Mobile phones, computers and television take up a growing share of the waking day of young people. They are used for study, messaging, entertainment and games. In a long-term birth cohort, television viewing in childhood and adolescence predicted overweight, poor fitness and raised serum cholesterol in adult life [1]. Heavy use of information and communication technology in adolescents has also been linked with poorer perceived health, largely through irregular sleep and daytime tiredness [2].

The effects are not only behavioural. Video game play raises heart rate, blood pressure and energy expenditure compared with rest [3]. Computer game playing and internet use are associated with later bedtimes and shorter sleep [4], and mobile phone use after lights out predicts greater tiredness one year later [5]. Playing a computer game on a bright display before bed raises pre-sleep heart rate, lengthens sleep latency and reduces REM sleep [6]. Medical students are a group at particular risk. They report high levels of academic stress [7], and long study hours and irregular schedules add to recreational screen use.

Heart rate variability (HRV) is the beat-to-beat variation in the RR interval. It is a simple non-invasive marker of the balance between sympathetic and parasympathetic control of the heart [8]. According to the neurovisceral integration model, vagally mediated HRV reflects the capacity of prefrontal and limbic networks to regulate emotion, attention and physiological arousal [9]. Low vagal function is linked with several cardiovascular risk factors and with higher morbidity and mortality [10]. Mental stress during computer work lowers high-frequency power and raises the LF/HF ratio [11]. HRV can therefore capture the autonomic cost of habits that keep the nervous system in an aroused state.

There is some direct evidence that screen-based activity alters autonomic tone. In boys, playing a violent television game changed HRV during play and during the following night compared with a non-violent game [12]. Most such studies, however, have been done in children under laboratory conditions. Few have looked at ordinary daily screen time in healthy young adults.

Reaction time is the interval between a stimulus and the voluntary response to it. It reflects sensory processing, central integration and motor output, and it serves as a simple index of alertness and sensorimotor performance. In Indian adults, auditory reaction time is shorter than visual reaction time, and both differ between males and females [13]. Chronic sleep restriction causes dose-dependent slowing of responses and lapses of attention [14]. Long screen use also causes visual fatigue and eye strain [15]. Both effects may slow responses to visual and auditory stimuli. Few studies have examined whether daily screen time relates to reaction time in medical students.

We therefore assessed the association of daily screen time with short-term HRV and with auditory and visual reaction time in first-year medical students. We also examined whether any association was independent of sex, body mass index, physical activity and sleep quality. We hypothesised that higher screen time would be associated with lower vagal indices of HRV and longer reaction times.

Study design and setting

This cross-sectional observational study was carried out in the Department of Physiology, Dr. Vasantrao Pawar Medical College & Hospital, Nashik, Maharashtra, from January 2010 to April 2010. Written informed consent was taken from all participants. The study followed the Declaration of Helsinki and the ICMR Ethical Guidelines for Biomedical Research on Human Participants (2006).

Sample size

The sample size was calculated to detect a correlation coefficient of 0.30 between screen time and RMSSD, with a two-sided alpha of 0.05 and power of 80%. This gave a minimum of 85 participants. A total of 120 students were enrolled, which allowed for incomplete or unusable recordings and gave 40 participants in each screen-time group.

 Participants

First-year MBBS students aged 18 to 22 years who gave consent were included. Students were excluded if they had known cardiovascular, respiratory, endocrine, neurological or psychiatric illness; were taking any medication that affects autonomic function (such as beta blockers, antihistamines, antidepressants or bronchodilators); smoked or used tobacco; drank alcohol regularly; had uncorrected visual or hearing impairment; or had an acute illness in the preceding two weeks. Trained athletes were also excluded, as regular endurance training alters resting vagal tone.

 Assessment of screen time

Daily screen time was recorded with a structured screen-use diary that each participant kept for seven consecutive days before testing. Participants noted the start and end time of each session of mobile phone use. Retrospective recall of mobile phone use is often inaccurate [16], so a prospective diary was used to reduce recall error. Mean daily screen time was expressed in hours per day. Participants were grouped as low users (less than 4 h/day), moderate users (4 to 7 h/day) and high users (more than 7 h/day). Screen use in the hour before sleep was recorded separately in minutes per day.

