The effects of adolescent physical activity participation on cell phone dependence: The mediating role of self-control

Abstract

BACKGROUND:

The rapid development of science and technology and the swift improvement of people’s material living standards enabled smartphones to be indispensable of people’s daily lives.

OBJECTIVE:

The purpose of this paper was to examine the influence of self-control in adolescents’ participation in physical activity on cell phone dependence.

METHODS:

The Physical Activity Rating Scale (PARS-3), the Self-Control Scale, and the Cell Phone Dependence Scale were used to measure the influence of self-control in adolescents’ participation in physical activity on cell phone dependence among 649 adolescents.

RESULTS:

The results show: (1) There were significant differences (p < 0.01) in the physical activity levels of adolescents under different gender, birthplace, and education background. (2) Each dimensional variable of physical activity was negatively correlated with the cell phone dependence variable, and positively correlated with each variable of self-control, and the self-control variables were negatively correlated with cell phone dependence. (3) Self-control was partially mediating the effects of physical activity on cell phone dependence, with the mediating effect accounting for 39.68%.

CONCLUSIONS:

Adolescents’ participation in physical exercise activities will improve self-control and ultimately reduce cell phone dependence. Curbing the negative and malignant events of cell phone addiction among adolescents, timely investigation of mobile phone and Internet addiction, pathological formation mechanisms and intervention measures are important measures to reshape the healthy lifestyle of adolescents and have great practical significance for the prosperity and development of families, society, nation and country.

Keywords

Physical exercise self-control cell phone dependence adolescents

1 Introduction

The rapid development of science and technology and the swift improvement of people’s material living standards enabled smartphones to be indispensable of people’s daily lives. Phubber (someone who ignores the person that they are with and gives attention to their mobile phone instead) has become a heated word. According to the data of the 45^th Statistical Report on the Development Status of the Internet in China, as of April 28, 2020, China’s online education users have reached up to 423 million, an increase of 110.2% over 2018, occupying 46.8% of the Internet user group, and 265 million school students generally turned to online courses during the COVID-19 pandemic [1]. Despite the convenience, mobile phones have actively seized the pockets of time, and the curiosity and fascination of young people for new things make mobile phones indispensable for young people, therefore, the phenomenon of cell phone dependence has been increasingly noticeable. According to psychiatrist Ahmed, cell phone dependence is the leading non-drug addiction of the 21st century [2]. It is undeniable that mobile phones are providing convenience to people while slowly destroying the physical and mental health due to cell phone dependence. Studies have shown that talking for more than 20 minutes a day can lead to endocrine disruption [3], risk of carcinogenesis [4], anxiety, loneliness, stressful emotions, depression, impulsive suicidal behavior, and academic difficulties in adolescents [5]. Considering that mobile phone addiction affects the physical and mental health of adolescents and the future development of the nation, in order to curb the emergence of negative and malignant incidents of adolescent mobile phone addiction, it is an important measure to reshape the lifestyle of adolescents, which is of great practical significance for the prosperity and development of family members, society, the nation and the country.

Among the influencing factors of cell phone dependence in adolescents, an increasing number of scholars have begun to focus on the interventions and methods of cell phone dependence in adolescent populations, especially the utility of exercise interventions on cell phone dependence. Some scholars have a relative lack of empirical studies on cell phone dependence interventions, while other scholars have mainly focused on studies explicit individual interventions on micro-level and invisible environmental interventions on macro-level [6].There has been an active international discussion on exercise therapy, and in 2013 Kim proposed a relatively systematic view of intervention [7]. Existing research results show that exercise will give spiritual relief and psychological transcendence to mobile phone-addicted patients, and achieve moderate psychological relief, thus easing the dependence and attachment of mobile phones. The results of Ge Renkai’s study showed that medium- and long-term volleyball can effectively divert college students’ negative emotions, improve peer group relationships and significantly reduce the frequency and duration of cell phone use [8]. Outward bound and group sports are important interventions to effectively alleviate cell phone dependence among adolescents [9, 10]. In addition, compared with the utility of other external environmental and cognitive interventions, interventions from the exercise can mobilize participants’ enjoyment and motivate them to get rid of their cell phones, thereby improving the symptoms of cell phone dependence in college students [11]. However, some scholars have also suggested that exercise interventions can significantly ameliorate moderate and mild cell phone addiction, but have little impact on severely addicted patients, which is one of the major problems with exercise interventions [12]. Although previous scholars have explored studies on the impact of physical activity participation on cell phone addiction among adolescents, the extent of the association and whether the influence is positive or negative are still unknown.

