Cognitive failures and hand/neck discomfort: Does smartphone addiction contribute?

Abstract

BACKGROUND:

Due to the emergence of smartphone addiction, as a 21th century phenomenon, investigating its subsequent negative effects is essential.

OBJECTIVE:

The present study aims to test the predicting effect of smartphone addiction on musculoskeletal discomfort in hand/neck region as well as cognitive failures.

METHODS:

A cross-sectional study was designed in which 533 smartphone users (60.2% females and 39.8% males; mean age: 35.9±11.0 years) participated. Smartphone Addiction Scale (SAS), Cognitive Failures Questionnaires (CFQ), Neck Disability Index (NDI) and Cornell Hand Discomfort Questionnaire (CHDQ) were used for data collection. Age, gender and occupational use of electronic devices were considered as socio-demographic factors. Independent t-test, chi-square, and logistic regression were used for data analysis.

RESULTS:

The overall prevalence of smartphone addiction was 37.9% among Iranian 20–61 years users. Addicted smartphone users were 3.3, 2.2, 2.9, and 2.8 times more likely to develop musculoskeletal discomfort in right hand, left hand, neck, and cognitive failures than non-addicted smartphone users. High occupational use of electronic devices predicted right-hand discomfort (by 2.6 times) as well as cognitive failures (by 3.4 times). Women were significantly more likely to develop all the studied outcomes. Age did not predict any of the studied outcomes.

CONCLUSIONS:

Addiction to smartphones can lead to a sharp rise in the prevalence of neck and hand discomfort, as well as cognitive failures, particularly among working age people. This is concerning issue that requires effective preventive and corrective measures. Developing behavioral approaches to address this problem can help reduce its impact on society.

Keywords

Addiction cognition dysfunction hand musculoskeletal pain neck smartphone

1 Introduction

The usage of smartphones has become an indispensable part of people’s daily lives providing a wide range of online and offline functionalities such as social media, banking and shopping, schedule tracking, entertainment, watching movies and sports, and surfing the internet for information [1, 2]. As of the end of 2020, around 80 percent of the global populations were smartphone users [3]. Interestingly, many individuals use more than one smartphone, which is expected to result in 7.7 billion smartphone subscriptions by 2027 [3]. Consequently, excessive use of smartphones, and even smartphone addiction, have become one of the most pressing global issues [4]. The Diagnostic and Statistical Manual of Mental Disorders version 5 (DSM-5) outlines a set of criteria for addictive and substance-related disorders including “withdrawal”, “repeated attempts to control or quit”, “given up activities”, “physical or psychological problems related to use”, “hazardous use”, “tolerance” and “craving” [5]. Behavioral (non-chemical) addiction arises when an individual is unable to resist a behavior associated with a psychological need, leading to long-term negative effects [4]. Integrating DSM-5 criteria for addictive and substance-related disorders with other definitions proposed for behavioral addiction, Sunday et al. [4] defined smartphone addiction as “a condition where the use of smartphone has fulfilled a deep need (dependency, habitual, and addictive behavior) to the extent that the individual has difficulty conducting basic activities of daily life without the concurrent use of a smartphone, and as such caused neglect of other aspects of one’s life” [4].

In recent years, with the growth of teleworking and online jobs, conventional offices have been transformed into remote work setups where individuals can work from home or any location using mobile computing technology (e.g. notebook or laptop computers, tablet computers, mobile phones and smartphones) via access to data over the internet [6, 7]. Based on the available evidence, it appears that the COVID-19 pandemic has significantly increased teleworking [8]. Some people prefer smartphones over personal computers due to various smartphone functionalities [1] which may serve as a justification for excessive usage of these devices.

