Effect of Smartphone Usage on Mental Health and Sport Performance among Canadian Athletes: A Multiple Case Study
Bibliographic record
Abstract
The purpose of this multiple case study was to investigate smartphone usage in a sample of Canadian athletes. More precisely, the study had three objectives, namely to examine (a) experiences and features of smartphone usage, (b) the perceived impact of smartphone usage on mental health and performance in sport and (c) the relation between real-time and self-perceived smartphone usage estimates. \n\nData were collected over nine months via focus groups, in-depth interviews, surveys and objective tracking of smartphone usage through a novel mobile application. These data were then investigated through document and content analysis and mapped out as narratives to represent athletes’ voices.\n\nThe main findings showed that athletes have multifaceted and nuanced experiences with their smartphones. Social media applications (e.g., Instagram, Snapchat, and Facebook) accounted for a staggering amount of the participants’ total smartphone usage over the assessed nine-month period. Most athletes had two or more social media-related apps in their top three most frequently used apps. The findings revealed that the prevalence of overnight smartphone usage from 12 am to 6 am compromised up to twenty percent of total active screen time.\n\nThe results supported the hypothesis that smartphone usage in and around the sport context elicited both beneficial and detrimental consequences. Data analysis suggested that social media usage was associated with more harmful than helpful effects on mental health and performance than other smartphone features and applications that the athletes utilized. The findings allude to associated changes in self-regulation capacity. Finally, the findings revealed a gap between real-time and self-perceived smartphone usage, The overall findings provide the fundamentals for the development of guiding principles for smartphone usage for athletes and sports programs, and suggest areas of future research. It is, however, premature to propose universal guidelines for the use of smartphone and social media within the athletic community. Continued research is required to facilitate optimal performance outcomes and overall mental health of athletes within our device–driven society.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".