In this regard, in order to further sort out the efficacy of physical activity participation on mobile phone addiction, this study attempts to find a relevant chain of evidence from previous scholarly results. Scholars from two groups of experiments, open and closed sports skills, concluded that there was a significant difference (P < 0.05) between the two exercise interventions, with open sports skills being more effective [13]. Some scholars have also conducted medium- and long-term comparative experiments and found that after 12 weeks of physical activity intervention, there was a significant downward trend in cell phone addiction scores in the experimental group compared to the control group (p < 0.05) [14]. Scholars generally agree that participation in sports can effectively alleviate students’ emotional states and play a significant role in the adjustment and concentration of students’ attention [15]. Based on the above-mentioned studies, it is known that scholars have obtained relevant results on the effect of physical exercise participation on cell phone addiction, but the effect and prediction level of cell phone addiction have still not been specifically explained. Here, the hypothesis 1 of the present study is proposed: participation in physical exercise among adolescents can effectively alleviate the symptoms of cell phone dependence and has a negative predictive effect on cell phone dependence.

Self-control is the ability of an individual to constrain, regulate, and sort out a series of behavioral, emotional, and cognitive activities in accordance with social standards or their own will [16]. Studies have shown that cell phone dependence in college students is significantly negatively correlated with self-control [7], and scholars believe that intervention groups experience significant improvement in self-control group counseling activities over the medium and long term [17], and cell phone dependence is negatively correlated with self-control, and individuals with cell phone dependence demonstrate lower self-control [18], suggesting that self-control has an influence on important variables of cell phone dependence. Based on the aforementioned studies, it can be concluded that scholars believe that self-control can significantly predict cell phone dependence, but there is a lack of examination and discussion of longitudinal cross-education background groups. In this regard, based on the examination of students in four longitudinal cohorts, this study proposes hypothesis 2: self-control in adolescents is negatively related to cell phone dependence and negatively predictive of cell phone dependence.

It is worth exploring in detail that self-control is both an important factor influencing physical activity participation in adolescent groups and an important intervention variable influencing cell phone dependence [19, 20]. Scholars have conducted in-depth discussions on positive psychological capital, exercise attitudes, exercise behaviors, and cell phone dependence among college students, and concluded that positive psychological capital in terms of good exercise attitudes and behaviors can have a significant negative impact on cell phone dependence [21]. Some scholars have conducted studies on the effects of physical exercise participation on related maladaptive behaviors (smoking, aggression, and disciplinary behavior) and the role of self-control within them without mentioning cell phone addiction maladaptive habits [22, 23]. In this regard, based on the above literature review and the articulation of research hypotheses 1 and 2, the present study proposes hypothesis 3: self-control plays a mediating role in the influence of adolescents’ physical activity participation on cell phone dependence.

On the basis of the aforementioned literature review and related research hypotheses, this study investigates the direct effects of physical activity on cell phone dependence and the internal mechanisms of self-control.

2 Participants and methodology

2.1 Research participants

In this study, Questionnaire Star, an e-survey platform (https://www.wjx.cn) was selected to edit the questionnaire and collect opinions of the respondents by means of WeChat, QQ, Weibo and other platforms, and the questionnaire was tested by snowball sampling method. Students aged 7–24 in Sichuan Province, PRC were selected to participate in this study. A total of 663 questionnaires were collected, with missing values and question values and other unqualified questionnaires removed, among which 649 questionnaires were valid, with an effective response rate of 97.8% and an overall validity of Cronbach coefficient of 0.93. There were 316 boys, 333 girls, with 203 from primary schools, 87 from middle schools, 359 from universities in terms of education background, and with 386 from urban areas, 165 from rural counties and 98 from rural areas in terms of birthplace. When completing the questionnaire, all were informed in detail about the specific use of the questionnaire for this study and related privacy treatment.