Excessive and uncontrollable internet use has previously been found to be associated with structural abnormalities in multiple cortical and subcortical brain areas [9]. Both neuroimaging and clinical evidence demonstrate the changes in gray matter volume among individuals addicted to the internet lead to dysfunction in cognitive control ability [10]. Cognitive control is the process of coordinating thoughts and actions by focusing on goal-relevant data and suppressing irrelevant distractions [9]. Dysfunction in cognitive control typically results in cognitive failure mainly characterized by concentration deficits, memory loss and reduced perception [11, 12]. Given the results of these studies, and the similarities between Internet addiction and smartphone addiction, it can be assumed that smartphone addiction can also have long-term effects [13]. This means that addicted people to smartphones if separated from their own gadget, in addition to anxiety and other psychological consequences [14, 15], may experience cognitive failures. Former studies have highlighted the significant impact of cognitive failures on both work performance and individual safety behavior [16 –19]. For instance, a review study conducted by Busch and McCarthy [20], showed that using smartphones problematically affects both the user’s professional and social performance [20]. As a priority for people with smartphone addiction, they tend to allocate a significant portion of their attention to their smartphones even if they are busy doing other activities. Therefore, the capacity of their cognitive resources for other activities is reduced, so their performance is affected [21]. Duke and Montag [22] also supported the notion that habitual checking and tendencies towards smartphone addiction can diminish productivity in both home and workplace settings [22]. Their research revealed a significant relationship between smartphone addiction and the amount of work hours lost due to smartphone use [22]. Additionally, using smartphones simultaneously with other activities enhance errors and reaction times due to the limited capacity of attention, and in work places, this can decrease productivity, both qualitatively and quantitatively [23].

On the other hand, a remarkable body of scientific research has accrued to support the effect of problematic smartphone use on Musculoskeletal Disorders (MSDs) [24 –26]. This could be mainly attributed to the prolonged and frequent usage of these devices along with awkward postures and repetitive movements resulting in an elevated biomechanical load especially in neck, wrists, fingers and thumbs. The earliest sign of smartphone overuse is discomfort, which may be transient at first, but gradually turn into chronic pain following the frequent use of smartphones [27]. Previous surveys showed MSDs were resulting in sickness absence [28], reduced productivity at work (for example lost time) [28, 29], psychological symptoms like decreased concentration and stress-related pain which can negatively affect productivity [30, 31].

The prevalence of smartphone addiction is estimated to be 12% to 62.6% in several Asian countries [32 –35]. Literature review reveals that the majority of studies conducted on negative consequences of smartphone overuse have either focused on psychosocial factors (e.g. depression, anxiety, quality of life), or musculoskeletal disorders particularly in neck region of younger adults [36 –38]. Cognitive failures due to smartphone addiction have rarely been examined. Therefore, the main aim of the present study was to test the predictive effect of smartphone addiction on hand/neck musculoskeletal discomfort as well as cognitive failures. In this regard, we hypothesized that smartphone-addicted people would be more susceptible to the aforementioned problems compared to non-addicted people.

2 Methods

The study had a cross-sectional design and was conducted from April to July 2021. An online questionnaire (described below) was designed through “Google Forms”, and was disseminated through WhatsApp. Before proceeding with data collection, information about the purpose of the study as well as its voluntary and anonymous nature were given to participants. Giving informed consent was required prior to answering the questions. The study protocol was approved by the local research ethics committee (Reg. IR.SBMU.PHNS.REC.1399.102). The survey took about 15–20 minutes to be completed by each subject.

2.1 Sample size and participants

The minimum sample size of 523 was determined using the formula, (Z_1 - α/2 + Z_1 - β)² P (1-P) / d²; where Z_1 - α/2 = 1.96 (the value of normal deviate at 0.05 level of confidence), Z_1 - β = 0.84 (the value of normal deviate at the study power of 0.8), P = 0.6 (the mean prevalence of smartphone addiction, and d = 0.06 (expected absolute allowable error in the mean) [39 –41].

Initial subjects were students and staff of a main university of Tehran (capital of Iran). They were asked to send the link of the survey to their relatives and friends by WhatsApp. Participants who self-reported being diagnosed with musculoskeletal, neurological, or cognitive disorders were not included in the study. Data collection was continued until the required sample size was obtained. Ultimately 533 participants were included in the study.

