Current Use and Discrepancies in the Adoption of Health-Related Internet of Things and Apps Among Working Women in Japan: Large-Scale, Internet-Based, Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Demographic changes and a low birth rate have led to a workforce shortage in Japan. To address this issue, the government has promoted engagement of female employment. However, increased female employment can impact women's health. Using Internet of Things (IoT) and apps to manage women's health has gained attention, but few studies have focused on working women. OBJECTIVE: This study aimed to clarify the current situation of working women and their use of IoT or apps to manage their health. METHODS: A large-scale, nationwide internet survey was conducted among 10,000 female participants aged from 20 years to 64 years in Japan. Participants were recruited from a marketing research company's active survey panel of 5.24 million members. The survey included questions about health status, sociodemographic factors, psychological characteristics, and the use of IoT or apps for health management. We compared perceived health status and reasons for current IoT use using t tests and assessed participant characteristics that predicted IoT use using the C5.0 decision tree algorithm. Ethical approval was granted by St. Luke's International University. RESULTS: Among participants, 14.6% (1455/10,000) currently used IoT or apps, 7% (695/10,000) used them previously, and 78.5% (7850/10,000) had never used them. Current users (42.7 years old) were older than past users (39.7 years old). Discrepancies were observed between participants' perceived health problems and the purpose for using IoT or apps, with 21.3% (2130/10,000) of all women reporting they experienced menstrual symptoms or disorders but only 3.5% (347/10,000) used IoT or apps to manage the same symptom. On the other hand, current users were more likely to use IoT or apps to manage nutrition-related problems such as underweight or obesity (405/1455, 27.8%). Device use was highest among current users, with 87.3% (1270/1455) using smartphones, 19.7% (287/1455) using smartwatches, and 13.3% (194/1455) using PCs. Decision tree analysis identified 6 clusters, the largest consisting of 81.6% (5323/6523) of non-IoT users who did not exercise regularly, while pregnant women were more likely to use IoT or apps. CONCLUSIONS: Our findings highlight the idea that woman with particular health problems (ie, menstrual symptoms or disorders and premenstrual syndrome) have lower use of IoT or apps, suggesting an unmet need for IoT and apps in specific areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".