 2 hours 16 1

2 – 3 hours 452 27

3 – 4 hours 500 30

4 – 5 hours 605 36

≥5 hours 86 5

13 ^# Smartphone usage places:

Before sleeping 1586 95

While studying 1205 72

While eating 752 45

During travelling 813 4

Others 32 1

14 ^# Maximum usage of smartphone for:

Gaming 1340 80

Video watching 1203 72

Social-media 1355 81

Web browsing 1020 61

Texting 1438 86

Calling 1659 100

Others 484 29

15 If gaming is selected, specify type of game

Endless Runner Game 753 45

Match-Three Puzzle Game 540 33

Player versus Player Shooter Game 211 13

Kingdom Building Game 155 9

16 Preferrable body posture maintained while using smartphone

Standing 31 2

Walking 0 0

Sitting 983 59

Lying down 645 39

Others 0 0

17 General neck/trunk posture maintained while using a smartphone

Flexed 427 26

Neutral 980 59

Extended 252 15

18 ^# Any sort of uneasiness do you feel after prolonged (continuous 1 hour) usage?

Eye strain 1251 75

Headache 835 50

Neck/shoulder pain 1187 72

Arm/elbow pain 367 22

Hand/wrist pain 38 2

Fingers or thumb pain 378 23

Waist or lower back pain 643 39

Legs and feet pain 5 0

Others 94 6

19 Do you feel any neck or shoulder pain while using a smartphone?

Never 168 10

Rarely 349 21

Sometimes 382 23

Often 667 40

Always 93 6

20 If yes, after how much duration of smartphone usage you feel neck/shoulder pain?

Immediately 18 1

With-in 1 hour 231 14

After 1 hour and less than 2 hours 438 26

After 2 hour and less than 4 hours 323 19

After 4 hours 649 39

21 Pain intensity at any place due to prolonged (continuous 1 hour) smartphone usage

I have no pain at that moment 538 32

The pain is very mild at that moment 630 38

The pain is moderate at that moment 478 29

The pain is severe at that moment 13 1

The pain is the worst imaginable at that moment 0 0

22 Do you feel any muscle spasms or numbness after smartphone usage?

Never 546 33

Rarely 528 32

Sometimes 478 35

Often 7 0

Always 5 0

23 Do you face any mental (smartphone addiction) symptoms?

No 187 11

Sleeping disorders 564 34

Depression 254 15

Exhaustion at work 140 8

Addiction 317 19

Anxiety 113 7

Fear 5 0

Others 79 5

24 How often involved in physical activities?

Never 317 19

Occasionally 354 21

Weekly once 282 23

Weekly twice 297 18

Daily 314 19

25 Have you ever felt, the use of smartphone will cause any issues in the near future?

Yes 861 52

No 326 20

Maybe 472 28

26 Are you following precautions to minimize health risks associated with smartphone usage?

Yes 477 29

No 1182 71

Q. no	Questions	Responses (N = 1659)
1	Age
	Below 17 years	62	4
	18–30 years	1542	93
	31–44 years	45	3
	45 years and above	10	0
2	Gender
	Male	1247	75
	Female	412	25
	Other	0	0
3	Dominant hand
	Right	1444	87
	Left	215	13
4	Visual acuity / eyesight
	Very good- zero eyesight	530	32
	Fairly good less than 1 power eyesight	462	28
	Moderate vision - more than 1 power eyesight	419	25
	Poor vision - more than 3 power eyesight	248	15
5	Do you have any neck or shoulder problems in your history?
	Yes	232	14
	No	1457	86
6	Education level
	12th class or below	25	2
	Graduation	1457	88
	Post-graduation or above	83	5
	Others	92	6
7	Occupation
	Student	1487	90
	Involving computers or smartphones	95	6
	Not involving computers or smartphones	53	3
	Others	24	1
8	From how many years you are using smartphone?
	≤2 years	77	5
	≥2 and ≤4 years	702	42
	≥4 and ≤8 years	785	47
	≥8 years	95	6
9	Smartphone screen size (inch)
	Less than 5 inches	14	1
	5–6 inches	755	46
	More than 6 inches	795	54
10	How often you use smartphone for your work practices (including studies)?
	≤1 hour	16	1
	1 – 2 hours	403	24
	2 – 3 hours	557	34
	3 – 4 hours	516	31
	4 – 5 hours	108	7
	Above 5 hours	59	4
11	Number of messages sent daily including messengers
	<25 per day	54	3
	26–50 per day	323	19
	50–100 per day	275	17
	51–100 per day	310	19
	101–200 per day	331	20
	>200 per day	366	22
12	Average time spent on smartphone (per day) including work and entertainment
	≤1 hour	0	0
	1 – 2 hours	16	1
