Pain is a key factor in any survey that looks at health, well-being, or respondent happiness. But pain is hard to measure objectively; it depends on so many different factors such as mood, coping mechanisms, context, and perception.
As a result, researchers use the visual analog scale (VAS), a simple and effective technique that allows respondents to rate their pain severity on a continuous line. The VAS is widely used because it is simple to administer, understand, and measure.
The visual analog scale also has good psychometric properties and can detect subtle changes in pain intensity over time. In this article, we will explore how the VAS works, its strengths, and its limitations.
The Visual Analog Scale (VAS) is a tool that allows you to measure respondent pain intensity. You can also use it to assess other subjective feelings, such as anxiety, fatigue, or happiness.
When administering the VAS works, you start by asking respondents to mark a point on a line or a scale that represents how much pain or discomfort they feel. There are two endpoints on the line or scale that represent the extreme range of your sensations.
For example, one endpoint may say “absolutely no pain” and the other endpoint may say “the worst pain imaginable”. Then you ask the respondent to mark their pain level.
With the VAS, you can measure your pain on a line from 0 to 10 (no pain) or the worst pain imaginable. This gives you more choices than categorical scales, which have only a few options.
You can also measure small changes in pain more precisely with the VAS. For example, the VAS makes it easier to indicate a slight change in pain intensity than traditional Yes or No categorical scales such as, “Are you in pain?”
The VAS process is straightforward and fast. All you have to do is mark a spot on the line corresponding to your pain level. There are no words or numbers to read or memorize. It is also easy for researchers and clinicians to use and understand.
The VAS understands that pain is different for everyone. What you think of as “mild” pain might be “severe” to someone else. With VAS, you can report your pain severity without basing your pain on what people expect or think.
Lots of studies and settings have been done with VAS, and it’s proven to be a reliable and accurate way of gauging pain intensity. It also has a good correlation with other pain scales and pain markers, such as physiological reactions and functional results.
The first thing you need to do is come up with a question that’s specific to what you’re measuring. For example, if you’re trying to how much pain a patient with a sprain in their ankle is in, you might ask them, “How much pain are you feeling in your ankle?”
Also, avoid using vague or ambiguous terms, such as “good” or “bad”, that may have different meanings for different respondents.
Next, choose two words or phrases that represent the opposite ends of the scale. These endpoints should be relevant to the question and easy to understand.
For example, you could use “Extremely Painful” and “No pain at all” as the endpoints. Make sure that the endpoints are mutually exclusive and cover the full range of possible responses.
Decide whether you want to use a horizontal, vertical, or digital scale. A horizontal scale is a line that goes from left to right, with endpoints on each side, while a vertical scale goes from bottom to top.
You can also use a digital scale; it is a slider that can be moved along a line on a screen, with endpoints on each side. The choice of format depends on your preference and the mode of data collection.
For example, a digital scale may be more suitable for online surveys, while a horizontal or vertical scale may be easier to use on paper.
Give respondents clear instructions for using the VAS. Start by telling them how to mark their answers on the scale, if they can use any points on the line or just discrete intervals, and what to say if they’re not sure or don’t have an opinion.
Also, assure respondents that there are no “correct” or “wrong” answers and that answers should be honest and spontaneous. You could start with a disclaimer: “Your responses are confidential to you and are only to evaluate your pain levels.”
Ask clear and specific questions that match your survey purpose and the concept you want to measure. For example, if want to know how exhausted a respondent feels after a fitness test, you could ask them, “How exhausted are you right now?”
You should also explain the meaning of the scale endpoints and how to use them. For example, you could say: “Please mark a point on the line below that best represents your level of exhaustion. The left end means ‘extremely exhausted’ and the right end means “Not exhausted at all.”
You should provide a visual aid or an example to help respondents understand the question and the scale better. For example, you could show a picture of a smiling face and a crying face to illustrate the meaning of pain.
You could also give an example of a previous respondent’s answer and explain why they chose that point on the scale. For example, you could say: “This is an example of a previous respondent’s answer. They marked this point because they were in extreme pain, and needed attention.”
Before you start using the question and scale in your survey, test it out with a small group of your target audience. That way, you can make sure it’s clear, easy to understand, and right for your respondents.
You can also collect feedback from the pre-test participants and make any necessary adjustments to improve your VAS question and scale.
Read Also – Pilot Testing: Meaning, Importance + [Question Example]
Different cultures and languages may express or interpret subjective feelings or sensations differently. For example, some cultures may be more reserved or polite in expressing their opinions, while others may be more direct or blunt.
Also, use culturally sensitive and neutral language in your question and scale. Translate your survey questions and scale into the respondent’s language so they fully understand your survey questions and their context.
A VAS has two endpoints that represent the extremes of the concept you want to measure. For example, if you want to measure pain, you can use a VAS with endpoints labeled “no pain” and “worst pain imaginable”.
VAS data is majorly considered to be either continuous or interval, which means you can use tests like t-tests and ANOVAs to compare the average scores of different groups or conditions.
However, some scientists argue VAS results are either randomized or non-randomized, which means that you can find the median scores of the different groups or conditions using non-randomized tests like the Mann–Whitney U or Kruskal–Wallis test.
Report the descriptive statistics (such as mean, standard deviation, median, and range). Also, state the inferential statistics (such as test statistic, p-value, and effect size) of your VAS data.
Next, explain how these statistics relate to your particular phenomenon. For example, if you see that pain scores are significantly different between two patients who were on different treatment plans, you can infer that one treatment reduces pain more than the other.
Self-report measures like VAS rely on the subjective perception and expression of pain by the individual. This can introduce some biases and limitations, such as:
The VAS isn’t standardized or calibrated, so there’s no universal format or objective reference points for the VAS. As a result, anyone can use whatever anchors or labels they want for endpoints, and pain severity can’t be generalized because people have different interpretations of thresholds for pain.
Given the limitations and considerations of the VAS, you may want to consider using alternative pain assessment tools. Here are some of the tools with their pros and cons:
Read More – What is a Survey Rating Scale? + [Types & Question Examples]
The VAS is a useful tool for assessing individuals and changing pain intensity. It can help you provide more accurate and meaningful feedback about the patient experience.
But if the VAS isn’t tailored to fit your target audience and concept, there’s a high likelihood of biases and inaccurate conclusions.
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