 Covariates

Sleep quality over the past month was assessed with the Pittsburgh Sleep Quality Index (PSQI). A global score above 5 was taken as poor sleep [17]. Physical activity was assessed with the short form of the International Physical Activity Questionnaire (IPAQ) and expressed in MET-minutes per week [18]. Height and weight were measured with standard methods and BMI was calculated. BMI was classified using the cut-offs recommended for Asian Indians [19].

 Recording of heart rate variability

All recordings were made between 9:00 and 11:00 a.m. in a quiet, temperature-controlled laboratory (24 to 26 °C). Participants were asked to avoid tea, coffee and other caffeinated drinks for 12 hours and strenuous exercise for 24 hours, and to have a light breakfast at least two hours before the test. They were also asked not to use screens for 30 minutes before testing. After 10 minutes of supine rest, a 5-minute lead II ECG was recorded at a sampling rate of 1024 Hz using a digital polygraph, RMS Polyrite D (Recorders & Medicare Systems Pvt. Ltd., Chandigarh, India), and analysed with its built-in HRV software. Participants breathed normally and were asked not to talk or move during the recording.

HRV analysis followed the Task Force standards of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [8]. RR intervals were checked visually, and ectopic beats and artefacts were removed. Time-domain indices were mean RR interval, resting heart rate, SDNN, RMSSD and pNN50. Frequency-domain indices were derived by fast Fourier transform: low-frequency power (LF, 0.04 to 0.15 Hz), high-frequency power (HF, 0.15 to 0.40 Hz), LF and HF in normalised units (nu) and the LF/HF ratio [8]. RMSSD and HF were taken as markers of vagal activity, and the LF/HF ratio as an index of sympathovagal balance [20].

 Measurement of reaction time

Auditory and visual reaction times were measured with an audio-visual reaction time machine, RTM-608 (Medicaid Systems, Chandigarh, India), which has a resolution of 0.001 s. A red light was used as the visual stimulus and a high-pitch tone as the auditory stimulus. Participants were seated comfortably and asked to press the response key with the index finger of the dominant hand as quickly as possible after each stimulus. Stimuli were given at random intervals of 2 to 5 seconds. After three practice trials, ten trials were recorded for each modality. The mean of the ten trials, in milliseconds, was used for analysis.

 Statistical analysis

Data were entered in Microsoft Excel and analysed with SPSS Statistics for Windows, version 17.0 (SPSS Inc., Chicago, IL, USA). Normality was checked with the Shapiro-Wilk test. Normally distributed data were expressed as mean ± standard deviation (SD) and compared across the three groups with one-way ANOVA followed by Tukey's post hoc test. Skewed data (age, screen time, heart rate, PSQI score and LF/HF ratio) were expressed as median (interquartile range, IQR) and compared with the Kruskal-Wallis test followed by Dunn's test with Bonferroni correction. Categorical variables were compared with the chi-square test. Male and female participants were compared with the unpaired t test, and auditory and visual reaction times with the paired t test. The relationship of screen time with HRV indices and reaction time was examined with Spearman's correlation. Multiple linear regression was used to test whether screen time predicted RMSSD, the LF/HF ratio and reaction time independently of sex, BMI, IPAQ score and PSQI score. A p value below 0.05 was taken as statistically significant.

All 120 enrolled students completed the study and were included in the analysis. There were 63 males (52.5%) and 57 females (47.5%), with a mean age of 19.56 ± 1.11 years (range 18 to 22). Mean daily screen time was 5.74 ± 2.61 h (range 1.58 to 10.77 h). Each screen-time group had 40 participants.

The three groups were similar in age, sex distribution and BMI (Table 1). Screen use in the hour before sleep rose from 42.33 ± 21.80 min in low users to 87.62 ± 22.70 min in high users (p < 0.001). Physical activity was lowest in high users (1118.15 ± 505.56 MET-min/week against 1944.45 ± 562.43 in low users, p < 0.001). The median PSQI score rose from 4.0 in low users to 7.5 in high users (p < 0.001). Poor sleep was present in 36 (90.0%) high users against 14 (35.0%) low users (p < 0.001).

