Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents.

OBJECTIVE: This study attempted to address the limited knowledge regarding the impact of screen time (ST) on lifestyle behaviors in Indian adolescents during the ongoing COVID-19 pandemic. The objectives were to 1) evaluate frequency and duration of using screens, and screen addiction behaviors in 10-15 years old adolescents in Mumbai during the COVID-19 pandemic and 2) examine the association of ST with lifestyle behaviors- eating habits, snacking patterns, physical activity (PA) levels, sleep quality and depression symptoms.
METHODS: An online survey was completed between January and March 2021. Eating habits, snacking patterns, time spent in different screen-based activities, and screen addiction behaviors were reported. The PA levels, sleep quality, and depression symptoms were evaluated using the Physical Activity Questionnaire for Children/Adolescents (PAQ C/A), Pittsburg Sleep Quality Index (PSQI), and Patient Health Questionnaire-2 (PHQ-2) respectively. Multiple linear regression analyses were performed to determine the impact of ST on lifestyle behaviors.
RESULTS: Adolescents (n = 1298, Mage 13.2(1.1), 53.3% boys) reported the mean weekday and weekend ST as 442.3 (201.5) minutes/d and 379.9 (178.2) minutes/d respectively. Overall, 33.4% spent > 6hours/d for studying or doing homework, 65.4% used social networking sites for at least 2-3 hours/d, and 70.7% agreed that ST had taken up the majority of their leisure time. Only 12% engaged in moderate to vigorous PA (PAQ C/A scores <2). More than half (52.5%) reported PSQI >5 indicating poor sleep quality and 8.6% scored ≥ 3 on PHQ-2 to suggest a risk of depression. A higher ST was associated with lower PA and increased sleep problems and a greater screen addiction was inversely associated with the eating habit, PA, and sleep-related variables.
CONCLUSION: The study draws attention to a high prevalence of excess ST and its impact on eating habits, PA levels, and sleep quality in Indian adolescents during the COVID-19 pandemic. Targeted health promotion interventions that encourage judicious use of screens for education and entertainment and emphasize the adverse health effects of excess ST are required.

Introduction

With the advent of the global COVID-19 pandemic and a need to mitigate the spread and transmission of the coronavirus, many countries, including India, had to issue stay-at-home advisories and impose lockdown restrictions and social distancing protocols [1,2]. The extended closures of schools prompted the educational institutions to adopt online teaching-learning models for students and the movement bans resulted in greater use of screens for entertainment and social interactions. While these measures have helped to a certain extent to maintain normalcy, they have also inadvertently increased the usage of varied screens, such as television, computers, video gaming, and mobile devices, in adolescents, much beyond the recommended duration of two hours per day [1-5]. The negative health consequences of excess screen time (ST), such as weight gain, behavioral problems, and sleep disturbances are well documented [6-9]. So, the increase in ST during the ongoing COVID-19 pandemic is likely to pose a greater risk of adverse health outcomes in adolescents, many of whom may have already been engaging in unhealthy lifestyle behaviors. This brings forth a need to investigate the prevalence of excess ST during the pandemic and evaluate its potential influences on lifestyle behaviors, such as eating habits, physical activity (PA) levels, and sleep patterns in adolescents.

Few studies that assessed the impact of the COVID-19 pandemic on adolescent lifestyle behaviors have suggested a complex, nonlinear, and context-driven effect. For instance, a recent study observed an increase in both ST and habitual PA in adolescents during the pandemic [4], but another cross-sectional study reported a drastic reduction in PA and a substantial increase in ST [10]. Besides the influence of ST on PA levels, there is evidence that adolescents who spend more time using screens tend to have a lower intake of fruits and vegetables and higher consumption of energy-dense snacks [6,11-13]. However, a majority of these studies were conducted before the pandemic and little is known about the association between excessive ST and adolescents’ eating habits during the COVID-19.

Excess ST has also been associated with poor sleep quality, daytime dysfunction, and an increased risk of anxiety and depression in adolescents [7,8,14]. While the evidence regarding the association of screen-based sedentary behaviors with sleep latency, and sleep insufficiency is unequivocal [6,15,16], the influence of ST on mental health seems to vary based on the duration and content of screen exposure in adolescents [17-19]. Given that unhealthy lifestyle behaviors, such as physical inactivity, unhealthy diet, and inadequate sleep tend to co-occur [20], the impact of excessive ST may transude into multiple behaviors of adolescents, thereby escalating the health risks during the pandemic.

To date, there is no clarity on how long the lockdown restrictions will continue or the COVID-19 pandemic will last. As the lockdown restrictions continue in India and the chances of a third wave to commence persist, the impact of the increased ST during the COVID-19 on the lifestyle habits of adolescents must be better understood. This information can help customize ST recommendations and guide effective policies and interventions aimed at moderating the health risks associated with excess ST in adolescents. Yet studies investigating the prevalence and magnitude of ST during the ongoing pandemic and its association with lifestyle behaviors and mental health in adolescents in India are still lacking.

To the best of our knowledge, this study is the first to provide a comprehensive investigation into several lifestyle behaviors, such as eating habits, PA levels, sleep quality, and depressive symptoms among adolescents in India during the pandemic and to assess these behaviors as a function of ST. The primary aim was to assess the impact of ST during COVID-19 on lifestyle behaviors in Indian adolescents and the specific objectives were to 1) evaluate frequency and duration of using screens, and screen addiction behaviors in 10–15 years old adolescents in Mumbai during the COVID-19 pandemic and 2) examine the association of ST with eating habits, physical activity (PA) levels, sleep quality and depression symptoms.

Methods

Study design, setting, and adolescents

This cross-sectional study was conducted among 10–15 years old adolescents attending grades 6 to 10 of six private schools and four government-aided schools in the city of Mumbai, India. The study sites were selected using a purposive sampling method. An online survey was conducted to collect data as an in-person survey was not feasible due to the ongoing pandemic-induced closures of educational institutes in India since late March 2020 [2]. Information leaflets containing details of the study and a link to provide parental consent were sent to each of the participating schools and colleges. The parents were informed to provide consent within a week of receiving the information sheets. Adolescents who provided signed parental consent (n = 1512) were invited to join virtual meetings scheduled separately for each of the study sites. The online survey was completed by 1298 adolescents in the presence of the investigators, research staff, and school representatives. Data were collected from January 2021 to March 2021 after obtaining ethical approval from an independent ethics committee, Intersystem Biomedica Committee, Mumbai (ISBEC protocol Version 1b, dated 16 December 2020).

Sample size estimation

Based on a recent study that reported the prevalence of excessive ST (using screens >2hours/day) in urban adolescents in India as 68% [21] and after using 95% confidence level, a 5% margin of error, a non-response rate of 25%, and a proportional representation of adolescents from private and government schools, the final sample size was estimated as 805.

Measures

An online survey including questions related to socio-demographic characteristics, eating habits, snacking behaviors, physical activity levels, screen time and screen addiction, sleep patterns, and depression symptoms were administered through google forms on a virtual meeting platform.

Demographic characteristics

Adolescents were asked to provide demographic information, such as gender, date of birth, class of study, type of living arrangement, father’s present occupation, mother’s working status, and type and number of screens (television/mobile phones/computers/laptops/tablets) owned by them and their families.

Screen time

In this study, the term ‘screen time’ indicates the time spent working/studying/playing using any screen device, and ‘screen usage’ refers to different screen devices, such as laptops, mobile phones, television, and more, that were used by the adolescents. The type, frequency, and duration of screen usage were reported on a brief five-item questionnaire, that was developed by the researchers after an extensive review of similar instruments [7,17,22] used in previous studies among adolescents. The questionnaire administered to the adolescents is provided as Supplemental Material (File S1). In summary, the frequency of using different screens was reported from ‘never (0 days)’ to ‘every day (7 days)’, and the time spent using these screens on a typical weekday and a typical weekend was reported as minutes/d. To estimate the daily time spent in screen-related activities, the adolescents were asked ‘In the last 7 days, how much time did you spend in the following screen-related activities? and to evaluate the adolescents’ addiction to screen usage, a five-point Likert scale (strongly disagree to strongly agree) was used. Additionally, two statements evaluated adolescents’ perceived increase/decrease in their screen usage and ST during the lockdown.

Eating habits and snacking patterns

To evaluate the eating habits, the adolescents were asked to report the frequency of consuming breakfast, having lunch or dinner with family, watching television while having meals, eating out with family and/or friends, and ordering takeaways in the past week. The response options—never, 1–2 days, 3–4 days, 5–6 days, and every day were scored 0–4. A brief 24 item food frequency questionnaire, that was validated in our previous study for the same population [23], estimated the consumption of fruits, freshly prepared fruit juices, packaged 100% fruit juices, vegetables (green leafy vegetables, orange and yellow vegetables, salad, and other vegetables), unhealthy snacks (foods high in fat, salt, and sugar) and carbonated beverages. Adolescents were asked ‘In the last 7 days, how many days did you consume the following foods/beverages?’. The responses were evaluated on a five-point scale, from never to 2 or more than twice a day, scored 0 to 4 for fruits and vegetables, and reverse coded as 4–0 for unhealthy snacks and carbonated beverages to ensure that higher scores indicated healthier eating habits. For each of the listed food items, the adolescents reported if their consumption has increased, decreased, or remained the same during the pandemic as compared to the pre-pandemic times.

