Evidence map›Paper›PMID 24608002›Full record

ArticleSensors (Basel, Switzerland)2014

Ontological knowledge engine and health screening data enabled ubiquitous personalized physical fitness (UFIT).

Chuan-Jun Su, Chang-Yu Chiang, Meng-Chun Chih

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Content and quality of physical activity ontologies: a systematic review.The international journal of behavioral nutrition and physical activity · 2023
    Pooled it
  2. Pooled it
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chuan-Jun SuDepartment of Industrial Engineering & Management, Yuan Ze University, No.135, Yuandong Rd., Zhongli City, Taoyuan County 320, Taiwan. iecjsu@saturn.yzu.edu.tw.
Chang-Yu ChiangDepartment of Industrial Engineering & Management, Yuan Ze University, No.135, Yuandong Rd., Zhongli City, Taoyuan County 320, Taiwan. s978905@mail.yzu.edu.tw.
Meng-Chun ChihProject Management, Shun On Electronic Co. Ltd, 6F., No.22, Taiyuan St., Zhubei City, Hsinchu County 302, Taiwan. Baby1121tw@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Good physical fitness generally makes the body less prone to common diseases. A personalized exercise plan that promotes a balanced approach to fitness helps promotes fitness, while inappropriate forms of exercise can have adverse consequences for health. This paper aims to develop an ontology-driven knowledge-based system for generating custom-designed exercise plans based on a user's profile and health status, incorporating international standard Health Level Seven International (HL7) data on physical fitness and health screening. The generated plan exposing Representational State Transfer (REST) style web services which can be accessed from any Internet-enabled device and deployed in cloud computing environments. To ensure the practicality of the generated exercise plans, encapsulated knowledge used as a basis for inference in the system is acquired from domain experts. The proposed Ubiquitous Exercise Plan Generation for Personalized Physical Fitness (UFIT) will not only improve health-related fitness through generating personalized exercise plans, but also aid users in avoiding inappropriate work outs.

Indexed as

HealthKnowledgeMass ScreeningExerciseHumansMalePhysical FitnessSearch EngineSoftwareSurveys and Questionnaires

Identifiers

PMID24608002
PMCPMC4003957

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.