Background

The number of people using the internet has surged in the last few years and the internet has become a fundamental part of daily life for many of us. The proportion of the global population using the internet increased from 17% in 2005 to 53.6% in 2019, with more than half of the internet users living in Asia [1]. While the internet penetration is high in developed countries (86.6%), compared to developing (47.0%) and the least developed countries (19.1%), a steady increase can be observed in low-income countries like Nepal in recent years. A recent survey in Nepal reported 51.1% of the households had access to the internet, with 44.2% of the population using it at least once a week [2]. In Kathmandu valley, about 70% of the households had access to the internet [3].

Adolescents and young adults are more likely to use the internet for a wide range of activities such as information search, social networking, communicating, music playing, emailing, playing games and fulfilling their social needs [4]. Increased internet availability with widespread devices such as mobile phones and tablets provide adolescents access to and use of the internet during daytime as well as bedtime. The accessibility of such facilities in developing countries has put adolescents at risk of internet addiction [5–7]. Internet addiction among adolescents have been reported to be associated with poor outcomes including poor mental [8, 9] and physical [10, 11] health, substance abuse [12], academic difficulties [13], social isolation [14], self-injurious behavior and suicidal tendencies [15–17] and low self-esteem [9, 18].

Internet addiction is also reported to be associated with poor sleep quality and sleep disturbances [19–23]. Adolescents who used the internet go to bed later in the night, needed more time to fall asleep and had an increased number of awakenings in the night [24]. 16.5% of Nepalese undergraduate students [25] and 23.8% of Indian medical students [26] internet users had poor sleep quality which led to depressive symptoms.

Although it is a growing concern, data has been limited in Nepal. Various researches have been conducted on internet addiction, and sleep quality and mental health problems in different countries. However, very less evidence is generated in relation to internet addiction, poor sleep quality and related areas among adolescent students in Nepal. The study will help to explore evidence on sleep quality, internet addiction, and its contributing factors. The purpose of this study was to assess the prevalence of sleep quality on the internet using adolescents in a peri-urban setting of Nepal.

Method

Results

Table 1 provides the socio-demographic characteristics of the study sample (N = 390). The mean age was 15.0 (SD = 1.0) years and more than half were female (55.4%). In terms of ethnic backgrounds, 47.2% of the participants belonged to the Indigenous ethnicity. Nearly half of the participants (47.4%) identified their family belonging to the upper-middle-income class. It was found that the parents of participants mostly completed bachelor’s level education. Majority of the participant’s main source of family income was business (43.8%). Most participants had access to at least a smartphone (67.4%) and/or laptop (46.2%). A higher proportion of participants reported better self-perceived relationships with parents and friends compared to relatives.

Table 1

Socio-demographic characteristics of the adolescent participants.

Socio-demographic characteristics		Frequency	Percentage
Age	≤15 years	287	73.6
Mean±SD (range): 15.0±1.0 (13–20) years	>15 years	103	26.4
Sex	Male	216	55.4
	Female	174	44.6
Religion	Hindu	323	82.8
	Other	67	17.2
Ethnicity	Indigenous	184	47.2
	Brahmin	90	23.1
	Chhetri	89	22.8
	Dalit	23	5.9
	Others	4	1.0
Self-reported family income class	Upper middle class	185	47.4
	Middle class	163	41.8
	Upper class	42	10.8
Parent’s education (any parent)	Bachelor’s and higher	110	28.2
	Secondary	109	27.9
	Primary	76	19.5
	Higher secondary	69	17.7
	Illiterate	26	6.7
Main source of household income	Business	171	43.8
	Service	114	29.2
	Agriculture	69	17.7
	Labour	31	7.9
	Others	5	1.3
Devices for internet use (Multiple responses possible)	Smartphone	263	67.4
	Laptop	180	46.2
	Tablet	90	23.1
	Other	21	5.4
Relation with parents	Good	361	92.6
	Neutral	29	7.4
Relation with friends	Good	307	78.7
	Neutral	74	19
	Bad	9	2.3
Relation with relatives	Good	199	51.1
	Neutral	176	45.1
	Bad	15	3.8

The mean Internet Addiction Test score was 12.1 (SD = 6.5). Around one-fifth (21.5%) of the participants had borderline internet addiction and 13.3% had possible internet addiction. The mean Pittsburgh Sleep Quality Index score was 4.6 (SD = 2.3), with 31.0% of the participants falling in the poor sleep quality category (Table 2).

Table 2

Prevalence of Internet addictions and sleep quality among adolescent participants.

Variables	Frequency	Percentage
Internet Addiction test, mean±SD	12.1±6.5
Internet addiction category
No internet addiction	254	65.2
Borderline internet addiction	84	21.5
Possible internet addiction	52	13.3
Likely internet addiction	0	0.0
Pittsburgh Sleep Quality Index, mean±SD	4.6±2.3
Sleep quality category
Good sleep quality	269	69.0
Poor sleep quality	121	31.0

In bivariate and multivariable analyses (Table 3), sleep quality was significantly associated with internet addiction, age, and ethnicity. Adolescents with borderline and possible internet addiction were more likely to experience poor sleep quality compared to no internet addiction. Higher age group participants were found to be associated with poor sleep quality. In terms of ethnicity, the participants from Indigenous and Chhetri caste groups were more probable to experience poor sleep quality in comparison to other groups.

Table 3

Bivariate and multivariable analysis of socio-demographic characteristics, internet addictions and poor sleep quality among adolescent participants.