2.2 Instruments

2.2.1 Amount of physical exercise

The amount of physical exercise was collected by the Physical Activity Rating Scale compiled by Japanese psychologist Kimio Hashimoto and revised by Liang Deqing et.al from Wuhan Sports University [24]. The scale focuses on a comprehensive examination of three physical activity aspects: intensity, duration, and frequency of physical activity in the past week, with the amount of physical activity as a quantitative measure and physical activity = intensity x (time-1) x frequency.

The intensity, duration, and frequency of physical exercise in the past week were subjectively evaluated using the 5-point Likert scale, with 1 to 5 levels corresponding to 1 to 5 points, and the range of physical exercise scores is from 0 to 100 points. For rating of the physical exercise amount, the score range for low exercise participation group is≤19 points, the score range for medium exercise participation group is from 20 to 42 points, the score range for high exercise participation group is≥43 points. The test-retest reliability for the scale is 0.82 [14]. The consistency test coefficients and the test values of relevant parameters for the survey data in this study are shown in Table 1.

Table 1
Basic participation of adolescents in physical exercise (N = 649)

Item Level Physical exercise level (%) Total X² P

Low Medium High

Gender Male 117 (37.03) 102 (32.28) 97 (30.07) 316 (48.69) 63.75 0.000^

Female 215 (64.56) 87 (26.13) 31 (9.31) 333 (51.31)

Birthplace Urban 175 (45.34) 124 (32.12) 87 (22.54) 386 (59.48) 23.62 0.001^

County 5 (27.78) 9 (50.00) 4 (22.22) 18 (2.77)

Township 95 (64.63) 34 (23.13) 18 (12.24) 147 (22.65)

Rural 57 (58.16) 22 (22.45) 19 (19.39) 98 (15.10)

Education background Elementary 129 (63.55) 44 (21.67) 30 (14.78) 203 (31.28) 24.48 0.000^**

Secondary 50 (57.47) 21 (24.14) 16 (18.39) 87 (13.41)

University 153(42.62) 124(34.54) 82(22.84) 359(55.32)

Item	Level	Physical exercise level (%)	Total	X²	P
Gender	Male	117 (37.03)	102 (32.28)	97 (30.07)	316 (48.69)	63.75	0.000^**
	Female	215 (64.56)	87 (26.13)	31 (9.31)	333 (51.31)
Birthplace	Urban	175 (45.34)	124 (32.12)	87 (22.54)	386 (59.48)	23.62	0.001^**
	County	5 (27.78)	9 (50.00)	4 (22.22)	18 (2.77)
	Township	95 (64.63)	34 (23.13)	18 (12.24)	147 (22.65)
	Rural	57 (58.16)	22 (22.45)	19 (19.39)	98 (15.10)
Education background	Elementary	129 (63.55)	44 (21.67)	30 (14.78)	203 (31.28)	24.48	0.000^**
	Secondary	50 (57.47)	21 (24.14)	16 (18.39)	87 (13.41)
	University	153(42.62)	124(34.54)	82(22.84)	359(55.32)

Note: ^**P < 0.01 by the chi-square test.

2.2.2 Self-control scales

The Self-Control Scale (SCS), revised by Tan et al. [25] in 2008, is used to assess the self-control ability of the participants. The SCS mainly examines the cognitive and behavioral aspects of the participants’ self-control ability and is widely used by scholars to assess the self-control of secondary school students [26, 27]. The scale has 19 assessment items, mainly using the Likert 5-point scoring method, with 1 to 5 representing (completely non-conforming, non-conforming, uncertain, conforming, and very conforming), and higher values of the assessment items represent a higher degree of conformity. The scale consists of five factors: impulse control, resistance to temptation, healthy habits, concentration on work, and abstinence from entertainment. The consistency test coefficients, correlation parameter test values, and fit index indices for the self-control scale module of this study are shown in Table 1.

2.2.3 Cell phone dependence scale

The self-assessment questionnaire for adolescents’ mobile phone dependence compiled by Tao is used, which mainly consists of 3 dimensions such as withdrawal symptoms, physical and psychological influence and craving, with 13 items in total [28]. The Likert five-point scale is used, with 1 to 5 representing “Never, Rarely, Sometimes, Often, Always”, and a higher score represents a more prominent tendency to rely on mobile phones.

