Development of a Monitoring System for Smartphone Overuse
Bibliographic record
Abstract
Background: Smartphone overuse has become an epidemic public health concern around the world. Nowadays, the measurement of smartphone overuse, which comes to be known as smartphone addiction, is still relying on self-reported questionnaire. However, this leads to inaccuracy and it cannot perform continual measurement. Objective: The aim of this study is to develop an IT based system monitoring daily usage behavior of smartphone automatically, which was named Smartphone Overuse Monitoring System (SOMS). Methods: The monitoring system consists of an Android Smartphone application (SOMS App) and a web application server. The App was designed to fulfill the following core functions: 1. To collect users’ general demographic data and identify the IMEI of smartphones; 2. To monitor using behavior and assess smartphone overuse; 3. To give instant feedback to users. The web server stores the data collected by the App and execute statistical analysis. Results: We invited 11 participants to test the SOMS. The users were asked to fill out a short questionnaire at the first logon, which includes demographic information, such as name, sex, age etc. The SOMS App recorded the smartphone behavior as follows: power on/off, call in/out, screening on/off/unlock, programs usage. The data of program usage include which app and how long it was used. Once a day, the participants received a notification of the smartphone usage statistics for the last 24 hours. The users can also find key messages from the interface of the SOMS App, such as the top 10 most frequently and longest used Apps. Moreover, the SOMS App draws a fragment map, which illustrates the interrupted daily life by smartphone. Conclusions: The monitoring system can tally the length and frequency of smartphone use and analyze the most influential Apps on people’s daily life. It is not only significant for the screening of smartphone overuse but also for the development of intervention strategy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".