Evaluation of a Sarcopenia Prediction Model and Development and Effectiveness of a Nursing Guidance Mobile App

NCT ID: NCT05363033

Last Updated: 2022-05-05

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-07-01

Study Completion Date

2022-12-31

Brief Summary

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We evaluated a model for the prediction of high-risk populations for sarcopenia in home settings, developed a sarcopenia nursing guidance mobile application, and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care behavior among participants.

Detailed Description

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Conditions

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Prediction of High-risk Populations for Sarcopenia

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Study Groups

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one group

Using a one-group pretest-posttest design

sarcopenia nursing guidance mobile application was developed

Intervention Type DEVICE

The application enhanced participants' sarcopenia-related knowledge and awareness regarding self-care

Interventions

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sarcopenia nursing guidance mobile application was developed

The application enhanced participants' sarcopenia-related knowledge and awareness regarding self-care

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

Inclusion criteria were being a member of the public, aged ≥20 years and \<80 years, and having experience using mobile devices or smartphones.

Exclusion Criteria

Exclusion criteria were not belonging to a high-risk population for sarcopenia and/or being unable to use the application platform.
Minimum Eligible Age

20 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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National Taipei University of Nursing and Health Sciences

OTHER

Sponsor Role lead

Responsible Party

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Pei-Hung Liao

Assistant Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Cheng Hsin General Hospital

Taipei County, , Taiwan

Site Status

Countries

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Taiwan

References

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Liao PH, Huang YJ, Ho CS, Chu W. Application of machine learning and its effects on the development of a nursing guidance mobile app for sarcopenia. BMC Nurs. 2023 Oct 9;22(1):369. doi: 10.1186/s12912-023-01545-w.

Reference Type DERIVED
PMID: 37814285 (View on PubMed)

Other Identifiers

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108A-06

Identifier Type: -

Identifier Source: org_study_id

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