2.2.4 Measurement of demographic variables

It mainly involves the variables related to gender, birthplace, education background, parental education, and monthly household income of the participants.

2.3 Test of common method bias

As it was not convenient to conduct an on-site visitor questionnaire survey during the COVID-19 pandemic, a network survey was conducted, so the research questionnaire was tested by common method bias [29].

2.4 Parametric testing of exploratory factor correlations

To further understand the internal correlations, covariance, and reliability and validity between the three variables. The relevant parameters for each configuration are shown in Table 2. The first value of each non-standardized construct is set as the reference index, and the non-standardized coefficients of the variables to be tested for each construct are all positive, with Z value > 1.96. The variables of each construct are significant (P < 0.01). The coefficients of the variables in the standardized constructs are between 0.6 and 0.7, and the SMC coefficients are all > 0.36, which is a relatively good reference index and indicates that the confidence level of the items and the explanatory ability of the items in each construct are relatively good. The composite reliabilities (CR) of each construct are all > 0.7, and the average explanatory ability of the items in each construct is 0.545, 0.454 and 0.459 respectively, which indicates that each construct has good reliability and validity.

Table 2
Parameter test for each construct

Construct Item Parameter significance estimates Item reliability Composite reliability Convergent validity

Unstd S.E Z-value P Unstd S.E

Cell phone dependence Withdrawal symptoms 1.000 0.711 0.506 0.782 0.545

Physical and psychological influence 0.916 0.126 7.288 ^* 0.793 0.629

Craving 0.724 0.106 6.810 ^* 0.708 0.501

Self-control Abstinence from entertainment 1.000 0.721 0.520 0.806 0.454

Concentration on work 0.761 0.086 8.803 ^* 0.680 0.462

Resistance to temptation 0.893 0.087 10.260 ^* 0.678 0.460

Healthy habits 0.876 0.108 8.098 ^* 0.655 0.429

Impulse control 0.880 0.096 9.147 ^* 0.630 0.397

Physical exercise Duration 1.000 0.745 0.555 0.717 0.459

Frequency 0.692 0.107 6.446 ^* 0.636 0.404

Intensity 0.952 0.137 6.939 ^* 0.646 0.417

Construct	Item	Parameter significance estimates	Item reliability	Composite reliability	Convergent validity
Cell phone dependence	Withdrawal symptoms	1.000				0.711	0.506	0.782	0.545
	Physical and psychological influence	0.916	0.126	7.288	^***	0.793	0.629
	Craving	0.724	0.106	6.810	^***	0.708	0.501
Self-control	Abstinence from entertainment	1.000				0.721	0.520	0.806	0.454
	Concentration on work	0.761	0.086	8.803	^***	0.680	0.462
	Resistance to temptation	0.893	0.087	10.260	^***	0.678	0.460
	Healthy habits	0.876	0.108	8.098	^***	0.655	0.429
	Impulse control	0.880	0.096	9.147	^***	0.630	0.397
Physical exercise	Duration	1.000				0.745	0.555	0.717	0.459
	Frequency	0.692	0.107	6.446	^***	0.636	0.404
	Intensity	0.952	0.137	6.939	^***	0.646	0.417

Note: ^*p < 0.05, ^**p < 0.01, as follows.

3 Results

3.1 Basic comparison of adolescents’ participation in physical activity

To understand the situation of teenagers’ participation in sports activities, this study uses gender, student source, and school stage as the clusters to perform chi-square test. The survey results show that there are significant differences (P < 0.01) in the physical exercise levels of adolescents according to gender, birthplace and education background (see Table 2), as follows: senior male > senior female, senior urban > senior county > senior rural > senior township, senior university > senior elementary > senior secondary; junior female > junior male, junior township > junior rural > junior urban > junior county.

3.2 Correlation coefficient matrix between variables

After the non-standardized treatment, it is necessary to further understand the covariance between the internal aspects of each variable and other variables, to solve the phase relationship between the variables and the covariance matrix is processed. The results of the correlation analysis are shown in Table 3. The variables of physical exercise were negatively correlated with cell phone dependence (r coefficients all < 0, P < 0.01) and positively correlated with self-control variables (r coefficients all > 0, P < 0.01); the variables of self-control were negatively correlated with cell phone dependence (r coefficients all < 0, P < 0.01).

