| Literature DB >> 35787276 |
Asanka Rathnasiri1, Harsha Rathnayaka2, Nirmani Yasara2, Sachith Mettananda3,4.
Abstract
BACKGROUND: Excessive use of screen devices and screen time are increasing health problems in children. We aim to describe the electronic screen device usage and determine the factors associated with their use among preschool-attending children in a suburban population in Sri Lanka.Entities:
Keywords: Preschool children; Screen device; Screen time; Television use
Mesh:
Year: 2022 PMID: 35787276 PMCID: PMC9251924 DOI: 10.1186/s12887-022-03452-6
Source DB: PubMed Journal: BMC Pediatr ISSN: 1471-2431 Impact factor: 2.567
General characteristics of the study population
| Sex of the childa | ||
| Male | 154 | 48.1 |
| Female | 166 | 51.9 |
| Age of the child | ||
| 3 – 4 years | 139 | 40.9 |
| 4 – 5 years | 201 | 59.1 |
| Age of the mother | ||
| < 20 years | 5 | 1.5 |
| 21–30 years | 71 | 20.9 |
| 31–40 years | 240 | 70.6 |
| > 40 years | 24 | 7.1 |
| Education level of the motherb | ||
| Primary education | 1 | 0.3 |
| Secondary education (Ordinary Level) | 52 | 15.4 |
| Secondary education (Advance Level) | 160 | 47.3 |
| Higher education | 125 | 36.9 |
| Employment status of the motherc | ||
| Unemployed | 165 | 49.3 |
| Employed | 170 | 50.7 |
| Age of the father | ||
| < 20 years | 1 | 0.3 |
| 21–30 years | 33 | 9.7 |
| 31–40 years | 241 | 70.8 |
| > 40 years | 65 | 19.1 |
| Education level of the fatherd | ||
| Primary education | 6 | 1.8 |
| Secondary education (Ordinary Level) | 93 | 28.1 |
| Secondary education (Advance Level) | 139 | 42.0 |
| Higher education | 93 | 28.1 |
| Number of children in the family | ||
| One | 139 | 40.9 |
| Two | 156 | 45.9 |
| Three | 42 | 12.4 |
| Four | 3 | 0.9 |
| Monthly family incomee | ||
| < LKR 25,000 | 19 | 5.7 |
| LKR 25,000–50,000 | 129 | 38.9 |
| LKR 50,000–100,000 | 127 | 38.3 |
| > LKR 100,000 | 57 | 17.2 |
| Age of commencement of using electronic screen devices | ||
| Less than 6 months | 3 | 0.9 |
| 7 – 12 months | 56 | 16.5 |
| 13 – 24 months | 116 | 34.9 |
| 25 – 36 months | 89 | 26.8 |
| 37 – 48 months | 58 | 17.5 |
| 49 – 59 months | 9 | 2.7 |
Data missing from:
a20 subjects
b2 subjects
c5 subjects
d9 subjects
e8 subjects.; USD 1 = LKR 200
Distribution of children by daily screen time and devices [N = 339](%)
| Screen time per day | Type of the screen devices | |||||
|---|---|---|---|---|---|---|
| None | 14 (4.1) | 47 (13.9) | 131 (38.6) | 314 (92.6) | 320 (94.4) | 299 (88.2) |
| < 30 min | 40 (11.8) | 92 (27.1) | 140 (41.3) | 15 (4.4) | 12 (3.5) | 27 (8.0) |
| 31 min – 1 h | 80 (23.6) | 117 (34.5) | 43 (12.7) | 8 (2.3) | 3 (0.9) | 9 (2.7) |
| 1–2 h | 134 (39.5) | 73 (21.5) | 12 (3.5) | 1 (0.3) | 2 (0.6) | 2 (0.6) |
| 2–3 h | 46 (13.6) | 7 (2.1) | 12 (3.5) | 1 (0.3) | 2 (0.6) | 2 (0.6) |
| 3–4 h | 16 (4.8) | 2 (0.6) | 1 (0.3) | 0 | 0 | 0 |
| > 4 h | 9 (2.7) | 1 (0.3) | 0 | 0 | 0 | 0 |
Factors associated with screen time exceeding one hour per day a
| Gender of the child | |||
| Male ( | 90 (60.8%) | 0.99 (0.61–1.60) | 0.97 |
| Female ( | 94 (61.4%) | ||
| Age of the child | |||
