The mediating effect of impulsivity between mindfulness and smartphone addiction

doi:10.21203/rs.3.rs-3240625/v1

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Research Article

The mediating effect of impulsivity between mindfulness and smartphone addiction

https://doi.org/10.21203/rs.3.rs-3240625/v1

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Objective

Problematic smartphone usage may have negative effects on various psychological traits: e.g., studies have indicated that it is accompanied by low levels of mindfulness and high impulsivity. This study explored the psychological mechanism of problematic smartphone usage via the relationship between addictive smartphone use patterns, mindfulness and impulsivity.

Methods

Ninety participants were evaluated with the Smartphone Addiction Proneness Scale and classified into the problematic smartphone usage group (n=42; 24 women; mean age: 27.6±7.2 years) or normal usage group (n=48; 22 women; mean age: 30.1±5.7 years). All participants completed self-report questionnaires evaluating their affective symptoms, impulsivity and mindfulness and computerized comprehensive attention tests that assessed selective, sustained and divided attention. We compared the variables between the groups and explored the relationship between mindfulness, impulsivity and addictive smartphone use patterns through mediation analysis.

Results

The problematic smartphone usage group showed higher impulsivity and lower mindfulness than the normal usage group. There were no significant group differences in attention test performance. The level of addictive patterns was significantly correlated with impulsivity and mindfulness levels but not attention test performance. In the mediation analysis, acting with awareness (a facet of mindfulness) reduced levels of addictive smartphone use patterns through attentional impulsivity.

Conclusion

Acting without sufficient awareness could influence addictive smartphone use patterns by mediating attentional impulsivity. This supports existing evidence that the executive control deficits present in high impulsivity contribute to problematic smartphone use. Cognitive behavioural therapy for mindfulness could strengthen executive control over smartphone usage by promoting awareness.

attention

awareness

problematic smartphone use

mindfulness

impulsivity

Smartphone use has spread worldwide since the advent of the iPhone in 2007, and the current penetration rate of the smartphone has reached more than half of the world’s population [1]. However, the number of people exhibiting problematic smartphone usage—a maladaptive use pattern that may include withdrawal symptoms, resulting in disrupted and dysfunctional daily life [2]—has also increased over time, regardless of the country [3]. Many studies have indicated that problematic smartphone usage could be conceptualized as a type of behavioural addiction [4] with negative mental effects, such as depression, anxiety and stress [5], and physical effects, such as musculoskeletal pain and sleep problems [6].

Accordingly, studies are increasingly exploring the antecedent factors of problematic smartphone usage to prevent and interrupt it. Mindfulness, in particular, is a notable protective factor in behavioural addiction [7]. Mindfulness is ‘a process of regulating attention to bring about non-judgemental awareness of current experiences and to connect with one’s own experiences in a curious and receptive manner’ [8]. As mindfulness increases, so does awareness of the sensations, thoughts and emotions associated with cravings, which reduces addictive behaviour [9]. Actually, mindfulness has been shown to protect against addictive behaviours, such as alcohol abuse [10], smoking [11] and workaholism [12]. Several studies have also suggested that mindfulness may act as a protective factor in problematic smartphone usage [13, 14].

The mechanism by which mindfulness-based interventions mitigate addictive patterns of smartphone use has not been sufficiently identified. Identifying the antecedent factors affected by mindfulness could aid the development of mindfulness-based interventions targeting problematic smartphone usage. First, mindfulness appears to affect impulsivity, the behavioural tendency to respond quickly to certain stimuli without considering negative consequences [15]. High impulsivity reinforces addictive behaviours by making us more susceptible to immediate pleasure and insensitive to the negative consequences that follow [16]. High impulsivity appears to be antecedent to behavioural addictions, such as gambling addiction [17], Internet addiction [18] and problematic smartphone usage [19–21]. Furthermore, studies have found that mindfulness-based interventions can reduce impulsivity [22]. Thus, we predicted that low levels of mindfulness would affect problematic smartphone usage, mediated by high impulsivity.

