Explaining Screen-Time Behavior Among Preschoolers in Northern India Using Multi Theory Model: A Parental Cross-Sectional Survey

Abstract

Background

Screen-time (ST) is the time spent on digital media. The American Academy of Pediatrics recommends the daily ST of less than an hour for preschoolers. However, increased ST among preschoolers is becoming a public health concern.

Objective

This study assessed the multi-theory model (MTM)'s applicability in explaining the ST behavior change among preschoolers through parents.

Methods

A quota sample of 72 parents was drawn from Northern India. Data were analyzed using multiple regression.

Results

Behavioral confidence (p < 0.001) and changes in the physical environment (p < 0.001) significantly predicted the initiation of reducing ST. The sustenance of limiting ST was significantly predicted by the emotional transformation (p < 0.001), practice for change (p < 0.001), and changes in the social environment (p = 0.001).

Conclusions

The study highlights the usability of the MTM model in designing and testing interventions for parents to limit ST among their children.

Keywords

multi-theory model fourth generation theory screen time behavior interventions early childhood initiation sustenance

The problem of increased use of television and alternative electronic/digital media (assumed name: “digital candies”) such as cell phones, iPad, and tablet have already been identified as a contributing factor of several health risks among children.^1,2 While media aids in connectivity and enhances knowledge, the overwhelming screen use among children is a pressing concern.² Children in the age group 8–10 years use electronic media for more than 8 hours/day, while teens spend nearly half of the day on such activities.³ Television watching has become the leading activity (other than sleeping) among children.⁴ Expert groups, such as the American Academy of Pediatrics (AAP), the Canadian Society for Exercise Physiology, the World Health Organization (WHO) recommend screen watching for 60 minutes or less per day (or 420 minutes or less per week) for children aged 2–5 years.^5–7 Given the emergence of technology innovations, small children, including toddlers and preschoolers, view media as a new playing environment. As a result, electronic media uptake has dramatically increased in this group.⁸ In the United States, nearly 3 in 10 children under one year of age have had interactions with the mobile device, with 40% of children had the first exposure as early as three months of age.^9,10 The proportion of children watching television increased to 90% at two years.¹⁰ In India, the average screen time is higher (2.7 hours) compared to the one reported among the U.S. preschoolers (1–1.5 hours).^10,11 The increased screen time among preschoolers may lead to significant changes in the body mass index (BMI), thereby increasing the risk of childhood obesity.^12–14 Screen time is a strong predictor of the high BMI among children aged 2–5 years.^14,15

According to the previous evidence, the age of onset of media use, cumulative hours of screen time, and exposure to specific content were strongly associated with poor executive abilities and inattention problems among preschoolers.^16–19 Social-ecological models were used to explain behavioral, family, socio-demographic, cultural, and environmental factors to predict the screen time behaviors among children.^20,21 Environmental factors, including negative cues from excessive ST among parents and access to electronic devices were also investigated.²¹ Several interventions aimed to change the knowledge, attitudes, norms, behaviors, and environment were introduced to limit the screen time among children.²² Use of television-control device, educating parents to develop screen time budgeting plans, setting goals, and having children engaged in alternative activities, were some approaches used within these interventions, which emphasized provision only, behavioral and environmental change components.^22–27 Overall, these interventions yielded small effects in the ST reduction, with body mass index being the main foci of attention.²⁷ Strengthening the empirical evidence to evaluate the effectiveness of the already designed interventions and developing new interventions accounting for the holistic view of the problem is essential. In this context, fourth-generation theoretical models, such as the Multi Theory Model (MTM) of health behavior change, may play a critical role in explaining the ST behavior among children.^28–34 This theory encompasses external and internal mediators such as behavioral, social, environmental, and educational components to design precision interventions.^28–34 The MTM has successfully been utilized in predicting a wide array of health behaviors among different population groups over the past few years.^28–37 Therefore, we sought to determine the predictability and applicability of MTM in assessing the intention for changing ST behaviors among preschoolers of Northern India through a parental survey.

Methods

Study Design, Population and Sampling

A convenience quota sample of North Indian parents of 2 to 5-year-old preschoolers from Derabassi (a city in the vicinity of Chandigarh, Punjab) was recruited to participate in an online cross-sectional survey. Institutional Review Board approval (IRB # NDC/31/10/2019) was obtained from the National Dental College and Hospital, Derabassi, Chandigarh. G*Power software (version 3.1) was used to perform an a priori power analysis.^36,38,39 The power analysis used a predetermined alpha of 0.05, power of 0.80, a medium effect size of 0.15 (corresponding to the norm in behavioral and social sciences), and three predictors (the three constructs in each MTM component).²⁵ The estimated sample size was 77, which was deemed appropriate for the convenience quota sample. Parents with children spending more than 60 mins per day of screen time (or more than 420 minutes per week) were included in the study. Parents with more than one child in the age group of 2 to 5 years were asked to think of only one child per questionnaire while giving their responses. The explanatory variables were the constructs of MTM. The outcome variables were the intention to initiate the behavior change of limiting ST to less than 60 minutes per day (in the initiation model) and the intention of continuing to practice this behavior (in the sustenance model).