| 4–5 years ( | 113 (63.5%) | 1.34 (0.82–2.17) | 0.23 |
| 3–4 years ( | 71 (57.7%) | ||
| Mother's education level | |||
| > Ordinary level ( | 159 (62.4%) | 1.28 (0.19–1.28) | 0.51 |
| ≤ Ordinary level ( | 25 (54.3%) | ||
| Father's education level | |||
| > Ordinary level ( | 140 (67.6%) | 2.65 (1.45–4.84) | < 0.01 |
| ≤ Ordinary level ( | 44 (46.8%) | ||
| Mother's employment status | |||
| Employed ( | 99 (66.4%) | 1.28 (0.76–2.14) | 0.33 |
| Unemployed ( | 85 (55.9%) | ||
| Number of children | |||
| Only child ( | 82 (65.1%) | 1.30 (0.79–2.13) | 0.29 |
| More than one child ( | 102 (58.3%) | ||
| Monthly family income | |||
| > LKR 50,000 ( | 106 (63.1%) | 1.15 (0.67–1.96) | 0.60 |
| ≤ LKR 50,000 ( | 78 (58.6%) | ||
aThe analysis was done using binary logistic regression. 301 subjects with completed socio-demographic data were included in the analysis. Adjusted odds ratios were determined by adjusting to all other variables in the table
Factors associated with television, smartphone and laptop use of study populationa
| Socio-demographic factor | Television users | Smartphone users | Laptop users | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Gender of the child | |||||||||
| Male ( | 127 (85.5%) | 0.77 (0.38–1.56) | 0.47 | 102 (68.9%) | 1.70 (1.02–2.83) | < 0.05 | 25 (16.9%) | 1.34 (0.68–2.62) | 0.39 |
| Female ( | 136 (88.9%) | 89 (58.2%) | 21 (13.7%) | ||||||
| Age of the child | |||||||||
| 4–5 years ( | 164 (92.1%) | 2.84 (1.39–5.82) | < 0.01 | 108 (60.7%) | 0.76 (0.46–1.28) | 0.31 | 28 (15.7%) | 1.14 (0.58–2.25) | 0.69 |
| 3–4 years ( | 99 (80.5%) | 83 (67.5%) | 18 (14.6%) | ||||||
| Mother's education level | |||||||||
| > Ordinary level ( | 223 (87.5%) | 1.06 (0.34–3.25) | 0.91 | 167 (65.5%) | 0.76 (0.35–1.66) | 0.50 | 43 (16.9%) | 0.71 (0.16–2.98) | 0.63 |
| ≤ Ordinary level ( | 40 (87.0%) | 24 (52.2%) | 3 (6.5%) | ||||||
| Father's education level | |||||||||
| > Ordinary level ( | 180 (87.0%) | 0.64 (0.25–1.63) | 0.35 | 149 (72.0%) | 3.39 (1.81–6.33) | < 0.001 | 43 (20.8%) | 6.67 (1.75–25.3) | < 0.01 |
| ≤ Ordinary level ( | 83 (88.3%) | 42 (44.7%) | 3 (3.2%) | ||||||
| Mother's employment status | |||||||||
| Employed ( | 133 (89.3%) | 1.26 (0.59–2.65) | 0.54 | 107 (71.8%) | 1.59 (0.92–2.74) | 0.09 | 33 (22.1%) | 2.22 (1.07–4.58) | < 0.05 |
| Unemployed ( | 130 (85.5%) | 84 (55.3%) | 13 (8.6%) | ||||||
| Number of children | |||||||||
| Only child ( | 109 (86.5%) | 0.85 (0.42–1.74) | 0.67 | 93 (73.5%) | 2.16 (1.26–3.68) | < 0.01 | 22 (17.5%) | 1.26 (0.65–2.47) | 0.48 |
| More than one child ( | 154 (88.0%) | 98 (56.0%) | 24 (13.7%) | ||||||
| Monthly family income | |||||||||
| > LKR 50,000 (N = 168) | 151 (89.9%) | 1.94 (0.89–4.23) | 0.09 | 114 (67.9%) | 0.95 (0.54–1.67) | 0.88 | 34 (20.2%) | 1.51 (0.70–3.26) | 0.28 |
| ≤ LKR 50,000 (N = 133) | 112 (84.2%) | 77 (57.9%) | 12 (9.0%) | ||||||
aThe analysis was done using binary logistic regression. 301 subjects with completed socio-demographic data were included in the analysis. Adjusted odds ratios (AOR) were determined by adjusting to all other variables in the table