Table 1: Baseline characteristics of participants according to daily screen time

Values are mean ± SD unless stated otherwise. ᵃ p < 0.05 against low users; ᵇ p < 0.05 against moderate users (post hoc test). BMI: body mass index; IPAQ: International Physical Activity Questionnaire; PSQI: Pittsburgh Sleep Quality Index.

Resting heart rate was higher and mean RR interval shorter in high users than in the other two groups (Table 2). SDNN, RMSSD and pNN50 fell steadily from low to high users, and each pairwise difference was significant. In the frequency domain, HF power fell from 896.33 ± 236.22 ms² in low users to 369.40 ± 201.32 ms² in high users, while LF power rose from 779.02 ± 221.97 to 1068.40 ± 250.98 ms². HF nu fell from 53.52 to 24.87, and the median LF/HF ratio rose from 0.83 to 3.24. All three groups differed significantly from one another for these frequency-domain indices.

 Table 2: Short-term heart rate variability indices according to daily screen time

Values are mean ± SD unless stated otherwise. ᵃ p < 0.05 against low users; ᵇ p < 0.05 against moderate users (post hoc test). SDNN: standard deviation of NN intervals; RMSSD: root mean square of successive differences; pNN50: percentage of successive NN intervals differing by more than 50 ms; LF: low frequency; HF: high frequency; nu: normalised units.

Both reaction times were longer in high users (Table 3, Figure 2). Auditory reaction time differed significantly between all three groups. Visual reaction time did not differ between low and moderate users (p = 0.128) but was clearly longer in high users than in the other two groups (p < 0.001). Across all participants, auditory reaction time (199.53 ± 21.31 ms) was shorter than visual reaction time (249.12 ± 27.37 ms, p < 0.001).

Table 3: Auditory and visual reaction time according to daily screen time

Values are mean ± SD. ᵃ p < 0.05 against low users; ᵇ p < 0.05 against moderate users (Tukey's test).

Daily screen time correlated negatively with mean RR interval, SDNN, RMSSD, pNN50, HF power and HF nu, and positively with heart rate, LF power, LF nu, the LF/HF ratio and both reaction times (Table 4). The strongest correlations were with HF nu, LF nu and the LF/HF ratio (rho = 0.78 in absolute value). Figure 1 shows the inverse relation between screen time and RMSSD. Screen time also correlated positively with pre-sleep screen use (rho = 0.66) and PSQI score (rho = 0.57), and negatively with IPAQ score (rho = −0.53), all p < 0.001.

 Table 4: Correlation of daily screen time with HRV indices, reaction time, sleep quality and physical activity (n = 120)

p < 0.05 considered significant.

Figure 1: Scatter plot of daily screen time against RMSSD (n = 120)

The line shows the least-squares regression fit. RMSSD: root mean square of successive differences.

Figure 2: Auditory and visual reaction time according to daily screen time group

Bars show mean values and error bars show standard deviation (n = 40 per group).

On multiple linear regression adjusted for sex, BMI, IPAQ score and PSQI score, screen time was an independent predictor of RMSSD (B = −3.10 ms per hour, 95% CI −3.95 to −2.26, p < 0.001; model R² = 0.45). It also independently predicted the LF/HF ratio (B = 0.63, 95% CI 0.44 to 0.82, p < 0.001; R² = 0.40) and auditory reaction time (B = 4.55 ms per hour, 95% CI 2.95 to 6.16, p < 0.001; R² = 0.43). PSQI score, IPAQ score, sex and BMI did not predict any of these three outcomes. For visual reaction time, screen time (B = 3.54, 95% CI 1.38 to 5.71, p = 0.002), PSQI score (B = 2.34, 95% CI 0.20 to 4.47, p = 0.033) and IPAQ score (B = −0.009 ms per MET-min/week, 95% CI −0.016 to −0.002, p = 0.018) were each independent predictors (R² = 0.37).

