Ignite Creation Date:
2025-12-26 @ 11:11 AM
Ignite Modification Date:
2026-01-14 @ 12:03 AM
Study NCT ID:
NCT06810128
Status:
COMPLETED
Last Update Posted:
2025-05-13
First Post:
2024-12-07
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predictors For the Prognosis of CMLBP Among Healthcare Professions
Sponsor:
Alaa shaker Mohamed Mohamed
Organization:
Study Overview
Official Title:
CLINICAL PREDICTION RULE IN PROGNOSIS OF CHRONIC MECHANICAL LOW BACK PAIN AMONG HEALTH CARE PROFESSIONS
Status:
COMPLETED
Status Verified Date:
2025-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Identifying clinical predictors that anticipate the prognosis of pain intensity and level of disability of mechanical low back pain among healthcare professions.It will be hypothesized that:There will be no statistical significance correlation between the prognosis of chronic mechanical low back considering pain intensity, function disability and the independant variables.
Detailed Description:
The annual incidence of CMLBP among healthcare professions has been found to be 26% with a high yearly recurrence. Chronic mechanical Low back pain is the leading cause of years lived with disability worldwide. There are individual factors that can increase the risk of pain chronicity such as biomechanical, psychological, social, environmental, lifestyle, and personal factors. Therefore the main objective of the current study will be to investigate the anticipated clinical predictors that may help the prognosis of chronic mechanical LBP among health care professions. Up to author's knowledge, no studies have been carried on to identify anticipating factors related to CMLBP among healthcare professions. Studies simply identify the factors related to chronicity, they do not however study whether the presence of 1 factor is sufficient or whether a certain mix of factor is required. Therefore, the present study will be carried on to develop more comprehensive model including connections between multiple factors, in addition to consider which factors are truly important.The study procedures were explained in detail to every patient before the assessment. All patients were informed about the purpose and nature of the study, and written consent was obtained before participation in the study.
Sample size calculation is based on power analysis using the G\*power program 3.1.9 (G power program version 3.1, Heinrich-Heine-University, Düsseldorf, Germany). The sample size calculation is depended on a pilot study including 10 patients diagnosed with chronic mechanical low back pain to study multi-regression between dependent variable and independent variables. The squared multiple correlation (R2) for this pilot study = 0.140. Sample size calculation based on F tests (Linear multiple regression: Fixed model, R² deviation from zero), Type I error (α) = 0.05, power (1-β error probability) = 0.80, and Effect size f2= 0.1627907 with a total sample size for 118 participants. Considering a 10% drop out rate, the appropriate minimum sample size for this study will be 130 participants diagnosed with chronic mechanical low back pain. The sample size calculation was depended on a pilot study including 10 patients diagnosed with chronic mechanical low back pain to study multi-regression between dependent variable and independent variables . The squared multiple correlation (R2) for this pilot study = 0.140. Sample size calculation based on F tests (Linear multiple regression: Fixed model, R² deviation from zero), Type I error (α) = 0.05, power (1-β error probability) = 0.80, and Effect size f2= 0.1627907 with a total sample size for 118 participants. Considering a 10% drop out rate, the appropriate minimum sample size for this study will be 130 participants diagnosed with chronic mechanical low back pain.
Statistical Analysis:
1. Statistical analysis will be conducted using SPSS Version 15.0 statistical software package (SPSS Inc, Chicago, IL, USA) to determine whether any potential prognostic variables among health care progressions with low back pain.
2. Demographic data will be expressed as mean ± standard deviation for all continuous variables and Chi square for gender distribution between groups.
3. Prior to final analysis, data will be screened for normality assumption, homogeneity of variance, and presence of extreme scores.
4. Tests of normality including Shapiro Wilk test and Kolmogorov-Smirnov test will be used.
5. Sensitivity, specificity, and positive likelihood ratios will be calculated for each potential predictor variable. To determine the most accurate set of variables for prediction of prognosis of mechanical low back pain , potential predictor variables will be entered into a step-wise logistic regression model (p\<0.05).
Sample size calculation is based on power analysis using the G\*power program 3.1.9 (G power program version 3.1, Heinrich-Heine-University, Düsseldorf, Germany). The sample size calculation is depended on a pilot study including 10 patients diagnosed with chronic mechanical low back pain to study multi-regression between dependent variable and independent variables. The squared multiple correlation (R2) for this pilot study = 0.140. Sample size calculation based on F tests (Linear multiple regression: Fixed model, R² deviation from zero), Type I error (α) = 0.05, power (1-β error probability) = 0.80, and Effect size f2= 0.1627907 with a total sample size for 118 participants. Considering a 10% drop out rate, the appropriate minimum sample size for this study will be 130 participants diagnosed with chronic mechanical low back pain. The sample size calculation was depended on a pilot study including 10 patients diagnosed with chronic mechanical low back pain to study multi-regression between dependent variable and independent variables . The squared multiple correlation (R2) for this pilot study = 0.140. Sample size calculation based on F tests (Linear multiple regression: Fixed model, R² deviation from zero), Type I error (α) = 0.05, power (1-β error probability) = 0.80, and Effect size f2= 0.1627907 with a total sample size for 118 participants. Considering a 10% drop out rate, the appropriate minimum sample size for this study will be 130 participants diagnosed with chronic mechanical low back pain.
Statistical Analysis:
1. Statistical analysis will be conducted using SPSS Version 15.0 statistical software package (SPSS Inc, Chicago, IL, USA) to determine whether any potential prognostic variables among health care progressions with low back pain.
2. Demographic data will be expressed as mean ± standard deviation for all continuous variables and Chi square for gender distribution between groups.
3. Prior to final analysis, data will be screened for normality assumption, homogeneity of variance, and presence of extreme scores.
4. Tests of normality including Shapiro Wilk test and Kolmogorov-Smirnov test will be used.
5. Sensitivity, specificity, and positive likelihood ratios will be calculated for each potential predictor variable. To determine the most accurate set of variables for prediction of prognosis of mechanical low back pain , potential predictor variables will be entered into a step-wise logistic regression model (p\<0.05).
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
False
Is an FDA AA801 Violation?: