Evidence map›Paper›PMID 34804451›Full record

ArticleJournal of healthcare engineering2021

Retrospection of Analytical Data Collected through Smart Devices for Diseases and Disability Caused by Physical Inactivity.

Xiaoyu Wang

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 45% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Xiaoyu WangSports Department, Jilin Technology College of Electronic Information, Jilin 132000, China.ORCID 0000-0002-9039-4650
Jilin Vocational College of Industry and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The basic meaning of inactive lifestyle is doing sedentary activities. This includes playing video games, watching TV, operating computers, and sitting in a particular place for a long time. It may be sitting on the train or in bus, car, or office. This has many adverse effects on our bodies. This paper emphasizes the importance of physical activity, and a case study is presented to support the hypothesis that physical activity can certainly help improve human health. The diseases caused by inactive life are discussed in our paper. In this paper, a case study is described in which 200 individuals have participated in an activity called "Activity is Life," with the goal of improving the sedentary lifestyle and getting rid of physical ailments. The usage of analytical tools is made to analyse the data collected from the empirical research study, and IoT-based smart devices are used to capture the runtime data. The physicians and nurses have odd working hours, they have to go through many stress-induced situations, and most of them are suffering from life-threatening diseases such as depression, hypertension, cancer, and cardiovascular diseases. The main reason is inactivity in their lifestyles. To carry out an experimental study and to see the impact of regular activities on the health of doctors and nurses, a program was designed where 200 participants have participated. It is found that the activity-based 30-day program yielded great health benefits, including reduction in stress level, improvement in sleep quality index, and improvement in blood pressure values.

Indexed as

Sedentary BehaviorSleep QualityComputersExerciseHumansMotivation

Identifiers

PMID34804451
PMCPMC8601795
OpenAlexW3212439631

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.