	2 – 3 hours	452	27
	3 – 4 hours	500	30
	4 – 5 hours	605	36
	≥5 hours	86	5
13	^# Smartphone usage places:
	Before sleeping	1586	95
	While studying	1205	72
	While eating	752	45
	During travelling	813	4
	Others	32	1
14	^# Maximum usage of smartphone for:
	Gaming	1340	80
	Video watching	1203	72
	Social-media	1355	81
	Web browsing	1020	61
	Texting	1438	86
	Calling	1659	100
	Others	484	29
15	If gaming is selected, specify type of game
	Endless Runner Game	753	45
	Match-Three Puzzle Game	540	33
	Player versus Player Shooter Game	211	13
	Kingdom Building Game	155	9
16	Preferrable body posture maintained while using smartphone
	Standing	31	2
	Walking	0	0
	Sitting	983	59
	Lying down	645	39
	Others	0	0
17	General neck/trunk posture maintained while using a smartphone
	Flexed	427	26
	Neutral	980	59
	Extended	252	15
18	^# Any sort of uneasiness do you feel after prolonged (continuous 1 hour) usage?
	Eye strain	1251	75
	Headache	835	50
	Neck/shoulder pain	1187	72
	Arm/elbow pain	367	22
	Hand/wrist pain	38	2
	Fingers or thumb pain	378	23
	Waist or lower back pain	643	39
	Legs and feet pain	5	0
	Others	94	6
19	Do you feel any neck or shoulder pain while using a smartphone?
	Never	168	10
	Rarely	349	21
	Sometimes	382	23
	Often	667	40
	Always	93	6
20	If yes, after how much duration of smartphone usage you feel neck/shoulder pain?
	Immediately	18	1
	With-in 1 hour	231	14
	After 1 hour and less than 2 hours	438	26
	After 2 hour and less than 4 hours	323	19
	After 4 hours	649	39
21	Pain intensity at any place due to prolonged (continuous 1 hour) smartphone usage
	I have no pain at that moment	538	32
	The pain is very mild at that moment	630	38
	The pain is moderate at that moment	478	29
	The pain is severe at that moment	13	1
	The pain is the worst imaginable at that moment	0	0
22	Do you feel any muscle spasms or numbness after smartphone usage?
	Never	546	33
	Rarely	528	32
	Sometimes	478	35
	Often	7	0
	Always	5	0
23	Do you face any mental (smartphone addiction) symptoms?
	No	187	11
	Sleeping disorders	564	34
	Depression	254	15
	Exhaustion at work	140	8
	Addiction	317	19
	Anxiety	113	7
	Fear	5	0
	Others	79	5
24	How often involved in physical activities?
	Never	317	19
	Occasionally	354	21
	Weekly once	282	23
	Weekly twice	297	18
	Daily	314	19
25	Have you ever felt, the use of smartphone will cause any issues in the near future?
	Yes	861	52
	No	326	20
	Maybe	472	28
26	Are you following precautions to minimize health risks associated with smartphone usage?
	Yes	477	29
	No	1182	71

# - Participants can select multiple options.

2.4 Study design

In this study, the right-handed individuals were included as test participants for the experiment, and the “subway surfers” game was selected; the participants were asked to play the smartphone game for 1-h in a sitting posture, which was the preferred posture by 59% of the participants. Subway Surfers game is an endless runner game and the most preferred (45%) among the test participants. It’s a visually animated game, involving motions of swiping right, left, up, and down to avoid crashing into obstacles. The participants react by tilting their heads in the real world, matching the movements of the character in the game with higher head flexion angles. First, the participants were allowed to comfortably sit in a nonadjustable office chair with a cap affixed with the gyroscope, and EMG sensor. Then, the participants were asked to play the game on a smartphone by using both thumb fingers with the hands resting on the armrests and maintaining their comfortable head flexion angle (Fig. 1) with an eye-screen distance of 30–40 cm. The experimental location was maintained with an illumination of 75–80 lux.

Fig. 1

Subject’s posture maintained for smartphone gaming.

2.5 Experimental procedure

The experiment involved three stages. In stage 1, the procedure, technique, equipment, and data extracted were explained to the participants, and their written informed consent was collected prior to experiments.

