Cognitive and Spontaneous Brain Activity in Nonaddictive Smartphone Users Among Older Adults in China: Cross-Sectional Study
Bibliographic record
Abstract
Background: The effects of smartphone use on mental health and brain activity in adolescents have received much attention; however, the effects on older adults have received little attention. As more and more older adults begin to use smartphones, exploring the effects of nonaddictive smartphone use on mental health, cognitive function, and brain activity in older adults is imperative. Objective: This study aimed to examine differences in cognitive performance, emotional symptoms (depression, anxiety, and insomnia), and brain functional activity between older adults who use smartphones and those who do not. Methods: A total of 1014 community-dwelling older adults aged 60 years and above were surveyed in a rural area of China. Participants were categorized into 2 groups based on their smartphone use status. The Patient Health Questionnaire, Generalized Anxiety Disorder Scale, Insomnia Severity Index, and Montreal Cognitive Assessment-Basic were used to evaluate the symptoms of depression, anxiety, insomnia, and cognitive function of the participants by trained medical staff. To explore neural mechanisms, a subsample of 130 participants (89 smartphone users and 41 nonusers) was selected using stratified random sampling for resting-state functional magnetic resonance imaging scanning. Participants with contraindications for magnetic resonance imaging (eg, metal implants or claustrophobia) or who refused to participate were excluded. Functional brain activity was analyzed and compared between groups. Results: Among all 1015 older adults, 641 reported using smartphones, while 373 reported never using smartphones. Older adults who use smartphones exhibited better cognitive function compared with those who never use smartphones (z=3.806, P<.001), especially in the domains of fluency (z=3.025, P=.002) and abstraction (z=5.311, P<.001). However, there were no significant differences in levels of depression (z=0.689, P=.49), anxiety (z=0.934, P=.35), and insomnia (z=0.340, P=.73). In terms of the magnetic resonance imaging findings, a total of 130 participants completed functional magnetic resonance imaging scanning, including 89 who use smartphones and 41 who never use smartphones, and results showed that older adults who were smartphone users exhibited higher degree centrality values in the left parahippocampal gyrus. Conclusions: These findings suggest that smartphone use among older adults is associated with better cognitive performance and fewer emotional symptoms, potentially linked to enhanced brain activity in key cognitive regions. Promoting digital engagement may offer cognitive and emotional benefits for aging populations. Longitudinal studies are warranted to examine causal relationships.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".