Physical activity levels

The validated self-reported instruments, the Physical Activity Questionnaire for Children and/or Adolescents (PAQ-C/-A) assessed the physical activity levels of adolescents. The PAQ -C/A has been extensively used to evaluate general physical activity levels of children and adolescents in previous studies [24,25]. The PAQ C is typically administered in children, ages 8 to 14 years and comprises of 9 items providing a 7-day recall of the type and frequency of activities performed in spare time, during physical education (PE) classes and recess breaks, right after school, in the evenings, and on weekends. The PAQ-A for adolescents > 14 years is a modified version of PAQ-C that includes the same items except for the question regarding activities performed during recess. In both the questionnaires, each item is scored from 1 to 5 to derive item-specific composite activity scores. The mean of these composite scores is used to determine the PAQ summary score that ranges from 1–5 with higher scores (≥ 2) indicating moderate to vigorous level of PA and scores < 2 as light PA [25].

To determine the changes in the frequency and duration of screen usage, the frequency of intake of specific food items, and engagement in different physical activities during the COVID-19 pandemic as compared to before pandemic, the adolescents were asked to report whether they perceived the changes as increased, decreased or remained similar. The responses to these questions generated quantitative data that helped estimate the impact of the pandemic on the selected measures.

Sleep quality

The sleep patterns including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, and daytime dysfunction were evaluated using the Pittsburg Sleep Quality Index (PSQI) [26]. The validity and reliability of PSQI to identify sleep-related problems in adolescents have been established in previous studies [26-28]. The PSQI comprises Likert-type and open-ended questions that are scored from 0 to 3. Of a maximum score of 21, a total score > 5 is considered indicative of poor sleep quality.

Depression symptoms

The frequency of experiencing depression symptoms was assessed using Patient Health Questionnaire-2 or PHQ-2, a widely accepted and validated screening tool for major depressive disorders among adolescents [29,30]. The PHQ-2 includes the two items that inquire about the frequency of ‘having little pleasure in doing things’ and ‘feeling down, depressed or hopeless in the past two weeks. The frequency options for each item include ‘not at all’ to ‘nearly every day’ (scored 1 to 3). Adolescents reporting an overall score ≥ 3 are considered to be at risk for depression.

Statistical analysis

All analyses were performed using SPSS version 24. Descriptive statistics were calculated as mean and standard deviations or n (%). Comparison of categorical and continuous variables was performed using the chi-squared tests and one-way ANOVA, stratified by sex and age categories (10–12 years and 13–15 years). Multiple linear regression analyses were performed to assess the association of ST items, such as frequency and duration of screen usage, screen-related activities, and screen addiction scores with the dependent variables—eating habits (total healthy eating habit score was calculated by aggregating eating habit and snacking behavior item scores), physical activity levels (mean summary PAQ-C/A scores), sleep patterns (mean global PSQI scores) and depression symptoms (mean PHQ 2 scores) while controlling for adolescents’ age, sex, and type of school attended (used as a proxy for socioeconomic status) as covariates. Univariable regression analyses were first performed to determine the unadjusted effect of factors associated with each of the dependent variables. Next, the independent variables with a significance level <0.1 were entered into the mixed-effects multivariable regression models to determine the ST variables that were associated significantly with eating habits, PA, sleep, and depression symptoms. Analysis of residuals confirmed the assumptions of linearity and as indicated in previous studies, the lowest values of Akaike’s and Schwarz’s Bayesian information criteria measures were used to test the goodness of fit of the final models. To test multicollinearity, Pearson correlation coefficient (r) values > 0.5 or the variance inflation factor (VIF) value > 10 were used as the diagnostic tests. The VIF values ranged from 2.12 to 8.15 (mean 5.44) for the majority of variables, except for an ST-related activity item (reading/listening to music) and an ST addiction statement (I use screens for a longer duration than is good for me), so these variables were excluded in the final model. Results were reported as standardized regression coefficients (β) and standard error (SE). All tests were two-tailed and considered statistically significant at a p-value ≤ 0.05.

Results

Sample characteristics

Of 1512 adolescents for whom parental consent was provided, 89 were not present on the survey day and 155 had provided > 20% incomplete/implausible data in the online survey. So, the analyses were performed on the final sample (n = 1298, 85.8% of those with parental consent) of adolescents, aged 10–12 years (n = 724) and 13–15 years (n = 574). The mean age of the adolescents was 13.2 (1.2) years). Overall, 53.3% were boys, 60.2% attended private schools, and 78.9% and 70.9% mentioned their living arrangement as a nuclear family and the mother’s working status as a homemaker respectively (Table 1). Almost all adolescents reported their families having a television (95.1%) and mobile/smartphones (93.4%). The most common types of screen devices owned by the adolescents were smart/mobile phones (68.5%), laptops (43.5%), and Tablets or iPads (12.5%).

Table 1

Demographic characteristics of 10–15 years old adolescents in the study (n%).

Variables	Overall (n = 1298)
Gender
Boys	692 (53.3)
Girls	606 (46.7)
Age categories
10–12 years	724 (55.8)
13–15 years	574 (44.2)
Type of school attended
Private school	782 (60.2)
Government-aided school	516 (39.8)
Type of living arrangement
Single parent family	19 (1.5)
Nuclear family	1024 (78.9)
Joint family	179 (13.8)
Extended family	76 (5.9)
Father’s present occupation
Service	459 (35.4)
Business	389 (30.0)
Menial jobs	234 (18.0)
Self-employed	140 (10.8)
Does not know	76 (5.9)
Mother’s working status
Works full time (> 6h/day)	256 (19.7)
Works part-time (< 5 h/day)	106 (8.2)
Homemaker	920 (70.9)
Does not know	16 (1.2)
Number of screen devices owned by the family (including television, mobile phone)
≤ 2	12 (0.9)
3–5	870 (67.0)
≥ 6	416 (32.0)
Type of screens owned by the family (Response -Yes)
Television	1235 (95.1)
Desktop computer	321 (24.7)
Laptop	689 (53.1)
Smartphone/Mobile phone	1212 (93.4)
Tablets/iPads	378 (29.1)
Others (X Box/PlayStation/smartwatch)	181 (13.9)
Type of screens owned by the participant (Response -Yes)
Television (in own bedroom)	112 (8.6)
Desktop computer	67 (5.2)
Laptop	564 (43.5)
Smartphone/Mobile phone	889 (68.5)
Tablets/iPads	162 (12.5)
Others (game consoles/handheld video games)	104 (8.0)

Screen time

Among adolescents, 37.7%, 30.2%, and 64.9% reported using television, laptops/desktop computers, and mobile phones every day in the past week for study or entertainment respectively. The total time spent using screens on weekdays was 442.29 (201.5) minutes/d and during weekends was 379.90 (178.2) minutes/d. One third (33.4%) had spent > 6hours/d using screens for studying or doing homework, two thirds (65.4%) reported being on social networking sites 2–3 hours/d and a majority reported that their screen usage (85.6%) and ST (94.9%) had increased during the pandemic. For screen addiction behaviors, 70.7% agreed/strongly agreed that screen time takes up the majority of their leisure time and 20.5% reported that they prefer socializing online than meeting people face to face (Table 2).

Table 2

Screen related behaviors of 10–15 years old adolescents (n = 1298) in the study.

Frequency of screen usage/week n (%)
Variables	Never (0 days)	Sometimes (1–2 days)	Often (3–4 days)	Frequently (5–6 days)	Every day (7 days)
Television	103 (7.9)	112 (8.6)	246 (19.0)	348 (26.8)	489 (37.7)
Laptop/desktop computer	301 (23.2)	126 (9.7)	212 (16.3)	267 (20.6)	392 (30.2)
Mobile/Smart phone	38 (2.9)	45 (3.5)	59 (4.5)	313 (24.1)	843 (64.9)
Tablets/iPads	712 (54.9)	174 (13.4)	152 (11.7)	102 (7.9)	158 (12.2)
Time spent in screen-related activities/day n (%)
Variables	<30 minutes/d	30 minutes- 1hour	1hour–2hour	> 2 hours	> 4 hours
Studying/doing homework	6 (0.5)	25 (1.9)	462 (35.6)	372 (28.7)	433 (33.4)
Using social networking sites	137 (10.6)	312 (24.0)	754 (58.1)	62 (4.8)	33 (2.5)
Playing games	93 (7.2)	192 (14.8)	877 (67.6)	98 (7.6)	38 (2.9)
Watching movies/YouTube	26 (2.0)	58 (4.5)	1051 (81.0)	101 (7.8)	62 (4.8)
Reading/listening to music	588 (45.3)	290 (22.3)	362 (27.9)	38 (2.9)	20 (1.5)
Screen addiction behaviors n (%)
Variables	Strongly disagree	Disagree	Neither agree nor disagree	Agree	Strongly agree
I can’t imagine going anywhere without my mobile devices	172 (13.3)	246 (19.0)	371 (28.6)	383 (29.5)	126 (9.7)
Screen time takes up a majority of my leisure time	62 (4.8)	170 (13.1)	148 (11.4)	631 (48.6)	287 (22.1)
I use screens for a longer duration than is good for me	117 (9.0)	297 (22.9)	333 (25.7)	347 (26.7)	204 (15.7)
I prefer socializing online than meeting people face to face	520 (40.1)	389 (30.0)	123 (9.5)	74 (5.7)	192 (14.8)

