Elsevier

The Spine Journal

Volume 1, Issue 1, January–February 2001, Pages 57-65
The Spine Journal

Original submission
Predicting recovery using continuous low back pain outcome measures

https://doi.org/10.1016/S1529-9430(01)00003-1Get rights and content

Abstract

Background context: There is a lack of research evaluating multiple follow-up visits, specifically when using continuous outcome measures. Continuous outcome measures with several follow-up assessments would allow us to evaluate rate of recovery.

Purpose: To predict low back pain outcomes based on the quantification of initial conditions.

Study design/setting

This was a prospective study where patients were enrolled within the first month of low back pain symptoms and evaluated for 3 months. Patients were recruited from several primary care facilities.

Patient sample

Thirty-two patients with local low back pain symptoms were recruited for the study.

Outcome measures

There were four major outcome measures, including functional performance probability, symptom intensity, impairment of activities of daily living, and a summary outcome measure.

Methods

Regression models were constructed using the initial conditions, including psychological, psychosocial, physical workplace, and personal factors, to predict the rate of recovery for each outcome measure.

Results

Twenty-eight patients completed the study. The r2 value for the rate of recovery regression models were 0.77 symptom intensity prediction, 0.85 activities of daily living prediction, 0.87 functional performance probability prediction, and 0.96 summary outcome measure prediction. Two functional performance patterns of recovery were found, including a steady improvement and a large jump in improvement. A discriminant function model identified the pattern of recovery in 91% of cases given initial conditions.

Conclusions

Continuous outcome measures can be accurately predicted given the initial conditions.

Introduction

Low back disorders (LBDs) are one of the most common ailments plaguing society today. According to the epidemiological literature, lifetime prevalence may be as high as 80% [1]. Fortunately, nearly 90% of those with LBD will recover in 2 to 8 weeks regardless of treatment. However, there is a lack of research evaluating multiple follow-up evaluations, which would allow researchers to evaluate the rate of recovery pattern. It is hypothesized that predicting the rate of recovery pattern based on the patients' initial condition may improve health care by providing a realistic expectation of recovery for the patient, thereby alleviating anxiety about the length of recovery. In addition, it may also predict the 10% of patients at risk for developing chronic low back disorders.

There are a plethora of outcome studies evaluating low back pain recovery. The most common outcome measures in the literature include return to work 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, symptoms 2, 9, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, activities of daily living (disability questionnaires) 10, 12, 16, 23, 25, 26, 33, 34, 35, 36, 37, 38, and functional capacity 6, 22, 23, 24, 35, 39, 40, 41, 42, 43, 44, 45, 46. Typically, these outcome measures have been assessed as dichotomous measures indicating whether a patient has recovered. Symptoms, activities of daily living, and functional capacity or functional performance may be scored as continuous or interval outcome measures. It is hypothesized that continuous or interval outcome measures may provide more quantitative information regarding the extent of recovery or residual impairment than traditional dichotomous measures.

Patients often want to know how long it will be until they recover or are able to return to specific activities. Providing a realistic expectation of the length of time to recovery may reduce patient anxiety and enhance patient recovery. The length of time required for low back pain recovery may be influenced by several categories of factors, including psychological factors, psychosocial workplace factors, physical demand workplace factors, and personal factors.

The psychological measure of depression has been shown to influence return to work 14, 21, 47 and symptoms 4, 17, 27, 44. In addition to depression, psychological measures of anxiety 27, 48, 49 and stressful life events 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50 have also been shown to influence symptoms. There is a lack of research evaluating the influence of psychological measures on activities of daily living or functional performance outcome measures.

The psychosocial measures of job satisfaction 51, 52, 53 and job control [52] have been shown to influence return to work. Symptoms may be influenced by job enjoyment [51], relations with supervisors 30, 54, 55, job control [52], mental stress 56, 57, and mental workload 30, 58. These studies illustrate the multitude of psychosocial factors that may influence traditional dichotomous outcome measures. It is postulated that these types of factors may also influence continuous outcome measures.

Physical workplace demands have been found to influence return to work. Lifting [58], twisting [59], weight of lift 52, 59, and posture 58, 59 have been shown to influence return to work. Because physical job demands influence return to work, these measures should be assessed when evaluating recovery using other outcome measures.

There are several personal factors that have been evaluated in the literature. Age has been shown to influence return to work [60]. Cultural factors, personal philosophy, and monetary factors may also influence symptoms 61, 62. Smoking habits have been shown to influence symptoms 48, 54. Thus, these personal factors should be monitored when evaluating outcome measures.

Three of the four most commonly used outcome measures in the literature may be quantified as continuous measures. Continuous measures are those that can take on any numerical value [63]. There is a void in the literature quantifying low back pain recovery using continuous outcome measures. Furthermore, there is a lack of research evaluating the effects of factors on continuous outcome measures, which have been shown to influence traditional dichotomous outcome measures. Hence, the first goal of this project is to quantitatively assess recovery using continuous outcome measures. The second goal is to predict recovery given the initial conditions.

Section snippets

Approach

A prospective study was designed to monitor acute low back pain recovery using various outcome measures at fixed follow-up times for a period of 3 months. The study was limited to 3 months, because it was hypothesized that most of the changes in outcome would occur during the first 3 months of recovery for patients with acute low back pain. Four continuous outcome measures were used: present pain intensity, activities of daily living, the kinematic measure of functional performance probability,

Results

Twenty-eight of the initial 32 participants completed the study. The average age of the participants was 35 years with a standard deviation of 12 years. Sixty-four percent of the participants were male. Four participants dropped out for reasons not associated with the study. Table 2 lists the means and standard deviations for each of the major outcome measures as a function of time. On average, the participant's first visit was 2 weeks into their symptoms; therefore, Table 2 starts with time at

Summary of predictive outcome models

The results of this study illustrate that recovery can be accurately predicted as a continuous outcome measure. The model components predicting outcome show that functional performance, present pain intensity, activities of daily living or disability, and a summary outcome measure are all influenced by a combination of factors. The specific variables incorporated in the models changed as a function of the outcome measures. However, three specific variables consistently appear in the models

Conclusions

Continuous outcome measures of present pain intensity, functional performance probability, and MVAS can be accurately predicted. The models predicting LBD recovery suggest an interactive process of recovery between symptoms and functional performance measures. In addition psychosocial measures were predictive of all outcome measures. The objective functional performance probability was the single measure outcome tool that was most accurately predicted given the initial conditions. The rate of

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    Partial funding for this research was provided by the National Institute for Disability and Rehabilitation Research, The Ohio State University Graduate School, and The Ohio Center for Labor Relations.

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