Fatigue due to smartphone use? Investigating research trends and methods for analysing fatigue caused by extensive smartphone usage: A review

Abstract

BACKGROUND:

The easy accessibility of smartphones has led to a fivefold increase in their use. People use smartphones almost anywhere, including during travelling and studying. During the global COVID-19 pandemic, the average smartphone screen time has increased from 2.25 to 4.8 hours per day. In India, smartphone usage increased by 68%, and the average screen time increased from 2.42 to 6.9 hours. This dependency on smartphones has led to smartphone addiction. Inappropriate postures during the prolonged use of smartphones can exert adverse effects such as musculoskeletal disorders, digital eye strain, loss of focus and attention.

OBJECTIVES:

This study was undertaken to understand the effects of prolonged smartphone utilisation and explored fatigue measurement techniques.

METHODS:

A total of 130 studies examining the effects of smartphone utilisation published in the previous 10 years were identified from the following databases: IEEE, Science Direct, PubMed, PubMed Central, and Google Scholar are reviewed. This study was conducted from September 2019 to January 2021.

RESULTS:

One in every four adolescents were prone to smartphone addiction, which causes poor mental health. Moreover, India’s research on the effects of excessive smartphone usage is limited.

CONCLUSIONS:

Studies are required to establish the correlation between fatigue levels and smartphone usage patterns.

Keywords

Smartphone addiction digital eye strain musculoskeletal disorders smartphone fatigue smartphone India

1 Introduction

A smartphone allows flexibility in peoples’ lives and serves various features such as communication, instant messaging, web browsing, maps, digital camera, shopping, gaming, and data storage [1]. The smartphone adoption rate has been substantially increasing; globally, the number of users increased from 0.53 billion in 2010 to 3.5 billion by the end of 2020. It is also estimated to reach 4.5 billion, as per the predicted 2022 statistics, which resembles five times the digit growth [2]. Technological advancements have increased the number of smartphone users by 42.5% over feature phone users. In 2020, China, India, and the United States comprised 62% of the total global smartphone users [2, 3]. The growth rate of active Internet users has increased by eight times compared with that of the overall population [4]. In 2017, an average of 46.8% of the global population had access to the Internet; this percentage increased to 52.4% in 2020 and is expected to reach 53.7% by 2021 [5].

India ranks second worldwide in terms of the number of smartphone and Internet users. In India, the number of smartphone users increased from 100 million in 2013 to 350 million in 2020 [6]. A study conducted by PwC and ASSOCHAM demonstrated that the number of smartphone users would reach 859 million (an increase of 84%) by 2022 [7]. In India, a regular Internet user spends an average of 17 hours a week on multiple social networking websites; this duration is higher than that observed in other developing nations such as China. Globally, a regular Internet user spends 15.8 hours a week on various social media platforms [8].

During the global COVID-19 pandemic, the number of smartphone users increased from 4.2 to 4.8 billion. The utilisation of smartphones has increased by 70% compared with that of desktops - laptops (40%) and smart televisions (30%) [9]. The average smartphone screen time has rapidly increased from 2.25 to 4.8 hours [10]. Similarly, in India, smartphone usage increased by 68%, and the average screen time increased from 2.42 to 6.9 hours [11]. Since the beginning of the pandemic, the time spent on a smartphone for work has increased by 75% [12]. However, the increase in the adoption of smartphones or other digital devices has led to various mental and physical health problems. Approximately 70% of people indicated that smartphone use can likely affect their mental health if their current usage continues. Furthermore, approximately 74% of users periodically turned off their smartphones to reduce mental illness due to digital devices [13]. A steep increase in digital activity resulting from spending more than 8 hours on work from home, online teaching, or learning has caused spinal cord related problems, leading to lower back pain and muscle fatigue [14].

When looking at a smartphone for an extended period, the blue light from smartphones can result in blurred vision, reduce the blink rate, and cause insomnia [15] and other vision-related problems [16]. In addition, increased smartphone use can lead to various musculoskeletal disorders such as thumb arthritis [17] and pain in the shoulder [18], upper back [19], neck [20] and headache due to inappropriate body posture [21]. Incorrect posture while using a smartphone can affect the viewing distance, lateral bending, and flexion angles [22]. One in every four adolescents is prone to smartphone addiction, which can result in poor mental health and compulsive behaviour [23]. Smartphone addiction affects an individual’s focus and attention, causing depression and anxiety [24]. In addition, excessive use of smartphones can lead to the development of a psychological condition called Nomophobia, labelled as DSM-4 (panic disorder). A study found that 23% of individuals were nomophobic and 64% showed signs of early stages of nomophobia [25].