Instrumentation

The instrument comprised of 39 items, of which 10 asked the parents about their basic socio-demographic information (gender, age, number of children, relationship with the child, income, type of screen, amount of ST) and 29 additional items measured the MTM constructs for the initiation and sustenance models.

According to MTM, there are three constructs of initiation for assessing ST behavior, including participatory dialogue in which advantages overshadow disadvantages, behavioral confidence (or surety of performance of behavior), and changes in the physical environment. For screen time behavior's sustenance, emotional transformation, practice for change, and changes in the social environment were the three constructs.

Initiation Model

The advantages of limiting screen time to less than an hour daily were the following, (1) child will be healthier (such as not have eye problems, neck problems, etc.); (2) sleep more; (3) not get overweight or obese; (4) have more time for other activities; and (5) have more energy. Each item was scored on a five-point scale (0= never, 1= almost never, 2 = sometimes, 3 = fairly often, 4 = very often). The disadvantages were also measured through five items. If their child engaged in less than 60 minutes of screen time every day, he/she may not learn new technology, will not have adequate cognitive development, miss out on literacy skills, become more active in undesirable activities, and will require more attention from parents. The total score for all items was calculated for advantages and disadvantages separately with a possible score range of 0–20 points. To derive the score on the construct of participatory dialogue, the total scores of disadvantages were subtracted from the scores of advantages (possible scores ranging from -20 to +20 units).

Behavioral confidence was measured using five items to self-assess the confidence of parents in the following: 1) restricting child's screen time to less than 60 minutes per day for the current week; (2) helping the child learn new technology; 3) finding programming for child’s mental development; 4) finding programming for developing literacy (3Rs) skills; 5) spending more time with children. The scoring was on a five-point scale (0= not at all sure, 1 = slightly sure, 2 = moderately sure, 3 = very sure, and 4= completely sure). The scores in this subscale ranged from 0 to 20 units after deriving a summative score of the five items.

Changes in the physical environment construct was also measured using the same scale as behavioral confidence and asked about parental ability to do the following, 1) take away screen time devices from the children after 60 minutes every day; 2) find alternatives to screen time for the children; 3) divert the attention of children from spending time in front of the screen. The summative scores of three items on this subscale ranged from 0 to 12 units.

The construct of the intention of initiation was measured as a rating on the likelihood of restricting their child's ST to less than 60 minutes every day beginning tomorrow. This was measured on a five-point scale of not at all likely (0), somewhat likely (1), moderately likely (2), very likely (3), and completely likely (4) with the scores ranging from 0–4 units.

Sustenance Model

The construct of emotional transformation was measured using three items that asked parents about the confidence in directing their feelings toward setting a goal of restricting their children ST to less than 60 minutes every day, inspiring their child toward restricting his/her ST to less than 60 minutes every day and resisting self-doubt in meeting the goal of restricting their child's ST to less than 60 minutes. The same rating scale as the behavioral confidence subscale was used and the summative score of three items could yield a possible range from 0 to 12 units.

The construct of practice for change was assessed using three items that asked parents of their sustenance in keeping a daily log to monitor the total time of your child spends in front of the screen, maintaining the goal of restricting their child's ST to less than 60 minutes daily if they came across barriers, and changing their plan of restricting their child's ST to less than 60 minutes every day if they encountered difficulties The same rating scale as behavioral confidence subscale was used and the summative score of three items could yield a possible range from 0 to 12 units.

The construct of changes in the social environment was assessed using three items that asked parents regarding their surety of getting the help of their spouse in restricting their child's ST to less than 60 minutes every day, a family member in restricting their child's ST to less than 60 minutes every day, a friend (or daycare worker if their child goes to daycare) in restricting their child's ST to less than 60 minutes every day. The same rating scale as the behavioral confidence subscale was used, and the summative score of three items could yield a possible range from 0 to 12 units.

The construct of the intention of sustenance was measured as a rating on the likelihood of restricting their child's ST to less than 60 minutes every day until school age. This was measured on a five-point scale of not at all likely (0), somewhat likely (1), moderately likely (2), very likely (3), and completely likely (4) with the scores ranging from 0–4 units.

Face and Content Validity

Instrument was developed by taking into consideration the cultural aspects of India. Six experts in the areas of health behavior instrumentation, MTM, and work with preschoolers were invited from different institutions in India and the U.S. to verify the face and content validity of the instrument. We did not encounter any cross-culture challenges in the process of survey validation because five out of six experts were of Indian origin and were well acquainted with the Indian culture. One U.S. native expert was a theory expert and had a great range of familiarity with other cultures, including Indian culture. Experts were requested to provide an item-by-item evaluation in terms of readability (Was the content understood at the eighth-grade reading level in India?), face validity (Whether each item appeared to measure the intended construct as per the provided definition?), and content validity (Whether the items adequately tapped into each MTM construct within the universe of content?). It took two rounds to finalize the instrument and achieve mutual consensus. The Flesch Reading Ease of the final instrument was 77.1, and the Flesch Kincaid Grade Level of the instrument was 4.1.

