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Patterns of sleep, outdoor activity, emotion, and media use among adolescents during COVID-19: A cross-sectional survey
*Corresponding author: Roshan Fakirchand Sutar, Department of Psychiatry, All India Institute of Medical Sciences, Bhopal, Madhya Pradesh, India. roshidoc@yahoo.co.in
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Received: ,
Accepted: ,
How to cite this article: Kushwah A, Sutar RF, Revadi G, Patil R. Patterns of sleep, outdoor activity, emotion, and media use among adolescents during COVID-19: A cross-sectional survey. J Neurosci Rural Pract. doi: 10.25259/JNRP_71_2025
Abstract
Objectives:
The COVID-19 pandemic disrupted adolescents’ routines, impacting sleep, physical activity, and media use, with significant consequences for their physical and mental health. This study aims to analyze these interconnected patterns among Indian adolescents to understand their overall well-being during the pandemic.
Materials and Methods:
A cross-sectional online survey was conducted among adolescents aged 14–19 years, who attended regular school or college till March 2020, using snowball sampling through social media platforms from January to March 2022. The study utilized the CoRonavIruS Health Impact Survey V0.3 to assess daily behaviors, emotions, and media use. The survey aimed to understand adolescent behavior and emotional patterns during the pandemic. Data analyzed with R software version 4.2.1. Continuous variables are presented as mean (standard deviation), and categorical variables as number (%). Socio-demographic characteristics were examined as independent predictors for outcomes related to sleep patterns, emotional behaviors, and usage of TV, social media, and video games. Subsequently, media usage patterns were analyzed as risk factors for emotional behaviors. Associations between categorical variables were tested using Chi-square or Fisher’s exact tests, with a significance threshold of P < 0.05.
Results:
The survey of 413 adolescents revealed significant associations between sleep patterns and factors such as gender, education level, age group, religion, and location, with rural adolescents being more likely to get >8 h of sleep. The study found significant associations between physical activity, outdoor time, and socio-demographic factors, with younger adolescents, males, and 11th graders showing higher engagement and gender and academic year strongly influencing weekend outdoor activity patterns. The study found significant associations between adolescents’ use of TV, social media, and video games with emotional characteristics such as worry, fatigue, anxiety, and irritability, influenced by age, gender, location, and education level.
Conclusion:
The study explores adolescent perception regarding physical and mental health during difficult times and emphasizes the preparation for daunting tasks to address sleep disturbances, physical inactivity, emotional resilience, and balanced media use among adolescents with the help of community and school-based interventions.
Keywords
Adolescents
Anxiety
COVID-19
Depression
Media use
Physical activity
Sleep
INTRODUCTION
The COVID-19 pandemic has significantly influenced our daily lives, with adolescents facing unique challenges and disruptions to their routines. Like the rest of the world, central India witnessed an unprecedented shift in societal norms, education systems, and adolescents’ daily activities. Adolescence is a critical phase of physical, emotional, and social development. Therefore, understanding adolescents’ adaptation during this period is crucial from the perspective of teachers, parents, and mental health professionals. With countrywide lockdowns, school closures, and social distancing measures, the pandemic has uniquely impacted adolescents’ sleep, which could further impact physical and cognitive development.[1]
The disruptions brought about by the pandemic, including altered school schedules, increased screen time, heightened stress levels, and changes in regular physical activity, can impact sleep quality and duration.[2] Similarly, the surge in digital communication, online learning, and entertainment during lockdowns has transformed media use patterns among adolescents. While technology has been a lifeline for education and networking, excessive screen time and exposure to specific content are known to pose risks to mental health. Surveys showed that excessive media exposure was associated with increased depressive and anxiety symptoms during COVID-19.[3] The findings indicate altered sleep, physical activity, and reduced media usage and screen time among Polish children and adolescents during the COVID-19 pandemic.[4] Other studies on adolescent girls found that physical activity and social media use may impact later adolescent sleep timing during the COVID-19 pandemic.[2,3]
The pattern of physical exercise and outdoor activity was reduced during the pandemic, and it had negative consequences, leading to altered emotions in the form of worries, irritability, anger, sadness, and anxiety among adolescents.[5] Increased concurrent exposure to digital media and smartphones could have also played a significant role in behavioral manifestation among adolescents during a pandemic. To the best of our knowledge, very few studies from India have uncovered the interconnectedness of the pattern of sleep, exercise, emotions, and media use in shaping the overall well-being of adolescents during COVID-19.[6] In this view, a cross-sectional survey was conducted to understand adolescents’ mental health in India. The aim was to comprehensively explore and analyze the patterns of sleep, exercise, emotions, and media use patterns among adolescents during COVID-19.