Table 3
Correlation coefficient matrix

M SD Intensity Frequency Duration AE CW RT IC HH WS PPI Craving

Intensity 3.013 1.021 1.000

Frequency 3.381 0.894 0.321^ 1.000

Duration 3.139 0.930 0.493^ 0.400^** 1.000

AE 3.210 1.108 0.129^* 0.109 –0.006 1.000

CW 3.126 1.049 0.141^* 0.125^* 0.048 0.442^ 1.000

RT 2.981 1.052 0.223^ 0.238^ 0.155^ 0.539^ 0.383^ 1.000

IC 3.458 1.116 0.069 0.292^ 0.032 0.435^ 0.321^ 0.410^ 1.000

HH 3.681 1.261 0.079 0.054 0.035 0.363^ 0.329^ 0.300^ 0.456^ 1.000

WS 2.945 1.049 –0.129^* –0.154^ –0.168^ –0.319^ –0.341^ –0.353^ –0.274^ –0.265^ 1.000

PPI 3.010 1.153 –0.201^ –0.164^ –0.152^ –0.278^ –0.234^ –0.253^ –0.245^ –0.289^ 0.412^ 1.000

Craving 2.645 1.063 0.025 –0.160^ –0.078 –0.206^ –0.140^* –0.200^ –0.305^ –0.276^ 0.374^ 0.301^** 1.000

M	SD	Intensity	Frequency	Duration	AE	CW	RT	IC	HH	WS	PPI	Craving
Intensity	3.013	1.021	1.000
Frequency	3.381	0.894	0.321^**	1.000
Duration	3.139	0.930	0.493^**	0.400^**	1.000
AE	3.210	1.108	0.129^*	0.109	–0.006	1.000
CW	3.126	1.049	0.141^*	0.125^*	0.048	0.442^**	1.000
RT	2.981	1.052	0.223^**	0.238^**	0.155^**	0.539^**	0.383^**	1.000
IC	3.458	1.116	0.069	0.292^**	0.032	0.435^**	0.321^**	0.410^**	1.000
HH	3.681	1.261	0.079	0.054	0.035	0.363^**	0.329^**	0.300^**	0.456^**	1.000
WS	2.945	1.049	–0.129^*	–0.154^**	–0.168^**	–0.319^**	–0.341^**	–0.353^**	–0.274^**	–0.265^**	1.000
PPI	3.010	1.153	–0.201^**	–0.164^**	–0.152^**	–0.278^**	–0.234^**	–0.253^**	–0.245^**	–0.289^**	0.412^**	1.000
Craving	2.645	1.063	0.025	–0.160^**	–0.078	–0.206^**	–0.140^*	–0.200^**	–0.305^**	–0.276^**	0.374^**	0.301^**	1.000

Note: ^*P < 0.05, ^**P < 0.01. AE: abstinence from entertainment, CW: concentration on work, RT: resistance to temptation, IC: impulse control, HH: Healthy habits, WS: withdrawal symptoms, PPI: physical and psychological influence.

3.3 Mediation model testing

The Bootstrap method is used to test the mediation effect to prove the degree of influence among the dependent variable, the independent variable and the mediation variable, and to verify the size of the path coefficient and the mediation. Self-control (M) between physical activity (X) and mobile phone dependence (Y) was tested according to the procedure of mediation model test (see Table 4). After controlling for variables such as gender, birthplace, and education background, the mediation model test was conducted and found that the p-values of the standardized coefficients were less than 0.01 and the effect values of each regression equation were between LLCI (The lower limit confidence interval) and ULCI (the upper limit confidence interval), indicating that self-control holds a partial mediating effect between the effects of physical exercise on cell phone dependence, with the mediating effect accounting for 0.364×–0.496/–0.1455 = 39.68%. The structural equation model was used to test the goodness of fit of the model for the self-control mediating effect, chi-square = 103.597, degrees of freedom = 41, x2/df = 2.527, p-value< 0.01, GFI (goodness-of-fit index) = 0.943, AGFI (adjusted goodness-of-fit index) = 0.908, CFI (comparative fit index) = 0.917, RMSEA (Root Mean Square Error of Approximation) = 0.070.