The aforementioned numbers indicate a leap in smartphone dissemination globally. Because of its excessive usage and associated health risks, it is much needed to study the effects of prolonged smartphone usage. Thus, the current study examined reasons for fatigue caused by smartphone addiction; evaluated how prolonged smartphone usage can affect the eyes, brain, and muscles; and determined various methods and technologies available to measure fatigue induced due to prolonged smartphone usage. In addition, this review explored smartphone dissemination and research status in India and globally.

2 Method

In this study, a systematic review (Fig. 1) was performed. Research articles published on effects of excessive smartphone usage in the past 10 years were identified using different keywords and their combinations: smartphone addiction, digital eye strain, musculoskeletal disorders, and smartphone India along with ‘smartphone fatigue. These articles were searched in electronic databases, namely IEEE, Science Direct, PubMed, PubMed Central, and Google Scholar. A total of 250 articles published between 2010 and 2021 were selected. The following three-step strategy was implemented to identify relevant articles. First, the titles of articles were rapidly scanned to identify related studies. Second, the complete abstract was read to identify suitable articles. Third, a detailed review of full paper was performed to understand and analyse the in-detailed process. Finally, 130 articles that met the inclusion criteria were included in this review. This study was conducted from September 2019 to January 2021.

Fig. 1

Methodology for the inclusion of relvant studies.

Global research trends of factors and effects of fatigue induced due to excessive smartphone usage are discussed in the following sections. In addition, measurement techniques used for analysing the fatigue level along with the research status in India are discussed.

3 Results and discussion

Several studies examining the effects of excessive smartphone usage were included in this review to identify factors that cause fatigue, explore different technologies available to assess fatigue, and evaluate the research status in India. Multiple articles examining how smartphone addiction, blue light technology, and improper posture cause fatigue due to smartphones were included. In addition, various technologies used to measure fatigue including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), Eye trackers, Open-source computer vision (Open-CV) techni-ques, and existing mechanical sensors were explored. Different assessment scales were compared. The research trends are summarised in Fig. 2.

Fig. 2

Research trends on excessive smartphone usage.

3.1 Factors causing fatigue

The extensive utilisation or dependence on smartphones has resulted in smartphone addiction, leading to various psychological traits that are discussed in detail in section 3.1.1 [26]. Blue light emitted from smartphones affects the eyes, causing vision-related discomfort and digital eye strain [27]. Digital eye strain refers to the discomfort caused due to any digital medium; further discussed in section 3.1.2 [28]. Maintaining an inappropriate posture while using a smartphone for a longer duration can cause pain in the shoulder, neck, lower back, and wrist; described in detail in section 3.1.3 [29].

3.1.1 Smartphone addiction

Various studies on smartphone addiction have examined how smartphone use affects the mental health of individuals. Multiple advantages and easy accessibility of smartphones and the Internet have made our lives comfortable and convenient. The smartphone’s convenience has made its usage more of a habit, which became an addiction with few factors (Fig. 3) [30 –32].

Fig. 3

Reasons for smartphone addiction.

Internet and smartphone addiction are observed more in adolescents than in adults [26]. Many studies from prominent researchers adopted a web-based survey to examine individuals’ mental health and their addiction level toward smartphones and the Internet. A web-based survey includes the questionnaire with socio-demographic factors, types of smartphone usage, data to assess smartphone addiction levels, social interactions with others, and mental health issues faced due to smartphone addiction [33 –43]. In addition, telephonic interviews were conducted to examine the mental state of individuals [44].

Fig. 4

Parameters affected by smartphone addiction.

Studies have suggested that a poor parent–child relationship or family dysfunction can reduce the quality of life; thus, the child turns towards social media for comfort [45, 46]. Features such as dependency on the social environment, loss of control, abuse, and avoidant attachment lead to an increase in smartphone addiction [36 , 48]. Other demographic factors such as low economic status, marital status of being single, and poor academic performance can lead to smartphone addiction, affecting self-esteem [38 , 50]. Trend, narcissism, attention, and caring for others are critical factors for addiction to social networking sites [33 , 41].