In stage 2, the participants were prepared for testing. This study followed the guidelines of SENIAM and ISEK for standard electrode placement and procedure. To acquire a favorable EMG signal and reduce impedance, the skin was cleansed with alcohol, the hair on the muscle area was removed, and electrodes were attached to the dry skin. According to the protocol for the SCM muscle, both bipolar electrodes were placed 20–30 mm along the sternal portion, with the electrode center 1/3 of the distance between the mastoid process and the sternal notch. The reference electrode was placed the right acromion process [16]. The gyroscope sensor was attached to the head’s cap and a web camera was fixed at a 1-m distance from the participant at a height of 0.8 m for capturing motion and examining the head flexion angle. In addition, the participant was asked to set their head at a 0° angle, and two reflective green markers with a diameter of 4 cm were attached to the neck and cap (50 mm above the ear; Fig. 1).

In stage 3, three data signals were extracted from the muscle. First, the participants were asked to sit in a relaxed position, and data were recorded for 90 s; these data were considered Rest_EMG. Subsequently, the MVC, a standardized parameter used for measuring extreme muscle strength, was calculated for 10 s and considered MVC_EMG. This study computed the MVC of the SCM muscle by using a headband attached to a rigid surface (Fig. 2). In each MVC test, the participants were asked to gradually exert their maximum force and pull the head for 3–5 s, hold the same position for 3 s, and progressively reduce the force in 3 s. After the MVC measurement, the participants rested for 20 min. Then, the participants were allowed to play the smartphone game by using both the thumb fingers with hands resting on the armrests for 1-h in the sitting posture. Both the flexion angle and EMG (Task_EMG) were recorded. The smartphone was set in flight mode and all electronics except the experimental equipment were turned off to prevent interference. After smartphone gaming, a post-subjective assessment form was filled by each participant.

Fig. 2

MVC measurement technique for sternocleidomastoid muscle.

2.6 Data processing

The Myoware EMG and gyroscope sensors were connected to the laptop through a microcontroller and recorded data were exported to Excel. Finally, the datasheet was analyzed to correlate changes in the flexion angle with those in muscle activity.

First, the raw signal received from the EMG was amplified and subjected to common-mode rejection. Then, Fourier analysis was performed to estimate the frequency potential and related muscle fatigue. The signal was collected at a sampling rate of 1000 Hz, and a Butterworth second-order band-pass filter was utilized with a cutoff range of 20–500 Hz. Later, the signal was refined through full-wave rectification, followed by the smoothing process of the root mean square (RMS) method. During the smoothing process, the time window width of 50 ms was used to recognize fast movements and reflex actions. Thus, the RMS of EMG was directly related to fatigue. Finally, MVC-based normalization was performed to compare the data between the participants [16]. $Normalized EMG % = \frac{{Task}_{RMS} - {Rest}_{RMS}}{{MVC}_{RMS} - {Rest}_{RMS}} \times 100$ (1)

This postprocessing method utilized the RMS value at the task condition (i.e., Task_EMG) to normalize subsequent EMG data values. The result is presented as a percentage of normalized EMG, as shown in equation (a), which is utilized to establish the common ground while comparing the fatigue level of the participants. Green markers (i.e., region of interest [ROI]) were detected and tracked using the hue saturation value algorithm in OpenCV. The angle was identified between the two marks by using equation (b) (Fig. 3) where (x₁, y₁) are marker coordinates in the neck region and (x₂, y₂) is on the cap.

Fig. 3

Head flexion angle measurement utilizing OpenCV method with representation.

$θ_{2} = 90 - \cos^{- 1} (\frac{x_{2} - x_{1}}{\sqrt{{(x_{2} - x_{1})}^{2} + {(y_{2} - y_{1})}^{2}}})$ (2)

2.7 Data analysis

Statistical analysis was performed for the average head flexion angle, muscle activity, and load acting on the C7 vertebra. One-way repeated measure analysis of variance (r-ANOVA) was performed to investigate the effect of the usage duration on muscle activity and flexion angle. Before the analysis, we tested the normality of the variables and the result showed that all the variables are normally distributed using Shapiro-Wilk test of normality. Also, the sphericity check was performed using Mauchly’s sphericity check and sphericity was violated with p < 0.00 indicating the rejection of null hypothesis. The epsilon (ɛ) parameter is used for correction work of degree of freedom of F-value of the Greenhouse-Geisser procedure. The significant effect of the duration and body posture was estimated by performing Greenhouse–Geisser correction with Bonferroni post-hoc analysis and using SPSS 25.0 software.

