100 Participants Needed

Vital Sign Assessment for Healthy Subjects

EI
RW
Overseen ByRachel Ward, PT
Age: 18+
Sex: Any
Trial Phase: Academic
Sponsor: Youngstown State University
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to determine when checking vital signs, such as heart rate and blood pressure, is necessary during outpatient physical therapy visits. The goal is to enhance patient safety by identifying specific signs and symptoms that warrant these checks. Licensed physical therapists with at least five years of experience in outpatient settings are invited to join an expert panel. They will share insights through online surveys to help create a decision-making guide, the Decision Algorithm for Vital Sign Assessment. As an unphased trial, this study offers patients the opportunity to contribute to groundbreaking research that could improve safety protocols in physical therapy.

Do I need to stop taking my current medications for this trial?

The trial information does not specify whether participants need to stop taking their current medications. It seems unlikely, as the study focuses on physical therapists' opinions rather than patient treatments.

What prior data suggests that this decision algorithm for vital sign assessment is safe?

The study on improving vital sign monitoring aims to make outpatient physical therapy safer for patients. Currently, the study does not involve testing treatments or medications on individuals. Instead, it seeks to determine the optimal times to check vital signs, such as heart rate and blood pressure, to guide therapy.

As this is not a trial for a drug or medical device, typical safety concerns like side effects do not apply. The goal is to develop a tool that helps physical therapists decide when to monitor vital signs. This tool aims to enhance therapy safety by identifying potential issues early. No negative effects are expected, as the study focuses on gathering opinions and reaching a consensus, not testing a new treatment on individuals.12345

Why are researchers excited about this trial?

Researchers are excited about this trial because it explores a new decision algorithm for vital sign assessment. Unlike traditional methods that rely heavily on manual monitoring and subjective analysis, this algorithm aims to provide a more standardized and objective approach. By integrating advanced data analysis, the algorithm could potentially enhance accuracy and efficiency in assessing vital signs. This could lead to better-informed decisions in patient care, especially in outpatient settings.

What evidence suggests that this decision algorithm is effective for vital sign assessment?

Research has shown that using a decision-making tool to monitor vital signs can help detect health problems early. This method identifies physical issues, leading to better treatment and outcomes for patients. One study found that tracking vital signs and predicting health outcomes proved very effective. Another study discovered that using a tool to predict patient stability improved hospital care, such as enhancing patient sleep. These findings suggest that a well-designed tool, like the Decision Algorithm for Vital Sign Assessment studied in this trial, could be useful in outpatient settings to determine when vital sign checks are necessary.15678

Are You a Good Fit for This Trial?

Inclusion Criteria

I have worked as a physical therapist in an outpatient setting for at least 5 years.
I am a licensed physical therapist.
I can read and write in English proficiently.
See 1 more

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Round 1

Participants suggest signs, symptoms, and factors influencing VSA decisions

3 weeks
Online survey

Round 2

Participants rank the importance of factors on a 5-point Likert scale and may propose additional items

3 weeks
Online survey

Round 3

Participants review aggregated results and finalize consensus on criteria

3 weeks
Online survey

Follow-up

Participants are monitored for consensus stability and agreement

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

  • Decision Algorithm for Vital Sign Assessment

How Is the Trial Designed?

1

Treatment groups

Experimental Treatment

Group I: Physical Therapists in an Outpatient SettingExperimental Treatment1 Intervention

Find a Clinic Near You

Who Is Running the Clinical Trial?

Youngstown State University

Lead Sponsor

Trials
27
Recruited
2,000+

Citations

Decision Algorithm for Vital Sign Assessment in the ...

The resulting algorithm may improve early detection of physiological abnormalities, optimize treatment progression, and enhance patient outcomes ...

Prediction of serious outcomes based on continuous vital ...

This study presents an approach to real-time outcome prediction based on machine learning from continuous recording of vital signs.

Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort ...

Each parameter is individually scored according to severity and combined for a total NEWS 2 score. Observations are performed every 4–6 h for those with a score ...

New AI tool limits vital sign monitoring, improves inpatient sleep

Drs. Theodoros Zanos and Jamie Hirsch have helped develop an algorithm that predicts a hospitalized patient's overnight stability, ...

Effectiveness of an Analytics-Based Intervention for ...

The results of this trial indicate that augmenting physician judgment with a real-time prediction algorithm can help provide patients greater sleep opportunity.

Decision Algorithm for Vital Sign Assessment in the Outpatient ...

The primary purpose is to identify clinical signs, symptoms, and other factors that inform decision-making for VSA to improve patient safety and ...

Assessment of the Use of Patient Vital Sign Data ...

This paper evaluates the use of patient vital sign data, within an enabling artificial intelligence (AI) framework, for the purposes of patient identification.

A novel deep learning algorithm for real-time prediction of ...

To address outliers in the vital sign data, the upper and lower limits were set for each variable based on the physiological ranges.