Eating habits, physical activity levels, sleep quality, and depression symptoms of adolescents

The mean frequency of breakfast consumption and having meals sitting together with family was reported as 2.46 (1.31) d/week and 2.55 (1.22) d/week respectively. Adolescents mentioned watching television/any other screen while eating 4–5 times/week and consuming fast foods, carbonated beverages, and foods high in fat content > 3times/week. Overall, the fresh fruit items were consumed 4–5 times in the last week but the consumption of salads and healthy snacks were only 1–2 times/week. Increased frequency of consumption of fast food (66.8%), foods high in sugar (56.5%), fried foods (48,9%), carbonated beverages (59.1%), and fruits (72.3%) during the pandemic were reported. Adolescents reported the frequency of having online physical education (PE) classes as a part of the school curriculum and being active during these PE classes to be < 2times/week. Almost all adolescents (88.6%) mentioned a decrease in PA during the pandemic and a majority (88%) had light PA levels (as assessed using PAQ C/A scores < 2). Overall, 52.5% and 8.6% had PSQI >5 and PHQ-2 scores ≥ 3 respectively. In Fig 1, we have provided the results of the changes in the frequency of screen usage, eating habits, snacking patterns, and physical activity levels during the pandemic as reported by the adolescents.

Fig 1

Reported frequency of screen usage, eating habits, and physical activity levels before and during COVID-19.

Comparison between sex and age categories

No significant sex and age differences were observed in the eating habits and snacking patterns of adolescents except for the healthy snack consumption scores between girls (2.15 (0.89) d/week) and boys (1.76 (0.65) d/week, p = 0.033) (Table 3). For PA, we observed differences in the frequency of being active during PE classes and the mean PAQ C/A scores between boys and girls and between older and younger adolescents. Girls reported a higher frequency of using mobile/smartphones and using social networking sites as compared to boys. The mean scores for all screen addiction behavior-related statements were higher among older adolescents (13–15 years) as compared to the younger adolescents (10–12 years old). The mean sleep latency, daytime dysfunction, and global PSQI scores were higher among girls, and the mean depression symptom score for ‘having little pleasure in doing things’ was higher among 13–15 years old adolescents. Also, the proportion of adolescents having poor sleep quality and depression symptoms increased with age (p < 0.05).

Table 3

Descriptive results of eating habits, screen time, physical activity levels and sleep quality during COVID-19 pandemic of 10–15 years old adolescents, stratified by sex and age categories.

Variables	Total (n = 1298)	Boys (n = 692)	Girls (n = 606)	P value	10–12 years (n = 724)	13–15 years (n = 574)	P value
Eating habits (days/week) a
1. Had breakfast	2.46 (1.31)	2.49 (1.29)	2.39 (1.36)	0.722	2.41 (1.47)	2.52 (1.11)	0.672
2. Had lunch/dinner sitting together with family	2.55 (1.22)	2.52 (1.20)	2.61 (1.24)	0.743	2.68 (1.22)	2.40 (1.21)	0.241
3. Had an evening meal in front of the Television/any screen	4.76 (1.04)	4.65 (1.02)	6.00 (1.06)	0.238	4.86 (1.04)	4.65 (1.03)	0.449
4. Brought fast foods from outside/Ordered takeaways	0.58 (0.13)	0.54 (0.19)	0.70 (0.10)	0.227	0.58 (0.12)	0.59 (0.23)	0.962
Snacking pattern (days/week) b
1. Fresh Fruits	4.71 (2.17)	4.54 (2.66)	5.06 (1.89)	0.349	4.57 (2.21)	4.87 (2.74)	0.552
2. Salad	1.50 (1.01)	1.46 (1.01)	1.58 (1.02)	0.603	1.45 (0.97)	1.56 (0.78)	0.560
3. Healthy snacks	1.94 (0.79)	1.76 (0.65)	2.15 (0.89)	0.033 *	1.89 (0.72)	1.97 (0.81)	0.221
4. High sugar foods	2.94 (0.65)	3.00 (0.65)	2.82 (0.66)	0.186	2.96 (0.62)	2.92 (0.72)	0.712
5. High-fat foods	3.27 (0.60)	3.23 (0.59)	3.36 (0.60)	0.272	3.29 (0.59)	3.25 (0.60)	0.762
6. High salt foods	2.99 (0.71)	3.03 (0.69)	2.91 (0.72)	0.424	2.96 (0.66)	3.02 (0.75)	0.685
7. Fast foods	3.61 (0.56)	3.62 (0.57)	3.58 (0.56)	0.714	3.64 (0.55)	3.56 (0.58)	0.472
8. Carbonated beverages	3.68 (0.57)	3.66 (0.55)	3.73 (0.57)	0.572	3.70 (0.44)	3.63 (0.64)	0.319
Physical activity levels
Frequency of online PE classes/week	1.70 (0.92)	1.85 (0.85)	1.39 (0.99)	0.037 *	2.02 (0.62)	1.33 (0.82)	0.048 *
Frequency of being active in PE/week	1.44 (1.41)	1.63 (1.26)	1.03 (1.63)	0.020 *	1.91 (1.05)	0.90 (1.10)	<0.001 **
Mean PAQ- C/A score	1.15 (1.47)	1.44 (1.05)	0.85 (0.61)	<0.001 **	1.18 (0.71)	1.10 (0.73)	0.045 *
PA level (n%)
Light PA (score <2)	1142 (88.0)	590 (85.3)	552 (91.1)	0.001 *	629 (86.9)	513 (89.4)	0.168
Moderate to vigorous (score >2)	156 (12.0)	102 (14.7)	54 (8.9)	-	95 (13.1)	61 (10.6)	-
Screen related behaviors
Frequency of screen usage (d/week)
• Laptop/Computer	5.12 (3.29)	5.02 (2.89)	5.22 (3.45)	0.584	4.90 (2.28)	5.46 (3.40)	0.865
• Mobile Phone	6.67 (4.21)	6.62 (3.99)	6.69 (4.33)	0.498	5.82 (3.89)	7.02 (4.51)	<0.001**
• Television	4.78 (3.64)	4.99 (3.61)	4.56 (2.89)	0.123	5.12 (3.78)	4.32 (3.23)	<0.001**
• Tablet/iPad	1.23 (1.16)	0.99 (0.88)	1.69 (1.20)	0.003 *	1.22 (1.08)	1.24 (1.20)	0.539
• Game consoles	0.58 (0.12)	0.61 (0.14)	0.58 (0.09)	0.821	0.62 (0.10)	0.57 (0.15)	0.291
Time spent on screen-based activities c
• Studying/doing homework	2.57 (1.32)	2.52 (1.36)	2.67 (1.27)	0.605	2.64 (1.30)	2.52 (1.34)	0.684
• Using social networking sites	1.57 (0.93)	1.01 (0.87)	2.00 (1.01)	0.036 *	1.05 (0.87)	2.00 (0.99)	<0.001 **
• Playing games	1.93 (0.88)	1.98 (0.82)	1.78 (0.92)	0.099	2.01 (0.85)	1.88 (0.90)	0.158
• Watching movies/YouTube	2.24 (1.16)	2.17 (1.12)	2.39 (1.16)	0.219	2.21 (1.15)	2.25 (1.16)	0.312
• Reading/listening to music	0.56 (0.22)	0.38 (0.20)	0.72 (0.21)	< 0.001 **	0.48 (0.20)	0.64 (0.24)	0.002*
Total screen time/weekday (minutes)	442.29 (201.5)	438.34 (178.2)	451.12 (214.4)	0.645	448.45(167.34)	431.11 (221.12)	0.108
Total screen time/weekend (minutes)	379.90 (178.2)	398.22 (166.8)	365.30 (181.1)	0.612	369.98(166.23)	385.20 (184.10)	0.118
Screen addiction behaviors d
• I can’t imagine going anywhere without my mobile devices	2.01 (1.18)	1.87 (1.25)	2.30 (0.95)	0.083	1.73 (1.18)	2.33 (1.19)	0.009 *
• Screen time takes up a majority of my leisure time	2.68 (1.10)	2.65 (1.20)	2.76 (0.93)	0.638	2.52 (1.08)	2.88 (1.12)	0.099
• I use screens for a longer duration than is good for me	2.17 (1.20)	2.14 (1.28)	2.24 (1.03)	0.690	2.05 (1.22)	2.31 (1.17)	0.276
• I prefer socializing online	1.21 (1.13)	1.15 (1.04)	1.33 (1.16)	0.543	1.20 (1.10)	1.21 (1.12)	0.983
Sleep pattern using PSQI
Subjective sleep quality	0.84 (0.53)	0.80 (0.50)	0.86 (0.56)	0.218	0.77 (0.56)	0.87 (0.46)	0.044 *
Sleep Latency	1.10 (1.05)	0.87 (0.55)	1.58 (1.09)	< 0.001 **	1.05 (0.98)	1.12 (1.06)	0.714
Sleep Duration	0.97 (0.68)	0.98 (0.66)	0.97 (0.69)	0.443	0.85 (0.66)	1.10 (0.71)	0.836
Sleep Efficiency	0.89 (0.12)	0.91 (0.11)	0.88 (0.13)	0.631	0.88 (0.10)	0.89 (0.12)	0.479
Sleep Disturbances	0.77 (0.28)	0.82 (0.21)	0.73 (0.29)	0.335	0.82 (0.25)	0.79 (0.29)	0.492
Daytime Dysfunction	0.88 (0.41)	0.65 (0.30)	1.12 (0.55)	0.027 *	0.85 (0.40)	0.92 (0.42)	0.192
Global PSQI score	5.45 (3.21)	5.21 (2.97)	5.78 (3.30)	0.011 *	5.03 (3.02)	5.82 (3.10)	0.036 *
Mean Global PSQI score > 5 (n%)	682 (52.5)	340 (49.1)	342 (56.4)	0.008 *	360 (49.7)	322 (56.1)	0.021 *
Depression symptoms using PHQ-2
Little pleasure in doing things	1.24 (0.65)	1.27 (0.59)	1.23 (0.72)	0.992	0.89 (0.59)	1.58 (0.68)	<0.001 **
Feeling down, depressed or hopeless	0.64 (0.33)	0.63 (0.30)	0.64 (0.34)	0.712	0.62 (0.28)	0.65 (0.35)	0.760
Mean PHQ-2 score ≥ 3 (n%)	111 (8.6)	58 (8.4)	53 (8.7)	0.847	43 (5.9)	68 (11.8)	0.002 *

