Monitoring changes in patients within and between sessions is important for evaluating the effectiveness of interventions. The methods for monitoring these changes can be grouped into subjective and objective tests and measures, depending on the condition being treated.
Below are some widely used tests and measures to track within- and between-session changes:
1. Within-Session Changes (Immediate Changes)
These methods evaluate immediate responses after an intervention, such as spinal manipulation, mobilization, or TENS:
1.1. Pain Intensity Rating:
Visual Analog Scale (VAS): A simple 0-10 scale to rate pain before and after an intervention.
1.2 Range of Motion (ROM):
Goniometry or Inclinometry: Measures the degree of motion in joints before and after an intervention to assess immediate effects, especially after spinal manipulation or mobilization.
1.3 Pressure Pain Threshold (PPT):
Algometry: Measures the minimum pressure that elicits pain, which may change immediately after an intervention such as TENS or spinal manipulation.
1.4 Functional Tests:
Timed Up and Go (TUG) Test: Used for mobility assessment. Changes within a session can reflect immediate effects on functional capacity.
1.5 Patient-Reported Outcomes:
Global Rating of Change (GROC): A quick questionnaire to measure how the patient feels they have improved immediately after an intervention.
2. Between-Session Changes (Over Time)
These methods monitor progress over multiple sessions and evaluate longer-term improvements:
2.1 Outcome Measures:
Oswestry Disability Index (ODI): Commonly used for back pain, it monitors functional disability over time.
Roland-Morris Disability Questionnaire (RMDQ): For low back pain, this tracks changes in functional disability.
Neck Disability Index (NDI): Used for monitoring neck-related functional limitations.
2.2 Functional Measures:
6-Minute Walk Test: Monitors changes in endurance and functional capacity over time.
Sit-to-Stand Test or Step Test: Useful for tracking improvements in lower limb strength and balance.
2.3 Physical Performance Measures:
Gait Speed or Balance Tests: Can track motor function improvements over time.
Handheld Dynamometry: Measures muscle strength across sessions, particularly after rehabilitation or manual therapy.
2.4 Pain and Quality of Life Questionnaires:
Short-Form Health Survey (SF-36): Assesses overall health and well-being, including changes in physical function over time.
Pain Disability Index (PDI): Measures pain-related disability and can show progress or regression over time.
2.5 Biometrics/Physiological Markers:
Heart Rate Variability (HRV): Can monitor autonomic nervous system changes over time, reflecting recovery or adaptation, especially after interventions like TENS.
Electromyography (EMG): Tracks muscle activation changes in response to repeated interventions.
2.6 Patient Adherence and Feedback:
Patient Journals or Logs: Recording pain intensity, functional limitations, or adherence to home exercise programs helps track between-session changes and patient engagement.
Electronic Health Monitoring Tools: Devices or apps that allow patients to track pain, physical activity, or sleep patterns.
3. Advanced Trend Monitoring with Magnitude-Based Inference (MBI)
In clinical practice, selecting specific tests and measures depends on the patient’s condition, the intervention, and the desired outcome, to ensure an individualized and evidence-based approach to monitoring change.
In order to use these tests and measures effectively for monitoring patient changes within and between sessions, there is a need for quantitative assessment of trends over multiple testing time points to determine the extent to which the patient is on track for improvement or recovery. Furthermore, assessing acute deviations from the trend can determine the likelihood of benefit or harm from short-term changes in training or treatment.
To this end, a more advanced approach involves the use of magnitude-based inference (MBI), which allows clinicians and researchers to assess trends in patient outcomes across multiple time points with a focus on practical significance, rather than relying solely on traditional statistical significance.
3.1 Magnitude-Based Inference (MBI) and Trend Assessment
Using a spreadsheet designed for MBI analysis, users can track changes in patient performance or clinical outcomes across multiple tests over time. This tool enables users to evaluate key outcomes, such as:
Changes relative to a reference test or the average of selected tests: By selecting one test as a reference or averaging the performance across multiple tests, the spreadsheet helps assess how each individual test compares, allowing for better detection of trends or deviations.
Changes between consecutive tests: This feature is particularly useful for monitoring short-term progression or regression in clinical status, especially when interventions are applied in repeated sessions.
Deviations from a fitted linear trend: The spreadsheet fits a linear model to selected time points, allowing users to monitor if patient outcomes deviate from expected trends, providing insights into sudden improvements or deteriorations that may require attention.
Magnitude of the trend: In addition to identifying deviations, the spreadsheet helps quantify the magnitude of the observed trend, indicating whether a patient is improving, worsening, or remaining stable over time.
3.2 Defining Meaningful Changes and Trend Goals
Smallest meaningful change: Users can input the smallest worthwhile change between tests, which is critical for determining the clinical relevance of observed differences. This avoids over-interpretation of small, clinically insignificant changes.
Target changes over a longer period: Defining a desired or clinically meaningful target change over time allows the spreadsheet to assess whether the observed trends align with long-term therapeutic goals, providing actionable insights for adjusting treatment plans.
Typical measurement error: Users can input values for short-term measurement error if available; otherwise, the spreadsheet estimates this error based on the fitted linear trend. This improves the reliability of the trend analysis by accounting for variability that could skew results.
3.3 Time-Series Plot for Visualization
The spreadsheet also includes a time-series plot that visually represents patient performance over time, aiding in:
Test selection for trend analysis: By visualizing the data, clinicians can select the most relevant tests for further analysis and trend estimation.
Identifying notable changes: The graphical representation makes it easier to spot rapid improvements, deteriorations, or plateaus, enhancing the clinician’s ability to intervene in a timely manner.
3.4 Practical Significance and Magnitude-Based Inference
Rather than focusing on p-values and traditional statistical significance, the spreadsheet provides likelihoods of substantial or trivial changes based on the smallest worthwhile change. This allows for a more practical, clinical interpretation of changes and trends, emphasizing the clinical importance of results rather than statistical artifacts.
The use of the magnitude-based inference strategy to monitor and interpret immediate and long-term changes in patients offers a robust framework for clinicians. This allows for more nuanced and informed decision-making regarding patient progress, helping to tailor interventions more effectively over time.
Here is the link (xIndividualTrend) to access the spreadsheet template.
Reference: https://sportscience.sportsci.org/2017/wghtrend.htm