Construct Validity

For construct validation of the instrument, confirmatory factor analysis (CFA) of each subscale was conducted using the maximum likelihood method (MLM) on the study sample. For establishing a one-factor solution following the Kaiser criterion of Eigenvalue greater than or equal to 1.0 and factor loadings on each item greater than 0.287 were established a priori as per the generally accepted recommendations.^36,40 Given the small sample size, we doubled the critical value (0.287 × 2 = 0.57), as suggested by Stevens⁴¹ in 2009.

Reliability

For establishing the internal consistency reliability of the instrument, Cronbach's alpha was performed on each subscale, as well as the initiation and sustenance subscales and the entire scale. The calculations were made on the data from the study sample and have been reported in the results section. The acceptable level of Cronbach's alpha was determined a priori as 0.70 as this is a new scale.³⁶

Data Analysis

Participants’ responses, from Qualtrics, were exported to Microsoft Excel, and then imported to IBM SPSS version 26.0 (IBM Corp. Armonk, NY, USA) for the statistical analysis. Descriptive statistics, including means and standard deviations for all continuous variables and frequencies and percentages for categorical variables, were computed. The correlations among the variables were computed using the independent Pearson's correlations test. The two stepwise multiple regression models were fitted with initiation and sustenance constructs as dependent variables. For the initiation model, three predictors, namely participatory dialogue, behavioral confidence, and changes in the physical environment, were utilized. In the sustenance model, emotional transformation, practice for change, and changes in the social environment were used as predictors. P-values less than 0.05 (two-sided) were considered statistically signiﬁcant, and data were also reported as 95% confidence intervals.

Results

Demographic Characteristics

A total of 118 parents provided consent to participate in this study and offered information about their children. Of the entire study population, a total of 72 (61.1%) parents reported that their children had ST equal to or greater than 420 minutes/day. Therefore, they were included in the final analysis of predicting screen use behaviors. Every 3 out of 10 parents reported their income in the range of 100,001–500,00 INR (see Table 1). More than half of the parents reported having three or more screen devices in households. Nearly 69% of the children had access to one or two screen devices at home. The most commonly used devices were smartphones with other devices such as computers, iPad, tablets, and video game consoles. The mean age of children across the total sample of 72 was 3.94 (± 0.95) years. The demographic profile shows that 31 (56.9%) children were females, and 41 (43.1%) were males.

Table 1.

Demographic Characteristics of the Entire Study Population (n = 72).

Variable	Characteristics	Mean ± SD	n (%)
Age	–	3.94 ± 0.95	–
Gender	Female	–	31(56.9)
Gender	Male	–	41(43.1)
Number of Children	1Child	–	32 (44.4)
	2 Children	–	26 (36.1)
	>2 Children	–	12 (16.7)
	Not reported	–	2 (2.8)
Relationship with child	Father	–	13 (18.1)
	Mother	–	57 (79.2)
	Guardian	–	2 (2.8)
Income (in Indian Rupees)	≤50,000	–	9 (12.5)
	50,001–100,000	–	11 (15.3)
	100,001–500,000	–	28 (38.9)
	>500,001	–	13 (18.1)
	Not reported	–	11 (15.3)
Number of screen gazettes in the household	1	–	16 (22.2)
	2	–	14 (19.4)
	3	–	22 (30.5)
	>3	–	20 (27.7)
Number of device available to children	1	–	44 (37.3)
	2	–	38(32.2)
	3	–	23 (19.5)
	>3	–	13 (11.0)
Type of Screen	Smart phone	–	15 (20.8)
	Other devices with Smart phone	–	49 (68.1)
	Other devices without Smart phone	–	8 (11.1)

Note: Other devices include: Computer, I pad, TV, Tablet, Video game console.

The mean for the intention for initiation of limiting ST to less than 60 minutes daily in the upcoming week was 2.81 ± 1.01 units, with possible minimum and maximum scores ranging from 1 to 4 units (see Table 2). The mean for the intention for the sustenance of restricting screen time less than 60 minutes daily or less than 420 minutes weekly for the next five years was 2.90 (SD: ±1.10) units, with possible minimum and maximum scores ranging from 0 to 4 units. Except for the construct “changes in the social environment,” all the scales and subscales had Cronbach's alphas higher than 0.70 and thus acceptable. The construct validation using the Confirmatory Factor Analysis (CFA) with the maximum likelihood method was performed on the seven subscales. The results of construct validation yielded one-factor solutions for each subscale with Eigenvalues over 1.0 and factor loadings over the acceptable value of 0.287. Upon doubling the critical value to 0.57, all items of the subscales, except for a few items, met the criteria. Since the experts had confirmed the scales to be construct valid and these were new subscales with a small sample size, no item deletions were done.