In addition to the mediating effect of impulsivity, this study also aimed to explore the role of attention. Mindfulness can be conceptualized as a kind of ability to control attention, and several studies agree that mindfulness training improves attentional control [23]. Attentional control is related not only to problematic smartphone usage, as seen in the relationship between attention deficit hyperactivity disorder (ADHD) and smartphone overuse [24], but also to impulsivity [25, 26]. This existing evidence suggests that attentional control – and, therefore, impulsivity – may mediate the relationship between mindfulness and problematic smartphone usage.

In this study, we compared mindfulness, impulsivity and attentional control between subjects with problematic smartphone usage and those with normal usage. Attentional control and behavioural impulsivity were evaluated through the continuous performance task, and mindfulness and general impulsivity were evaluated through self-report questionnaires. Through this study, we explored whether the mindfulness levels of problematic smartphone users are lower than those of healthy users. In addition, we aimed to identify the mediating role of attentional control and impulsivity in the relationship between mindfulness and problematic smartphone usage, thus exploring the mechanism by which mindfulness-based interventions can improve problematic smartphone usage.

Participants

Participants aged 19 or older were recruited from May 2020 to September 2022 through online boards. We evaluated each subject’s psychiatric history through Structured Clinical Interviews for DSM-IV [27] and measured their full-scale intelligence quotients (FSIQs) through a short version of the Korean Wechsler Adult Intelligence Scale [28]. Four exclusion criteria were applied based on these assessments: 1) major psychiatric illness (e.g. major depressive disorder, schizophrenia, bipolar disorder, etc.), 2) current psychiatric medication use, 3) IQ less than 80 and 4) history of a medical/neurological disease that could affect cognitive function.

As a result, we included 104 participants. We assessed the degree of addictive smartphone use patterns using the Smartphone Addiction Proneness Scale (SAPS) [29], which has four sub-factors: disturbance of adaptive functions, virtual life orientation, withdrawal and tolerance. High-risk users were those who met the following criteria: (a) total SAPS score of ≥ 44 or (b) disturbance of adaptive functions, withdrawal and tolerance subscale scores of ≥ 15, ≥13 and ≥ 13, respectively. At-risk users were those who met the following criteria: (a) total SAPS total score of 40–43 or (b) disturbance of adaptive functions subscale score of ≥ 14. We classified the participants into a problematic smartphone usage group (including at-risk and high-risk groups) or a normal usage group. We sorted 42 participants (18 men and 24 women, mean age: 27.6 ± 7.2 years) into the problematic smartphone usage group and 48 participants (26 men and 22 women, mean age: 30.1 ± 5.7 years) into the normal usage group.

Measures

Depression and anxiety were evaluated through the Beck Depression Inventory (BDI) [30] and Beck Anxiety Inventory (BAI), respectively [31]. Mindfulness was evaluated using the Five Fact Mindfulness Questionnaire–Short Form (FFMQ-SF) [32] and the Korean version of the Mindful Attention Awareness Scale (MAAS) [33]. Impulsivity was assessed using the Korean version of the Barratt Impulsiveness Scale (BIS) [34], and alcohol use problems were evaluated using the Korean version of the Alcohol Use Disorders Identification Test (AUDIT) [35]. We also assessed the participants’ levels of Internet addiction using the Korean version of the Internet Addiction Test (IAT).

The participants’ selective, sustained and divided attention was assessed through a computerized comprehensive attention test (CAT) [36], validated for adults by Huh et al. [37], which evaluates levels of various attention abilities and identifies attention deficits. When assessing selective attention, the pictures appear in the centre of a computer screen and the participants must press the button as quickly as they see the given targets (e.g., shape ‘O’). For the sustained attention test, participants press the button whenever the pictures appear, but not when the picture shape is ‘X’. Finally, in the divided attention test, a picture and sound are presented simultaneously. The participants must remember the previous picture and sound and press the button if the current picture or sound is the same as the one before. We used two indicators for each test: commission errors, the number of incorrect responses to stimuli other than the target, and omission errors, the number of missed responses to the target stimuli. Commission errors are usually measures of impulsivity and omission errors measures of inattention [38].

Statistical analysis

We used the Statistical Package for the Social Sciences (SPSS) version 22.0 (SPSS Inc., Chicago, IL, USA) and a significance level of p < 0.05 (two-tailed) for all statistical analyses. Independent t-tests and χ² tests were performed to compare sociodemographic variables, clinical variables and the attention abilities of the two groups. In the group comparison of the BIS and FFMQ-SF subscales, we applied the Bonferroni correction for multiple comparisons.