To examine the posture, RULA was performed on retrieved angle data. The load acting on the cervical C7 vertebrae was theoretically calculated using the head flexion angle at 0°, 15°, 30°, 45°, and 60°, using equation (c). The load was estimated concerning the head weight of the individual at 0°. The head weight was assumed to constitute approximately 8% of the total body mass [31]. As shown in Fig. 3, HD represents the lateral distance between the C7 and the center of mass (i.e., moment arm), VD represents the longitudinal distance between the C7 and the center of mass, and θ represents the head flexion angle.

$\begin{matrix} Head Weight at Angle θ \\ = \frac{(Head Weight at 0^{\circ}) \times Vertical Distance \times sin θ}{Horizontal distance} \end{matrix}$ (3)

3 Results

The pre-subjective data indicated that 94.7% of the participants utilized smartphones from a moderate to a very high level as part of their daily work practice. These findings of the pre-subjective assessment are demonstrated in Table 2. The average head flexion angle and muscle activity maintained by the participants at different time ranges are presented in Figs. 4 and 5. The load acting on the neck at 0°, 15°, 30°, 45°, and 60° of the head flexion angles was theoretically calculated and is presented in Fig. 6. The average discomfort level in the neck, shoulder, wrist and lower back experienced by the participants is illustrated in Fig. 7. One-way r-ANOVA with Bonferroni post-hoc test was performed utilizing Greenhouse–Geisser correction with pairwise comparison, and the results are summarized in Table 3. The average muscle activity significantly differed among different time ranges and the load acting on C7 significantly differed among different flexion angles (p < 0.01; Figs. 5 and 6) and pairwise comparison results are reported in Table 3. No significant difference in the head flexion angle was noted among different time ranges. Although a significant difference (p < 0.01) was observed in the discomfort level in the neck, it is not presented in Fig. 7 for legibility.

Fig. 4

Head flexion angle to time within subjects.

Fig. 5

Muscle activity within subjects.

Fig. 6

Load acting at different head tilt angles.

Fig. 7

Discomfort level - post-subjective analysis.

Table 3

Statistical analysis of muscle activity, flexion angles, load acting on C7 and subjective discomfort level

Condition	Effect - time range	Mean±SD	Posture effect	Effect size and epsilon parameter	Post-hoc test
					Pairwise comparison	Significance
Head flexion	0 – 15 min (1)	27.46±4.04	F = 5.332, p = 0.145	η² = 0.120; ɛ= 0.00	–	–
Angle (degrees)	15 – 30 min (2)	28.55±3.83			–	–
	30 – 45 min (3)	28±5.53			–	–
	45 – 60 min (4)	30.56±5.76			–	–
Muscle	0 – 15 min (1)	14.13±1.99	F = 152.57, p = 0.000	η² = 0.796; ɛ= 0.659	1–2	0.000
Activity	15 – 30 min (2)	17.99±3.26			1–3	0.000
(% of MVC)	30 – 45 min (3)	20.16±3.07			1–4	0.000
	45 – 60 min (4)	22.82±2.65			2–3	0.000
					2–4	0.000
					3–4	0.000
Load acting on C7 (kgf)	0 degree (A)	4.89±0.79	F = 1018.6, p = 0.000	η² = 0.974; ɛ= 0.250	A-B	0.000
	15 degree (B)	10.13±1.63			A-C	0.000
	30 degree (C)	15.06±2.43			A-D	0.000
	45 degree (D)	18.45±2.98			A-E	0.000
	60 degree (E)	22.60±3.64			B-C	0.000
					B-D	0.000
					B-E	0.000
					C-D	0.000
					C-E	0.000
					D-B	0.000
Level of discomfort at neck (score)	After 15 min (a)	1.4±0.496	F = 447.10, p = 0.000	η² = 0.92; ɛ= 0.647	a-b	0.000
	After 30 min (b)	2.225±0.65			a-c	0.000
	After 45 min (c)	3.2±0.82			a-d	0.000
	After 60 min (d)	4.1±0.77			b-c	0.000
					b-d	0.000
					c-d	0.000

4 Discussion

4.1 Pre-subjective questionnaire

In the pre-subjective assessment, 99% of the participants indicated that they used the smartphone for a minimum of 2-h per day and 97% reported they are sending a minimum of 25–50 messages per day. Besides calling, 86% and 80% of the participants reported texting and gaming as the major smartphone usage. Also, 95% of participants reported that they use a smartphone before sleeping which leads to insomnia [5]. Furthermore, 80% of participants felt excessive smartphone usage could lead to various physical and mental issues, of which, only 29% are following precautions. In other studies, participants reported that they were addicted and could not survive without smartphones [22] and 76% of the respondents confirmed that their smartphone usage was more than 4 hours [25].