Table 2.

Descriptive Statistics of Multi-Theory Model Constructs of Behavior Change (n = 72).

Constructs	Possible score(Min. and Max.)	Observed score(Min. and Max.)	Mean ± SD	Cronbach’s alpha
Initiation	0–4	1–4	2.81 ± 1.01	—
Participatory dialogue: advantages	0–20	3–20	12.2 ± 4.45	0.82
Participatory dialogue: disadvantages	0–20	0–20	10.5 ± 3.80	0.77
Participatory dialogue: advantages/disadvantages score	–20±20	–10±17	1.7 ± 4.20	—
Behavior confidence	0–20	5–20	12.7 ± 3.32	0.73
Changes in the physical environment	0–12	2–12	8.24 ± 2.74	0.88
Entire initiation scale	—	—	—	0.86
Sustenance	0–4	0–4	2.90 ± 1.10	—
Emotional transformation	0–12	0–12	8.31 ± 2.70	0.88
Practice for change	0–12	0–12	7.36 ± 2.70	0.74
Changes in the social environment	0–12	3–12	8.21 ± 2.4	0.60
Entire sustenance scale	—	—	—	0.86
Entire scale	—	—	—	0.91

Prior to the multiple regressions, correlation matrices were generated for the MTM constructs (see Tables 3 and 4). The regression model with intention for initiation as a dependent variable explained 33.4% of the variance in the intention for initiation of limiting screen use, F (1, 67) = 35.059, p < 0.001, adjusted R² = 0.334 (Table 5). Behavioral confidence (standardized coefficient = 0.487, p < 0.001) and changes in the physical environment (standardized coefficient = 0.586, p < 0.001) statistically significantly predicted the intention for initiation of reducing ST behavior (see Table 5).

Table 3.

Correlation Matrix of the Initiation Model Constructs.

Construct	1	2	3	4
1. Initiation	–	0.086	0.487**	0.586**
2. Participatory dialogue: advantages/disadvantages score		–	0.032	0.437**
3. Behavioral confidence	–	–	–	0.616**
4. Changes in the physical environment	–	–	–	–

**Correlation is significant at the 0.01 level.

Table 4.

Correlation Matrix of the Sustenance Model Constructs.

Construct	1	2	3	4
1. Sustenance	–	0.637**	0.475**	0.396**
2. Emotional Transformation	–	–	0.661	0.381**
3. Practice for Change	–	–	–	0.563**
4. Changes in the Social Environment	–	–	–	–

** Correlation is significant at the 0.01 level.

Table 5.

Stepwise Multiple Regression Predicting Initiation of Screen Time (n = 72).

Variables	B	SE_B	β	p-value	95% CI
Behavioral confidence	0.148	0.32	0.487	<0.001	0.083, 0.213
Changes in the physical environment	0.216	0.036	0.586	<0.001	0.143, 0.289

F (1,67) = 35.059, p < 0.001, R² = 0.344, adjusted R² = 0.334.

The dependent variable is the intention of initiation of limiting screen use; the independent variables are participatory dialogue (excluded), behavioral confidence and changes in the physical environment; B = unstandardized coefficient; SEB = standard error of the coefficient; β = standardized coefficient; p = level of significance; CI = confidence interval; participatory dialogue (advantages/disadvantages) was excluded from the model (probability of F to remove ≥0.1).

The regression model with intention for sustenance as a dependent variable explained 39.7% of the variance in the intention for sustenance of reducing screen use, F (1, 65) = 44.480, p < 0.001, adjusted R² = 0.397 (Table 6). Emotional transformation (standardized coefficient = 0.637, p < 0.001), practice for change (standardized coefficient = 0.475, p < 0.001), and changes in the social environment (standardized coefficient = 0.396, p = 0.001) statistically significantly predicted the sustenance of limiting ST behavior (see Table 6).

Table 6.

Stepwise Multiple Regression Predicting the Sustenance of Screen Time (n = 72).

Variables	B	SE_B	β	p-value	95% CI
Emotional transformation	0.269	0.040	0.637	<0.001	0.188, 0.349
Practice for change	0.201	0.046	0.475	<0.001	0.109, 0.292
Changes in the social environment	0.185	0.052	0.396	<0.001	0.080, 0.290

F (1,65) = 44.480, p < 0.001, R² = 0.406, adjusted R² = 0.397.

The dependent variable is the intention of sustenance of limiting screen use behavior; the independent variables are emotional transformation, practice for change, and changes in the social environment; B = unstandardized coefficient; SEB = standard error of the coefficient; β = standardized coefficient; p = level of significance; CI = confidence interval.