*p <0.05,

** p < 0.001.

Significant measures are highlighted.

Abbreviations: PA, Physical Activity, PE, Physical Education. PSQI, Pittsburg Sleep Quality Index. PHQ, Patient Health Questionnaire.

a Eating habits- Frequency options-0-7.

b Snacking pattern- Healthy snack items (sandwich/sprouts/popcorn), High sugar foods (Chocolates/Cakes/Pastries/Ice cream), High fat foods (Fried Indian snacks, such as samosa/vadapav), High salt foods (Hakka noodles/fried rice/Manchurian), Fast foods (wafers/chips/French fries/Burger/pizza).

c Time spent in screen-based activities- Responses, < 30 min/d to > 4 h/d, scored from 0 to 4.

d Screen addiction was assessed on a five-point Likert scale (strongly disagree to strongly agree), scored 0 to 4.

Association of ST on lifestyle behaviors

The eating habit scores were observed to be associated with the time spent using social networking sites (β = -0.229 (0.110), p = 0.044) and the screen addiction score for ‘ST takes a majority of my leisure time’ (β = -0.658 (0.222), p = 0.017) (Table 4). Increase in the frequency of screen usage (laptops/computers) was associated with a decrease in the eating habit (β = -0.298 (0.089), p = 0.034) and PA (β = -0.845 (0.212), p <0.001) scores and also with an increase in PSQI scores (β = 0.612 (0.334), p = 0.044), indicating poor sleep quality in the sample. The total time spent using screens on both weekdays (β = -0.343 (0.130), p = 0.006) and weekends (β = -0.831(0.119), p = 0.002) showed inverse associations with time spent in PA/d. Also, the analyses indicated that higher screen addiction behavior scores (‘I can’t imagine going anywhere without my mobile devices’) were associated with reduced PA levels (β = -0.512 (0.215), p = 0.043) and poor sleep quality (β = 0.298 (0.089), p = 0.005) in adolescents.

Table 4

Association of screen time during COVID-19 pandemic with eating habits, physical activity levels, sleep quality, and depression symptoms of 10–15 years old adolescents in Mumbai, India.

Measures	Eating habits		Physical activity levels		Sleep quality		Depression symptoms
	β (SE)	P value	β (SE)	P value	β (SE)	P value	β (SE)	P value
Frequency of screen usage/week
• Laptop/desktop	-0.298 (0.089)	0.034*	-0.845 (0.212)	<0.001**	0.612 (0.334)	0.044*	0.245 (0.197)	0.071
• Mobile/smart phone	-0.123 (0.015)	0.520	-0.412 (0.330)	0.112	0.546 (0.221)	0.012*	0.212 (0.065)	0.156
• Television	-0.345 (0.135)	0.024*	-0.118 (0.067)	0.540	0.219 (0.110)	0.738	0.113 (0.101)	0.718
Time spent in screen activities/day
• Studying/doing homework	- 0.198 (0.065)	0.331	-0.089 (0.056)	0.566	0.329 (0.115)	0.038*	0.192 (0.031)	0.089
• Using social networking sites	- 0.229 (0.110)	0.044*	-0.312 (0.216)	<0.001**	0.312 (0.210)	0.119	0.412 (0.220)	0.018*
• Playing games	- 0.116 (0.078)	0.665	-0.032 (0.011)	0.328	0.448 (0.178)	0.002*	0.210 (0.114)	0.178
Total screen time/day
• Weekdays	-0.199 (0.123)	0.114	-0.343 (0.130)	0.006*	0.547 (0.312)	0.046*	0.125 (0.063)	0.348
• Weekends	-0.224 (0.112)	0.082	-0.831 (0.119)	0.002*	0.198 (0.148)	0.321	0.099 (0.045)	0.648
Screen addiction behaviors
• I can’t imagine going anywhere without my mobile devices	-0.121 (0.110)	0.412	-0.512 (0.215)	0.043*	0.298 (0.089)	0.005*	0.166 (0.103)	0.118
• Screen time takes up majority of my leisure time	-0.658(0.222)	0.017	-0.220 (0.119)	0.128	0.210 (0.116)	0.421	0.101 (0.049)	0.332
• I prefer socializing online than meeting people face to face	- 0.088 (0.034)	0.882	-0.240 (0.112)	0.019*	0.254 (0.188)	0.026*	0.034 (0.012)	0.614
Model Summary	Adj r2 = 0.133, F = 1.909, Significance = 0.012		Adj r2 = 0.154, F = 4.728, Significance = 0.002		Adj r2 = 0.142, F = 2.973 Significance = 0.026		Adj r2 = 0.080, F = 1.410, Significance = 0.136

Abbreviations: β, standardized regression coefficient. SE, standard error.

*Significant at p <0.05.

**Significant at p <0.001.

Eating habits indicate mean scores of eating habits (breakfast, family meals, eating out, ordering takeaways) related items and snacking pattern (frequency of healthy and unhealthy snack consumption) related items.

Physical activity levels indicate mean summary PAQ-C/A (Physical Activity Questionnaire for Children and Adolescents) scores.

Sleep quality indicates mean global PSQI (Pittsburg Sleep Quality Index) scores.

Depression symptoms indicate mean PHQ 2 (Patient Health Questionnaire-2) scores.

All models were adjusted for sex, age, type of school attended (private vs government schools).

Discussion

Several key findings emerged from this study– 1) Adolescents’ daily screen usage was substantially high with the mean reported ST being higher during weekdays as compared to the weekends. A considerable amount of time was spent using screens for studying/doing homework and a ubiquitous screen addiction behavior was observed in adolescents. 2) Only a few were involved in moderate to vigorous PA levels with the engagement in PA being even lower in girls, highlighting the magnitude of physical inactivity during the pandemic. 3) Skipping breakfast, infrequent family meals, and frequent consumption of fast foods, fried foods, and carbonated beverages was reported. Girls reported a higher prevalence of sleep latency, daytime dysfunction, and poor sleep quality, and the PHQ-2 scores (for risk of depression) were higher among older adolescents, ages 13–15 years. 4) Additionally, associations were observed between screen usage and eating habits, PA, and sleep quality among the sampled adolescents. A higher ST was associated with lower PA and increased sleep problems and a greater screen addiction was inversely related to healthy eating habits, PA, and sleep variables, though not with depression symptoms. At the time of the data collection, the adolescents had been confined to homes for almost a year due to the country-wide lockdown restrictions imposed since March 2020. This is likely to have resulted in substantial disruptions in the daily routines, lifestyle behaviors, and mental wellbeing of adolescents.

In line with our findings, several studies had reported excessive usage of digital devices among adolescents during the pandemic [3,8,18]. The adverse health consequences of excess ST on the risk of obesity, anxiety, depression, and cardiovascular problems are established [6,7,22,31]. However, there is also a growing interest to explore the use of screens as coping measures for learning, connecting with people, curbing boredom, and having better access to scientific information. A recent study observed that adolescents who were more active in social media were better equipped to handle pandemic induced environmental stressors [32], a report published in the child and adolescent health section of the Lancet journal recommended that the amount of time spent using screens should be tailored keeping into consideration other factors, such as snacking behaviors and activity patterns in adolescents [33] and a systematic review noted that the ST can be leveraged to extend better social and emotional support to children [34]. Given the established negative effects of increased ST and a need to make the most of the time spent using screens for educational and social benefits, it is imperative that age-appropriate resources that encourage judicious use of screens are emphasized and public awareness regarding the adverse health effects of excess ST are simultaneously promoted.