Before the mediation analysis, we conducted a correlation analysis between SAPS, BIS, CAT and FFMQ-SF for all subjects. We then used model 4 of PROCESS Macro version 4.2 [39] to explore the mediating effect of the BIS subscales in the relationship between the subscales of FFMQ-SF and SAPS. The mediation model was tested at a 95% confidence interval (CI) with 5,000 bootstrap samples. We performed several mediation analyses in which each FFMQ-SF subscale has an impact on SAPS through BIS subscales; for these analyses, the other subscales of FFMQ were used as covariates [39].

Sociodemographic and clinical differences between groups

The problematic smartphone usage group and the normal usage group did not show differences in age, gender or FSIQ (Table 1), but the SAPS score in the normal usage group was significantly lower than that in the problematic smartphone usage group (t = -11.631, p < 0.001). The groups showed no significant differences in smoking or drinking habits. The problematic smartphone usage group showed significantly higher BDI, BAI and IAT scores than the normal usage group (BDI: t = 4.572, p < 0.001; BAI: t = 4.305, p < 0.001; IAT: t = 9.463, p < 0.001), but the AUDIT scores were not significantly different.

Table 1

Comparison of sociodemographic and clinical valuables between groups
		Normal usage group (n = 48)	Problematic smartphone usage group (n = 42)	t/\({\varvec{x}}^{2}\)	p
Age		30.1 ± 5.7	27.6 ± 7.2	1.811	.074
Gender (n, %)	Male	26 (54.2)	18 (42.9)	1.147	.284
Gender (n, %)	Female	22 (45.8)	24 (57.1)	1.147	.284
FSIQ		111.6 ± 12.2	110.7 ± 10.2	0.390	.698
SAPS		29.8 ± 5.8	43.7 ± 5.5	-11.631	.000
Smoking (n, %)		7 (14.6)	4 (9.5)	0.534	.465
Years smoking		9.9 ± 7.3	6.8 ± 5.1	0.751	.472
Cigarettes per day		8.2 ± 4.4	6.8 ± 4.9	0.481	.642
Drinking (n, %)		34 (70.8)	30 (71.4)	0.004	.950
Prevalence of drinking		1.0 ± 0.8	0.9 ± 0.7	0.511	.611
Alcohol units per drinking		1.2 ± 1.2	0.8 ± 0.6	1.674	.100
BDI		4.3 ± 3.8	8.1 ± 4.0	4.572	.000
BAI		3.5 ± 4.6	8.4 ± 5.9	4.305	.000
IAT		26.3 ± 10.9	51.3 ± 14.1	9.463	.000
AUDIT		6.3 ± 5.6	6.0 ± 6.2	-0.220	.826
Note: AUDIT = Alcohol Use Disorders Identification Test; BAI = Beck Anxiety Inventory; BDI = Beck Depression Inventory; FSIQ = Full-Scale Intelligence Quotient; SAPS = Smartphone Addiction Proneness Scale

Differences in impulsivity, mindfulness and attention abilities between groups

The problematic smartphone usage group appeared to have significantly more impulsivity than the normal usage group (t = 4.349, p < 0.001; Table 2), showing higher scores on all BIS subscales (attentional impulsivity: t = 3.707, p < 0.001; motor impulsivity: t = 2.558, p = 0.012; non-planning impulsivity: t = 3.493, p = 0.001).

Table 2

Comparison of impulsivity and mindfulness between groups
	Normal usage group (n = 48)	Problematic smartphone usage group (n = 42)	t	p
BIS	55.6 ± 9.3	64.4 ± 9.9	4.349	.000
Attentional	14.9 ± 3.2	18.0 ± 4.6	3.707	.000
Motor	23.7 ± 5.2	26.3 ± 4.6	2.558	.012
Non-planning	17.0 ± 3.6	20.0 ± 4.5	3.493	.001
FFMQ-SF	87.8 ± 9.7	78.8 ± 9.9	-4.305	.000
Observing	13.3 ± 2.7	13.1 ± 3.4	-0.203	.840
Describing	19.7 ± 2.9	17.8 ± 3.8	-2.637	.010
Acting with awareness	20.2 ± 2.5	17.5 ± 3.8	-3.952	.000
Non-judgement	17.0 ± 4.1	14.5 ± 4.5	-2.671	.009
Non-reactivity	17.6 ± 3.1	15.8 ± 3.8	-2.383	.019
MAAS	74.1 ± 6.9	64.5 ± 11.2	-4.826	.000
Note: BIS = Barratt Impulsiveness Scale; FFMQ-SF = Five Facet Mindfulness Questionnaire–Short Form; MAAS = Mindful Attention Awareness Scale

