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Sleep Med Res > Volume 15(2); 2024 > Article
Paul, Angeline, Arasu, Maliyekkal, and Sabu: Quality of Sleep, Work Time, and Smartphone Usage in Migrant Workers of India: A Cross-Sectional Study


Background and Objective

Insufficient sleep associated with long work hours and prolonged smartphone usage is common among migrant workers. Considering their increased recruitment in Bangalore’s (South India) restaurant industry, it is essential to assess their sleep quality and its relationships with work-time and smartphone use.


Ninety migrant restaurant workers recruited using a snowball sampling technique were surveyed using a comprehensive semi-structured questionnaire to collect sociodemographic details, work schedules, and smartphone usage patterns. The Pittsburgh Sleep Quality Index (PSQI) was utilized to assess sleep quality of these migrant workers (global score of >5 indicated poor sleep quality). Associations between sleep quality, work schedules, and smartphone usage pattern were then assessed using relevant statistical tests.


The mean age of migrants was 26.4 ± 8.5 years. Their average work-time was 10.1 ± 1.6 hours with an average smartphone use of 60 (30–90) minutes per day. Approximately 22.2% of the population had poor sleep quality according to PSQI. Multiple logistic regression analysis revealed significant associations of poor sleep quality with work-time exceeding 10 hours (adjusted odds ratio [AOR] = 5.2; 95% confidence interval [CI] = 1.3–21.5), continuous smartphone usage of more than 1 hour (AOR = 5.4; 95% CI = 1.3–22.2), and frequent headaches due to smartphone use (AOR = 23.4; 95% CI = 3.4–161.4).


This study demonstrated that over one fifth of migrant workers had poor sleep quality, with excessive work-time, prolonged smartphone use, and frequent smartphone-induced headaches predicting a poor sleep quality. Consequently, migrant workers should receive awareness sessions on sleep hygiene and responsible smartphone use.


In India, millions of individuals and households rely on internal labor migration, where citizens move within the country in search of employment opportunities as a means of subsistence [1]. According to the World Economic Forum, the nation is estimated to have at least 139 million internal migrants based on an analysis of official employment figures from sectors predominantly employing migrant labor [2]. Migration rates have nearly doubled, with trends indicating a shift from rural to urban migration for nonagricultural purposes. The prevalence of temporary migration is exceptionally high due to various push and pull factors primarily driven by poverty of workers, compelling them to migrate and face challenges associated with urban migration such as social isolation, compromised living conditions, and abuse [3]. In the 2011 census, Bangalore Urban’s population of 8.7 million included 44.3 million (50.6%) of migrants (more than half of the population), of whom significant portion are migrants mainly from North Indian states. States like Uttar Pradesh, Bihar, and West Bengal contribute significantly to this migrant outflow for better job prospects in developed regions [4]. This makes the inter-state migrant population an ideal demographic for understanding health-related issues of migrants in a metropolitan setting.
One of the most prevalent health problems among migrants is sleep disorder. Its prevalence rate ranges from 39% to 99% [5], significantly higher than its prevalence of 10% in the general population [6].
Migrants experiencing social isolation often turn to smartphones for communication, making them their primary means of maintaining connections with friends and families back home. This effectively reduces social isolation and becomes a compelling phenomenon, reshaping cultural habits and giving rise to new behavioural patterns, ultimately contributing to activism through social media platforms [7]. Smartphones have streamlined communication and granted them broad access to information [8,9]. Given that these migrants can acquire a smartphone for as little as 2000 rupees ($24) with monthly call, SMS, and internet packages available for around 100 rupees ($1.2), these devices have become highly accessible and affordable for migrant workers. This accessibility has led to the widespread use of smartphones and social media platforms among this working population. As their smartphone usage during working hours is restricted, they tend to use them primarily after work, often during the night. An online study conducted among 23187 participants utilizing a smartphone app developed by Ginger.io (San Francisco) to measure smartphone usage revealed that longer average screen time (median: 3.7 [interquartile range: 2.2–5.5] minutes per hour) was associated with poor sleep quality (p < 0.004) [9]. Studies have also found that smartphone use at night can lead to increased depletion the following morning due to its impact on sleep quality, which subsequently diminishes daily work engagement. Furthermore, indirect effects of smartphone use on depletion and engagement the next day are incremental to effects of other electronic devices (e.g., computer, tablet, and television use) [10].
Biological rhythms of these workers often become disrupted due to altered daily schedule brought on by shift work, particularly night shifts, which detrimentally affects the quality of their sleep [11,12]. Various independent factors contribute to disrupted sleep, including hectic work, physically strenuous tasks, and shift work. Work stress, shift work, and physical workload further interfere with sleep and contribute to fatigue [13]. Physiological research has suggested that inadequate sleep is associated with significant drops in glucose levels and cerebral metabolic rates in the prefrontal cortex, this reduction in activity is noteworthy because the prefrontal cortex plays a pivotal role in executive functioning, which is inherently linked to self-control [10]. Consequently, recent research has established a connection between a lack of sleep and ego depletion, which in turn is linked to lapses in regulation of deviant and unethical behaviors [14]. Studies have demonstrated that individuals experiencing poor sleep exhibit heightened cognitive activity while in bed, even after accounting for global indices of depression and anxiety. Poor sleepers also report statistically significant increases in excessive noise in the bedroom, uncomfortable nighttime temperatures, and engagement in activities that are exciting, emotional, or demand high concentration close to bedtime [15]. These factors are present both in their workplaces and in accommodations provided in migrated areas.
Therefore, assessing the quality of sleep, smartphone use, and working conditions is crucial for understanding the occupational health of these migrant workers. However, existing literature highlights a significant gap in research that examines the quality of sleep and its relationships with work hours and smartphone usage among migrant restaurant workers in Bangalore. Consequently, this study aimed to evaluate the quality of sleep and its associations with work hours and smartphone usage among migrant restaurant workers in Maruthinagar and Koramangala areas of Bangalore, Karnataka.