Table 4
Mediating effects of self-control between physical activity and cell phone dependence

Effect Path Effect value SE t p LLCI Lower ULCI Upper

Direct effect Physical exercise ⇒ cell phone dependence –0.314 0.053 –5.885 0.000 –0.419 –0.210

Indirect effect Physical exercise ⇒ self-control 0.479 0.070 6.863 0.000 0.342 0.616

Process Self-control ⇒ cell phone dependence –0.431 0.041 –10.620 0.000 –0.511 –0.352

Total effect Physical exercise ⇒ cell phone dependence –0.521 0.058 –8.970 0.000 –0.635 –0.407

Effect	Path	Effect value	SE	t	LLCI Lower	ULCI Upper
Direct effect	Physical exercise ⇒ cell phone dependence	–0.314	0.053	–5.885	–0.419	–0.210
Indirect effect	Physical exercise ⇒ self-control	0.479	0.070	6.863	0.342	0.616
Process	Self-control ⇒ cell phone dependence	–0.431	0.041	–10.620	–0.511	–0.352
Total effect	Physical exercise ⇒ cell phone dependence	–0.521	0.058	–8.970	–0.635	–0.407

4 Discussion

4.1 The impact of physical activity on adolescents’ cell phone dependence and self-control

The results of this study showed that the physical exercise level of male students is better than that of female students, the physical exercise level of urban students is better than that of students in counties, towns and rural areas, and that the physical exercise level of college students is better than that of students in secondary and primary schools. The physical exercise level of girls is less than that of boys, which is consistent with the results of the daily physical exercise≥60 minutes detection rate (16.8% of boys, 10.93% of girls). In addition, compared with male students, female students insist on exercise for a shorter period of time with a lower intensity, and there is a relative lack of exercise frequency in extracurricular time, indicating poor overall persistence and dependence [30]. Furthermore, there is a significant difference between urban and rural adolescents’ participation in physical exercise, and the relative backwardness of rural adolescent families in terms of economic income and awareness of the concept of physical exercise is the main reason for the inadequate provision of basic sports facilities in urban and rural areas, as well as the main reason for the distinction between urban and rural adolescents’ physical exercise levels [31]. Some scholars believe that college students have more leisure time and more ample activity space as well as participation in various sports clubs, resulting in a higher intensity of physical exercise in this particular group than in primary and secondary schools, which is consistent with the results of this study [32].

The results of the study found that the correlation coefficient matrix between physical exercise and cell phone dependence was significantly negatively correlated, which has confirmed that physical exercise can reduce the symptoms of cell phone dependence in the adolescent population, indicating hypothesis 1 is valid. This also validates the previous findings that physical exercise is negatively correlated with cell phone dependence, and college students with higher the level of physical exercise show lower the tendency of cell phone addiction [32, 33]. From the theoretical level, participation in sports as an exercise prescription for mobile phone addiction has been accepted by an increasing number of beneficiary groups, and has gradually formed an exercise intervention mechanism, laying a solid foundation for the development of the theory. Scholars have confirmed that regular exercise behaviors can reduce time spent on cell phone use for cell phone dependent college students, lower the scores of various factors of cell phone dependence, and have a positive effect on cardiorespiratory fitness, speed, endurance, flexibility and other physical qualities of cell phone dependent groups [34]. Furthermore, scholars have reached a consensus that the mechanism of exercise therapy for addiction-like interventions is the recovery and recuperation of nerve cells after exercise, which enhances resistance and adaptation to the outside world [35]. Thus, we can argue that physical exercise can be used as an effective measure and tool to prevent cell phone addiction.

Meanwhile, it can be seen that physical exercise is positively correlated with self-control, which has long been confirmed by relevant scholars. Some scholars have found that physical exercise and self-control are positively correlated in college groups, and physical exercise can change self-control, indirectly affecting aggressive behavior, cell phone addiction, psychological dependence symptoms etc., with self-control playing an mediating role between the two [36 –39]. The research related to physical exercise on self-control has long been applied to real life and scientific research, and this theoretical model has been popularized and applied by scholars, with secondary schools in general considering exercise interventions as a mechanism to achieve self-control enhancement and positively influence self-control [40]. This is also consistent with the findings of the present study that physical activity positively influences self-control ability. Concurrently, scholars concluded that self-control had a significant negative predictive effect on cell phone dependence, and cell phone dependence showed a significant negative correlation with self-control among middle school students, and cell phone dependence had a negative predictive effect on self-control [18, 41]. Therefore, we argue that self-control plays a mediating role between physical activity on cell phone addiction, again arguing that hypothesis 2 holds.

