The association between social media use and physical activity among Canadian adolescents: a Health Behaviour in School-aged Children (HBSC) study

OBJECTIVE

To determine the association between social media use (SMU) and physical activity (PA) among Canadian adolescents.

METHODS

We used data from 12,358 participants in grades 6 to 10 who responded to the Canadian component of the 2017/2018 Health Behaviour in School-aged Children (HBSC) survey. Social media intensity and problematic SMU were assessed using a 4-point mutually exclusive scale that contained three categories based on intensity (non-active, active, and intense SMU) and one category based on the presence of addiction-like symptoms irrespective of intensity (problematic SMU). PA was assessed for five domains (i.e., school curriculum, organized sport, exercise, outdoor play, and active transport) and dichotomized using the first quartile to represent high PA engagement in each domain. Meeting PA recommendation of 60 min per day of moderate-to-vigorous PA was calculated using the sum of the five domains. Logistic regression models were used to assess the association between SMU and PA, with active SMU used as the reference group for all models.

RESULTS

Non-active SMU was associated with lower odds of meeting the daily PA recommendations and of high engagement in all five domains of PA when compared to active SMU. Intense SMU was associated with higher odds of meeting the daily PA recommendations. Problematic SMU was not associated with meeting daily PA recommendations, but it was significantly associated with lower odds of high PA engagement in the exercise domain.

CONCLUSION

The findings of this study suggest that non-active SMU was significantly associated with lower PA levels. Problematic SMU was only significantly associated with lower PA levels in the exercise domain. Intense SMU was associated with higher odds of meeting the PA recommendation.

Rapid On-Cell Selection of High-Performance Human Antibodies

Phage display is a critical tool for developing antibodies. However, existing approaches require many time-consuming rounds of biopanning and screening of potential candidates due to a high rate of failure during validation. Herein, we present a rapid on-cell phage display platform which recapitulates the complex in vivo binding environment to produce high-performance human antibodies in a short amount of time. Selection is performed in a highly stringent heterogeneous mixture of cells to quickly remove nonspecific binders. A microfluidic platform then separates antigen-presenting cells with high throughput and specificity. An unsupervised machine learning algorithm analyzes sequences of phage from all pools to identify the structural trends that contribute to affinity and proposes ideal candidates for validation. In a proof-of-concept screen against human Frizzled-7, a key ligand in the Wnt signaling pathway, antibodies with picomolar affinity were discovered in two rounds of selection that outperformed current gold-standard reagents. This approach, termed μCellect, is low cost, high throughput, and compatible with a wide variety of cell types, enabling widespread adoption for antibody development.