Discussion

The study aimed to explain the intention to change screen time (ST) behavior among preschoolers in Northern India to the recommended levels of less than 420 minutes per week as reported by their parents utilizing the MTM framework. This study indicates that behavioral confidence (p < 0.001) and changes in the physical environment (p < 0.001) were statistically significant constructs to explain the intention of initiation of reducing ST behavior of preschoolers by their parents. Furthermore, these two constructs accounted for a substantial proportion of variance (33.4%) in the intention of initiation of reducing ST behavior among preschoolers by their parents. In behavioral sciences, this magnitude of effect size (33.4%) can be considered practically significant.^35,36 To our knowledge, this study is the first one to utilize the MTM model to predict ST behavior among preschoolers.

Behavioral confidence is akin to the construct of self-efficacy, with the difference being that it is futuristic and has sources in addition to self. In previous literature, the role of parental self-efficacy has been significantly associated with the ability to restrict preschoolers' ST.⁴² Consistent with previous studies, the current study found that changes in the physical environment by limiting access to the screen devices at home can be useful in reducing the screen use among preschoolers.²¹ In other MTM based studies conducted among the Indian population with other behaviors, a significant association between the constructs of MTM initiation and sustenance was observed.²⁸ For instance in Indian studies conducted to explain the initiation of physical activity behavior change in upper elementary school children and in limiting use of sugar containing beverages among university students, behavioral confidence was statistically significant.³³ Additionally, behavioral confidence was statistically significant for predicting the initiation of intention for changing unhealthy sleep patterns among dental students in India.³⁴ The findings of this study point to the usefulness of both building behavioral confidence among parents of preschoolers to reduce their children's ST and to make changes in the physical environment conducive to a reduction in ST. These measures could include finding alternatives to ST, spending more play and reading time with the preschoolers, taking away ST devices, and diverting the attention of the preschooler from ST devices to other activities.

The study did not find the role of participatory dialogue in contributing to the intention of starting ST behavior change. The mean score of this construct was 1.7 ± 4.20 units with a possible range from -20 to + 20 points. The positive yet lower magnitude can partially suggest the likelihood of a minimum threshold not being reached to contribute to the ST behavior change in the absence of a behavior change program. A related aspect worth discussing is that the mean disadvantages score was high with a value of 10.5 ± 3.80 units (possible range 0–20 units). Possibly, the parents believed that the disadvantages of not indulging in ST behavior, such as not being able to learn new technology, not having adequate cognitive development, missing out on literacy skills, becoming active in undesirable activities, and requiring more time from parents outweighed the advantages of restricting ST behavior. This underscores the need for interventions to increase parental awareness and education to alleviate the fears of perceived disadvantages of limiting screen use in addition to building behavioral confidence and addressing changes in the physical environment.

In this study, all three constructs of MTM namely, emotional transformation (p < 0.001), practice for change (p < 0.001), and changes in the social environment (p = 0.001) were statistically significant. Additionally, these constructs accounted for a substantial proportion of variance (39.7%) in the intention of sustaining the reduction of ST behavior among preschoolers by their parents. These findings are particularly important and lend support to the use of MTM in designing ST reduction interventions among preschoolers through their parents in India. Previous MTM based studies explaining behaviors such as sustenance of physical activity among upper elementary school children support parental involvement in the behavior change among children.²⁸ It was found that the emotional transformation and practice for change were statistically significant. These findings were consistent with other MTM based studies performed in India among university students to explain the different behaviors, such as change in maintaining sugar-sweetened beverage consumption and healthy sleep patterns.^33,34

In our study, parents reported that their children had screen time equal to or greater than 420 minutes/day which is the upper limit of the recommended levels. This estimate is higher than 162 minutes spent/week by preschoolers in Western India.¹¹ This may point to a technology boom that might be contributing to greater ST behavior among preschoolers in India. It is important to note that the data for this study were collected before the emergence of the COVID-19 pandemic. Given the school closures and transition to the online learning environment in the pandemic, screen time among children can be presumably high. Investigating ST behavior among children during COVID-19 can be a possible avenue for future studies.

Implications for Practice

The study provides support for the applicability of MTM in designing pilot and efficacy studies for influencing ST behavior for preschoolers through parental interventions. Such interventions would need to build behavioral confidence of parents by mastery of skills to find alternatives to screen time in caring for their children. Parents will also need to be convinced that their children can learn new technology, find adequate stimulation for sound cognitive development, and find adequate and suitable programming for developing literacy (3Rs) skills without overspending time in front of the screen. Such interventions should encourage parents to spend more time playing and reading with their preschool children. It would also be important to influence changes in the physical environment by restricting the use of screen devices at home. As indicated earlier, while the construct of the participatory dialogue was not statistically significant, it might still be useful to help swing the decisional balance in favor of the advantages outweighing the disadvantages for reducing ST behavior among preschoolers by their parents. Parents will need to be convinced in the intervention through participatory dialogue so that they can themselves see that the benefits of reducing ST behavior in their children are indeed better than the potential detriments. For maintaining the long-term behavior change in parents the construct of emotional transformation whereby parents can be taught how to direct their emotions into goals will be beneficial. If the parent is feeling angry or frustrated, instead of providing the child with a screen time device, they can set a goal of reading or play with them. This can be built using role-playing or psychodrama or other effective measures in a traditional health education intervention. Likewise, practice for change construct also needs to be built in interventions by providing the parents with a monitoring tool such as an app or another method of recording the total time spent in front of the screen and regulating it. Finally, the construct of changes in the social environment can be influenced by mobilizing natural social support in the form of family and friends who can provide alternatives to screen time. The artificial social support that can come from health professionals or researchers in the form of reminders, text messages, and other such means to sustain the habit of reducing ST behavior in the preschoolers would also be useful.