The problematic smartphone usage group also showed lower scores on both measures of mindfulness (FFMQ-SF: t = -4.305, p < 0.001; MAAS: t = 4.826, p < 0.001). After the Bonferroni correction, the problematic smartphone usage group showed significantly lower scores on the acting with awareness and non-judgement subscales of FFMQ-SF (acting with awareness: t = -3.952, p < 0.001; non-judgement: t = -2.671, p = 0.009).

There was no significant difference in CAT indexes between the two groups (Table 3). Only the difference in selective attention omission errors was close to being statistically significant (t = 1.910, p = 0.063).

Table 3

**Group differences in the indexes of comprehensive attention test**
	Normal usage group (n = 48)	Problematic smartphone usage group (n = 42)	t	p
Selective attention
Omission error	0.0 ± 0.1	0.3 ± 1.1	1.910	.063
Commission error	0.5 ± 0.9	0.7 ± 1.3	0.796	.428
Sustained attention
Omission error	1.3 ± 2.7	0.7 ± 1.2	-1.234	.222
Commission error	4.7 ± 4.8	5.8 ± 6.3	0.961	.339
Divided attention
Omission error	3.0 ± 3.1	3.9 ± 3.6	1.363	.176
Commission error	2.7 ± 1.4	2.8 ± 1.6	0.105	.916

Correlation between clinical variables

The SAPS and BIS scores, including the subscales, were all positively correlated (total BIS: r = 0.477, p < 0.001; attentional impulsivity: r = 0.453, p < 0.001; motor impulsivity: r = 0.322, p = 0.002; non-planning impulsivity: r = 0.340, p < 0.001; Table 4). The correlations between the SAPS and total mindfulness scales were significantly negative (FFMQ-SF: r = -0.428, p < 0.001; MAAS: r = -0.481, p < 0.001). Except for observing, all other FFMQ-SF subscales showed significant correlations with the SAPS (description: r = -0.248, p = 0.019; acting with awareness: r = -0.448, p < 0.001; non-judgement: r = -0.233, p = 0.027; non-reactivity: r = -0.329, p = 0.002). Furthermore, the total BIS and mindfulness scores were all negatively correlated (FFMQ: r = -0.498, p < 0.001; MAAS: r = -0.626, p < 0.001). There were no correlations between the clinical variables and the CAT indexes.