2.2 Instruments- Main outcomes

The first outcome was smartphone addiction, which was tested by means of the Farsi short version of Smartphone Addiction Questionnaire (SAS-short) [42]. Designed by Kwan et al. (2013), the SAS-short is a self-administered questionnaire containing 10-items each rated on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree) to constitute a 10–60 scale. The cut-off scores of ≥33 and ≥31 were set for men and women, respectively [43].

The next outcome was self-reported hand discomfort assessed by the Cornell Hand Discomfort Questionnaire (CHDQ). The CHDQ measuring musculoskeletal disorders, was developed by Hedge et al. in 1999. It assesses pain and discomfort in six distinct areas of the palms and fingers of both right and left hands (Fig. 1) on the basis of frequency, severity, and impact of discomfort on work capacity during the past week. Scores can be analyzed by multiplying the frequency score (never = 0, 1-2 times/week = 1.5, 3-4 times/week = 3.5, every day = 5, several times every day = 10) by the discomfort score (1,2,3) by the interference score (1,2,3) [44].

Fig. 1

The CHDQ map used in the present study.

Neck pain was another outcome of the study, which was tested through Farsi version of the Neck Disability Index (NDI). Adapted from the Neck Disability Index questionnaire of Vernon et al. [45], the NDI is a self-rated questionnaire used to identify disabilities induced by neck pain in everyday life. It contains ten 6-item sections, including head and neck pain, personal activities, lifting, reading, concentrating, job-related activities, driving, sleeping, and recreational activities. In each question, score 0 indicates no amount of pain and discomfort and score 5 indicates the most amount of pain and discomfort in neck and head regions. The final score is obtained from the total score of the 10 questions that can be between 0 and 50. The questionnaire has good psychometric properties [46].

The last outcome was cognitive failure, which was assessed through Farsi version of the Cognitive Failures Questionnaire (CFQ) [47]. This questionnaire was developed by Broadbent et al. [48]. This questionnaire includes 25 items on a 5-point Likert scale (0 = never to 4 = always). A higher score indicates higher cognitive failures in daily activities. The CFQ has four components describing distraction, memory problems, inadvertent mistakes, and lack of name memorization.

Information considering age, gender and occupation were also recorded as socio-demographic variables because of their well-documented relationship with musculoskeletal disorders and cognitive failures [49, 50]. Based on consensus agreement from the authors, participants were divided into three subgroups of low (e.g. blue-collar workers), medium (e.g. salesclerks, nurses), and high (e.g. students, office workers, lecturers) users, taking into consideration their job nature and occupational use of electronic devices such as smartphones, laptops, and computers.

2.3 Statistical analysis

Independent t-test, chi-square, and logistic regression were used for data analysis using IBM SPSS Statistics software v.25 (IBM SPSS Statistics, ARMONK, USA). A backwards stepwise multivariate logistic regression analysis was used for the relevant body region and cognitive failures. Hand discomfort, neck discomfort and cognitive failures were analyzed separately as dependent variables and smartphone addiction, age, gender, and occupational use of electronic devices entered into the models as covariates. Factors with the largest likelihood ratio probability were omitted first, until P≤0.05 for the remaining factors in the model. Values of P≤0.05 were considered significant and odds ratios (ORs) were presented with 90% CI (confidence intervals).

3 Results

3.1 Descriptive statistics

A total of 533 smartphone users (60.2% females and 39.8% males) with mean age of 35.9±11.0 years participated in the study. Their age ranged from 20 to 61 years. Socio-demographic characteristics of participants are presented in Table 1.