Variables	COR (95% CI)	P-value	AOR (95% CI)	P-value
Internet addiction
No IA	Ref.		Ref.
Borderline IA	1.85 (1.09–3.13)	0.022*	1.790 (1.034–3.096)	0.037*
Possible IA	3.98 (2.15–7.40)	<0.001*	3.789 (1.984–7.237)	0.000*
Age
≤15 years	Ref.		Ref.
>15 years	1.81 (1.13–2.90)	0.013*	1.599 (0.965–2.649)	0.068
Sex
Male	Ref.		-
Female	0.87 (0.56–1.33)	0.5
Religion
Hindu	Ref.		-
Other	1.30 (0.75–2.26)	0.352
Ethnicity
Brahmin	Ref.		Ref.
Chhetri	2.73 (1.37–5.44)	0.004*	2.823 (1.378–5.787)	0.005*
Indigenous	2.65 (1.43–4.92)	0.002*	2.204 (1.158–4.196)	0.016*
Dalit	0.69 (0.18–2.62)	0.590	0.800 (0.208–3.075)	0.745
Others	4.63 (0.61–35.32)	0.140	3.119 (0.364–26.762)	0.300
Self-reported family income class
Upper middle class	Ref.		-
Middle class	0.878 (0.56–1.38)	0.575
Upper class	0.930 (0.45–1.93)	0.847
Parent’s education (any parent)
Bachelor’s and higher	Ref.		-
Secondary	0.905 (0.33–2.49)	0.846
Primary	1.453 (0.56–3.76)	0.442
Higher secondary	1.448 (0.53–3.93)	0.468
Illiterate	1.163 (0.45–3.03)	0.757
Main source of household income
Business	Ref.		-
Service	0.96 (0.51–1.82)	0.902
Agriculture	0.83 (0.45–1.51)	0.533
Labour	0.95 (0.39–2.35)	0.916
Others	0.50 (0.05–4.73)	0.546
Relation with parents
Good	Ref.		-
Neutral	1.00 (0.44–2.27)	0.999
Relation with friends
Good	Ref.		-
Neutral	1.17 (0.29–4.77)	0.828
Bad	1.27 (0.74–2.16)	0.389
Relation with relatives
Good	Ref.		-
Neutral	1.70 (0.58–5.00)	0.333
Bad	1.29 (0.83–1.99)	0.260
Goodness of fit χ2 (Hosmer-Lemeshow) = df = 7, p = 0.864

*P<0.05, COR = Crude Odds Ratio, AOR = Adjusted Odds Ratio

Discussions

This study aimed to assess the prevalence of internet addiction and poor sleep quality among adolescents in a peri-urban setting in Nepal. Borderline internet addiction and possible internet addiction were prevalent in 21.5% and 13.3% respectively. Around one-third (31.0%) were found to experience poor sleep quality. These results corroborate with findings of similar other studies conducted in Nepal [25] and elsewhere [9, 31].

Higher age during adolescence depicted poorer sleep quality compared to younger age groups. In contrast to other studies where girls were more likely to have poor sleep quality than boys [32], this study did not find the association between sleep quality and sex. A statistically significant association was found between internet addiction and poor sleep quality in the present study. The current study findings were supported by research conducted among female adolescents in Taiwan [33] and the high school students in Turkey [34] and Japan [35] with the positive relationship between internet addiction and poor sleep quality. Contrary to this study findings, no association was established between poor sleep quality and internet use in a study conducted among university students in Canada. The young population is often found to surf the internet and use other media to cope with sleep problems [36]. Based on our findings, it was found that as the adolescents got older, their sleep quality worsened as well. Of the studies conducted in the UK, one study found that using phones during bedtime reduced sleep duration by 21 minutes [37] and another study associated 45 minutes of reduced sleep duration with the use of phones at bedtime among pre-teens [38, 39]. However, a study in Canada found a negative association between the presence of phones and sleep duration but no association between phone use and sleep duration [40]. The current study showed around 29% of the participants had slept on an average of 7 hours and 23% had slept 8 hours. Whereas, a study conducted in Korea found that participants slept for 6 hours (26%), 7 hours (31.1%), 8 hours (29%) and 9 hours (13.9%). Excessive internet use was presumed to be the cause of sleep deprivation by displacing sleep time [41].

Awareness of poor sleep quality as a result of increased internet use and screen time should be carried out. Parents and school authorities may be at the central focus of the interventions aiming to reduce internet addiction as well as sleep quality.

The results in this study are reliant on self-reported behavior by the participants and it represents their views and perspectives. There might be a discrepancy between self-reported behavior and actual behavior practice. Negligence and underestimation of their own addictive behaviors might be underrepresented based on self-reported behavior. The study has not covered mental health problems associated with internet addictions. Being a cross-sectional study design, the results of this study does not provide causality, instead provide evidence between internet addiction and poor sleep quality.

The study provides background information on internet addiction and the sleep quality to design large scale studies in different age groups, their cognitive and behavioural development as well as overall health impact.

Conclusions

The current cross-sectional study demonstrated the one-third prevalence of internet addiction with poor sleep quality in peri-urban settings of Nepal. Ethnic groups such as Chhetri and Indigenous groups were more prone to problems related to internet addiction and poor sleep quality. These socio-demographic variables depict the complex relationships and influence over the problems of adolescents. The findings also suggest that there are significant associations between internet addiction and sleep quality. The findings that adolescents with internet addiction are more likely to experience poor sleep quality supported the relevancy of this topic in recent times. It would benefit future studies to emphasize and dig deeper into the underlying causes and patterns of these problems. The findings have implications for adolescents, parents, school authorities and researchers.