In this study, we observed that the extended screen usage coincided with other unhealthy lifestyle behaviors, such as lower than recommended MVPA, poor eating habits, and inadequate sleep duration and quality in adolescents. The staggering prevalence of physical inactivity observed in our study can be explained by the lockdown that led to restricted access to organized sports activities, limited free play in playgrounds, parks, and areas around apartments, and a general feeling of fear among parents to send children outside for playing. Adolescents reported increased consumption of unhealthy foods, such as fast foods, fried foods, and carbonated beverages, and also of healthy foods, such as fruits and salads during the pandemic. Emotional overeating and unhealthy snacking behaviors to reduce boredom and stress have been reported in previous studies [35-37]. The finding related to the perceived increase in healthy food consumption concur with similar studies that observed an improvement in the overall diet quality of adolescents due to greater parental involvement in cooking meals at homes and better monitoring of food intake at mealtimes. Regarding sleep and depression variables, more than half of the adolescents had poor sleep quality and 8.6% were observed to be at risk of depression. Alterations in the sleep-wake cycles due to delay in school start time [38], increased vulnerability to anxiety and depression symptoms due to COVID-related fear and social isolation [19], and limited peer interactions to handle academic frustrations and loneliness [39] seem to have further worsened the already pervasive sleep problems and mental health crisis in adolescents globally and India. Similar to our findings, other studies had shown the prevalence of sleep and depression to be higher among females and older adolescents [38,40], indicating that this group might require particular attention during the pandemic.

The associations between screen-related variables and eating habits, PA, sleep, and depression symptoms were explored. Regression analyses indicated that a higher frequency of screen usage and time spent in different screen-based activities and a greater screen addiction behavior score were associated with lower eating habit and MVPA scores and higher sleep disturbances in adolescents. Research has shown a direct relationship between television viewing and unhealthy eating behaviors [6,11,12], increased screen time to displace the time that can be used for engaging in PA [4,17,41], and excess usage of digital devices to suppress melatonin secretion, delay sleepiness, and increase sleep disturbances [17,42,43]. No significant associations were observed between screen-related variables and PHQ 2 scores. These results are inconsistent with previous studies that reported associations between ST and increased mental health problems [8,19], but concurwith others that did not observe statistically significant associations between ST, anxiety, and other mental health indicators in youth [44,45]. As the end of the COVID-19 pandemic remains uncertain, the use of digital devices and screens has become an unavoidable necessity. In this context, the role of parents in limiting the sedentary screen-based recreational activities, setting ground rules for screen media usage, and encouraging adolescents to participate in fun indoor activities, such as dancing, rope skipping, playing with hoops, and online fitness classes is of key importance. The families must also use the enforced stay-at-home advisories as opportunities to have more frequent family meals together, model healthier diet and sleep habits, and build better bonds with adolescents to overcome the hardships encountered during the pandemic. Finally, close attention and committed efforts are required from policymakers in India to revisit the ST guidelines, address the emerging challenges of longer screen exposure, inadequate PA levels, and insufficient sleep during the pandemic, and provide feasible solutions to mitigate the associated short and long-term health problems in adolescents.

Despite a fairly large sample size and selection of sample across age and socioeconomic categories (the adolescents, ages 10–15 years were recruited from both private and public schools in Mumbai), our study has a few limitations that must be considered while interpreting the results. Most importantly, the study sites were selected using the convenience sampling method and the study design was cross-sectional, both of which may have limited the generalizability of the findings and temporal associations between screen time and lifestyle behaviors in adolescents. Due to the ongoing pandemic-induced closure of educational institutes in India since late March 2020, conducting an in-person survey was not feasible. So, we had to conduct an online survey to collect data, which may have introduced a selection bias. Also, the variables were self-reported by the adolescents so can be subject to overestimation/underestimation. Investigating the lifestyle behaviors and risk of depression between adolescents who use ST predominantly for leisure vs school work may help design targeted strategies and evaluating these behaviors across diverse settings (rural and urban), geographical regions, and age categories can guide the development of appropriate health-promoting policies and designing of culturally relevant interventions for improving the physical and mental wellbeing of adolescents in India as elsewhere.

Conclusions

The present study brought forth a high prevalence of excessive ST, physical inactivity, and poor sleep quality in 10–15 years old Indian adolescents during the ongoing COVID-19 pandemic. Moreover, the results revealed that a greater time spent using screens was associated with a higher engagement in unhealthy eating behaviors, lower PA levels, and increased sleep disturbances in adolescents. It is difficult to predict if these transient behaviors will continue to persist post-pandemic. Nevertheless, amid continued national school closures, the extended screen time and its impact on adolescents’ lifestyle behaviors need to be managed effectively. Using screens to involve adolescents in active play, harnessing mobile applications and digital platforms to provide nutrition, PA, and mental health counseling, and involving social media to raise public awareness about adverse health consequences of excess ST present expedient opportunities to support the educational and entertainment needs of adolescents whilst ensuring optimum physical, sleep, and mental health during the pandemic. Proactive parental support for fostering a tighter control on the screen usage, being role models for appropriate ST and PA engagement, ensuring stringent monitoring of adolescents’ diet and sleep routines, and prioritizing an early identification of anxiety and depression symptoms in adolescents may add further leverage.

20 Sep 2021

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Reviewer #1: Summary:

The authors have conducted a well-designed and interesting study, in which the aim to determine the increase in screen-based activities, and thereby screen time, and whether this causes an decrease in physical activity, sleep quality and mental health. The find that the majority of participants report an increase in screen time, as well as unhealthy behaviors. Also, screen time appears to be associated with these unhealthy behaviors. I have however several reservations about the analysis performed, and the structure of the paper. I think the authors should therefore address these reservations to make it ready for publication.

General recommendations:

- If you use related (such as COVID-related or screen-related) there should be a dash (-) between related and the word before related. So, not COVID related or screen related, but COVID-related and screen-related.

- I should consider re-analyzing the risk factors/associations to be able to draw conclusions about the predictive value of your studied variables. As I explain in my comments below, the regression analyses should involve potentially confounding factors. It is too bold to state that for instance screen time was a significant predictors if you don’t account for any other variables. I would suggest performing univariate regression models to determine which baseline variables that were collected show a high correlation, i.e., a p-value below 0.10, and add those to the regression models too. This will make your results way more clinically relevant.

- In contract to what the authors have claimed in the data availability statement, the doi’s given only lead us to the results of the regression analysis and the used questionnaire. As such, the underlying data is not available to the reader. This is not in line with PLOS One’s Data policy.

- I think the level of English within the manuscript could be improved. Although it is for the most part grammatically correct, the constructions of sentences may be improved. Consider to ask a native English speaker to copy-edit the manuscript to improve readability.

Abstract

- The abstract should be changed according to the author changes made during the revision process.

- I would suggested using a structured abstract, as it is more suitable for this type of research in my opinion. This is however the author’s choice.

Introduction:

I think the introduction is a bit chaotic and thereby hard to read through. First, I think the message given can be described much more condense. Also, it would be helpful if the terminology in the introduction would be more uniform, especially screen-time related terms. I think the authors have tried to use the same terminology as used in the papers they refer to, but it would help the reader is findings are summarized. By summarizing findings, it will be much easier to shorten the introduction.

Methods

- “Measures”; I would suggest placing screen time directly below ‘demographics’, as this is your main outcome. This way, it will also be uniform to the results section.

- “The sleep patterns … Index (PSQI)” (lines 195-197) It this questionnaire also validated in adolescents? I could hypothesize that the sleep patterns in adults aren’t necessarily the same as in adolescents.

- Is the Patient Health Questionnaire-2 validated in adolescents?

- “Of 1512 … years (n= 574)” (Lines 207-210); This is a results and should therefore be moved to the Results section.

Results

- Try to be uniform; sometimes participants are referred to as ‘participants’ and sometimes as ‘adolescents’. In would benefit the readability to only use one term.

- “Screen time”: It is not clear to me what is the difference between screen usage and screen time, is there a difference of is it the same. If there is a difference, it should be more clearly described in the methods section, if there is no difference, use the same terms throughout the manuscript.

- I think the generalizability of the results would benefit from adding 95% CI to the percentages given.

- I would suggest the ‘Eating habits and snacking patterns’, ‘Physical activity levels’ and ‘Sleep quality and depression symptoms’ sections to be combined.

- “Figure 1”: I would recommend to place ‘remained same’ before ‘decreased (it is a more logical order that way). Additionally, would recommend to replace ‘Remained same’ with ‘Remained Similar’.

- “Table 3”: I would recommend describing the stratifications used in this table in the ‘Statistical analyis’ section.

- I would suggest add the descriptive results of the ‘Eating habits and snacking patterns’, ‘Physical activity levels’, and ‘Sleep quality and depression symptoms’ in the supplements, as they are now only displayed within the text.

- “Association of ST on lifestyle behaviors”; I think it is too bold to use the term ‘predictor’ in this section. The term association, as used in the subheading, would be more appropriate. Also, you switch between ‘association’ and ‘predictors’ in this section; these terms are not the same. Because there was not adjusted for any confounding factors (especially confounders as age and gender), no conclusions can be drawn about the predictive value of screen time/screen usage on these outcomes. If you are really interested in finding any possible predictors, the authors should study literature to determine for which variables should be adjusted in the analyses. Another way could be to perform univariate regression analyses with as predictors the baseline variables, one by one, and as dependent variables the ‘Healthy eating habit’, ’Physical activity’, ‘Sleep Quality’ and ‘Depression’ variables. This would certainly benefit the reliability of your results.

