Evidence map›Paper›PMID 34781953›Full record

ArticleBMC medical informatics and decision making2021

Design of a seniors and Alzheimer's disease caring service platform.

Cheng-Wen Lee, Hsiu-Mang Chuang

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
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

2 authors.

Cheng-Wen LeeCollege of Business, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan City, 320314, Taiwan, ROC.
Hsiu-Mang ChuangCollege of Business, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli Dist., Taoyuan City, 320314, Taiwan, ROC. man521@ms48.hinet.net.ORCID 0000-0002-1301-4466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo meet the needs of aging and dementia patients in Taiwan, this study designed a nursing system that includes communication, location tracking, and fall detection, and early warning services. The main purpose of this research is to provide timely services to the elderly and patients and hope to reduce the burden when the number of nursing staff decreases. This article is a remote disease care service platform with the Internet of Things (IoT) devices to monitor the location of the elderly and whether they have dropped warning alerts.

resultsThe device is connected to the patient's waist and chest, monitors the patient's movement and behavior, and transmits messages to the back-end system, and informs caregivers through mobile phone applications when unexpected or shocking events occur. The system can identify whether the patient has fallen, accidentally, or long-term inactivity. The device is equipped with sensors that enable it to monitor the patient's location and behavior data through Bluetooth and GPS technology. Finally, we proposed a basic model and an integrated model that will industrialize the system and is expected to play a role in a larger patient population.

conclusionsThe system developed in this research has passed the Activities of Daily Living (ADL) test and verification, and is expected to provide appropriate safety care services for nursing homes and elderly residences.

Indexed as

Alzheimer DiseaseInternet of ThingsActivities of Daily LivingAgedCaregiversHumansNursing HomesAlzheimerFall detectionInternet of ThingsLocation tracking

Identifiers

PMID34781953
PMCPMC8594091

What OpenQuestion holds

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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.