The restaurant industry is a thriving sector in Bangalore city, employing a significant number of migrants in various roles. These workers often find themselves performing a wide range of tasks, regardless of their designated positions. In some restaurants, managers may need to take on responsibilities of a waiter, cook, or even engage in cleaning duties. It is typically only in high-end establishments that employees are confined to specific roles, resulting in an increased workload. Furthermore, these workers have long work schedules, which can lead to health problems, including poor sleep quality [12]. Limited existing literature on occupational health of these workers motivated us to conduct this study.
A cross-sectional study was conducted among migrant restaurant workers in Bangalore city, South India, from October 2022 to December 2022. In a study done in South India, the prevalence of sleep disorders among migrants was 5.8% [16]. With a fixed precision of 5% and a confidence interval of 95%, the sample size was estimated at 90. Ethical clearance for this study was obtained from the Institutional Ethics Committee (IEC) of St. John’s National Academy of Health Sciences (IEC no. 255/2022). Permission was obtained from concerned local authorities. Snowball recruitment was followed. Restaurants were approached and the study was discussed with managers of the restaurant. After obtaining permission from restaurant authorities, phone numbers of workers were obtained. Workers were contacted over the phone and a consent form was shared through a Google form. Subjects were asked to check the agree/disagree option. If they agreed to participate in this study, the questionnaire was administered over the phone. Once the interview was finished, they were asked about nearby restaurants having migrant workers. These restaurants were visited and the process was continued until sample size was attained. All migrants working in restaurants in the study area were included in this study except for those who lacked smartphones and those who gave history of having treatment for mental illnesses.
A semi-structured face-validated questionnaire was developed to assess sociodemographic details (age, sex, native place, marital status, socioeconomic status), pattern of smartphone usage (total duration of smartphone usage as well as face validated questions to assess problematic smartphone use), and work timings such as starting time of work, ending time of work, break time, and total effective work time (total work time [in hours]– break time [in hours]).
Quality of sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI), a 19-item questionnaire comprising seven components (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction). A global score was calculated, with a score greater than 5 indicating a poor sleep quality [17].
E-survey was developed according to CHERRIES (Checklist for Reporting Results of Internet E-Surveys) guidelines [18]. No rewards were offered for participation in this study. Questionnaires were cross-checked for completeness of data, followed by data entry using Epicollect5 (https://five.epicollect.net/).

Statistical Analysis

Data was collated into Microsoft Excel 2021 and analyzed using SPSS Statistics version 21 (IBM Corp., Armonk, NY, USA). Sociodemographic details and occupational history were described using mean, standard deviation, frequencies, and percentages. Work time and smartphone usage time were measured as continuous variables and presented using mean and standard deviation. Both are also presented as categorical variables using median as the cut-off point. Quality of sleep and characteristics of smartphone usage were recorded as frequencies and percentages. For bivariate analysis, chi-square test was used. Associations with a p-value of <0.20 in bivariate analysis were included in a multiple logistic regression model.