MATERIALS AND METHODS
A cross-sectional online survey was conducted among adolescents aged between 14 and 19 years of either gender who attended regular school or college till March 2020. The snowball sampling was adapted using an online link to various social media platforms from January 2022 to March 2022. At the beginning of the questionnaire, the participants were contacted through an online link, which would open with a briefing about the study’s purpose and the measures used in the study, followed by an online consent statement and instructions regarding how to fill out the form. The online link was shared with people, parents, and teachers in schools in Bhopal through emails and WhatsApp so they could further share within their social circle to complete the snowball sampling. The data were collected using a questionnaire prepared by psychiatrists and psychologists with the consent and assent of adolescents. A semi-structured performa socio-demographic data sheet looks at independent variables such as age, sex, religion, location, and class grade. To assess the daily behavior, emotions, and media-use, we used the 16 items from respective domains of CoRonavIruS health Impact Survey (CRISIS) version 0.3 numbered from 38 to 54 (known as Youth Self-Report, Follow up from: current form). CRISIS was developed through a collaborative effort between the research teams of Kathleen Merikangas and Argyris Stringaris at the National Institute of Mental Health Intramural Research Program Mood Spectrum Collaboration and those of Michael P. Milham at the Child Mind Institute and the NYS Nathan S. Kline Institute for Psychiatric Research. It can be used to assess the past 2 weeks’ experience during the pandemic on the following domains: COVID–19 health/exposure status, life changes due to COVID-19, daily behavior, emotions/worries, media use, and substance use. The scale is free to use, and information regarding the study was emailed to the authors. The scale items were pretested on the first 10 entries to know if the participants quickly understood the questions. The study also used a media-information perception questionnaire adapted from the SURVEY TOOL AND GUIDANCE, rapid, simple, and flexible behavioral insights on COVID-19 by the World Health Organization.[7] However, this article discusses only dependent variables such as daily behavior, emotions/worries, and media use in CRISIS. Permission to use the tools was obtained by writing to the author. The sample size was not predetermined. We employed snowball sampling, which is a non-probability sampling technique. Therefore, we expected the initial participating adolescents’ parents to refer to additional participants, which is an easier approach in such a survey. The objective of the study was to identify daily behaviors (which included sleep patterns and physical activity), emotions, and media use. This survey is a part of the original study named COVID-19-related media-information perception among adolescents: An online survey from India (COMPASION) approved by IHEC-LOP/2021/IM0390. The data obtained from the survey were kept confidential and accessible only to the research team.
Statistical analysis
The data entry was done in Microsoft Excel 2016, then cleaned, and analyzed using R software version 4.2.1. The continuous variables were summarized as mean (standard deviation [SD]) and categorical variables as number (%). The socio-demographic characteristics were the independent predictors for the outcomes related to sleep patterns, emotional behaviors (yes/no), and TV, social media, and video game usage (yes/no). Later, the usage of media patterns was studied as a risk factor against emotional behaviors as the outcome. The association between categorical variables was tested using the Chi-square/Fisher’s exact test. P < 0.05 was considered significant.
Operational definition
Based on the healthy sleep habits and CRISIS questionnaire, we defined sleep time as before 10 pm, referring to the initiation of sleep, that is, time when the participant goes to bed and begins attempting to sleep anytime between 6:00 pm and 10:00 pm. We defined sleep time after 10 pm as the initiation of sleep time when the participant goes to bed and begins attempting to sleep anytime from 10:00 pm onward. Inadequate sleep was defined as <8 h/night, and adequate sleep as more than or equal to 8 h/night. We defined more than or equal to 1 day/week of exercise (e.g., increased heart rate and breathing) for at least 30 min as adequate exercise, and more than or equal to 1 day/week spent outdoors as adequate environmental exposure required for health.