Female participants had a slightly higher resting heart rate than males (75.56 ± 7.95 against 72.83 ± 5.91 bpm, p = 0.033). RMSSD (42.94 ± 11.54 against 43.67 ± 11.37 ms, p = 0.731), HF nu, the LF/HF ratio and both reaction times did not differ between the sexes.

This study examined the association of daily screen time with cardiac autonomic function and reaction time in first-year medical students. Higher screen time was associated with lower time-domain and high-frequency HRV indices, a higher LF/HF ratio, a faster resting heart rate and longer auditory and visual reaction times. These relations followed a graded pattern across the three groups. They remained significant after adjustment for sex, BMI, physical activity and sleep quality.

The fall in RMSSD, pNN50 and HF power in high users points to reduced cardiac vagal activity, and the higher LF/HF ratio to a shift toward sympathetic predominance [8,20]. RMSSD in high users was about one third lower than in low users. HF power fell by more than half. These results agree with the HRV changes seen in boys after violent game play [12] and with the rise in heart rate and blood pressure during video games [3]. Our study extends these observations from short laboratory sessions in children to habitual daily screen use in healthy young adults. Moderate users already differed from low users in most HRV indices. The autonomic effect therefore seems to follow a gradient of exposure.

Several mechanisms may explain this association. Continuous messaging, entertainment and gaming keep the user in a state of mild cognitive and emotional arousal. Mental stress at a screen lowers vagal HRV within minutes [11], and within the neurovisceral integration model this reflects weaker prefrontal inhibition of subcortical arousal circuits [9]. Screen time also displaces physical activity, and high users in our sample did about 40% fewer MET-minutes per week than low users. Sleep is a further pathway. Screen use in the evening delays bedtime and shortens sleep [4,5], and bright screens before bed raise pre-sleep arousal [6]. Acute sleep deprivation itself increases sympathetic and decreases parasympathetic cardiac modulation [21]. In our sample, 90% of high users had poor sleep. Yet neither PSQI score nor IPAQ score predicted RMSSD or the LF/HF ratio once screen time was in the model. This suggests that the autonomic change is not explained only by poor sleep or low activity. It may reflect a more direct effect of sustained arousal during screen use.

Reaction time rose with screen time in both modalities. Auditory reaction time showed the closer relation. It differed between all three groups and remained independently related to screen time after adjustment. Visual reaction time rose clearly only in high users, and it was also predicted by poor sleep and low physical activity. Eye strain is common with long screen use and causes blurred vision, dry eyes and difficulty refocusing [15]. It may add to the delay in visual responses at high exposure. Restricted sleep also slows responses in a dose-dependent way [14], which fits with the role of PSQI score in our visual reaction time model. The graded change in auditory reaction time, however, points to acentral effect on alertness and processing speed rather than a purely visual one. Auditory reaction time was shorter than visual reaction time in all groups, in keeping with earlier Indian data [13].

Sex is a known determinant of heart rate and HRV. Women have a higher resting heart rate than men, and sex differences in HRV vary with age [22]. Female students in our study had a slightly higher resting heart rate, but vagal indices did not differ between the sexes. Sex distribution was similar across the groups, and sex did not predict any outcome in the regression models. The findings therefore do not appear to be explained by differences between male and female students.

Reduced HRV in young adults is not a disease in itself. It is, however, linked with a less favourable cardiovascular risk profile in later life [10]. Screen habits formed in the first year of medical college often persist. The findings support including screen-use guidance in student orientation and mentorship programmes. Limiting screen use before bed, taking regular breaks and keeping up physical activity are practical, low-cost measures.

 Strengths and limitations

The strengths of this study include equal-sized exposure groups, a prospective seven-day diary instead of a single recall estimate, standard HRV recording and analysis [8], and adjustment for sleep, physical activity, sex and BMI. The study also has limitations. Its cross-sectional design does not allow causal inference. Screen time was self-recorded and may still differ from actual use [16]. Short-term recordings reflect resting autonomic state only, and 24-hour recordings may give a fuller picture. The content of screen use, whether academic or recreational, was not analysed separately. The sample came from a single institution, which may limit generalisation. Mental stress and anxiety were not formally measured and may have confounded the association.