- “Table 4”: It would be informative to describe in the legend of the table which variables were used for the ‘Healthy eating habit’, ‘Physical Activity’, ‘Sleep Quality’, and ‘Depression’ variables; readers should be able to understand all tables and figures in the manuscript without actually reading the methods or results section.

Discussion

- I think the first paragraph of the discussion can be omitted. The introduction is the section where you highlight the importance of your study, not the discussion. Now it is just a repetition of what you have already written in the introduction. You may briefly repeat the main goal of the study, but thereafter you should write about your most important findings, not about the relevance of the study.

- I have some reservations about your second paragraph, which should become your first paragraph

- In your second key finding (“Results indicated …. during the pandemic”), you claim that the low proportion of adolescents reporting to be involved in moderate to vigorous PA levels highlight the magnitude of physical activity during the pandemic. My first concern is that you refer to percentages which are not shown in the results section nor in the tables or figures. It is an absolute no-go to present new /unmentioned results first in the discussion section. To make this claim, describe this finding first in the results section. Also, you can claim that the proportion of adolescents involved in MVPA is low, but no conclusions can be drawn about the effect of COVID-19 (to do this, you should have retrospectively assessed their involvement in MVPA before COVID-19, but I cannot find this data in your manuscript.

- In your fourth key finding (“Additionally, significant … depression symptoms”: I would advice the authors to choose the appropriate term for your findings, i.e., association, throughout the manuscript. Here, you use three terms, which are not the same.

- “… and 45.1% mentioned the time spent in on or other screen-based activities to be > 6 hours” (lines 341-42). The 45.1% is the exact sum of the percentages, so this means that there was no overlap, is that correct?

- “Regression analysis … in adolescents” (lines 375-377): I would omit the use of predictor as previously explained.

- “Limitations”; I think the recruitment of only Indian adolescent should be considered as a limitation, as it limits the generalizability to other countries.

- “Future studies … in India.” (lines 401-403) I think the authors can broaden this claim; why would it only be interesting to study this in India rather than in other countries?

Conclusions:

- “In summary … the pandemic. Though the … sleep patterns.” (Lines 405-410) I would soften this statement.

- I would suggest shortening the conclusions, and only describe conclusions that can be drawn from your own results. For instance: “As such, … lifestyle behaviors” is not a finding of your study, and shouldn’t be stated in the conclusions section but in the introduction and/or discussion.

Reviewer #2: Here are my comments and suggestions for this interesting and topical paper:

1) The authors should be mindful of wording with regards to the use of PHQ-2 and screening for depression. A PHQ-2 score of >3 is only an indication of likely depression therefore the statement in the abstract “8.6% had depression (PHQ-2 ≥ 3)” is incorrect. Similarly in the discussion, “8.6% reported the presence of depression” (line 367).

2) In the introduction, the authors state that “Studies have consistently shown that the total time spent using screen devices far exceeds the recommended duration among adolescents(6–8)”. It would be interesting to state what the recommended duration is in order to compare to your findings.

3) The authors results show whether the participants’ ST, unhealthy eating patterns, PA have increased/decreased, but do the authors have any quantitative results for this? It would be interesting to quantify these changes comparing pre and during COVID-19 pandemic data if this is available.

4) It would be interesting to note if there was a difference in lifestyle behaviours or depressive symptoms between those adolescents who use ST for predominantly leisure vs predominantly educational purposes?

5) Re-consider the use of the word “dismal” in line 329.

6) Have the authors considered a selection bias in their sample as the surveys were done exclusively online that this would be skewed towards participants that would have higher ST usage?

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Reviewer #1: Yes: J.H. Vlake

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4 Nov 2021

Response to comments related to the journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We have formatted the manuscript in line with PLOS ONE’s style requirements.

2. Please include additional information regarding the survey used in the study, particularly the sample size calculation, and ensure that you have provided sufficient details that others could replicate the analyses.

Response: The questionnaire used to collect the data and the dataset analyzed during the current study are available in the [Figshare] [10.6084/m9.figshare.16934107]. We have added the details of sample size estimation between lines 128-132.

RESPONSE TO REVIEWER 1

Reviewer #1: Summary:

The authors have conducted a well-designed and interesting study, in which the aim to determine the increase in screen-based activities, and thereby screen time, and whether this causes an decrease in physical activity, sleep quality and mental health. The find that the majority of participants report an increase in screen time, as well as unhealthy behaviors. Also, screen time appears to be associated with these unhealthy behaviors. I have however several reservations about the analysis performed, and the structure of the paper. I think the authors should therefore address these reservations to make it ready for publication.

Response: Thanks for acknowledging the relevance of the study and for providing the opportunity to further strengthen the manuscript. The suggestions have been noted and a point-by-point clarification on each of the specified aspects is provided.

General recommendations:

1. If you use related (such as COVID-related or screen-related) there should be a dash (-) between related and the word before related. So, not COVID related or screen related, but COVID-related and screen-related.

Response: Noted and revised.

2. I should consider re-analyzing the risk factors/associations to be able to draw conclusions about the predictive value of your studied variables. As I explain in my comments below, the regression analyses should involve potentially confounding factors. It is too bold to state that for instance screen time was a significant predictors if you don’t account for any other variables. I would suggest performing univariate regression models to determine which baseline variables that were collected show a high correlation, i.e., a p-value below 0.10, and add those to the regression models too. This will make your results way more clinically relevant.

Response: Thanks for your valuable feedback. In this study, the associations between screen time and lifestyle behaviors were examined using multivariable linear regression models that were built by applying the backward elimination method (threshold of 0.05 to stay in the model) for selection of predictive variables while controlling for adolescents’ age, sex, and type of school attended (used as a proxy for socioeconomic status) as covariates. Further to your suggestion and our internal discussion with our statistician, we have re-analyzed our data. We have first performed univariable regression analyses to determine the unadjusted effect of factors associated with each of the dependent variables and have then entered the independent variables with a significance level <0.1 into the mixed-effects multivariable regression models to determine the ST variables that were associated significantly with eating habits, PA, sleep and depression symptoms. As indicated in previous studies, we have used the lowest values of Akaike’s and Schwarz’s Bayesian information criteria measures to test the goodness of fit of the final models. Minimal data were missing as we had analyzed data from participants who had provided complete information in the survey. The revised method of statistical analyses is included between lines 211-217 and the revised results are reported in Table 4 as standardized regression coefficients and standard error of coefficients, considered significant at p<0.05

3. In contract to what the authors have claimed in the data availability statement, the doi’s given only lead us to the results of the regression analysis and the used questionnaire. As such, the underlying data is not available to the reader. This is not in line with PLOS One’s Data policy.

Response: Datasets analyzed during the current study are available in the [Figshare Repository] [10.6084/m9.figshare.16934107]

4. I think the level of English within the manuscript could be improved. Although it is for the most part grammatically correct, the constructions of sentences may be improved. Consider to ask a native English speaker to copy-edit the manuscript to improve readability.

Response: We have revised the text of the manuscript to improve readability.

5. The abstract should be changed according to the author changes made during the revision process. I would suggested using a structured abstract, as it is more suitable for this type of research in my opinion. This is however the author’s choice.

Response: As suggested, the abstract has been provided in a structured format.

6. I think the introduction is a bit chaotic and thereby hard to read through. First, I think the message given can be described much more condense. Also, it would be helpful if the terminology in the introduction would be more uniform, especially screen-time related terms. I think the authors have tried to use the same terminology as used in the papers they refer to, but it would help the reader is findings are summarized. By summarizing findings, it will be much easier to shorten the introduction.

Response: Thanks for the feedback. We have shortened the introduction to include summarized findings of previous literature and ensured uniformity in the usage of screen time-related terms.

7. Measures- I would suggest placing screen time directly below ‘demographics’, as this is your main outcome. This way, it will also be uniform to the results section.

Response: Yes, we agree that placing screen time before eating habits and physical activity measures will improve uniformity. Revisions have been made (lines 141-160).

8. “The sleep patterns … Index (PSQI)” (lines 195-197) It this questionnaire also validated in adolescents? I could hypothesize that the sleep patterns in adults aren’t necessarily the same as in adolescents.

Response: Thanks for the question. Yes, several studies have established the validity and reliability of PSQI to identify sleep problems in adolescents. We have included an additional sentence in the methods to clarify the same (Line 190-191).

9. Is the Patient Health Questionnaire-2 validated in adolescents?

Response: The validity of PHQ 2 has been evaluated in adolescents in previous studies. The instrument is a widely accepted screening tool for major depressive disorders among adolescents and youth. This information has been added between lines 195-197.

10. Of 1512 … years (n= 574)” (Lines 207-210); This is a results and should therefore be moved to the Results section.

Response: Noted and revised (Lines 226-229.

11. Results- Try to be uniform; sometimes participants are referred to as ‘participants’ and sometimes as ‘adolescents’. In would benefit the readability to only use one term.

Response: Thanks for your suggestion. We have replaced participants with adolescents in the manuscript.