RESULTS
Sleep patterns among adolescents
In total, 413 adolescents completed the survey, comprising 256 (62%) boys and 157 (38%) girls, with a mean (±SD) age of 15.9 (±1.2) years. Table 1 shows that primarily, boys exhibited a trend of bedtime after 10:00 pm on both weekdays and weekends compared to girls. Adolescents in the 9th and 11th grades reported earlier sleeping times before 10 pm compared to others. Across all age groups, adolescents slept for <8 h regardless of weekdays or weekends, as shown in Table 1. In terms of sleep patterns, there was a significant association between bedtime on weekdays and gender (P < 0.001) and religion (P = 0.01), while gender (P = 0.013) and education class (P < 0.001) were associated with bedtime on weekends. Furthermore, there was an association between duration of sleep: Age group (P = 0.016) and location (P = 0.008) on weekdays, but only with location (P = 0.004) on weekends. In terms of duration of sleep, rural adolescents dominated in getting >8 h of sleep on weekdays and weekends, with P (0.008), as shown in Table 2.
| Pattern | Going to bed on weekdays | Going to bed on weekends | ||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | Overall, n=4071 (%) | Before 10 pm, n=2051 (%) | ≥10 pm, n=2021 (%) | P-value2 | Overall, n=4061 (%) | Before 10 pm, n=2311 (%) | ≥10 pm, n=1751 (%) | P-value2 |
| Age group | ||||||||
| 14–16 | 295 (72) | 148 (72) | 147 (73) | 0.9 | 294 (72) | 174 (75) | 120 (69) | 0.13 |
| 17–19 | 112 (28) | 57 (28) | 55 (27) | 112 (28) | 57 (25) | 55 (31) | ||
| Gender | ||||||||
| Male | 153 (38) | 59 (29) | 94 (47) | <0.001 | 151 (37) | 74 (32) | 77 (44) | 0.013 |
| Female | 254 (62) | 146 (71) | 108 (53) | 255 (63) | 157 (68) | 98 (56) | ||
| Location | ||||||||
| Urban | 129 (32) | 64 (31) | 65 (32) | 0.8 | 129 (32) | 66 (29) | 63 (36) | 0.11 |
| Rural | 278 (68) | 141 (69) | 137 (68) | 277 (68) | 165 (71) | 112 (64) | ||
| Class | ||||||||
| 9th std | 87 (21) | 49 (24) | 38 (19) | 0.6 | 85 (21) | 64 (28) | 21 (12) | <0.001 |
| 10th | 56 (14) | 27 (13) | 29 (14) | 56 (14) | 27 (12) | 29 (17) | ||
| 11th | 195 (48) | 97 (47) | 98 (49) | 195 (48) | 108 (47) | 87 (50) | ||
| 12th | 59 (14) | 29 (14) | 30 (15) | 60 (15) | 30 (13) | 30 (17) | ||
| Post 12th | 10 (2.5) | 3 (1.5) | 7 (3.5) | 10 (2.5) | 2 (0.9) | 8 (4.6) | ||
| Religion | ||||||||
| Hindu | 397 (98) | 203 (99) | 194 (96) | 0.015 | 396 (98) | 226 (98) | 170 (97) | 0.6 |
| Christian | 1 (0.2) | 1 (0.5) | 0 (0) | 1 (0.2) | 0 (0) | 1 (0.6) | ||
| Muslim | 6 (1.5) | 0 (0) | 6 (3.0) | 6 (1.5) | 4 (1.7) | 2 (1.1) | ||
| Jain | 3 (0.7) | 1 (0.5) | 2 (1.0) | 3 (0.7) | 1 (0.4) | 2 (1.1) | ||
| Others | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | ||
| Pattern | Number of hours per night sleep during weekdays | Number of hours per night sleep during weekends | ||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | Overall, n=4051 (%) | <8 h, n=2881 (%) | >8 h, n=1171 (%) | P-value2 | Overall, n=4001 (%) | <8 h, n=2451 (%) | >8 h, n=1551 (%) | P-value2 |
| Age group | ||||||||
| 14–16 | 295 (73) | 200 (69) | 95 (81) | 0.016* | 289 (72) | 177 (72) | 112 (72) | >0.9 |
| 17–19 | 110 (27) | 88 (31) | 22 (19) | 111 (28) | 68 (28) | 43 (28) | ||
