The Impact of Digital Technology on the Physical Health of Older Workers: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital technologies are increasingly present in workplaces; however, their impact on the physical health of older workers remains unclear. OBJECTIVE: This scoping review aims to examine and summarize the scientific evidence on how digital technology affects the physical health of older workers. METHODS: Following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews) guidelines, we conducted a scoping review of English-language peer-reviewed studies extracted from MEDLINE, Cochrane, ProQuest, Web of Science, Scopus, APA PsycInfo, and ERIH PLUS. The review followed the population, concept, and context (PCC) framework, including studies on workers aged 50 years or older, any form of digital technology (eg, teleworking and the use of digital tools at work), and its impact on physical health (eg, vision loss and musculoskeletal disorders). Studies that focused only on mental health were excluded. A 13-member research team screened studies in 3 stages, namely title and abstract screening, full-text review, and data extraction. Each study was independently reviewed by at least 2 researchers, and disagreements were resolved through discussion. Data extraction and synthesis were conducted using the web-based systematic review platform Covidence (Veritas Health Innovation Ltd). RESULTS: In total, 18 studies were selected, published between 2012 and 2024, with most conducted in Europe (n=8) and Asia (n=6), followed by North America (n=2), Oceania (n=1), and Africa (n=1). We identified 6 key physical health areas impacted by digital technology in older workers, including eye health, musculoskeletal health, metabolic and cardiovascular health, workplace sound levels, and user experiences of new technologies. Findings showed mixed effects, with notable negative impacts on eye strain, musculoskeletal disorders, and hearing health issues, but positive effects on weight management, cardiovascular health, physical activity, and perceived physical well-being. CONCLUSIONS: Digital technology presents both risks and benefits for the physical health of older workers. While prolonged screen use and digital work environments contribute to eye strain, musculoskeletal issues, and hearing concerns, other technologies support better weight management, cardiovascular health, and increased physical activity. These findings also underscore the need for workplace intervention to reduce health risks. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/59900.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.027 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".