4.2 Head flexion angle

On average, the participants maintained different head flexion angles (Table 3) throughout the test period [28° (0–15 min) < 29° (15–30 min) > 28° (30–45 min) < 31° (45–60 min)]. Although the head flexion angle maintained by the participant varied with time, no significant difference in the head flexion angle was noted among different time ranges (0–15, 15–30, 30–45, and 45–60 min). For the 1-h period, most of the participants used an average of 28.46°±4.04° for a duration of 43 min (more than 71% in an hour) in sitting posture. Hence, it is considered as maintaining increased head flexion angle or flexed posture for a prolonged period. A study reported that the flexion angle varied from 27.3° to 34.2° for one-hand browsing and from 35.7° to 41° for two-hand texting while walking [6]. While utilizing a smartphone, the head flexion angle reached up to 53.7° in 8-h of school duration [14]. The flexion angle is restricted to 15° while browsing on computers, whereas it can be between 20° and 37° while using a smartphone [20]. When measured using a 3D motion camera, the flexion angle ranged from 33° to 44° during texting in the sitting posture [19].

4.3 Muscle activity

The activity at the SCM muscle reached an average of 23% of MVC after 1-h continuous smartphone usage. Compared with the average muscle activity throughout the period, an increase of 14.13% (0–15 min), 17.99% (15–30 min), 20.16% (30–45 min), and 24.82% (45–60 min) was observed. When comparing muscle activity between the initial (0–15 min) to the end (45–60 min) of the experiment cycle, the muscle activity increased by 1.61 times. A significant difference (p < 0.01) with pairwise comparison was observed in average muscle activity among different time ranges. This increased muscle activity caused an increase in fatigue levels, which is in line with published studies [13 , 16]. Excessive smartphone usage produces significant pain and stress on SCM muscle [8], leading to deformation in anatomical structure and nerve compression [32]. In a study, the muscle activity was 2.4% in the SCM muscle while using a smartphone for a 5-min duration [16], similarly, SCM muscle showed dominant results and experienced more fatigue in taping conditions while utilizing smartphones compared with non-taping conditions [15]. During a 10-min laptop typing, the SCM muscle activity increased to 1.64% in flexed head posture and 1% in neutral head position [29]. While lifting 10 kg for 1–2 s, the muscle activity reached 44% of MVC. However, in the current study, the muscle activity reached up to 23%, even without handling heavy loads, which is solely due to prolonged smartphone usage in flexed posture. This increased muscle activity would lead to severe damage or reduction in muscle stiffness [33].

4.4 Load acting

The load acting on the neck region (C7) was calculated for various head flexion angles; the load increased with the head flexion angle (i.e., 4.89±0.79 kgf at 0° to 22.60±3.64 kgf at 60°). The load at the neck increased to 10.13 kgf (15°), 15.06 kgf (30°), 18.45 kgf (45°), and 22.60 kgf (60°) compared with the neutral head position (5 kgf at 0°). A significant difference (p < 0.01) was observed with pairwise comparison in the load acting on C7 among different flexion angles. For the 1-h period, most of the participants used an average of 28.46°±4.04° for a duration of 43 min (more than 71% in an hour) in sitting posture, at 30° flexion angle the head weight increased to 15.06 kgf (3 times of actual weight). It resembles that a constant load of additional 10 kgf was acting on C7 for a duration of 43 min. This is because the change in the head flexion angle caused a shift in the center of gravity and gravitational pull [5, 21]. The findings indicated that an increase in the head flexion angle increased the load acting on the neck region (C7). This increased load at C7 could increase the intervertebral disc pressure and shear forces [34]. This behavior leads to capsular ligament laxity of the faced joint [33]. According to the RULA chart, a head flexion angle of > 20° is a high-risk factor and not an acceptable posture [20]. Thus, a low head flexion angle should be maintained.

4.5 Post-subjective analysis

The results of post-subjective analysis indicated that 80% of the participants reported a score of > 4 (uncomfortable) for the neck on a 5-point Likert scale. A significant difference (p < 0.01) was observed with pairwise comparison in the post-subjective analysis among different time ranges. The discomfort level at the neck increased exponentially while comparing discomfort at the neck between the initial (0–15 min) to end (45–60 min). Similarly, 18.8% stated that they developed MSD [18] and 95% experienced pain in the neck and upper extremities [25], which lead to increased discomfort [24] due to smartphone use.