For the 90 workers interviewed, their average age was 26.4 ± 8.5 years with a total effective work time of 10.1 ± 1.6 hours. Median values of smartphone usage and PSQI global scores were found to be 60 (range, 30–90) minutes and 3 (range, 2–5), respectively. It was found that 58.9% of the population were Hindus, 62.2% belonged to nuclear families, 72.2% were unmarried, 54.4% had a high school education, 65.6% belonged to upper middle class of socioeconomic status according to modified BG Prasad socio economic status scale, 53.3% had a privilege of break time, and 57.8% had night shifts. Social media was found to be the most commonly used application on smartphones (48.9%). In addition, 22.2% of workers in this study had a poor sleep quality (Table 1).
Variables used to assess problematic smartphone usage are shown in Fig. 1. The majority (86.7%) of these workers were using smartphone while going to sleep. However, most of them did not have other features of problematic smartphone usage. Notably, 36.7% of workers reported that smartphone usage was causing a delay in the onset of sleep and 24.4% had received warnings from peers about excessive use of smartphone (Fig. 1).
Chi-square test showed that poor sleep quality had statistically significant associations with effective work time >10 hours (p = 0.006), continuous usage of smartphone >60 minutes (p = 0.006), delay of sleep due to smartphone usage (p = 0.003), frequent headaches due to smartphone usage (p < 0.001), and receiving warning from peers about their smartphone usage (p = 0.003). These variables were used in the multiple regression model. Frequent headaches might be both cause and effect of poor sleep quality. We used it as a predictor variable considering previous studies [19]. However, age, sex, place of origin, religion, education status, marital status, type of family, socioeconomic status, break time, and night shift did not have any statistically significant association with quality of sleep (Tables 2 and 3).
The multiple logistic regression showed that workers who had a total effective work time more than 10 hours had 5.2 (95% CI = 1.3–21.5) times higher odds of having a poor sleep quality, those who had a smartphone usage of more than 1 hour had 5.4 (95% CI = 1.3–22.2) times higher odds, and those having frequent headaches due to smartphone usage had 23.4 (95% CI = 3.4–161.4) times higher odds of having a poor sleep quality (all p < 0.05) (Table 4).


This study sought to assess the quality of sleep among migrant restaurant workers and its associations with work time and smart phone use. On evaluating the sleep quality of participants, we found that 22.2% were having poor sleep quality according to PQSI, similar to study done in China showing that 25.4% of internal migrant workers in the service industry had a poor sleep quality [20]. These percentages were lower than that in a study conducted in Ghana on waiters (74%) in high end restaurants [21]. The main difference might be due to differences in the type of workers and restaurants included in the study, as the study done in Ghana was focused on waiters in high end restaurant, where as our study was focused on all types of workers and restaurants.
The present study showed that the migrant restaurant workers who had a total effective work more than 10 hours had 5 times more chance of having a poor sleep quality. This finding was similar to a finding of migrant construction workers in Chennai [16] and a study done on migrant workers in Malayasia [22]
Continuous smartphone use for more than 1 hour had 5 times more chance of having a poor sleep quality, consistent with a previous study on smartphone usage and sleep quality in a general population [23]. This might be attributed to the fact that using smartphones was the preferred leisure activity of migrant workers who lacked other forms of entertainment, social support, and medical services compared to the native population. As a result, these workers are less likely to be diagnosed with psychophysiological problems, which could eventually lead to sleep-related issues [24].
Finally, individuals who experienced frequent headaches due to smartphone usage had a 23 times higher chance of having a poor sleep quality compared to their counterparts. Frequent headaches can be a cause of a poor sleep quality [19]. They are sometimes associated with smartphone usage [25,26].
No significant associations were identified between work hours and smartphone usage in our study. This lack of correlation might be attributed to the fact that workers were not allowed to use smartphones during their working hours. Smartphone usage was confined to break times and evening hours before sleep. Furthermore, fixed working hours ensured that workers did not need to exceed their designated work hours to complete pending tasks. It was worth noting that the study did not assess the quality of work as it was beyond the scope of our research. Future studies are recommended to delve into the relationship between smartphone usage, sleep patterns, and work quality. Existing research indicates that smartphone use can have an adverse impact on sleep, potentially leading to sleep deprivation, which in turn can result in diminished executive judgment and ethical behavior [10].
Providing guidance on maintaining proper sleep hygiene and using smartphones wisely is essential for enhancing the sleep quality of migrants. They need to avoid sleep-disrupting substances like caffeine, steer clear of engaging in stimulating activities close to bedtime, and restrict the use of their beds solely for sleeping, as these behaviors can impede their ability to fall asleep and stay asleep. Furthermore, stressing that avoiding smartphone use at night before bedtime is essential as it can have a negative impact on their sleep hygiene, hindering their ability to obtain sufficient rest and recover depleted resources [15].
One limitation of this study was that it was conducted among migrant restaurant workers in a specific area of Bangalore city. As a result, findings of this study might only reflect conditions unique to the study area. They might not be easily generalized. Therefore, it is advisable to undertake future studies on a larger scale using probability sampling to gain insights into the overall situation of migrants across the entire Bangalore city. Language barrier was another problem that might have affected results of this study. Several workers were not native Hindi speakers. They had acquired the language later in life, which might have contributed to their lower proficiency in speaking Hindi.
Advantages of this study were that the workers could open up about their problems as they were interviewed in their free time and over the phone without their bosses being there to monitor them. It also had the advantage of getting more participants as the interview could be scheduled according to comfort of the worker rather than busy restaurant timings.
In conclusion, this study showed that more than one-fifth of workers had poor sleep quality. The average effective work time was 10 hours and the average continuous smartphone usage time was 1 hour. Workers who had work time of more than 10 hours, continuous smartphone usage time for more than 1 hour, and frequent headaches due to smartphone had a higher chance of having a poor sleep quality. Smartphone usage before sleep was not found to have any independent association with sleep quality. Therefore, workers should be given advice on their sleep hygiene and judicious use of smartphones.