Smartphone addiction affects a person’s personality traits (Fig. 4). The increase in smartphone addiction levels increases depression, stress, social anxiety, loneliness, and self-esteem [33 , 52]. Extreme conditions of social stress can result in various attributes, such as a reduction in academic performance, life-satisfaction and conditions such as insomnia, tiredness, and sleeping disorders [36 , 54]. Smartphone and Internet addiction also cause compulsive behaviour, impulsivity, intolerance, and loss of self-control [23 , 56]. Addiction increases neuroticism, which reduces openness, conscientiousness, and emotional stability [40, 57]. An increase in texts and calls reduces social interaction, physical activities, and exercise, which, in turn, increase lower back pain, loneliness, and excessive smartphone usage [37 , 59]. Furthermore, excessive smartphone usage reduces individuals’ quality of life and psychological well-being [44, 60].

3.1.2 Blue light emitted from smartphone

The human eyes continuously absorb various types of lights, with the most prominent being blue light emitted from different computerised screens. Blue light is part of the visible spectrum’s electromagnetic radiation, with the highest energy and a shorter wavelength ranging from 380 to 500 nm. LED televisions, computer monitors, smartphones, tablets, and other digital devices are the sources of blue light. Children and adolescents are more affected by smartphones and their blue light [61]. Blue light emission can severely damage the eyes because the lens or cornea cannot reflect or block blue light, enabling it to reach the macula, causing macular degeneration. Exposure to blue light for a longer duration due to smartphones reduces melatonin release, thus causing sleep disorders and digital eye strain. An increase in illumination also reduces sleep quality [16, 62]. In the long term, excessive exposure to blue light can lead to eye cancer.

Two studies examined the difference between reading from a monitor and a paper. It was observed that reading speed reduced and duration increased while reading on the monitor due to blue light exposure [63, 64]. In addition, the increase in blue light exposure increases body temperature, alertness, and the commission to error [15, 65].

3.1.3 Postures

People have a sedentary lifestyle wherein they spend hours at a desk or glaring into a computer or smartphone [66]. Individuals tend to sit for hours with hunched shoulders and incorrect back posture. Because of spending many hours in the same position, the user tends to adopt a bad posture [21]. These inappropriate body postures can lead to musculoskeletal disorders and muscle fatigue, which develop over time. People are not aware of adverse effects of incorrect postures, including pain or discomfort in the fingers, wrist, shoulder, and neck, which result in multiple health issues such as text neck. Text neck [67] is the term utilised to indicate neck pain mainly caused by the usage of technological gadgets such as smartphones and tablets [68]. In addition, smartphone use leads to thumb arthritis that causes pain and tenderness at the thumb base due to texting and gripping the smartphone with thumbs [17]. Approximately 90% of people worldwide use technological gadgets in the bed, office, living room, or while driving [16, 69]. Thus, they bend the neck while using digital devices by adopting an improper position, leading to neck pain. Usage of smartphones while walking can affect the gait pattern [70] and velocity [71] and result in deviation from the path [72].

Various studies have used subjective assessments, such as questionnaires, and objective assessments, such as EMG, EEG, cameras, eye trackers, and mech-anical sensors, to evaluate muscle fatigue induced due to improper postures.

3.2 Techniques used to measure fatigue

Both subjective and objective measurement tools are utilised for analysing fatigue levels induced due to smartphone use. Subjective measurement include assessment scales with questionnaires. Objective measurement tools include EEG, EMG, EOG, eye trackers, and mechanical sensors that are used to retrieve data from the brain, muscles, and eyes while using smartphones [73, 74].

3.2.1 Subjective evaluation: assessment scales

Assessment scales are adopted to analyse fatigue levels due to smartphone usage and posture maintained. Each assessment scale comprises a different set of questionnaires. In subjective evaluation, smartphone users’ responses were recorded using either pen and paper or a web-based method. Responses collected from questionnaires were analysed and assigned scores accordingly. Numerous researchers performed a subjective evaluation while using a smartphone and collected various information including socio-demographic factors, personal opinions, judgment, emotions, smartphone usage patterns, usage duration, postures adopted, and usage purpose to correlate with multiple factors such as addiction, postures (flexion angle), muscle fatigue, mental fatigue, and visual fatigue (Table 1) [67 , 75–94].