Table 1
Socio-demographic characteristics of the study participants (n = 533)

Variable Number (%)

Age (years) 20–30 194 (36.4)

31–45 217 (40.7)

46–65 122 (22.9)

Gender Female 321 (60.2)

Male 212 (39.8)

Occupational use of electronic devices Low 77 (14.4)

Medium 116 (21.8)

High 340 (63.8)

Variable		Number (%)
Age (years)	20–30	194 (36.4)
	31–45	217 (40.7)
	46–65	122 (22.9)
Gender	Female	321 (60.2)
	Male	212 (39.8)
Occupational use of electronic devices	Low	77 (14.4)
	Medium	116 (21.8)
	High	340 (63.8)

As shown in Table 2, the overall prevalence of smartphone addiction was 37.9 percent, with the rate being slightly higher in males compared to females. The prevalence of musculoskeletal discomfort was 28.8% (n = 154) in the right-hand region and 25.7% (n = 137) in the left-hand region, while it was 88.2% (n = 470) in the neck area. Participants who reported pain or discomfort in at least one region of their hand or neck were classified the “with pain” group. Regarding cognitive failures, 20.8% (n = 111) of participants were categorized as cognitive failure group.

Table 2

Prevalence of musculoskeletal problems, cognitive failures, and smartphone addiction among study participants

Variable		Status	Number (%)	Mean (SD)
Hand discomfort	Right	Yes	154 (28.8)	44.4 (77.2)
		No	379 (71.2)	0
	Left	Yes	137 (25.7)	31.8 (78.9)
		No	396 (74.3)	0
Neck pain		Yes	470 (88.2)	7.1 (5.6)
		No	63 (11.8)	0
Cognitive failures		Yes	111 (20.8)	62.9 (10.3)
		No	422 (79.2)	29.6 (11.9)
Smartphone addiction	Female	Yes	113 (35.2)	40.7 (6.9)
		No	208 (64.8)	21.0 (6.1)
	Male	Yes	89 (42.0)	40.2 (7.0)
		No	123 (58.0)	21.5 (5.4)
	Total	Yes	202 (37.9)	40.5 (6.9)
		No	331 (62.1)	20.7 (6.6)

Based on the SAS questionnaire, individuals were divided into two groups: addict and non-addict smartphone users. We found significant differences between the two groups in terms of age, cognitive failures, neck, and right-hand discomfort (P < 0.05) based on the results of independent sample t-test. The mean age of addict users (34.1 years) was significantly (P = 0.006) lower than non-addict users (36.9 years). We also found that mean scores of CHDQ, NDI and CFQ questionnaires of addict users (23.52, 8.48 and 42.8) were significantly (P < 0.0001) higher as opposed to non-addict users (6.78, 4.93 and 31.89). No other significant difference was found between addict and non-addict groups.

3.2 Prediction models

A backwards stepwise multivariate logistic regression analysis was carried out to determine the significant contributory factors affecting right/left-hand discomfort, neck disorder and cognitive failures. Logistic regression also compared each category to one reference category which in this case was the first category of each variable. Although there was no significant relationship between age and other variables, this variable remained in all models due to the importance of age in individual habits and behaviors, as well as its well-known correlation with health status [51]. Significant relationships were observed between right-hand discomfort and gender, occupational use of electronic devices, and smartphone addiction (P < 0.05; for all cases). Specifically, the likelihood of experiencing right-hand discomfort increased by 0.508, 2.569 and 3.293 in males compared to females, high occupational users compared to low occupational users, and smartphone addicted users compared to their reference group, respectively. More details can be seen in Table 3.

Table 3
Relationship between right hand discomfort with demographic variables and smartphone addiction

Variable B S.E P-value Exp (B)