4.2 The mediating effect of self-control between physical activity and cell phone dependence

In this study, self-control was used as a mediating variable to explore the relationship between physical exercise and cell phone dependence, to demonstrate the extent and impact of the mediating effect, and it was concluded that self-control plays a partially mediating role between the two. It is shown that the process of physical exercise reducing cell phone dependence symptoms is complex. Exploring the mediating effect pathway, physical exercise can directly reduce the symptoms of cell phone dependence, and this effect is a medium- to long-term empirical study, which belongs to the category of exercise intervention [2]. Furthermore, with regard to the mediating path level, the influence of physical exercise on cell phone dependence is not only a direct process, there is also a parallel mediating utility, which is simply divided into two links: first, when the physical exercise level is high, the effectiveness of the mediating variables is also high; second, increased self-control ability affects the degree of cell phone dependence. This continuum of mediating effect links has corresponding sources of empirical analysis, laying a solid theoretical analysis. The first link, the explanation of the independent variable (physical exercise) on the control variable (self-control ability) in the mediating effect path is more in line with the self-control resource model, i.e., physical exercise enhances self-control ability [15]; the second link, the improvement of self-control ability reduces the symptoms of cell phone dependence, i.e., the significant negative predictive effect, has also been confirmed by a wide range of scholars [11]. Combining the research results and scholars’ previous theoretical results, empirical findings, etc., a more appropriate explanation can be given for the mediating effect of self-control ability that physical exercise as an intervention and means has some effect on cell phone dependence, but the role is not too obvious, while self-control ability as a special mediating effect, acting as a lever between the two, plays an important role in cell phone dependence. The greater the degree of self-control ability, the more significant effect on cell phone dependence was expressed. A summary postulate can be formed that self-control plays a significant role in the effect between physical activity on cell phone dependence, i.e., confirming hypothesis 3.

4.3 Research significance, limitations and suggestions for future research

Based on previous research, this study for the first time explores the influence of self-control ability between physical exercise and cell phone dependence, reveals the overall degree of influence and effect value of mediating variables, explains how physical exercise affects self-control ability and then influences the degree of cell phone dependence, and provides empirical material for a comprehensive interpretation of the mechanism of physical exercise intervention in cell phone dependence. First, the measurement of cell phone dependence via questionnaire in the current study is conducted from the psychological level, in addition, the similarity between network dependence and cell phone dependence in the questionnaire compiled is also a point of controversy among many scholars [11, 42]. Second, this study is based on a questionnaire survey, an individual subjective measurement, and lacks a longitudinal examination of empirical individual sources, which requires further exploration. Finally, excluding the influence of mediating variables on cell phone dependence, the explanatory power of the intervention of physical activity on cell phone dependence is also relatively limited, which may be related to other latent variables of social attributes on adolescents’ cell phone dependence, such as the influence of gender, age, family climate, and parents’ dependence on cell phone, all of which may be latent intervention variables [34]. Therefore, future studies should focus on exploring latent variables and longitudinal objective observation in order to avoid the systematic design flaws brought by the research process, and then better sort out the psychological process and related mechanisms of cell phone dependence in physical exercise, so as to lay a more solid theoretical foundation for sports intervention for cell phone addiction.

5 Conclusion

Participating in physical activity can effectively curb the symptoms of cell phone dependence and has a negative predictive effect on cell phone dependence. Adolescents’ self-control ability is negatively correlated with cell phone dependence, serving as an important variable that influences cell phone dependence. Physical exercise can improve adolescents’ self-control ability, thus curbing the symptoms of cell phone dependence. Therefore, adolescent groups should strengthen the awareness of physical exercise, increase the time and investment in physical exercise, and effectively improve their self-control ability, so as to comprehensively alleviate and eliminate the tendency psychology and symptoms of cell phone dependence.

Conflict of interest

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The study was supported by start-up funding for Ph.D. scientific research of Huaibei Normal University of China (No. 03106098)

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