Table 1
Assessment scales used to measure fatigue levels

Ref. Scale name Scale type Parameters measured No. of questions

[37] UCCA loneliness scale 5 Point Likert scale Quality of life &life satisfaction 20 items

[39] Mobile phone involvement questionnaire 6 Point Likert scale Phone usage pattern 18 items

Internet addiction test 7 Point Likert scale Internet usage pattern

Depression, anxiety and stress scales 5 Point Likert scale Anxiety-related questions

[41] Mobile phone problematic usage scale 4 Point Likert scale Demographic types of smartphone usage 24 items

Patient health questionnaire Depression, OCD

[42] Smartphone addiction scale short version 6 Point Likert scale Mood, depression 10 items

[43] Smartphone application based addition scale (SAPAS) 6 Point Likert scale Silence, mood, modification, tolerance 6 items

Bergen social media addiction scale (BSMAS) 5 Point Likert scale Related to social media 6 items

9-Item Internet gaming disorder scale-short form (IGDS-SF9) 5 Point Likert scale Internet gaming 9 items

[49] Smartphone addictive scale (SPAS-SF) 6 Point Likert scale Psychological well being 10 items

[50] Smart mobile phone addiction scale (MPAS) 6 Point Likert scale Smartphone usage pattern 350 items

Scale of university students daily life stress 5 Point Likert scale Academic performance

Scale of university students life satisfaction 6 Point Likert scale Quality of life

[53] Smartphone addictive inventory scale (SPAI) 4 Point Likert scale Depression 26 items

Patient health questionnaire - 9 Demographic data and health-related data 9 items

[60] Psychological well-being scale 6 Point Likert scale Psychological aspects 84 items

Positive and negative affective schedule 5 Point Likert scale How frequent subject have emotions 20 items

Problematic smartphone usage 5 Point Likert scale Smartphone usage pattern duration 27 items

Satisfaction with life scale 7 Point Likert scale Quality of life 5 items

Smartphone usage scale 5 Point Likert scale Habitual smartphone usage Entertainment usage 18 items

Communication usage

[95] Smartphone addictive inventory scale (SPAI) 4 Point Likert scale Depression 26 items

[96] Computer vision symptom scale (CVSS17) 10 Point Likert scale Vision-related questionnaire 10 items

[97] Smartphone addiction proneness scale 4 Point Likert scale Smartphone addiction and behavior 15 items

[98, 99] Chinese language test 4 Essay reading Multiple choice questions related 4 essay How legibility, font style, font size effect visual fatigue 4 items

[100] Open-ended questionnaire 7 Point Likert scale Eye discomfort in imaging 10 items

[101] Gaming disorder test 3 Point Likert scale Game related behavior questions 11 items

Internet gaming disorder test 3 Point Likert scale Game related behavior questions 9 items

3 Item loneliness scale 4 Point Likert scale Symptoms of depression 3 items

Patient health questionnaire 4 Point Likert scale Symptoms of depression 9 items

[102] 170 OHCS test (hydroxycortico steroids) Drowsiness, dullness, concentration, physical abnormalities 30 items

[103 –105] Index of community socio-educational advantage scale –Technology use questionnaire Yes / No Parent occupation, education level 58 questions

Ref.	Scale name	Scale type	Parameters measured	No. of questions
[37]	UCCA loneliness scale	5 Point Likert scale	Quality of life &life satisfaction	20 items
[39]	Mobile phone involvement questionnaire	6 Point Likert scale	Phone usage pattern	18 items
	Internet addiction test	7 Point Likert scale	Internet usage pattern
	Depression, anxiety and stress scales	5 Point Likert scale	Anxiety-related questions
[41]	Mobile phone problematic usage scale	4 Point Likert scale	Demographic types of smartphone usage	24 items
	Patient health questionnaire		Depression, OCD
[42]	Smartphone addiction scale short version	6 Point Likert scale	Mood, depression	10 items
[43]	Smartphone application based addition scale (SAPAS)	6 Point Likert scale	Silence, mood, modification, tolerance	6 items
	Bergen social media addiction scale (BSMAS)	5 Point Likert scale	Related to social media	6 items
	9-Item Internet gaming disorder scale-short form (IGDS-SF9)	5 Point Likert scale	Internet gaming	9 items
[49]	Smartphone addictive scale (SPAS-SF)	6 Point Likert scale	Psychological well being	10 items
[50]	Smart mobile phone addiction scale (MPAS)	6 Point Likert scale	Smartphone usage pattern	350 items
	Scale of university students daily life stress	5 Point Likert scale	Academic performance
	Scale of university students life satisfaction	6 Point Likert scale	Quality of life
[53]	Smartphone addictive inventory scale (SPAI)	4 Point Likert scale	Depression	26 items
	Patient health questionnaire - 9		Demographic data and health-related data	9 items
[60]	Psychological well-being scale	6 Point Likert scale	Psychological aspects	84 items
	Positive and negative affective schedule	5 Point Likert scale	How frequent subject have emotions	20 items
	Problematic smartphone usage	5 Point Likert scale	Smartphone usage pattern duration	27 items
	Satisfaction with life scale	7 Point Likert scale	Quality of life	5 items
	Smartphone usage scale	5 Point Likert scale	Habitual smartphone usage Entertainment usage	18 items
			Communication usage
[95]	Smartphone addictive inventory scale (SPAI)	4 Point Likert scale	Depression	26 items
[96]	Computer vision symptom scale (CVSS17)	10 Point Likert scale	Vision-related questionnaire	10 items
[97]	Smartphone addiction proneness scale	4 Point Likert scale	Smartphone addiction and behavior	15 items
[98, 99]	Chinese language test 4 Essay reading	Multiple choice questions related 4 essay	How legibility, font style, font size effect visual fatigue	4 items
[100]	Open-ended questionnaire	7 Point Likert scale	Eye discomfort in imaging	10 items
[101]	Gaming disorder test	3 Point Likert scale	Game related behavior questions	11 items
	Internet gaming disorder test	3 Point Likert scale	Game related behavior questions	9 items
	3 Item loneliness scale	4 Point Likert scale	Symptoms of depression	3 items
	Patient health questionnaire	4 Point Likert scale	Symptoms of depression	9 items
[102]	170 OHCS test (hydroxycortico steroids)		Drowsiness, dullness, concentration, physical abnormalities	30 items
[103 –105]	Index of community socio-educational advantage scale –Technology use questionnaire	Yes / No	Parent occupation, education level	58 questions