12. “Screen time”: It is not clear to me what is the difference between screen usage and screen time, is there a difference of is it the same. If there is a difference, it should be more clearly described in the methods section, if there is no difference, use the same terms throughout the manuscript.

Response: In this study, we had used the term ‘screen time’ to indicate the time spent working/ studying/playing using any screen device and the term ‘screen usage’ to indicate different screen devices (such as laptops, mobile phones, television) that were used by the adolescents. We have added this explanation in the methods section (between lines 143-145).

13. I would suggest the ‘Eating habits and snacking patterns’, ‘Physical activity levels’ and ‘Sleep quality and depression symptoms’ sections to be combined.

Response: Thanks for the suggestion. We have combined the results of eating habits, physical activity levels, sleep, and depression-related variables under a single subheading (Lines 246-258)

14. “Figure 1”: I would recommend to place ‘remained same’ before ‘decreased (it is a more logical order that way). Additionally, would recommend to replace ‘Remained same’ with ‘Remained Similar’.

Response: Noted and revised Fig 1.

15. “Table 3”: I would recommend describing the stratifications used in this table in the ‘Statistical analysis’ section.

Response: Between lines 203-205, we have mentioned that comparison of variables was performed stratified by sex and age categories (10-12 years and 13-15 years)

17. I would suggest add the descriptive results of the ‘Eating habits and snacking patterns’, ‘Physical activity levels’, and ‘Sleep quality and depression symptoms’ in the supplements, as they are now only displayed within the text.

Response: The descriptive results of eating habits, snacking patterns, physical activity levels, sleep quality and depression symptoms of adolescents, stratified by sex and age categories are provided in Table 3.

18. “Association of ST on lifestyle behaviors”; I think it is too bold to use the term ‘predictor’ in this section. The term association, as used in the subheading, would be more appropriate. Also, you switch between ‘association’ and ‘predictors’ in this section; these terms are not the same. Because there was not adjusted for any confounding factors (especially confounders as age and gender), no conclusions can be drawn about the predictive value of screen time/screen usage on these outcomes. If you are really interested in finding any possible predictors, the authors should study literature to determine for which variables should be adjusted in the analyses. Another way could be to perform univariate regression analyses with as predictors the baseline variables, one by one, and as dependent variables the ‘Healthy eating habit’, ’Physical activity’, ‘Sleep Quality’ and ‘Depression’ variables. This would certainly benefit the reliability of your results.

Response: We agree that the term association is better suited to indicate the relationship between the explanatory ST variables and the dependent variables such as healthy eating habits, physical activity, sleep quality, and depression symptoms as compared to the term predictor. Hence, we have replaced the term ‘predictor’ with ‘association’ in the results. As mentioned previously, the data was re-analyzed and the revised results of mixed effect multivariable regression models adjusted for sex, age, and type of school attended are reported in revised Table 4.

19. “Table 4”: It would be informative to describe in the legend of the table which variables were used for the ‘Healthy eating habit’, ‘Physical Activity’, ‘Sleep Quality’, and ‘Depression’ variables; readers should be able to understand all tables and figures in the manuscript without actually reading the methods or results section.

Response: Thanks for the feedback. We have added the details of the variables used in the regression analyses as footnotes in Table 4.

20. Discussion- I think the first paragraph of the discussion can be omitted. The introduction is the section where you highlight the importance of your study, not the discussion. Now it is just a repetition of what you have already written in the introduction. You may briefly repeat the main goal of the study, but thereafter you should write about your most important findings, not about the relevance of the study.

Response: The discussion text has been revised to restate the research questions and includes discussions regarding the key findings of the current study.

21. I have some reservations about your second paragraph, which should become your first paragraph

- In your second key finding (“Results indicated …. during the pandemic”), you claim that the low proportion of adolescents reporting to be involved in moderate to vigorous PA levels highlight the magnitude of physical activity during the pandemic. My first concern is that you refer to percentages which are not shown in the results section nor in the tables or figures. It is an absolute no-go to present new /unmentioned results first in the discussion section. To make this claim, describe this finding first in the results section..

Response: In Table 3, row 6 under physical activity levels, we have reported that 12% of adolescents had PAQ score> 2 and 8.9% of girls had PAQ score>2 (p <0.001). As this information was already provided under the Results section (Table 3), we have referred to the same percentages as key findings in the second paragraph of the Discussion to highlight the magnitude of physical inactivity among the sampled adolescents (line 329-331). We have included an additional sentence in text between lines 257-258 regarding physical activity levels of adolescents. The claim that the proportion of adolescents who were engaged in MVPA was low during the pandemic is justified in view of the findings, as mentioned in text and Table 3.

22. Also, you can claim that the proportion of adolescents involved in MVPA is low, but no conclusions can be drawn about the effect of COVID-19 (to do this, you should have retrospectively assessed their involvement in MVPA before COVID-19, but I cannot find this data in your manuscript

Response: We agree that the impact of COVID 19 on screen time and/or lifestyle behaviors cannot be ascertained due to the cross-sectional design of the study that did not have access to retrospective data to infer the changes brought upon adolescents’ lifestyles by the pandemic. This study aimed to evaluate the impact of screen time during the COVID 19 pandemic (and not the effect of the pandemic on screen time or lifestyle habits) on eating habits, physical activity levels, sleep quality, and depression symptoms in adolescents in India. To avoid potential misinterpretation, we have refrained from the usage of terms such as lower, higher, greater with reference to practices during the pandemic as compared to before COVID 19 in the revised manuscript. We have also explicitly stated the research questions in the introduction of the revised manuscript (Lines 104-106).

23. In your fourth key finding (“Additionally, significant … depression symptoms”: I would advice the authors to choose the appropriate term for your findings, i.e., association, throughout the manuscript. Here, you use three terms, which are not the same.

Response: Revised.

24. “… and 45.1% mentioned the time spent in on or other screen-based activities to be > 6 hours” (lines 341-42). The 45.1% is the exact sum of the percentages, so this means that there was no overlap, is that correct?

Response: The percentage (45.1%) was calculated by aggregating the responses received from 586 adolescents who reported the time spent in one or other screen-based activities to be> 6 hours/d. However, an overlap might have existed between adolescents who spent >6 h/d in different screen-based activities. So, we have revised the sentence to include 33.4% who reported spending >6h/d for studying/ doing homework.

25. “Regression analysis … in adolescents” (lines 375-377): I would omit the use of predictor as previously explained.

Response: We have replaced the term predictor with associated.

26. “Limitations”; I think the recruitment of only Indian adolescent should be considered as a limitation, as it limits the generalizability to other countries.

Response: This study attempted to address the limited knowledge regarding the potential influences of excess ST on lifestyle behaviors in adolescents in India during the COVID 19 pandemic. Moreover, a comprehensive investigation of eating habits, PA levels, sleep quality, and depressive symptoms during the pandemic among adolescents in India was performed. Given the differences in the lockdown restrictions imposed between countries during the pandemic and that the socio-cultural determinants of lifestyle behaviors tend to vary between adolescents residing in different geographical regions, we believe that the topical nature of this study is the novelty and in fact a strength of the study.

27. “Future studies … in India.” (lines 401-403) I think the authors can broaden this claim; why would it only be interesting to study this in India rather than in other countries?

Response: Though this study is the first large scale survey to assess the association of screen time during the COVID 19 pandemic with several lifestyle behaviors in Indian adolescents, the results are limited by the convenience sampling method and the urban setting of the survey (the sites were located in the city of Mumbai and may not reflect the impact of ST during pandemic on the lifestyle behaviors of adolescents in rural and smaller cities of the country). Hence, we suggested that future studies must be conducted in diverse settings (rural, semi-urban, and urban areas) and across a broader age category of adolescents in India to better understand the influences of excess ST on lifestyles. This information is particularly pertinent to guide the development of appropriate health-promoting policies and designing age and culturally relevant interventions aimed at improving the physical and mental wellbeing of adolescents. As suggested, we have included this sentence in Discussion (Lines 413-416).

28. Conclusions: “In summary … the pandemic. Though the … sleep patterns.” (Lines 405-410) I would soften this statement. I would suggest shortening the conclusions, and only describe conclusions that can be drawn from your own results. For instance: “As such, … lifestyle behaviors” is not a finding of your study, and shouldn’t be stated in the conclusions section but in the introduction and/or discussion.

Response: Thanks for the suggestion. We have made the conclusion more concise and highlighted the recommendations that can be drawn from the results of this study.

RESPONSE TO REVIEWER 2

Reviewer #2: Here are my comments and suggestions for this interesting and topical paper:

Response: Thanks for your valuable feedback and suggestions to improve the manuscript. We are grateful for your acknowledgment of the relevance of this topical study.

1) The authors should be mindful of wording with regards to the use of PHQ-2 and screening for depression. A PHQ-2 score of >3 is only an indication of likely depression therefore the statement in the abstract “8.6% had depression (PHQ-2 ≥ 3)” is incorrect. Similarly in the discussion, “8.6% reported the presence of depression” (line 367).

Response: We have revised the sentences to indicate that the adolescents with PHQ 2 scores above the established threshold score of 3 were identified to be at risk of depression.