Age (year) – – 0.449 –

20–30^r 0 – – 1.00

31–45 0.116 0.261 0.656 1.123

46–65 0.378 0.302 0.211 1.459

Gender Female ^r 0 – – 1.00

Male –0.676 0.240 0.005^* 0.508

Occupational use of electronic devices – – 0.042^* –

Low^r – – – –

Medium 0.282 0.298 0.345 1.326

High 0.943 0.389 0.015^* 2.569

Smartphone addiction No^r – – – –

Yes 1.192 0.227 <0.001^* 3.293

Constant – –1.661 0.339 <0.001^* 0.190

	Variable	B	S.E	P-value	Exp (B)
Age (year)		–	–	0.449	–
	20–30^r	0	–	–	1.00
	31–45	0.116	0.261	0.656	1.123
	46–65	0.378	0.302	0.211	1.459
Gender	Female ^r	0	–	–	1.00
	Male	–0.676	0.240	0.005^*	0.508
Occupational use of electronic devices		–	–	0.042^*	–
	Low^r	–	–	–	–
	Medium	0.282	0.298	0.345	1.326
	High	0.943	0.389	0.015^*	2.569
Smartphone addiction	No^r	–	–	–	–
	Yes	1.192	0.227	<0.001^*	3.293
Constant	–	–1.661	0.339	<0.001^*	0.190

^*indicates significant differences of P≤0.05. ^rindicates reference group.

In the initial analysis, a significant relationship was found between left-hand discomfort and smartphone addiction, while age, gender, and occupational use of electronic devices did not show significant relationships. Subsequently, the variable of occupational use of electronic devices was eliminated through this procedure. As a result, gender reached significance and retained in the final model. Finally, left-hand discomfort was significantly associated with gender and smartphone addiction (P < 0.05). The model explained that men were 0.625 times more likely to develop left-hand discomfort than women, while this symptom was 2.236 times more likely to be observed in smartphone addicted individuals than in others. See Table 4 for more details.

Table 4

Relationship between left hand discomfort with demographic variables and smartphone addiction

	Variable	B	S.E	P-value	Exp (B)
Age (year)	–	–	–	0.528	–
	20–30^r	–	–	–	–
	31–45	0.169	0.237	0.477	1.184
	46–65	0.303	0.273	0.266	1.354
Gender	Female ^r	–	–	–	–
	Male	–0.470	0.215	0.029^*	0.625
Smartphone addiction	No ^r	–	–	–	–
	Yes	0.805	0.208	<0.001^*	2.236
Constant	–	–1.373	0.213	<0.001^*	0.253

^*indicates significant differences of P≤0.05. ^rindicates reference group.

Table 5 represents contributing factors for neck pain. The initial model did not demonstrate significant relationship between age, gender, occupational use of smartphone addiction and neck pain. However, upon removing occupational use of smartphone addiction from the model, smartphone addiction and gender were found to influence neck pain (P < 0.05). Smartphone addicted individuals and male gender were 2.902 and 0.489 times more likely to suffer neck pain as compared with their reference groups, respectively.

Table 5

Relationship between neck pain with demographic variables and smartphone addiction

	Variable	B	S.E	P-value	Exp (B)
Age (year)	–	–	–	0.132	–
	20–30^r	–	–	–	–
	31–45	0.212	0.321	0.509	1.236
	46–65	0.743	0.377	0.049^*	2.102
Gender	Female ^r	–	–	–	–
	Male	–0.714	0.277	0.010^*	0.489
Smartphone addiction	No ^r	–	–	–	–
	Yes	1.065	0.340	0.002^*	2.902
Constant	–	1.713	0.294	<0.001^*	5.547

^*indicates significant differences of P≤0.05. ^rindicates reference group.

Cognitive failures were significantly correlated with smartphone addiction, occupational use of electronic devices, and gender. As shown in Table 6, smartphone addicted people, those who overuse smartphones for occupational purposes, and men were 2.831, 3.387, and 0.423 times more likely to develop cognitive failures than their reference groups, respectively.

Table 6

Relationship between cognitive failures with demographic variables and smartphone addiction

	Variable	B	S.E	P-value	Exp (B)
Age (year)		–	–	–	–
	20–30^r	–	–	–	–
	31–45	–0.060	0.263	0.819	0.942
	46–65	0.229	0.304	0.452	1.257
Gender	Female ^r	–	–	–	–
	Male	–0.859	0.253	0.001^*	0.423
Occupational use of electronic devices	–	–	<0.001^*	–
	Low ^r	–	–	–	–
	Medium	0.151	0.282	0.592	1.163
	High	1.220	0.283	<0.001^*	3.387
Smartphone addiction	No^r	–	–	–	–
	Yes	1.041	0.231	<0.001^*	2.831
Constant	–	–1.441	0.245	0.0008	0.237

^*indicates significant differences of P≤0.05. ^rindicates reference group.