3.2.2 Objective evaluation

Objective evaluation is conducted to examine fatigue induced due to smartphone usage. Physiological measurement devices, cameras, mechanical sensors, and eye trackers are used in objective assessments. In objective evaluation, data is retrieved from various sensors and techniques, wherein non-invasive sensors or electrodes are attached to the body at various locations, and signals are collected for measuring fatigue.

3.2.2.1Physiological signals Various physiological signal measurement techniques such as EMG, EEG, ECG, and EOG are utilised for measuring fatigue (Table 2). Many studies have used surface EMG to examine muscle activity while using a smartphone. Visual fatigue can also be examined by measuring eye muscle activity through EMG sensors [106]. Furthermore, EMG can be used to examine muscle fatigue induced in the neck and shoulder due to different smartphone usage conditions such as incorrect posture (head flexion angle), single or double hand use, with or without taping, walking, and relaxed and sitting positions [18 , 107]. A normalisation technique was adopted to compare data between participants. Generally, the maximum voluntary contraction (MVC) normalisation technique was utilised, which is the most efficient method for generating the highest muscle activity for a specific individual [108]. In the normalisation technique, EMG readings are retrieved at both rest and MVC to generate a scale [109, 110]. The root mean square (RMS) value provides insights into the amplitude of the EMG signal because it is a measure of the power of signal obtained from the muscle. The RMS value of the EMG signal increases linearly with the load acting on the muscle, which is directly proportional to the fatigue level [111].

Table 2
Physiological measuring parameters for analysing fatigue

Ref. Measuring condition Parameters measured Inference

[18] Biopac MP 35 surface EMG Measure neck muscle activity Muscle activity of s ternocleidomastoid, cervical erector Shoulder taping reduce discomfort; but the fatigue levels due to smartphone remain same.

•while texting in iPhone for 30 minutes With or without shoulder tapping. spinae and upper trapezius (taped) muscles were recorded.

[19] Surface EMG Normalized muscle activity of upper trapezius and splenius capitis were recorded (% MVC). Smartphone placed at eye level with supports reduce muscle fatigue level.

Muscle activity at neck and shoulder

•Smartphone placed at eye, chest, lap level and self-selected position.

Two chairs with arms and back support and no arms and back support.

[20] Noraxon DTS wireless EMG % MVC of neck and head muscle were recorded. Tab use in lap causes more flexion.

Understand neck muscle activity in 4 Different positions of reading monitor and tablet on table and lap with seat in semi-reclined of 60⁰.

[93] Surface EMG Values of % MVC of trapezius pars descendens and sternocleidomastoid muscle were recorded. Values were compared in % of MVC; More muscle activity observed without head-rest compared with head-rest seating.

Measure neck muscle activity while watching TV at lounge seat

•Lying flat

•Sitting slouched with back-rest at 40⁰ reclined position with head-rest

Sitting without head-rest.

[106] 8-Channel Biomonitor ME6000 Values of % MVC of orbicularis oculi (OO) muscle were recorded. 3D display and 3H viewing distance causes more fatigue than 2D display and 6H viewing distance.