2) In the introduction, the authors state that “Studies have consistently shown that the total time spent using screen devices far exceeds the recommended duration among adolescents (6–8)”. It would be interesting to state what the recommended duration is in order to compare to your findings.

Response: Thanks for the suggestion. We have mentioned that the screen time far exceeds the suggested duration of two hours a day (line 59)

3) The authors results show whether the participants’ ST, unhealthy eating patterns, PA have increased/decreased, but do the authors have any quantitative results for this? It would be interesting to quantify these changes comparing pre and during COVID-19 pandemic data if this is available.

Response: To determine the changes in the frequency and duration of screen usage, and the frequency of intake of specific food items and engagement in different physical activities during the COVID 19 pandemic as compared to before pandemic, the adolescents were asked to report whether they perceived the changes as increased, decreased or remained similar. The responses to these questions generated quantitative data that helped estimate the impact of the pandemic on the selected measures. In Figure 1, we have provided the results of the changes in the frequency of screen usage, eating habits, snacking patterns, and physical activity levels during the pandemic as reported by the adolescents.

4) It would be interesting to note if there was a difference in lifestyle behaviours or depressive symptoms between those adolescents who use ST for predominantly leisure vs predominantly educational purposes?

Response: Indeed, a comparison of the lifestyle behaviors and depression symptoms between adolescents who use screens for leisure vs for studying/doing homework is an interesting line of investigation that will add to the knowledge. However, the questionnaire used in the present study does not allow the stratification in discrete groups of adolescents who engaged in a particular screen-based activity. For instance, the adolescents who reported spending >6 hours/day doing homework may also be using screens for leisure > 6/day. Though the data that we currently have cannot establish these differences, we have suggested this aspect as an area that merits further research (Lines 411-413).

5) Re-consider the use of the word “dismal” in line 329.

Response: Revised.

6) Have the authors considered a selection bias in their sample as the surveys were done exclusively online that this would be skewed towards participants that would have higher ST usage?

Response: Due to the ongoing pandemic-induced closure of educational institutes in India since late March 2020, conducting an in-person survey was not feasible. So, we had to conduct an online survey to collect data, which may have introduced a selection bias. We have added this as a limitation of the study in the discussion section.

Submitted filename: Response to Reviewers.docx

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22 Dec 2021

24 Jan 2022

Dear Academic Editor and Reviewer,

Thank you for providing us the opportunity to submit a revised version of our manuscript titled, ‘Impact of screen time during COVID 19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents’ for consideration at the PLOS ONE journal. We thank reviewer #1 for his constructive feedback and valuable suggestions. We are submitting a point-by-point response to the suggestions provided by the reviewer and the manuscript with revisions highlighted in a colored font. An unmarked revised version of our manuscript is also submitted.

We look forward to hearing from you.

Sincerely,

Authors

RESPONSE TO REVIEWER 1

Reviewer #1: Review round 2 – Screen time

Overall comments:

Overall, I am very pleased with the effort of the authors during the revisions, and I believe the manuscript has increased tremendously compared to the previous version. I am happy to read that the authors have successfully addressed all my comments. However, there are a few minor points that have to be addressed prior to this study being ready for publication.

Response: Thank you so much for appreciating our efforts and providing us the opportunity to further strengthen the paper. We are sincerely grateful for your time, expertise, and insightful suggestions that continue to help us tremendously to improve the manuscript. Each of the minor points highlighted by you has been addressed and a point-by-point response to your suggested revisions is provided.

General points of interest

1) I think the authors should carefully check the manuscript for their punctuation. To name some examples: “COVID 19” should be “COVID-19” (Abstract, line 26, line 48; Introduction, line 52…), “some countries, including India had” should be “some countries, including India, had” and there should be a comma prior to each “such as” (for instance line 60 in the introduction).

Response: Thanks for the suggestion. We have made the necessary punctuation-related revisions throughout the manuscript.

2) The authors should copy-edit the full manuscript for some minor grammatical errors or missing words (for instance “the transmission of coronavirus” should be “the transmission of the coronavirus” (Introduction, line 52-53)

Response: We have edited the text to minimize grammatical errors and improve readability.

3) I would suggest to place all reference at the end of a sentence instead of after the part of the sentence the reference relates to. This will improve readability, and the reader can still find the appropriate reference if needed.

Response: Thanks for your suggestion. All in-text citations have been placed in parentheses at the end of the sentences except for a few citations that had to be placed mid-sentence as they reflected the results of multiple studies as a part of a single sentence. For instance, reference 4 was placed mid-sentence, immediately after the finding of the study followed by reference 10 that indicated the findings of another study (Lines 69-70).

4) Abstract:

I’m glad that the authors have re-written the abstract in a structured way. I think it suits the research better.

I would place the characteristics of the included adolescents in the results section of the abstract (“n=1298, Mage 13.2 (1.1), 53.3% boys”).

Response: As suggested, the participant characteristics have been added in the results section of the abstract (Line 39).

5) Introduction:

I am happy to see that the authors improved their introduction quite a lot. I however still think the authors could be much more concise as the current introduction still counts 935 words. I would like to challenge the authors to limit the word count of the introduction to at most 600 words (preferably less than 500). The introduction should only focus on letting the reader understand why you did this research, i.e., why is this research important. Also, I would re-arrange the last paragraph of the introduction. I would start with the last sentence (“To the best of our knowledge.”, then the aims and research question (personally, I would not state the research question that specific, but embed it in a normal sentence, such as “We sought to evaluate what the impact of screen time during the COVID-19 pandemic would be on eating habits…”, and thereafter state your specific objectives.

Response: Thanks for your valuable feedback. As suggested, the introduction has been shortened from 934 to ~ 650 words to present a concise summary of the scope, context, and significance of the study. The last paragraph of the introduction has been rearranged and the research question has been restated as the primary aim of the study (Lines 90-97).

6) Methods:

I would rephrase the sentence “due to the ongoing … collect data” (lines 118-120) to “An online survey was conducted to collect data, as an in-person survey was not feasible due to the ongoing ..”.

Response: Revised (Lines 102-104).

7) When explaining the questionnaire concerning screen time. I would suggest omitting the description of specific items, and add the questionnaire to the supplement and refer to that supplement. Now you spend quite a lot words describing the questionnaire, when I will be more concise and also more comprehensive when just referring to the supplement with the questionnaire.

Response: As suggested, we have referred to the questionnaire as supplemental material (line 131) and have provided a summary of the questions, response options, and scoring methods of screen time related items in the main text between lines 130-138.

8) I think the references after the statement “The PAQ-C/A have been extensively used to evaluate general physical activity levels of children and adolescents in previous studies” (Line 177-179) are missing.

Response: The references have been added (Line 157, References 24-25).

9) Results:

“Of 1512 adolescents who provided ….”; I would rephrase this sentence, as the adolescents didn’t provide parental consent, but their parents did. I would suggest “Of 1512 adolescents, of whom parental consent was provided, “

Response: Noted and revised.

10) In the first paragraph, sometime you use the exact number of adolescents (for instance for the age categories), and sometimes you use percentages (for instance about their living arrangements). I would suggest use the same for the whole paragraph, and I would suggest giving the percentage.

Response: Revised (Lines 213-214).

11) Lines 243-244: respectively should always be placed in the end of the sentence (…for study or entertainment, respectively).

Response: Revised (Line 222).

12) For the headings “Comparison of variables between sex and age categories” and “Association of ST on lifestyle behaviors”: omit the first sentence, as this is information you’ll typically find in the methods and distract from the actual results. Also, I would omit “of variables” as this does not add anything to the subheading. Also, try not to explain how certain scores were calculated in the results section, this is also information that should be in the methods and not be repeated in the results.

Response: Noted and revised.

13) In the methods section you write in the statistical analysis that coefficients are presented as standard coefficient and standard error (SE); I however only see the standardized coefficients in the text, so please add the SE’s as standardized coefficients themselves doesn’t tell us much.

Response: Thanks for your feedback. The standardized regression coefficients and standard error values as mentioned in Table 4 have been added in the text and highlighted in red font (Lines 293-301)

14) Discussion:

The first paragraph nicely summarizes the main findings of the study. I would however not repeat the exact results (such as the mean ST of percentages), and also not explain here how for instance vigorous PA levels were estimated, as this is already explained in the methods. Try just to repeat the main findings in words rather than in numbers, as you already did that in the results. (For instance, the first key finding could be rephrased as: “First, we found that adolescents use screens for a considerable amount of time, and used their screens more during the week than during the weekend. Second, moderate to vigorous PA levels were reached by only a few adolescents, and was the lowest in girls.”).

Response: As suggested, we have rephrased the key findings to include only the summary of the main results in the first paragraph of the discussion section (Lines 304-309).

15) Try not to use the word significantly too often. Differences are statistically significant, otherwise you wouldn’t call it a difference, the same applies to an association; if an association would not be statistically significant, you wouldn’t call it an association.

Response: Thanks. The required revisions have been made throughout the paper.

Submitted filename: Response to Reviewer- Ver 2.docx

Click here for additional data file.

21 Feb 2022

Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents

PONE-D-21-24611R2

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28 Feb 2022

PONE-D-21-24611R2

Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: A cross-sectional study in Indian adolescents

Dear Dr. Moitra:

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