4 Discussion

The present study evaluated the predictive effect of smartphone addiction on musculoskeletal discomfort in the hand/neck region, as well as cognitive failures.

The results of our study showed a comparable yet higher prevalence of smartphone addiction among Iranian users compared to other populations, and emphasized that this type of behavioral addiction can predict certain mental and musculoskeletal problems. While previous studies reported the prevalence of smartphone addiction to be under 28% among Japanese students, about 36% among Korean students, near 17% among Swiss adolescents, and approximately 21% among Chinese undergraduates [7 , 53], our findings clearly showed that approximately 2 out of 5 Iranian smartphone owners are addicted to their devices. In this respect, the ample prevalence of smartphone addiction found in a sample of Iranian users aged 20–61 years is both surprising and thought-provoking.

As expected, all the four studied health outcomes were predicted by smartphone addiction. This means that smartphone-addicted people are significantly at higher risks of health issues compared to non-addicted individuals. The widespread use of smartphones can be attributed to numerous reasons, including but not limited to the rise of online jobs, teleworking, and online work communications. These activities have recently gained popularity and have become increasingly common [6 –8], and could at least partly explain the high prevalence of smartphone overuse in the studied population. Overuse of occupational-related electronic devices predicted two important health outcomes (i.e. right-hand pain and cognitive failures). Accordingly, individuals who excessively use media devices for job purposes seem to be approximately 3 times more likely to develop right hand pain, and cognitive failures than their counterparts. It could be suggested that regardless of the purpose of usage, excessive smartphone use has adverse effects on individual’s health and productivity.

Excessive outward curve of the cervical vertebrae, awkward postures of arms, wrists, and hands as well as repetitive movements of fingers are commonly adopted during prolonged smartphone use. This condition leads to an increased pressure on the cervical spine and upper extremities structure, and can explain the prevalence of certain musculoskeletal problems among smartphone users [38, 54]. Forward head posture, very common among smartphone users, causes an increased tension in neck muscles and related tissues which is significantly associated with chronic neck pain [24]. In addition, the risk of carpal tunnel syndrome is increased with wrist deviation of >15deg [55]. Nevertheless, neutral wrist posture is not seen in more than 90% of cases while using smartphone [56]. A study conducted by Inal et al. (2015) revealed pain in the thumb area, enlargement of the median nerve, and decreased grip strength and hand function following smartphone addiction [57].

As expected, smartphone addiction had significant relationship with cognitive failures. In other words, smartphone addicted users were approximately three times more likely to experience cognitive failures than non-addicted users in their routine activities. Stanovich [58] believed that humans are cognitive misers who are inherently inclined towards cognitive laziness and often tend to use minimal cognitive resources. To wit, they prefer to replace a part of their cognitive resources with digital devices, as an external memory source. The more people use these memory aids, the less they use their own cognitive abilities. Continuation of this situation can lead to reduced performance and increased errors in routine activities [58]. Moreover, based on the “brain drain” theory proposed by Ward et al. in 2017, the mere presence of a smartphone while performing an activity can attract our attention and affect our performance probably because it is reminiscent of a broader world and communication. This part of attention is spent dealing with automatic attention to the smartphone. Reduced available attentional resources would results in attenuated cognitive performance [59]. Smartphones can take users’ attention away from their work to the point that they are unable to attain a state of flow at work (i.e. a state in which people are completely absorbed by their activities and forget about time and space while being very productive) [22, 60]. Interestingly, Altmann et al. [61] by investigating the effect of momentary interruptions on performance of a cognitive task, found that interruptions averaging as short as 2.8 s and 4.4 s doubled and tripled the rate of sequence errors, respectively, and disturbed participants’ flow of concentration [61].