Measure visual fatigue using EMG

•Type of display (2D &3D)

•Distance from display device (3H &6H); H- screen height = 50 inches

Measured EMG after every 10 minutes.

[109] Surface EMG Electrodes placed at cervical extensor muscle at C4 level. Typing, device on table and iPad creates more fatigue than gaming and reading, device on hand and iPhone.

Muscle activity at cervical spine were examined

Device: iPhone and iPad

Location: Device on flat table at elbow height and hold device with left hand

Task: Reading, typing and gaming.

[110] Bagnoli 16-channel surface EMG % of MVC of left and right shoulder muscles and neck extensors were recorded. Documentation format in lower angle and visual content (pictures) in upper angle was recommended.

Normalized EMG of shoulder and neck muscles were discussed with monitor placed at 3 display tilt positions (far (15⁰), close (90⁰) and low (75⁰).

[113] Wearable ECG –ADS1292R Recorded data of 8 heart-rate variability indicators. Normal to normal interval, total spectral power were most significant factors out of 8 in identifying mental state of subject.

Collected data while taking quiz of 55 questions (30 –Logical and computational; 25 –memory) to understand the mental fatigue.

[116] Biopac MP150 Equipment Collected data for 12–15 minutes while playing game in smartphone to identify addicted users. Recorded data from occipital lobe (O1 and O2) of frequency alpha and theta. High alpha and theta frequency values in addicted smartphone users than non-addicted users.

[117] Neurosky Mindwave Recorded data from frontal lobe of all five frequencies. From image displayed to focussing and depth perception; the EEG and eye activity varies for different comfort levels.

Watching 120 stereoscopic images to examine visual comfort level.

[120] EEG and Polar RS800cx portable heart rate monitor R-R intervals. Heart rate variability and identify the subject’s decision-making skills.

To understand the subject’s physiological behaviour.

Ref.	Measuring condition	Parameters measured	Inference
[18]	Biopac MP 35 surface EMG Measure neck muscle activity	Muscle activity of s ternocleidomastoid, cervical erector	Shoulder taping reduce discomfort; but the fatigue levels due to smartphone remain same.
	•while texting in iPhone for 30 minutes With or without shoulder tapping.	spinae and upper trapezius (taped) muscles were recorded.
[19]	Surface EMG	Normalized muscle activity of upper trapezius and splenius capitis were recorded (% MVC).	Smartphone placed at eye level with supports reduce muscle fatigue level.
	Muscle activity at neck and shoulder
	•Smartphone placed at eye, chest, lap level and self-selected position.
	Two chairs with arms and back support and no arms and back support.
[20]	Noraxon DTS wireless EMG	% MVC of neck and head muscle were recorded.	Tab use in lap causes more flexion.
	Understand neck muscle activity in 4 Different positions of reading monitor and tablet on table and lap with seat in semi-reclined of 60⁰.
[93]	Surface EMG	Values of % MVC of trapezius pars descendens and sternocleidomastoid muscle were recorded.	Values were compared in % of MVC; More muscle activity observed without head-rest compared with head-rest seating.
	Measure neck muscle activity while watching TV at lounge seat
	•Lying flat
	•Sitting slouched with back-rest at 40⁰ reclined position with head-rest
	Sitting without head-rest.
[106]	8-Channel Biomonitor ME6000	Values of % MVC of orbicularis oculi (OO) muscle were recorded.	3D display and 3H viewing distance causes more fatigue than 2D display and 6H viewing distance.
	Measure visual fatigue using EMG
	•Type of display (2D &3D)
	•Distance from display device (3H &6H); H- screen height = 50 inches
	Measured EMG after every 10 minutes.
[109]	Surface EMG	Electrodes placed at cervical extensor muscle at C4 level.	Typing, device on table and iPad creates more fatigue than gaming and reading, device on hand and iPhone.
	Muscle activity at cervical spine were examined
	Device: iPhone and iPad
	Location: Device on flat table at elbow height and hold device with left hand
	Task: Reading, typing and gaming.
[110]	Bagnoli 16-channel surface EMG	% of MVC of left and right shoulder muscles and neck extensors were recorded.	Documentation format in lower angle and visual content (pictures) in upper angle was recommended.
	Normalized EMG of shoulder and neck muscles were discussed with monitor placed at 3 display tilt positions (far (15⁰), close (90⁰) and low (75⁰).
[113]	Wearable ECG –ADS1292R	Recorded data of 8 heart-rate variability indicators.	Normal to normal interval, total spectral power were most significant factors out of 8 in identifying mental state of subject.
	Collected data while taking quiz of 55 questions (30 –Logical and computational; 25 –memory) to understand the mental fatigue.
[116]	Biopac MP150 Equipment Collected data for 12–15 minutes while playing game in smartphone to identify addicted users.	Recorded data from occipital lobe (O1 and O2) of frequency alpha and theta.	High alpha and theta frequency values in addicted smartphone users than non-addicted users.
[117]	Neurosky Mindwave	Recorded data from frontal lobe of all five frequencies.	From image displayed to focussing and depth perception; the EEG and eye activity varies for different comfort levels.
	Watching 120 stereoscopic images to examine visual comfort level.
[120]	EEG and Polar RS800cx portable heart rate monitor	R-R intervals.	Heart rate variability and identify the subject’s decision-making skills.
	To understand the subject’s physiological behaviour.