Availability of Data and Material
The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.
Author Contributions
Conceptualization: Subin Paul. Data curation: Subin Paul, Don J. Maliyekkal, Jestin Sabu. Formal analysis: Subin Paul. Methodology: Subin Paul, Nancy G. Angeline, Sakthi Arasu. Software: all authors. Supervision: Nancy G. Angeline, Sakthi Arasu. Visualization: Subin Paul, Nancy G. Angeline, Sakthi Arasu. Writing—original draft: Subin Paul. Writing—review & editing: Nancy G. Angeline, Sakthi Arasu.
Conflicts of Interest
The authors have no potential conflicts of interest to disclose.
Funding Statement


The authors would like to express their gratitude to the BBMP South Zone and East Zone Health Officers, restaurant authorities and workers of the restaurants for granting permission and cooperating in conducting this study.


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Fig. 1.
Impact of smartphone usage among the migrant workers.
Table 1.
Characteristics of 90 workers participated in the study
Characteristic Value (n = 90)
Age (yr) 26.4 ± 8.5
Total effective working hours (h) 10.1 ± 1.6
Continuous usage of smartphone (min) 60 (30–90)
Global PSQI score 3 (2–5)
 Female 6 (6.7)
 Male 84 (93.3)
Native state
 Northern states 78 (86.7)
 Southern states 8 (8.9)
 Other countries 4 (4.4)
 Hindu 53 (58.9)
 Christian 17 (18.9)
 Muslim 12 (13.3)
 Buddhist 8 (8.9)
Family type
 Nuclear family 56 (62.2)
 Joint family 27 (30.0)
 Others 7 (7.8)
Marital status
 Unmarried 63 (72.2)
 Married 27 (27.8)
Educational status
 Degree and above 7 (7.8)
 Higher secondary/PUC 18 (20.0)
 High school 49 (54.4)
 Primary 9 (10.0)
 Uneducated 7 (7.8)
Socioeconomic status (modified B.G. Prasad)
 Upper 2 (2.2)
 Upper middle 59 (65.6)
 Middle 16 (17.8)
 Lower middle 12 (13.3)
 Lower 1 (1.1)
Break time
 No 48 (53.3)
 Yes 42 (46.7)
Night shift
 No 52 (57.8)
 Yes 38 (42.2)
Sleep quality
 Poor 20 (22.2)
 Good 70 (77.8)
Most frequently used applications
 Social media (Facebook, Instagram) 44 (48.9)
 Video apps (Youtube) 37 (41.1)
 Others (games, calling, and texting) 9 (10)

Values are presented as mean ± standard deviation, median (interquartile range), or n (%).