Study Limitations

Despite determining the usability of MTM in explaining ST behavior among preschoolers, the study had a few limitations. First, the study was conducted in a small geographical area in Northern India, which may limit the generalizability of the results to other populations and regions. Second, the ST behavior was measured through a past 7-day recall, which may introduce a recall bias. Failure to correctly observe the ST behaviors in children, and all the other shortcomings of self-report can also induce bias. Future research can utilize more objective measures of measuring ST behavior such as actual recording on an app or other such means. Third, the study used a cross-sectional design which limits its ability to provide conclusive evidence regarding causality. Future studies can utilize longitudinal designs. Fourth, the study did not establish test-retest reliability due to the lack of enough resources. Finally, in the initiation model and sustenance model, the intention for behavior was measured instead of actual behavior which is justified based on previous work with this theory and theory of planned behavior.⁴³ Prospective studies addressing the above limitations can be designed to strengthen empirical evidence.

Conclusions

Understanding ST behavior among preschoolers is critical because behaviors instilled in early childhood are more likely to persist in adulthood. Most scientific evidence points toward restricting this behavior to no more than one hour per day but with growing complexities in life, many parents are not able to restrict this behavior in their children. This study tested a novel paradigm of MTM and found it be useful in explaining substantial variance in the intention for both initiating and maintaining a behavior change by parents to curtail the screen time in their preschool children in Northern India. This new theory can be utilized in designing and testing for efficacy, interventions to reduce ST behavior among preschool children in India and maybe elsewhere.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical Approval

Institutional Review Board approval (IRB # NDC/31/10/2019) was obtained from the National Dental College and Hospital, Derabassi, Chandigarh.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Manoj Sharma

Kavita Batra

Author Biographies

Manoj Sharma, MBBS, Ph.D., MCHES ® is a Professor of Social and Behavioral Health in the Department of Environmental & Occupational Health in the School of Public Health. He is a publichealth physician with a medical degree from the University of Delhi and a doctorate in Preventive Medicine (PublicHealth) from The Ohio State University. He is also a master certified health education specialist certified from the National Commission on Health Education Credentialing. In his career, spanning over 30 years, he has trained/taught over 6,000 health professionals at 13 national and international universities working full time or part-time. He is aprolific researcher and as of August 2020 had published 10 books, over 280 peer-reviewed research articles, and over 450 other publications and secured funding for over $4 million (h- index 36, i-10 index 147, and over 7,000 citations). He has been awarded several prestigious honors including American Public Health Association’s Mentoring Award, ICTHP Impact Award, William R. Gemma Distinguished Alumnus Award from the College of Public Health Alumni Society at the Ohio State University and others.

Kavita Batra, serves as a Biostatistician withthe Nevada Department of Health and Human Services and the School of Medicine (UNLV). Over the past years, she has taught several public health and general education courses, including Epidemiology, Biostatistics, Human Anatomy, and Physiology. Dr. Batra received her Ph.D. from the University of Nevada, Las Vegas, in May 2020. Her researchfocuses on maternal and child health, particularly drug-exposed neonates. Her dissertation was aimed to assess the health and financial burden of Neonatal Abstinence Syndrome in Nevada. During 2019-2020, She was a successful candidate to participate in the final rounds of the Rebel Grad Slam. Dr. Batra is a UNLV Medallion recipient and a Graduate College Finishing Fellow due to her exceptional involvement in the graduate studies. She served the Graduate Professional Students’ Association for three years (2017-2020). Dr. Batra is an expert in qualitative and quantitative research and presented her work in several state and national public health conferences. She is currently leading several research projects to investigate the impact of COVID-19 on mental health across different demographic and workforce groups. Dr. Batra also served as a panelist for the Center for Evidence-based Policy in the Medicaid Evidence-Based Decisions Project discussion on the racial disparities of COVID-19. Currently, she serves as a guest editor at the Healthcare journal for a special issue on COVID-19. Besides, she serves on the reviewer panel of several academic journals.

Tejinder Pal Singh, BDS,MDS, MPH is an Associate Professor in the Divisionof Public Health, University of Utah. He has played a key role in fostering academic collaborations and engagingcommunities promoting the practice of Public Health globally.