Different studies utilised EEG data to understand the mental behaviour of participants while using smartphones. EEG can also measure the eyeball movement and blink frequency [112]. In EEG, an electrode is placed on the skin all around the skull over distinct parts of the brain. EEG readings were utilised to understand participants’ smartphone addiction levels by examining alpha and theta frequencies [113]. A reduction in alpha waves indicates an increase in stress. Reduction in theta waves indicate a reduction in the concentration. Similarly, a reduction in the beta ratio indicates a reduction in focus. The general sampling rate of signals collected from an EEG headset is 300 Hz. Among various filters, band-pass and adaptive filters are the most utilised [114, 115]. To examine the brain activity, ocular and muscle artifacts are reduced or eliminated. Wavelet independent component analysis is an efficient method for artifact removal [116, 117].

EOG is utilised for recording eye activity and strain. For eye movement measurements, a pair of electrodes are placed on the four sides of the eyes. The blink frequency determined from EOG can be correlated with the heart rate to understand smartphone addiction and eye strain [118]. Data obtained from EOG mostly consists of low frequencies with a sampling rate of 120–150 Hz. Because EOG signals include low frequencies, they are processed using multiple filters. First, a notch filter or a low-pass filter is used to attenuate noise, followed by a band-pass filter to limit the signal band from 0.1–35 Hz. Pattern recognition techniques are used to examine horizontal and vertical movements [119]. Although EOG is cheaper than other measuring techniques, it cannot measure all eye parameters such as the pupil diameter and saccade length. Hence, it is not comparable with commercially available eye trackers.

3.2.2.2Eye tracker and camera An eye tracker can identify the state of the eyes for drowsiness, focus, attention, consciousness, and mental state. Infrared technology and high-resolution cameras are utilised to track the pupil diameter, gaze direction, and pupil centre, where light is reflected from the cornea. Eye activity, including eye movements, and position can be measured using eye trackers. This technology has been utilised in various studies to measure fatigue levels (Table 3).

Table 3

Comparison between different types of eye trackers

Ref.	Eye tracker	Measuring parameters
[121]	Portable Fatigue Meter	Critical flicker fusion frequency small, portable, light-weight
[122]	Smart Eye Pro 5.6	Eye movement, eyelid opening, blink rate, blink fixation, pupil diameter, saccade
[123]	Dikablis Eye tracker	Eye movement
[124]	Eye Tracker – SensoMotoric Instrument	Diameter of pupil, eye movement
[125]	Optical Biometry – SUOER (SW9000)	Pupil diameter, lens thickness
[125]	Eye Tracker – Tobii T120	Eye movement
[126]	Infra-red EMR ACTUS eye tracking device	Identify mental fatigue using pupil data and blink behaviour
[127]	Near Point Accommodation tester – OMP2608	Near point accommodation
[128]	Eye Tracker Acton	Blink rate, blink frequency, pupil diameter
[127, 129]	Critical flicker fusion frequency Tester 12021	Critical flicker fusion frequency

The state of the eye can also be examined using a basic camera, where the Open-CV toolbox is used to detect facial features, followed by extracting information from the eye region to identify the blink rate and dark circles, which are vital factors of short-term fatigue [130, 131]. Similarly, foreground and background segregation are performed using normalisation techniques to locate the eye region and identify the percentage of closure (PERCLOS) of the eyes [64]. PERCLOS and blink rate are critical symptoms of visual fatigue [132]. A camera is also used to calculate the head tilt angle for analysing muscle fatigue. Researchers utilised motion sensing digital cameras to calculate the head angle by placing an angle chart on a fixed wall and the participant positioned between a camera and a chart [19 , 133].