| Gender | ||||||||
| Male | 150 (37) | 104 (36) | 46 (39) | 0.5 | 152 (38) | 87 (36) | 65 (42) | 0.2 |
| Female | 255 (63) | 184 (64) | 71 (61) | 248 (62) | 158 (64) | 90 (58) | ||
| Location | ||||||||
| Urban | 129 (32) | 103 (36) | 26 (22) | 0.008* | 129 (32) | 92 (38) | 37 (24) | 0.004* |
| Rural | 276 (68) | 185 (64) | 91 (78) | 271 (68) | 153 (62 | 118 (76) | ||
| Class | ||||||||
| 9th std | 86 (21) | 57 (20) | 29 (25) | 0.5 | 80 (20) | 43 (18) | 37 (24) | 0.079 |
| 10th | 55 (14) | 39 (14) | 16 (14) | 56 (14) | 30 (12) | 26 (17) | ||
| 11th | 194 (48) | 137 (48) | 57 (49) | 195 (49) | 133 (54) | 62 (40) | ||
| 12th | 60 (15) | 46 (16) | 14 (12) | 59 (15) | 34 (14) | 25 (16) | ||
| Post 12th | 10 (2.5) | 9 (3.1) | 1 (0.9) | 10 (2.5) | 5 (2.0) | 5 (3.2) | ||
| Religion | ||||||||
| Hindu | 395 (98) | 280 (97) | 115 (98) | 0.2 | 390 (98) | 237 (97) | 153 (99) | 0.7 |
| Christian | 1 (0.2) | 1 (0.3) | 0 (0) | 1 (0.2) | 1 (0.4) | 0 (0) | ||
| Muslim | 6 (1.5) | 6 (2.1) | 0 (0) | 6 (1.5) | 5 (2.0) | 1 (0.6) | ||
| Jain | 3 (0.7) | 1 (0.3) | 2 (1.7) | 3 (0.8) | 2 (0.8) | 1 (0.6) | ||
| Others | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | ||
Physical activity and time spent outdoors
The association between physical exercise, outdoor activity, and socio-demographic characteristics among adolescents reveals distinct patterns. Younger adolescents (14–16 years) are more likely to engage in physical exercise and outdoor activity compared to older adolescents (17–19 years). Gender differences are notable, with males participating in outdoor activities more frequently than females (46% vs. 21%, P < 0.001). Urban and rural disparities exist, though not statistically significant for outdoor activity (P = 0.3). Educational level impacts activity patterns, as 11th-grade students reported the highest engagement in both physical exercise and outdoor activity. Religion showed minimal influence on these behaviors. These findings highlight age, gender, and education could be key determinants of adolescent activity levels.
On weekdays, of the 406 participants, 269 (66.3%) participants were engaged in physical activity during the past 2 weeks. Around 139 (51.7%), 61 (22.7%), 56 (20.8%), and 13 (4.8%) of the 269 adolescents reported 1–2 days, daily, 3–4 days, and 5–6 days of physical activity of at least 30 min in the past 2 weeks. There was an increasing proportion of students engaging in physical activity and outdoor activity as they progressed through higher academic years. There was a significant association between those who reported physical activity and the academic year (P = 0.032).
On weekends, of the 410 participants, 281 (68.5%) participants engaged in outdoor activity during the past 2 weeks. Time spent in outdoor activities during the past 2 weeks was associated with gender (P < 0.001) and class of study (P < 0.001). Around 115 (40.9%), 82 (29.2%), 52 (18.5%), and 32 (11.2%) of the 281 adolescents reported 1–2 days, daily, 3–4 days, and 5–6 days of physical activity of at least 30 min in the past 2 weeks. The association between physical exercise and outdoor activity with socio-demographic characteristics among adolescents during weekdays and weekends is shown in Table 3.