Rohini Dua (MDS) is a professor with theDepartment of Pedodontics and Preventive Dentistry, National Dental College and Hospital, Dera Bassi, Mohali, Punjab,India.

Melinda Ickes, received her Ph.D. from the University of Cincinnati in 2010. Dr. Ickes is an Associate Professor and Director of Graduate Studies in the Department of Kinesiology within the College of Education. She has a joint appointment in the College of Nursing and is a Faculty Associate of the BREATHE (Bridging ResearchEfforts and Advocacy Toward Healthy Environments) research team, serving as Co-Director of the TobaccoPolicy Research Program and Director of Go Tobacco-free. Dr. Ickes and her colleagues have assisted over 400 campuses and organizations work toward tobacco-free policies.

Ravi Batra (MCA) is the Quality Assurance Architect and Analyst with the Coforge Ltd. He has published multiple meta-analyses related to the psychological impact of COVID-19.

Vinayak K. Nahar is an Assistant Professor and Director of Clinical Research in theDepartment of Dermatology at the University of Mississippi Medical Center. He received his medical degree from Vitebsk State Medical University, Belarus, and a master’s degree in health promotion and a doctorate with an emphasisin health behavior from the University of Mississippi. Dr. Nahar’s research focus is on the utilization of healthbehavior theoretical models to conceptualize preventative behavioral approaches that guide effective interventionsand provide a deeper understanding of the underlying factors of behavior change. Dr. Nahar has 110 publications inmany high impact, peer-reviewed journals, including JAMA Dermatology; the Journal of the American Academy of Dermatology; the British Journal of Dermatology; Archives of Dermatological Research; Photodermatology; Photoimmunology & Photomedicine; the Indian Journal of Dermatology, Venereology, and Leprology; JAMA Oncology; and the Journal of Cancer Research and Clinical Oncology. He has presented his work at national and international conferencesand is currently a reviewer for JAAD and several other journals. He also is on the editorial board of the International Journal of Women’s Dermatology and the Journal of Dermatology Nurses’ Association. Dr. Nahar’s work has been featured in major news outlets including CNBC, CNN, Los Angeles Times, Newsweek, and Yahoo News.

References

Barber

Kelly

Collings

, et al. Prevalence, trajectories, and determinants of television viewing time in an ethnically diverse sample of young children from the UK. Int J Behav Nutr Phys Act 2017; 14: 1–11.

Council on Communications and Media. Children, adolescents, and the media. Pediatrics 2013; 132: 958–961.

Rideout

Generation M2: media in the lives of 8- to 18-year-olds. Menlo Park, CA: Kaiser Family Foundation, 2010.

Strasburger

Jordan

Donnerstein

Health effects of media on children and adolescents. Pediatrics 2010; 125: 756–767.

Council on Communications and Media. Media and young minds. Pediatrics 2016; 138: e20162591.

Tremblay

Carson

Chaput

, et al. Canadian 24-hour movement guidelines for children and youth: an integration of physical activity, sedentary behaviour, and sleep. Appl Physiol Nutr Metab 2016; 41: S311–S327.

World Health Organization. WHO guidelines on physical activity, sedentary behaviour and sleep for children under 5 years of age. Geneva: World Health Organization, 2019.

American Psychological Association. Kids and the media, www.apa.org/topics/kids-media/">www.apa.org/topics/kids-media/.">https://www.apa.org/topics/kids-media/">www.apa.org/topics/kids-media/. https://www.apa.org/topics/kids-media/ (n.d., accessed 17 March 2021).

Kemp

Babies as young as 6 months using mobile media. AAP News 2015; 36. DOI: 10.1542/aapnews.20150425-3

10.

Zimmerman

Christakis

Meltzoff

AN.

Television and DVD/video viewing in children younger than 2 years. Arch Pediatr Adolesc Med 2007; 161: 473–479.

11.

Shah

Fahey

Soni

, et al. Screen time usage among preschoolers aged 2-6 in rural Western India: a cross-sectional study. J Family Med Prim Care 2019; 8: 1999–2002.

12.

Caroli

Argentieri

Cardone

, et al. Role of television in childhood obesity prevention. Int J Obes 2004; 28: S104–S108.

13.

Suglia

Duarte

Chambers

, et al. Social and behavioral risk factors for obesity in early childhood. J Dev Behav Pediatr 2013; 34: 549–556.

14.

Wen

Baur

Rissel

, et al. Correlates of body mass index and overweight and obesity of children aged 2 years: findings from the healthy beginnings trial. Obesity (Silver Spring) 2014; 22: 1723–1730.

15.

Hinkley

Verbestel

Ahrens

, et al.; IDEFICS Consortium. Early childhood electronic media use as a predictor of poorer well-being: a prospective cohort study. JAMA Pediatr 2014; 168: 485–492.

16.

Chen

Adler

JL.

Assessment of screen exposure in young children, 1997 to 2014. JAMA Pediatr 2019; 173: 391–393.

17.

