In our first article, we introduced the overarching challenge: how comorbidities—having two or more chronic conditions—can drastically elevate a person’s risk of falls. Now, let’s dive into the heart of our project: a custom 24‐item survey that translates the nuanced realities of daily mobility, balance confidence, and health issues into one total fall‐risk score.
Why did we opt for 1–5 ratings for each question, and how did we decide on 24 questions? Read on to discover how we balanced completeness with clinical practicality in our design.
Why a Custom Fall‐Risk Survey?
Plenty of established tools exist (Berg Balance Scale, Tinetti’s Assessment, etc.), but we noticed:
- Comorbidity Blind Spots: Many scales focus on a single aspect—e.g., gait or basic ADLs—rather than systematically accounting for multiple conditions.
- Clinical Feasibility: Longer or more technical tools can be time‐consuming, reducing adoption in busy primary care settings.
- Patient Engagement: We wanted a format that both providers and patients could easily understand, reinforcing a sense of collaboration during assessment.
Our solution: a 24‐question questionnaire covering a broad range of day‐to‐day tasks (e.g., dressing, bending, going to the bathroom) plus key mobility/confidence items (e.g., standing from a chair without arms, walking up stairs independently).
The Simplicity of 1–5 Ratings
We selected a 1–5 Likert scale for each question:
- 1 = Strongly Disagree (or worst performance/confidence)
- 2 = Disagree
- 3 = Neither Agree nor Disagree
- 4 = Agree
- 5 = Strongly Agree (or best performance/confidence)
Why 1–5?
- Ease of Use: Clinicians can quickly circle a value; patients, when asked, also find it intuitive.
- Meaningful Range: We didn’t want a yes/no approach to something as nuanced as mobility. A 5‐point scale captures subtle shifts in confidence and ability.
- Straightforward Summation: Summing across 24 questions yields a total score from 24 (all items=1, highest risk) to 120 (all items=5, lowest risk). This final number is easy to communicate—“Lower total score = higher risk.”
Highlighting Comorbidity in the Questions
We didn’t create separate questions for each chronic condition (like diabetes or arthritis) because we suspect their combined effect appears in real‐world tasks. For example:
- “I can stand up from a chair or bed without using my arms.”
People with multiple comorbidities—say, diabetes (neuropathy) plus arthritis (joint pain)—often struggle more with this action. - “I can walk freely on all floor surface types.”
Balance disorders, heart disease, or even kidney issues can impact stamina and sure‐footedness, reflected in how confident they feel walking on different surfaces.
By focusing on functional outcomes, our survey indirectly measures how comorbidities collectively degrade day‐to‐day functioning.
Building Confidence & Compliance
One of the driving principles behind this survey was clinician and patient buy‐in. No matter how technically robust a scale is, it loses value if:
- It’s too cumbersome for busy clinics.
- Patients don’t understand the questions.
- The final score isn’t easy to interpret or explain.
Through pilot testing in our clinic, we refined language to ensure each statement was clear and concise. We also provided short instructions—“Circle the number that best describes how strongly you agree with the statement”—to standardize the response process.
Balancing Breadth & Depth
Why 24 questions?
- Too few could overlook key functional aspects.
- Too many could bog down staff and patients, leading to inconsistent usage.
- Our internal testing found 24 to be a sweet spot: broad enough (from vision to balance confidence) without becoming a lengthy chore.
What’s Next?
In the following articles, we’ll detail:
- How we conducted the study (Article 3), from participant selection to data analysis.
- The key findings (Article 4) showing how participants with ≥2 comorbidities scored significantly lower, reinforcing our hypothesis.
- Real‐world stories & practical tips (Articles 5 & 6) so clinicians can adapt or adopt similar approaches in their everyday practice.
Stay tuned—our next article gives a behind‐the‐scenes look at Data & Methods, revealing how we tested this survey among our patients and used Python to analyze the results. If you have questions or want to share experiences with designing clinical tools, drop a comment below or email us at researchinfo@ahcpllc.com.
Posted by:
Dr. Pariksith Singh, Dr. Manjusri Vennamaneni, and Dr. Carlos Arias (Authors)
with Ed Laughman and Nawtej Dosanjh (Editors)
and Lynda Benson (Research Associate)
in collaboration with Access Health Care Physicians & Vedere University