The results of this study demonstrated that muscle activity increased after the prolonged usage of a smartphone, thus leading to muscle fatigue and discomfort. These results can be even implemented for the industrial workers working in flexed head and upper body postures of soldering, mems assembly, chip designing and assembly. The angle observed using the gyroscope and OpenCV showed that the participants maintained an average of 28.46°±4.04° for 43 min in the sitting posture. From the Table 3, it resembles that smartphone usage does not have significant effect on head flexion angle over time. But the smartphone usage of 1-hour has significant effect on muscle activity and post subjective assessment. Muscle has the ability of elasticity, where the muscle regains its original length after the removal or release of external force. The human musculoskeletal system comprises two types of fibers: (i) slow-twitch and (ii) fast-twitch. Slow-twitch fibers are suited for minimal forces with prolonged periods such as postural maintenance, whereas fast-twitch fibers are suited for short bursts of intense activities such as weightlifting and fast running [35]. Slow-twitch muscle fibers can resist fatigue for longer durations compared with fast-twitch muscle fibers. In this study, only slow-twitch fibers were considered. Hence, our finding of the increasing trend of muscle activity indicates that with extended smartphone use, the SCM muscle loses its resistive support movement due to postural maintenance. Therefore, maintaining the same posture for a prolonged period can increase muscle activity and lead to muscle fatigue. As shown in Fig. 4, the high standard deviation observed in the head flexion angle, particularly for 30–45 and 45–60 min, could result in the loss of resistive support movement in the SCM muscle. This reduction in resistive support movement in the SCM muscle can lead to pain/discomfort, causing individuals to alter or adjust their head flexion angle. This study demonstrated a finer correlation between flexion angles and muscle activity. The discomfort level at the neck increased gradually due to muscle fatigue. The theoretical estimation indicated that mechanical loads on C1–C7 increased by 4.62 times at 60° head flexion angle. In line with previous results, our findings indicated that an increase in the flexion angle leads to (i) an increase in mechanical loads on the neck by 3–5 times [21, 28] and (ii) more musculoskeletal and spinal disorders [5].

4.6 Limitations

In the pre-subjective evaluation, the participants reported different tasks, postures, and usage duration (3–4 h), this study examined the effects of only 1-h smartphone gaming in the sitting posture with a nonadjustable office chair on 40 male participants aged between 20 and 28 years. However, few other studies reported muscle fatigue–related analysis with a time range between 5–20 min [16 , 29]. Furthermore, the analysis was limited to only one muscle (SCM) with male participants. In addition, only theoretical estimation of loads acting on C1–C7 was performed by using equation (c). In future female participants and more smartphone contents can also be included to have a better understanding of gender differences and content-based disorders.

5 Conclusion

An experiment was performed on 40 subjects, where the participants were allowed to play a smartphone game by using both the thumb fingers with hands resting on the armrests for 1-h in the sitting posture. The muscle activity at SCM muscle reached an average of 23% (p < 0.001) of MVC after 1-h continuous smartphone usage. Although many studies related to smartphones and their effects were conducted in different countries, India is still lacking in smartphone-related studies. This paper targets the effects of smartphone gaming on Indian adolescents. Therefore, this study analyzes the head flexion angle and muscle activity during smartphone gaming for a prolonged period of 1-h. The findings indicated that the prolonged usage of a smartphone with flexed angles caused a significant increase in muscle activity (1.61 times; p < 0.001), spinal loads (4.6 times; p < 0.001), and discomfort (2.9 times; p < 0.001) causing severe damage or reduction in muscle stiffness, MSD and increase in loads on the cervical vertebra. It is evident that various aspects (duration, posture, content) play a vital role in smartphone-related MSD and there is a potential risk of cervical spine problems. The methodology of the study can be implemented in various fields such as human-computer interaction, rehabilitation, industrial workers fatigue and athletes.

Ethical approval

The study received ethical clearance from the SRM Medical College Hospital and Research Centre (number 1980/IEC/2020) and was conducted in accordance with the Declaration of Helsinki.

Informed consent

Informed consent was obtained from all subjects involved in the study.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

The authors thank the Robotics Lab of the Department of Mechanical Engineering and E-Mobility Research Centre from the SRM Institute of Science and Technology, Kattankulathur, India, for providing the required facilities for this research study.

Funding

This research received no external funding.

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