Table 4

Correlation matrix
	1	2	2 − 1	2–2	2–3	3	3 − 1	3 − 2	3–3	3–4	3–5	4	5 − 1	5 − 2	5 − 3	5 − 4	5–5	5–6
1. SAPS	1
2. BIS	\({.477}^{***}\)	1
2 − 1. Attentional	\({.453}^{***}\)	\({.712}^{***}\)	1
2–2. Motor	\({.322}^{**}\)	\({.790}^{***}\)	\({.279}^{**}\)	1
2–3. Non-planning	\({.340}^{**}\)	\({.810}^{***}\)	\({.430}^{***}\)	\({.471}^{***}\)	1
3. FFMQ-SF	-\({.428}^{***}\)	-\({.498}^{***}\)	\({.347}^{**}\)	-\({.469}^{***}\)	-\({.322}^{**}\)	1
3 − 1. Observing	.006	− .137	.129	-\({.341}^{**}\)	− .056	\({.370}^{***}\)	1
3 − 2. Describing	-\({.248}^{*}\)	-\({.387}^{***}\)	− .133	-\({.367}^{***}\)	-\({.379}^{***}\)	\({.703}^{***}\)	.160	1
3–3. Acting with awareness	-\({.448}^{***}\)	-\({.571}^{***}\)	-\({.489}^{***}\)	-\({.315}^{**}\)	-\({.544}^{***}\)	\({.585}^{***}\)	− .086	\({.484}^{***}\)	1
3–4. Non-judgement	-\({.233}^{*}\)	− .081	-\({.236}^{*}\)	− .062	.108	\({.603}^{***}\)	.015	.144	.123	1
3–5. Non-reactivity	-\({.329}^{**}\)	-\({.355}^{**}\)	-\({.258}^{*}\)	-\({.383}^{***}\)	− .160	\({.699}^{***}\)	.169	\({.361}^{***}\)	\({.243}^{*}\)	\({.302}^{**}\)	1
4. MAAS	-\({.481}^{***}\)	-\({.626}^{***}\)	-\({.604}^{***}\)	-\({.329}^{**}\)	\({.548}^{***}\)	\({.573}^{***}\)	− .123	\({.430}^{***}\)	\({.863}^{***}\)	\({.220}^{*}\)	\({.305}^{**}\)	1
5. CAT
5 − 1. Selective attention - Omission error	.152	.021	.030	.010	.009	.079	.026	.041	− .001	-\({.226}^{*}\)	− .019	− .055	1
5 − 2. Selective attention - Commission error	.031	.080	.106	.044	.039	.074	.102	.047	− .052	.005	.035	− .012	\({.276}^{**}\)	1
5 − 3. Sustained attention - Omission error	− .174	.084	.154	.033	.015	.010	.063	− .045	− .088	.042	.051	− .041	− .056	.204	1
5 − 4. Sustained attention - Commission error	.050	.184	.201	.058	.185	− .065	.093	− .042	− .110	− .032	− .085	− .178	.013	\({.594}^{***}\)	.147	1
5–5. Divided attention - Omission error	.178	− .013	.090	− .087	− .017	.056	.095	.091	− .048	.091	− .066	− .031	.107	− .126	− .025	.022	1
5–6. Divided attention - Commission error	.038	− .018	.042	− .003	− .081	.070	− .019	.199	.091	.044	− .106	.093	.061	\({.241}^{*}\)	.018	\({.387}^{***}\)	.198	1
Note: CAT = Comprehensive Attention Test; SAPS = Smartphone Addiction Proneness Scale; BIS = Barratt Impulsiveness Scale; FFMQ = Five Facet Mindfulness Questionnaire; MAAS = Mindful Attention Awareness Scale

The mediating effect of impulsivity between mindfulness and smartphone addiction

The mediation analysis identified only the acting with awareness subscale of FFMQ-SF as a significant predictor of SAPS scores (total effect β = 1.039, SE = 0.283, 95% CI [-1.604; 0.475]; Table 5). We found a significant indirect effect of the impact of acting with awareness on SAPS scores through attentional impulsivity (β = -0.287, SE = 0.150, 95% CI [-0.609; 0.017]); however, the direct effect of acting with awareness on SAPS scores was not significant. Taken together, attentional impulsivity completely mediated the relationship between acting with awareness and the SAPS score.

Table 5

The mediating effect of BIS subscales between FFMQ-SF subscales and SAPS
Independent variable	Dependent variable	Total effect	Direct effect	Mediators	Indirect effect	Confidence interval
Independent variable	Dependent variable	Total effect	Direct effect	Mediators	Indirect effect	Lower bound	Upper bound
Observing	SAPS	.006 (p = .983)	.063 (p = .834)	Attentional impulsivity	.056	− .081	.264
				Motor impulsivity	− .098	− .333	.134
				Non-planning impulsivity	− .015	− .123	.097
Describing		.101 (p = .732)	.061 (p = .840)	Attentional impulsivity	.103	− .041	.276
				Motor impulsivity	− .037	− .230	.054
				Non-planning impulsivity	− .026	− .166	.085
Acting with awareness		-1.039 (p = .000)	− .598 (p = .073)	Attentional impulsivity	− .287	− .609	− .017
				Motor impulsivity	− .065	− .250	.093
				Non-planning impulsivity	− .089	− .440	.230
Non-judgement		− .255 (p = .203)	− .232 (p = .263)	Attentional impulsivity	− .066	− .203	.022
				Motor impulsivity	.015	− .048	.108
				Non-planning impulsivity	.028	− .074	.150
Non-reactivity		− .524 (p = .051)	− .355 (p = .193)	Attentional impulsivity	− .092	− .307	.020
				Motor impulsivity	− .070	− .280	.083
				Non-planning impulsivity	− .006	− .105	.072
Note: BIS = Barratt Impulsiveness Scale; FFMQ-SF = Five Facet Mindfulness Questionnaire–Short Form; SAPS = Smartphone Addiction Proneness Scale.