Higher daily screen time in first-year medical students was associated with lower vagal HRV indices, a shift toward sympathetic predominance and slower auditory and visual reaction times. These changes were graded with exposure. The autonomic association was independent of sleep quality and physical activity. Longitudinal and intervention studies are needed to confirm whether reducing screen time improves autonomic function and reaction time in this group.

Acknowledgement

The author thanks the students who took part in the study and the technical staff of the Department of Physiology, Dr. Vasantrao Pawar Medical College & Hospital, Nashik, for their help with data collection.

 Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

1. Hancox RJ, Milne BJ, Poulton R. Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study. Lancet. 2004;364(9430):257-62.
2. Punamäki RL, Wallenius M, Nygård CH, Saarni L, Rimpelä A. Use of information and communication technology (ICT) and perceived health in adolescence: the role of sleeping habits and waking-time tiredness. J Adolesc. 2007;30(4):569-85.
3. Wang X, Perry AC. Metabolic and physiologic responses to video game play in 7- to 10-year-old boys. Arch Pediatr Adolesc Med. 2006;160(4):411-5.
4. Van den Bulck J. Television viewing, computer game playing, and Internet use and self-reported time to bed and time out of bed in secondary-school children. Sleep. 2004;27(1):101-4.
5. Van den Bulck J. Adolescent use of mobile phones for calling and for sending text messages after lights out: results from a prospective cohort study with a one-year follow-up. Sleep. 2007;30(9):1220-3.
6. Higuchi S, Motohashi Y, Liu Y, Maeda A. Effects of playing a computer game using a bright display on presleep physiological variables, sleep latency, slow wave sleep and REM sleep. J Sleep Res. 2005;14(3):267-73.
7. Supe AN. A study of stress in medical students at Seth G.S. Medical College. J Postgrad Med. 1998;44(1):1-6.
8. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93(5):1043-65.
9. Thayer JF, Lane RD. A model of neurovisceral integration in emotion regulation and dysregulation. J Affect Disord. 2000;61(3):201-16.
10. Thayer JF, Lane RD. The role of vagal function in the risk for cardiovascular disease and mortality. Biol Psychol. 2007;74(2):224-42.
11. Hjortskov N, Rissén D, Blangsted AK, Fallentin N, Lundberg U, Søgaard K. The effect of mental stress on heart rate variability and blood pressure during computer work. Eur J Appl Physiol. 2004;92(1-2):84-9.
12. Ivarsson M, Anderson M, Akerstedt T, Lindblad F. Playing a violent television game affects heart rate variability. Acta Paediatr. 2009;98(1):166-72.
13. Misra N, Mahajan KK, Maini BK. Comparative study of visual and auditory reaction time of hands and feet in males and females. Indian J Physiol Pharmacol. 1985;29(4):213-8.
14. Van Dongen HP, Maislin G, Mullington JM, Dinges DF. The cumulative cost of additional wakefulness: dose-response effects on neurobehavioral functions and sleep physiology from chronic sleep restriction and total sleep deprivation. Sleep. 2003;26(2):117-26.
15. Blehm C, Vishnu S, Khattak A, Mitra S, Yee RW. Computer vision syndrome: a review. Surv Ophthalmol. 2005;50(3):253-62.
16. Inyang I, Benke G, Morrissey J, McKenzie R, Abramson M. How well do adolescents recall use of mobile telephones? Results of a validation study. BMC Med Res Methodol. 2009;9:36.
17. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213.
18. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381-95.
19. Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India. 2009;57:163-70.
20. Malliani A, Pagani M, Lombardi F, Cerutti S. Cardiovascular neural regulation explored in the frequency domain. Circulation. 1991;84(2):482-92.
21. Zhong X, Hilton HJ, Gates GJ, Jelic S, Stern Y, Bartels MN, et al. Increased sympathetic and decreased parasympathetic cardiovascular modulation in normal humans with acute sleep deprivation. J Appl Physiol (1985). 2005;98(6):2024-32.
22. Umetani K, Singer DH, McCraty R, Atkinson M. Twenty-four hour time domain heart rate variability and heart rate: relations to age and gender over nine decades. J Am Coll Cardiol. 1998;31(3):593-601.