Smartphone addiction was more prevalent in younger adults of <30 years old. This finding is consistent with previous studies which showed that people at younger ages were more attracted to smartphones than older ones because of their stronger connection to technology, openness to new experiences, and use of a variety of smartphone applications. As individuals age, they may be less susceptible to smartphone addiction. This phenomenon could potentially be attributed to a reduction in social stress and a preference for face-to-face interactions over virtual relationships [51]. Despite the fact that ageing is related to several mental and physical health problems, none of the studied outcomes were predicted by age. Further studies are needed to explore the potential effect of prolonged smartphone addiction on certain mental and physical diseases in the elderly.

In line with some previous studies, our study revealed that smartphone addiction affects both genders equally [62, 63]. However, gender was found as a contributing factor for all the studied outcomes; suggesting that women were near 2 times more likely to develop right- and left- hand discomfort, neck pain and cognitive failures. According to Park et al. (2015), women tend to favor virtual relationships over men due to greater difficulty they experience in coping with social stress. As a rule of thumb, increased smartphone usage is likely to amplify associated risks [38].

Based on the findings, it appears that health policymakers should implement preventive, clinical, and rehabilitative strategies to mitigate the negative side effects of smartphone addiction. Information-enhancing strategies (i.e. straight warnings, informative guidelines, community campaigns, etc.), capacity-enhancing strategies (i.e. policies to strengthen self-disciplinary management skills including interventions focusing on physical activities, face-to-face interactions, actual social networks, etc.) and behavioral reinforcement strategies (i.e. constrain users manage their smartphone usage, for example, via deliberate design of usability features that make users shut them out from an application thoroughly once a certain period of time has passed) are suggested in a systematic literature review to correct problematic smartphone use [20]. Currently, there are ergonomic recommendations related to the usage of mobile computing technology including ergonomic workstation setups, work-rest schedules, exercise during work breaks, etc. However, future longitudinal studies are needed to clarify whether regulations need to be established in this domain.

There are several limitations to the study that should be taken into consideration. Firstly, the cross-sectional design is not sufficient for determining a direct causal relationship between the variables. To better understand the prediction effects, it would be preferable to conduct longitudinal studies that enable tracking changes over time. Application of more objective measures in future studies (muscle electromyography, posture analysis, etc.) as well as investigating the influence of more contributing factors (e.g. leisure activities, lifestyle, etc.) and other dependent variables (e.g. work productivity, work disability, etc.) would be of interest, opening the path for future studies.

5 Conclusion

Due to the impressive prevalence of smartphone addiction and its observed contributing effect on hand/neck musculoskeletal pain as well as cognitive failures, it is recommended to take this behavioral addiction more seriously into consideration and develop strategies to address it effectively, especially at work settings.

Ethical approval

The ethical review committee of the Shahid Beheshti University of Medical Sciences approved the study protocol (Code no. IR.SBMU.PHNS.REC.1398.019).

Informed consent

All participants signed a written consent form before participation.

Conflict of interest

The authors have no conflict of interest to report.

Footnotes

Acknowledgments

This article was part of a thesis conducted to fulfill the requirements for a Master degree in Ergonomics from Shahid Beheshti University of Medical Sciences (Code no. 16574).

Funding

This research is partly supported by research deputy of Shahid Beheshti University of Medical Sciences.

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Cognitive failures and hand/neck discomfort: Does smartphone addiction contribute?

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Methods

2.1 Sample size and participants

2.2 Instruments- Main outcomes

3 Results

3.1 Descriptive statistics

Table 1 Socio-demographic characteristics of the study participants (n = 533) Variable Number (%) Age (years) 20–30 194 (36.4) 31–45 217 (40.7) 46–65 122 (22.9) Gender Female 321 (60.2) Male 212 (39.8) Occupational use of electronic devices Low 77 (14.4) Medium 116 (21.8) High 340 (63.8)

5 Conclusion

Ethical approval

Informed consent

Conflict of interest

Footnotes

Acknowledgments

Funding

References