Relationships between internet addiction, quality of life and sleep problems: a structural equation modeling analysis

Abstract Objective: To assess the relationship between internet addiction, quality of life, and sleep problems among adolescents. Method: This research was conducted with a representative sample of 875 adolescents. This cross-sectional study used the Internet Addiction Test, Pediatric Quality of Life Inventory™ version 4.0, Pediatric Daytime Sleepiness Scale, and sleep duration. Sociodemographic factors were also analyzed. Structural equation modeling was used to investigate relationships between variables. Results: After adjusting the model for covariances between the latent variables of daytime sleepiness and correlations between the physical and emotional domains of quality of life, the authors obtained satisfactory fit indices (RMSEA = 0.031, CFI = 0.926, TLI = 0.909, SRMR = 0.058). Internet addiction was positively associated with daytime sleepiness (rho = 0.549, p < 0.001) and negatively associated with quality of life (rho = -0.173, p < 0.001). By contrast, sleep duration was negatively associated with daytime sleepiness (rho = -0.089, p = 0.007), positively associated with quality of life (rho = 0.105, p = 0.014), and dependent on school shift (rho = 0.453, p < 0.001). Conclusions: Adolescents with higher levels of internet addiction had lower perceptions of quality of life and higher daytime sleepiness. Moreover, sleep duration had a positive correlation with quality of life. Given its detrimental effects on quality of life and daytime sleepiness, parents should better supervise internet use in adolescents.

Relationships between internet addiction, quality of life and sleep problems: a structural equation modeling analysis.

OBJECTIVE: To assess the relationship between internet addiction, quality of life, and sleep problems among adolescents. METHOD: This research was conducted with a representative sample of 875 adolescents. This cross-sectional study used the Internet Addiction Test, Pediatric Quality of Life Inventory™ version 4.0, Pediatric Daytime Sleepiness Scale, and sleep duration. Sociodemographic factors were also analyzed. Structural equation modeling was used to investigate relationships between variables. RESULTS: After adjusting the model for covariances between the latent variables of daytime sleepiness and correlations between the physical and emotional domains of quality of life, the authors obtained satisfactory fit indices (RMSEA = 0.031, CFI = 0.926, TLI = 0.909, SRMR = 0.058). Internet addiction was positively associated with daytime sleepiness (rho = 0.549, p < 0.001) and negatively associated with quality of life (rho = -0.173, p < 0.001). By contrast, sleep duration was negatively associated with daytime sleepiness (rho = -0.089, p = 0.007), positively associated with quality of life (rho = 0.105, p = 0.014), and dependent on school shift (rho = 0.453, p < 0.001). CONCLUSIONS: Adolescents with higher levels of internet addiction had lower perceptions of quality of life and higher daytime sleepiness. Moreover, sleep duration had a positive correlation with quality of life. Given its detrimental effects on quality of life and daytime sleepiness, parents should better supervise internet use in adolescents.

Prevalence, capsular types, antimicrobial resistance and risk factors associated with pneumococcal carriage among children after long-term 10-valent pneumococcal conjugate vaccine use in Brazil.

BACKGROUND: The 10-valent pneumococcal conjugate vaccine (PCV10) was introduced for childhood vaccination in Brazil's National Immunization Program in 2010. After nine years of PCV10 use, we investigated the carriage prevalence, capsular types, antimicrobial resistance and risk factors among children living in Niterói city, RJ, Brazil. METHODS: Between September and December 2019, we conducted a cross-sectional study and recruited children under 6 years of age. Antimicrobial susceptibility was evaluated by the disk-diffusion method and MICs to beta-lactams and macrolides were determined by E-test®. Capsular types were deduced by multiplex PCR. Logistic regression was used to predict risk factors for pneumococcal carriage. RESULTS: Seventy-five (17.4%) of the 430 children were pneumococcal carriers. The most frequent capsular types were 6C/D (14.7%), 11A/D (13.3%), and 23B (9.3%). PCV10 serotypes represented 5.3%. All isolates were susceptible to levofloxacin, linezolid, rifampicin, and vancomycin. Penicillin non-susceptible pneumococci (PNSP) made up 37.3%, with penicillin and ceftriaxone MICs ranging from 0.12 to 4.0 µg/ml and 0.064-4.0 µg/ml, respectively. Of the 19 (25.3%) erythromycin-resistant (ERY-R) isolates (macrolide MICs of 6 to >256 µg/ml), most had the cMLSB phenotype (84.2%) and carried the erm(B) gene (73.7%). We detected 17 (22.6%) multidrug-resistant (MDR) isolates, strongly associated with serotype 6C/D. Presence of any symptoms, chronic diseases, childcare center attendance, living with young siblings, slum residence, and unstable income were predictors of pneumococcal carriage. CONCLUSIONS: Long-term universal childhood use of PCV10 has nearly eliminated carriage with PCV10 serotypes, but the high frequency of MDR isolates, especially associated with serotype 6C/D, remains a concern. Replacing PCV10 with PCV13 should reduce the proportion of ERY-R isolates and PNSP by at least 14% and 18%, respectively.

Ohmic heating processing of milk for probiotic fermented milk production: Survival kinetics of Listeria monocytogenes as contaminant post-fermentation, bioactive compounds retention and sensory acceptance.

The survival kinetics of Listeria monocytogenes (9 log CFU/mL) as a post-fermentation contaminant in probiotic fermented milk (Lactobacillus acidophilus La-5, 8-9 log CFU/mL) processed with milk subjected to ohmic heating (0, 4, 6, and 8 V/cm; CONV, OH4, OH6, OH8, 90-95 °C/5 min) was investigated using Weibull predictive model. Additionally, the presence of bioactive compounds (antioxidant activity, inhibition of the enzymes α-glucosidase, α-amylase, and angiotensin-converting) and sensory analysis (consumer test) of probiotic fermented milks were evaluated. Overall, OH provided a decrease in the viability of Listeria monocytogenes, suitable Lactobacillus acidophilus counts, and satisfactory results in the gastrointestinal tract survival. The Weibull model presented an excellent fit to the data of all conditions. Furthermore, lower δ values (217-298 against 665 h, CONV), and increased R2 values (0.99 against 0.98, CONV) were obtained for the OH-treated samples, emphasizing the best performance of OH data. In addition, OH improved the generation of bioactive compounds as well as the sensory acceptance. Indeed, considering functional and safety purposes, OH presented as an interesting technology to be used in milk for manufacturing probiotic fermented milk.