3.2.2.3Mechanical sensors Some studies used different mechanical sensors to determine the head flexion angle. Various mechanical sensors used to measure the head title angle include accelerometer, gyroscope, electrical magnetometer, goniometer, and dolorimeter. These sensors are attached to the head, and the corresponding head angles are determined through the microcontroller and analysed for further processing. The calculated head inclination of participants was correlated with muscle fatigue [17 , 134–139].

3.3 Research status in India

The aforementioned studies have mainly been conducted in emerging nations, which are the major manufacturers of smart displays and smartphones. These studies mainly focused on analysing fatigue caused by smartphones or any other digital devices. In emerging nations, although the smartphone dissemination rate is high (South Korea: 95.4% [140], Taiwan: 98% [3, 141], the United States: 84%, and India: 31.7% [142]), but the smartphone market is very low (South Korea: 4, Taiwan: 5, the United States: 5) than in India (India: > 12). These countries restrict to a maximum of five brands to benefit their society by providing high-quality products and reduce the retinal damage caused by smartphones. Gustafsson and Inal and Arslan [143, 144] recommended guidelines for smartphone utilisation such as (i) resting forearms, (ii) not sitting with the head bending forward, (iii) utilising both thumb fingers equally, and (iv) reducing the velocity of texting to prevent text neck and adopting ergonomic behaviour and active lifestyle.

In contrast, in India, the second-highest smartphone utilizer, the smartphone market is enormous. More than 12 smartphone brands are available in India; and no proper guidelines are followed while using smartphones. Moreover, according to the authors’ knowledge, limited studies are reported on the analysis of fatigue caused by smartphones; it is virtually zero. In a competitive market, companies are moving towards inexpensive displays to save manufacturing expenses and maximise profits. In turn, these measures directly affect the screen quality, opting for substandard screens and causing retinal strains.

Balhara et al. [145, 146] conducted three studies on Indian adolescents and their behavioural addiction due to the digital medium with the usage pattern. Similarly, a study [147] explored the use of Internet browsing, online gaming, and social media that cause smartphone and Internet addiction among high school students. In addition, another study [148] examined how smartphone radiation affects the human body and nervous system with an illustration of imposing microwave radiation on animals.

Also, the methods available to measure physiological signals and eye parameters need high investments. Hence, it is necessary to study the adverse effects caused by smartphones in India by developing a standard measurement setup as the anatomy of the eye [149], usage pattern, and duration varies from region to region. Few survey studies [145–148 , 150] have assessed smartphone addiction in Indian adolescents and its psychological effects with significant health risks. An extensive study is needed to understand muscle, visual, and mental fatigue in Indian adolescents with Indian usage patterns, as a variety of smartphones and usage patterns are observed.

3.4 Limitations

This study has some limitations that should be addressed. Although a systematic search was performed to identify studies published between 2010 and 2021 by using keywords focusing on visual, muscle, and mental fatigue caused due to extensive usage of smartphones, some studies might have been missed due to inaccessibility. In addition, laboratory studies, unpublished studies, and studies in languages other than English were excluded due to legibility issues. Digital media includes computers, laptops, smart TV, smartphones, and tablets; however, this study focused only on smartphone-related research because of their extensive usage.

4 Conclusion

Apart from the multiple advantages of a smartphone, users should be aware of their negative effects. The uncontrollability of smartphone usage leads to smartphone addiction. Smartphone addiction is recognised as the most concerning aspect among adolescents. Because adolescents are firmly attached to their smartphones, they have a significant risk of Internet and smartphone addiction that can damage the retina and cause digital eye strain. Inappropriate body postures can cause musculoskeletal disorders including pain or discomfort in the neck, shoulder, and thumb. Eventually, adolescents can experience several social, physical, and psychological health issues.

A systematic review study was performed to understand excessive smartphone usage and its adverse effects. A total of 130 research articles were evaluated in this review study. Various studies were summarised and categorised based on the factors and effects of smartphone addiction, with measuring techniques like subjective and objective assessment. This study also examined the research status in India. The findings reveal how smartphone utilisation can cause various physiological issues. It is also understood from this study, India is significantly lagging in research on the effects of smartphone usage. Studies should be conducted to understand the effect of smartphone use on Indian adolescents. Furthermore, studies are needed to establish the correlation between mental disorders, musculoskeletal disorders, and visual fatigue with extensive smartphone utilisation.

Footnotes

Acknowledgment

The authors thank the Department of Mechanical Engineering and Robotics lab from SRM Institute of Science and Technology, Kattankulathur, India, for providing the required facilities for this research study.

Conflict of interest

The authors declare that there are no conflicts of interest.

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