PSQI, Pittsburgh Sleep Quality Index; PUC, pre-university course.

Table 2.
Association between sociodemographic variables and sleep outcome (PSQI)
Variable Sleep (PSQI)
Good Poor
Age 0.503
 >26 years (n = 28) 23 (82.1) 5 (17.9)
 <26 years (n = 62) 47 (75.8) 15 (24.2)
Sex 0.498
 Female (n = 6) 2 (33.3) 4 (66.7)
 Male (n = 84) 18 (21.4) 66 (78.6)
Place of origin 0.783
 North Indian (n = 78) 18 (23.1) 60 (76.9)
 South Indian (n = 8) 1 (12.5) 7 (87.5)
 Outside India (n = 4) 1 (25.0) 3 (75.0)
Religion 0.360
 Hindu (n = 53) 10 (18.9) 43 (81.1)
 Others (n = 37) 10 (27.0) 27 (73.0)
Education status 0.927
 Uneducated (n = 7) 2 (28.6) 5 (71.4)
 Primary (n = 9) 2 (22.2) 7 (77.8)
 High school (n = 49) 12 (24.5) 37 (75.5)
 PUC (n = 18) 3 (16.7) 15 (83.3)
 Degree (n = 7) 1 (14.3) 6 (85.7)
Marital status 0.148
 Married (n = 25) 3 (12.0) 22 (88.0)
 Unmarried (n = 65) 17 (26.2) 48 (73.8)
Type of family 0.698
 Nuclear family (n = 56) 14 (25.0) 42 (75.0)
 Joint family (n = 27) 5 (18.5) 22 (81.5)
 Others (n = 7) 1 (14.3) 6 (85.7)
Socio economic status 0.498
 Lower (n = 1) 0 (0.0) 1 (100.0)
 Lower middle (n = 12) 4 (33.3) 8 (66.7)
 Middle (n = 16) 5 (31.3) 11 (68.8)
 Upper middle (n = 59) 10 (16.9) 49 (83.1)
 Upper (n = 2) 1 (50.0) 1 (50.0)

Values are presented as n (%).

PSQI, Pittsburgh Sleep Quality Index; PUC, pre-university courses.

Table 3.
Association between work related variables, smartphone use and sleep outcome (PSQI)
Variable Quality of sleep
Good Poor
Effective working hours 0.006
 ≤10 hours (n = 11) 6 (54.5) 5 (45.5)
 >10 hours (n = 79) 14 (17.7) 65 (82.3)
Break time 0.498
 No (n = 48) 12 (25.0) 36 (75.0)
 Yes (n = 42) 8 (19.0) 34 (81.0)
Night shift 0.775
 No (n = 52) 11 (21.2) 41 (78.8)
 Yes (n = 38) 9 (23.7) 29 (76.3)
Continuous usage of smartphone 0.006
 ≤60 min (n = 25) 10 (40.0) 15 (60.0)
 >60 min (n = 65) 10 (15.4) 55 (84.6)
Sleep delay 0.003
 No (n = 57) 7 (12.3) 50 (87.7)
 Yes (n = 33) 13 (39.4) 20 (60.6)
Frequent headaches <0.001
 No (n = 79) 66 (83.5) 13 (16.5)
 Yes (n = 11) 4 (36.4) 7 (63.6)
Warning by peers 0.003
 No (n = 68) 10 (14.7) 58 (85.3)
 Yes (n = 22) 10 (45.5) 12 (54.5)

Values are presented as n (%).

PSQI, Pittsburgh Sleep Quality Index.

Table 4.
Multiple logistic regression analysis of variables significantly associated with poor sleep quality (PSQI)
Variable Adjusted OR (95% CI) p-value
Total effective working hours
 ≤10 hours Ref
 >10 hours 5.199 (1.255–21.537) 0.023
Continuous usage of smartphone
 ≤1 hour Ref
 >1 hour 5.417 (1.322–22.192) 0.019
Sleep delay
 No Ref
 Yes 3.767 (0.993–14.294) 0.051
Frequent headaches
 No Ref
 Yes 23.357 (3.380–161.418) 0.001
Warning by peers
 No Ref
 Yes 3.848 (0.964–15.349) 0.056

PSQI, Pittsburgh Sleep Quality Index; OR, odds ratio; CI, confidence interval.