| Pattern | Number of days per week exercise (e.g., increased heart rate, breathing) for at least 30 min | Number of days per week time spent outdoors | ||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | Overall, n=4061 (%) | None, n=2371 (%) | Physical activity, n=1691 (%) | P-value2 | Overall, n=4101 (%) | None, n=1291 (%) | Outdoor activity, n=2811 (%) | P-value2 |
| Age group | ||||||||
| 14–16 | 294 (72) | 102 (74) | 192 (71) | 0.5 | 297 (72) | 99 (77) | 198 (70) | 0.2 |
| 17–19 | 112 (28) | 35 (26) | 77 (29) | 113 (28) | 30 (23) | 83 (30) | ||
| Gender | ||||||||
| Male | 154 (38) | 56 (41) | 98 (36) | 0.4 | 156 (38) | 27 (21) | 129 (46) | <0.001* |
| Female | 252 (62) | 81 (59) | 171 (64) | 254 (62) | 102 (79) | 152 (54) | ||
| Location | ||||||||
| Urban | 129 (32) | 35 (26) | 94 (35) | 0.054 | 129 (31) | 45 (35) | 84 (30) | 0.3 |
| Rural | 277 (68) | 102 (74) | 175 (65) | 281 (69) | 84 (65) | 197 (70) | ||
| Class | ||||||||
| 9th std | 83 (20) | 35 (26) | 48 (18) | 0.032* | 87 (21) | 14 (11) | 73 (26) | <0.001* |
| 10th | 56 (14) | 21 (15) | 35 (13) | 55 (13) | 24 (19) | 31 (11) | ||
| 11th | 197 (49) | 53 (39) | 144 (54) | 198 (48) | 73 (57) | 125 (44) | ||
| 12th | 60 (15) | 26 (19) | 34 (13) | 60 (15) | 18 (14) | 42 (15) | ||
| Post 12th | 10 (2.5) | 2 (1.5) | 8 (3.0) | 10 (2.4) | 0 (0) | 10 (3.6) | ||
| Religion | ||||||||
| Hindu | 396 (98) | 135 (99) | 261 (97) | >0.9 | 400 (98) | 127 (98) | 273 (97) | 0.13 |
| Christian | 1 (0.2) | 0 (0) | 1 (0.4) | 1 (0.2) | 0 (0) | 1 (0.4) | ||
| Muslim | 6 (1.5) | 1 (0.7) | 5 (1.9) | 6 (1.5) | 0 (0) | 6 (2.1) | ||
| Jain | 3 (0.7) | 1 (0.7) | 2 (0.7) | 3 (0.7) | 2 (1.6) | 1 (0.4) | ||
| Others | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | 0 (0) | ||
Emotional behaviors
The pattern and association between the emotions and socio-demographic characteristics reflect that the gender variable was significantly associated with emotions of being worried (P = 0.032), happy (P = 0.020), and fatigued (P = 0.008). The class of study was associated with being worried (P = 0.043), anxious (P = 0.047), fidgeting (P = 0.035), and fatigue (P < 0.001). The religious characteristic was associated with emotions of being happy (P = 0.003) and fidgeting (P = 0.014). Of all the emotions, fatigue was found to be associated with age (P = 0.008) and location (0.013). No significant association with age, gender, location, class, and religion was reported across distractibility and irritability. Figure 1 shows the self-reported mixture of emotions among the adolescents.
- Compound bar chart showing the self-reported mixture of emotions among the adolescents.
Television, social media, and video game usage patterns
Around 343 (84.9%), 302 (74.2%), and 164 (40.4%) of respondents reported television (n = 404), social media (n = 407), and video game usage (n = 406). The pattern of usage in the Supplementary file revealed that those adolescents of the 14–16 years age group, girls, belonging to the 9th–11th class, and residents of rural showed predominant television, social media, and video game usage as compared to others. From Tables 4-6, it is evident that there is a significant association between age (P = 0.021), gender (P < 0.001), location (P < 0.001), class (P = 0.007), and social media usage. Similarly, there was a significant association between age (P = 0.001), gender (P < 0.001), location (P < 0.016), class (P < 0.001), religion (P = 0.005), and video game usage.
Tables 4-6 [Supplementary file] show the association between TV/digital media, social media, and videogame usage as independent variables with the emotional characteristics (worry, fatigue, etc.,) as outcome. There was a statistically significant association between worry levels (P = 0.040), fidget (P = 0.002), fatigue (P < 0.001), irritability (P = 0.006), and TV/digital media usage, as shown in Table 4. Furthermore, there was a significant association between fatigue (P < 0.001), anxiety (P < 0.001), distractibility (P < 0.001), and social media usage, as shown in Table 5, while video game usage was found to be statistically associated with worry (P = 0.023), anxiety levels (P = 0.020), fidgeting (P = 0.011), fatigue (P < 0.001), and distractibility (P = 0.031), as shown in Table 6 [Table 6 supplementary file].