In this study, we compared several psychological characteristics of problematic smartphone users and normal users. The problematic smartphone usage group had higher scores for negative emotions, such as depression and anxiety, than the normal usage group. These findings are consistent with previous studies that reported a close association between problematic smartphone usage and emotional problems [40, 41]. The comparison of our subjects also suggested that problematic smartphone users had lower levels of mindfulness than the normal usage group, consistent with previous studies showing low levels of mindfulness in addictive diseases [42]. In this study, problematic smartphone users showed significant differences from the normal usage group in awareness and non-judgement among several sub-factors of mindfulness. In the context of mindfulness, awareness is the ability to notice what thoughts, feelings and sensations are currently going on inside oneself [43]. Loss of control over addictive behaviour is closely related to decreased awareness of interoceptive signals derived from addictive behaviour [44]. Our results suggest that a tendency to act without sufficient awareness may lead to a loss of control over excessive smartphone usage. On the other hand, a non-judgemental attitude has been negatively correlated with affective symptoms, such as depression and anxiety [45]. Thus, we speculate that low levels of non-judgement in problematic smartphone users may be associated with their high tendency toward emotional difficulties.

We also compared impulsiveness assessed through self-reports (BIS) and attentional capacity assessed through CATs between problematic and normal smartphone users. Problematic smartphone users reported a higher level of impulsivity than normal users, which is consistent with previous studies of impulsiveness in problematic smartphone users [46]. On the other hand, the CAT results showed no difference between the groups’ omission and commission errors of selective, sustained and divided attention. Despite the high correlation between ADHD and problematic smartphone use [47], a previous study reported that the adverse effects of smartphone use on attention are not remarkable in the general population [48]. A recent study also reported no impairment in the problematic smartphone use of cognitive tests evaluating executive function [49]. Another study reported that problematic smartphone users performed poorly on cognitive tests that assessed attentional control with interfering stimuli [50]. We did not conduct the Flanker test, which measures one’s ability to respond to target stimuli while ignoring interfering stimuli among the CAT items. Thus, although our study suggests that abnormalities in selective, sustained and divided attention are not prominent among problematic smartphone users, attentional control in the presence of distractions in this group requires further investigation.

Our CAT results appear to conflict somewhat with existing research: commission error, an inappropriate response to a nontarget, has been proposed as an indicator of impulsivity in cognitive tasks [51], but we found no correlation between CAT indicators and BIS scores. However, previous studies that measured impulsivity often report conflicting results between self-reports and cognitive tasks [52–54]. Impulsivity can be conceptualized and measured in many ways, and different cognitive tasks and self-report questionnaires measure different aspects of impulsivity. For example, three-dimensional constructs of impulsivity, including impulsive choice, behaviour and personality traits, have been proposed [55]. A cognitive task usually measures impulsive behaviour, whereas self-report lists measure impulsive personality traits. Our results suggest that although impulsive behaviour is not prominent in problematic smartphone users, impulsive personality traits are. These results are consistent with prior evidence that impulsive personality traits are linked to vulnerability to addictive disorders [56].