Nathanson

Aladé

Sharp

, et al. The relation between television exposure and executive function among preschoolers. Dev Psychol 2014; 50: 1497–1506.

18.

Madigan

Browne

Racine

, et al. Association between screen time and children’s performance on a developmental screening test [published correction appears in. JAMA Pediatr 2019; 173: 244–250.

19.

Tamana

Ezeugwu

Chikuma

, et al.; CHILD study Investigators. Screen-time is associated with inattention problems in preschoolers: results from the CHILD birth cohort study. PLoS One 2019; 14: e0213995. https://doi.org/10.1371/journal.pone.0213995

20.

Downing

Hinkley

Salmon

, et al. Do the correlates of screen time and sedentary time differ in preschool children? BMC Public Health 2017; 17: 1–12.

21.

Kaur

Gupta

Malhi

, et al. Screen time in under-five children. Indian Pediatr 2019; 56: 773–788.

22.

Maniccia

Davison

Marshall

, et al. A meta-analysis of interventions that target children’s screen time for reduction. Pediatrics 2011; 128: e193–e210.

23.

Dennison

Russo

Burdick

, et al. An intervention to reduce television viewing by preschool children. Arch Pediatr Adolesc Med 2004; 158: 170–176.

24.

Leblanc

Spence

Carson

, et al. Associations between factors within the home setting and screen time among children aged 0–5 years: a cross-sectional study. BMC Public Health 2012; 12: 1–8.

25.

Lipsey

Wilson

DB.

The efficacy of psychological, educational, and behavioral treatment. Confirmation from meta-analysis. Am Psychol 1993; 48: 1181–1209.

26.

Harrison

Burns

McGuinness

, et al. Influence of a health education intervention on physical activity and screen time in primary school children: switch off–get active. J Sci Med Sport 2006; 9: 388–394.

27.

Wahi

Parkin

Beyene

, et al. Effectiveness of interventions aimed at reducing screen time in children: a systematic review and meta-analysis of randomized controlled trials. Arch Pediatr Adolesc Med 2011; 165: 979–986.

28.

Khanna

Singh

, et al. Sharma testing multi-theory model (MTM) in predicting physical activity behavior among upper elementary school children in Northern India. J Health Soc Sci 2020; 5: 387–396.

29.

Sharma

Multi-theory model (MTM) for health behavior change. Webmed Central Behaviour, 2015; 6(9): WMC004982.

30.

Sharma

Catalano

Nahar

, et al. Using multi-theory model to predict initiation and sustenance of small portion size consumption among college students. Health Promot Perspect 2016; 6: 137–144.

31.

Sharma

Theoretical foundations of health education and health promotion. 3rd ed. Burlington: Jones & Bartlett Learning, 2017, pp. 250–256.

32.

Sharma

Priest

Nahar

, et al. Applying multi-theory model (MTM) of health behavior change to predict water consumption instead of sugar-sweetened beverages. J Res Health Sci 2017; 17: 370.

33.

Sharma

Jain

Nahar

, et al. Determining predictors of change in sugar sweetened beverage consumption behaviour among university students in India. Int J Adolesc Med Health. Epub ahead of print 25 January 2020. DOI: 10.1515/ijamh-2019-0078.

34.

Sharma

Jain

Nahar

, et al. Predictors of behaviour change for unhealthy sleep patterns among Indian dental students. Int J Adolesc Med Health. Epub ahead of print 3 February 2020. DOI: 10.1515/ijamh-2018-0256.

35.

Dokun-Mowete

Sharma

Beatty

Using multitheory model to predict low-salt intake among Nigerian adults living with hypertension. Int Q Community Health Educ 2019; 39: 245–255.

36.

Sharma

Petosa

RL.

Measurement and evaluation for health educators. 1st ed. Burlington: Jones & Bartlett Learning, 2014.

37.

Hayes

Sharma

Shahbazi

, et al. The evaluation of a fourth-generation multi-theory model (MTM) based intervention to initiate and sustain physical activity. Health Promot Perspect 2019; 9: 13–23.

38.

Faul

Erdfelder

Buchner

, et al. Statistical power analyses using G*power 3.1: tests for correlation and regression analyses. Behav Res Methods 2009; 41: 1149–1160.

39.

Lenth

RV.

Some practical guidelines for effective sample size determination. Am Stat 2001; 55: 187–193.

40.

Kaiser

HF.

The application of electronic computers to factor analysis. Educ Psychol Meas 1960; 20: 141–151.

41.

Stevens

JP.

Applied multivariate statistics for the social sciences. 5th ed. New York: Taylor and Francis Group; 2009.

42.

Jago

Wood

Zahra

, et al. Parental control, nurturance, self-efficacy, and screen viewing among 5- to 6-year-old children: a cross-sectional mediation analysis to inform potential behavior change strategies. Child Obes 2015; 11: 139–147.

43.

Ajzen

The theory of planned behavior. Organ Behav Hum Decis Process 1991; 50: 179–211.