DISCUSSION
The results of the study examining the patterns of sleep, physical exercise, outdoor activities, emotions, and media exposure provided important mental health parameters of the 413 adolescents who took the survey in central India. The age distribution indicates a relatively homogeneous age group with predominantly male participation in the survey. The results of the study show that the COVID-19 pandemic resulted in a considerable proportion of adolescents’ development of worries, depression, and anxiety symptoms, indicating the perception of the COVID-19 period as a stressful event by adolescents in central India.
Sleep, physical exercise, and outdoor activities
Adolescents commonly followed late-night sleep schedules, with many getting only moderate weekday sleep and rural youth sleeping more than urban peers. While outdoor and physical activity varied by age, gender, and location (with males, younger, and rural teens tending to be more active), these trends lacked statistical consistency across studies.[8] After COVID-19, the children and youth from Canada in an online survey also reported the adverse impact on their activities and play behaviors.[5] To re-establish the altered sleep and physical activities, developing at-home “play kits” for students is suggested by recent research based on a practical, theory-driven solution to address restricted physical activity in stressful periods.[9] The specific motivation and patterns of physical exercise, such as dance, drama, and sports, were also analyzed by some studies not considered in our survey.[10] More studies may be required to examine effective exercise and play in a restricted environment with a more consistent routine.
Emotional behavior
The impact of COVID-19 on adolescent mental health has been studied globally, reflecting findings similar to our study.[11] Our study findings suggest the importance of considering gender-specific factors influencing emotional behaviors in females, similar to findings from Indian study on academic stress and emotional well-being.[12] Although not significant, recognizing and understanding the religious and cultural variations are crucial for developing culturally sensitive interventions for mental well-being among adolescents. Anxiety due to COVID-19 was 20.4%, and females were reported to have higher levels of distress, similar to our study.[13] Although emotional disturbances were reported only by males and females gender in our study, there is also evidence of increasing distress in gender minorities among the adolescent population.[12] Academic advancement showed a possible link with anxiety and fidgeting, while age, gender, location, and school grade significantly correlated with happiness, worries, anxiety, fatigue, and fidgetiness, but not irritability or distractibility.[14] This may be because irritability and distractibility could be determined by factors such as acceptable adolescent behavior, affect dysregulation, and easy distractibility during adolescence. A similar study conducted in Delhi and Mathura also found higher stress levels among children during COVID-19, but using different tools.[15] Multiple other studies have also looked at the increasing levels of depression and anxiety in adolescents after the pandemic,[16] with wide variation across countries.[17]
Media use
Social media use during crises like the COVID-19 pandemic has both benefits and drawbacks. It enabled adolescents to stay socially connected, yet also fostered one-way communication, information overload, and anxiety, leading to worry, irritability, fatigue, and heightened distress. Reduced outdoor activity and amplified negative emotions may have exacerbated underlying mental health issues, a limitation this survey could not resolve. The study found varied emotional associations with exposure to television, social media, and video games. Notably, adolescents aged 14–16, girls, rural residents, and students in grades 9–11 used these media more than others, with social media use significantly linked to age, gender, location, and school grade. Similar findings in China suggest psychological distress may mediate the link between screen exposure and depression.[18] TV, video games, and digital media use were associated strongly with emotional symptoms, findings similar to adolescent samples from the Philippines and Turkey.[19] Family issues were reported to be associated with problematic use of social media and the internet among adolescents during COVID-19.[20] However, these factors were not considered in the current survey. Further research is needed to understand how differences in TV, social media, and video game exposure relate to screen habits, worry, sleep, and overall adolescent well-being and to determine how effectively parents, schools, and child-friendly communities can promote outdoor play, physical activity, and healthier digital use.[21]
Limitations
Limited access to digital devices and the internet may have resulted in a non-representative sample. Adolescents may provide socially desirable answers that may skew the dataset. Family-related factors influencing social media use were also not explored.
CONCLUSION
The study highlights the lifestyle patterns impacted by COVID-19 on adolescents in central India, emphasizing the need for promoting healthier routines. Physical activity and emotional well-being showed gender, urban-rural, and age-based variations. Media exposure, mainly social media and video games, was strongly associated with emotional disturbances such as anxiety, fatigue, and irritability, highlighting the dual-edged role of digital platforms during crises. These findings call for uniform efforts at local and global levels with community-based and school-led initiatives to foster healthier sleep, physical activity, emotional resilience, and balanced media use.
Ethical approval:
The research/study was approved by the Institutional Review Board at AIIMS Bhopal, number LOP/2021/IM0390, dated May 01, 2021.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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