In this study, we investigated the relationship between mindfulness, impulsivity and addictive smartphone usage. Evidence has shown that impulsivity is associated with mindfulness [57, 58] and addictive behaviour [59, 60]. Consistent with previous studies, BIS scores showed significant correlations with FFMQ-SF, MAAS and SAPS scores in this study’s correlation analysis. We further explored whether impulsivity plays a mediating role between mindfulness and addictive smartphone usage and found that the acting with awareness facet of mindfulness affects addictive smartphone usage through attentional impulsivity, the impulsiveness revealed during tasks requiring attentional control [61]. As attentional control is a major domain of executive function [62], attentional impulsivity refers to impulsiveness during executive functioning. Disease models of addictive behaviour have identified diminished executive functioning as a major pathophysiological cause of loss of control over addictive behaviour [63]. Therefore, we speculate that diminished executive functioning reflected by high attentional impulsivity is linked to addictive patterns of smartphone use. Acting with awareness refers to the ability to pay attention to activities in the present moment [32, 64]. A sufficient level of awareness is a prerequisite for self-monitoring and self-regulation, which are key elements in performing higher cognitive functions, such as executive function [65]. Our mediation analysis results imply that diminished executive functioning, which is reflected in high attentional impulsivity, mediates the relationship between low levels of awareness and addictive smartphone usage. These findings are in line with a previous study showing that mindfulness acts as a protective factor in problematic smartphone use, but high impulsivity can interfere with it [13]. In addition, previous studies on mindfulness have revealed that mindfulness meditation and mindfulness-based psychological interventions can strengthen executive control by improving awareness [66]. Therefore, our current findings support that mindfulness-based interventions can improve addictive smartphone use patterns [14].

The limitations of this study are as follows. First, we did not evaluate objective smartphone usage patterns in the real world, only through the self-reported questionnaire scale. Given the diversity of smartphone-based online activities, collecting objective data on time consumption and behavioural patterns of online activities through smartphones would be helpful. Second, the measurement tools used to evaluate the main variables in this study were limited. Mindfulness, impulsivity and attention are all composed of various psychometric aspects, so a comprehensive evaluation is needed. Mindfulness can be defined and evaluated not only as a trait factor, as in this study, but also as a state factor during mindful attention [67]. Our study did not include impulsive choice tasks, which can be used to evaluate a person’s tendency to choose smaller, immediate rewards over larger, delayed rewards [21]. As previously mentioned, this study also did not include the Flanker test, which evaluates the capacity to maintain attention despite a distractor. Third, the number of subjects in this study was not large enough to sufficiently explore the relationship between variables. For example, structural equation modelling, which has the advantage of providing a flexible framework for analysing complex relationships between multiple variables, usually requires 200 or more subjects [68]. Fourth, this study’s cross-sectional design limited our ability to evaluate the causal relationship between mindfulness and problematic smartphone usage. A longitudinal follow-up study is needed to explore changes in addictive smartphone usage according to changes in mindfulness levels.

In conclusion, this study evaluated the levels of mindfulness, impulsiveness and attentional control of problematic smartphone users and explored the relationships among the variables. Problematic smartphone users had a lower level of mindfulness than healthy users, and the difference was particularly noticeable in the acting with awareness and non-judgement facets of mindfulness. The problematic smartphone usage group had higher impulsivity than the normal usage group, but there was no difference in their performance on-attention tasks. In particular, our results suggest that a low level of awareness has a significant effect on addictive patterns of smartphone use and that diminished executive control, reflected by high attentional impulsivity, mediates the relationship between awareness and addictive smartphone usage. The results of this study imply that mindfulness-based interventions can be an effective approach to problematic smartphone usage.

Acknowledgements

Not applicable

Author contributions

DL and Y-CJ conceived and designed the study. GS recruited participants. MK and DL analyzed data and drafted the manuscript. M-JJ and Y-CJ provided critical revision of the manuscript and important intellectual content. All authors had full access to all data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and critically reviewed and approved the final version of this manuscript for publication.

Funding

This research was supported by the National Research Foundation of Korea, funded by the National Ministry of Science and ICT of Korea (No.2020R1C1C1011171).

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The protocols used in this study were approved by the Institutional Review Board at Yongin Severance Hospital, Yonsei University, South Korea, and all subjects were provided detailed explanations of the study and signed an informed consent form before participating. This study was conducted in accordance to the relevant guidelines and regulations.

Consent for publication

The results presented in this manuscript have not been published elsewhere, nor are they under consideration (from any of the authors) by another publisher.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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The mediating effect of impulsivity between mindfulness and smartphone addiction

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Abstract

INTRODUCTION

METHODS

Participants

Measures

Statistical analysis

RESULTS

Sociodemographic and clinical differences between groups

Differences in impulsivity, mindfulness and attention abilities between groups

Correlation between clinical variables

The mediating effect of impulsivity between mindfulness and smartphone addiction

DISCUSSION

Declarations

References

Additional Declarations

Status:

Version 1