In our first three articles, we discussed the importance of analyzing fall risk in patients with multiple health conditions, explained our 24‐item survey, and detailed the methodology behind it all. Now, let’s look at the actual results from our 673 participants—and see just how much having two or more comorbidities can shift a person’s fall‐risk score.
A Quick Refresher
- 24‐Item Survey: Each statement is scored from 1 (Strongly Disagree) to 5 (Strongly Agree), so total scores range from 24 (highest risk) to 120 (lowest risk).
- Comorbidity Groups:
- Group 0: Fewer than 2 chronic conditions (i.e., 0 or 1).
- Group 1: 2 or more chronic conditions.
- Welch’s t‐Test: We compared the average scores of these two groups to see if any difference was likely statistically significant rather than random.
The Numbers
- Group 0 (<2 comorbidities):
- Mean Total Score ≈ 97.77 (SD ~ 23.2)
- Group 1 (≥2 comorbidities):
- Mean Total Score ≈ 89.97 (SD ~ 24.3)
That’s a gap of nearly 8 points on a 24–120 scale, suggesting a notable difference.
Statistical Significance and Effect Size
- t‐Statistic = 3.91
- p‐Value ≈ 0.000105 (< 0.001)
- Cohen’s d ≈ 0.33
- A p‐value < 0.05 typically marks significance, but here we’re far below that threshold, indicating it’s highly unlikely the gap is due to chance.
- Cohen’s d of 0.33 may be considered a small‐to‐moderate effect size, meaning the difference—while not massive—still holds practical importance.
Interpreting the Results
- Lower Scores = Higher Risk: An average score near 90 for those with ≥2 comorbidities implies they generally rated themselves lower on key questions like standing up without arms, bending down safely, or feeling steady at night.
- The Impact of Multiple Conditions: This 8‐point difference aligns with the hypothesis that juggling multiple conditions (e.g., heart disease + arthritis + diabetes) can meaningfully reduce mobility, confidence, or both—leading to greater susceptibility to falls.
Real‐World Relevance
- Screening Priority: Patients with multiple comorbidities might warrant closer fall‐risk monitoring—perhaps they should be assessed more regularly or flagged for early intervention.
- Proactive Measures: Knowing their scores trend lower, clinicians might recommend extra physical therapy, home safety evaluations, or medication reviews specifically aimed at fall prevention.
- Patient Education: A transparent conversation about how conditions interact to raise risk can motivate patients to stick with prescribed exercises, balance training, or dietary plans.
What About Actual Falls?
Keep in mind:
- Our data shows a cross‐sectional snapshot. While participants with more comorbidities scored lower (i.e., had higher risk), this doesn’t confirm how many falls they actually experienced afterward.
- Future Studies: A longitudinal approach could track participants over time, checking if those lower‐scoring individuals indeed have more (or more severe) falls. We’ll dive deeper into potential follow‐ups in a later article.
Next Steps
In Article 5, we’ll share human stories behind these numbers—case vignettes and real‐life implications that put the data in context. From anecdotal patient experiences to daily living challenges, we’ll see how these abstract points play out in everyday life.
Have questions or thoughts on these findings? Drop a comment below or email us at researchinfo@ahcpllc.com. We’d love to compare notes, especially if you’re seeing similar patterns in your practice.
Posted by:
Dr. Pariksith Singh, Dr. Manjusri Vennamaneni, Dr. Carlos Arias, and Dr. Jeremy Tharp (Authors)
with Ed Laughman and Nawtej Dosanjh (Editors)
and Lynda Benson (Research Associate)
in collaboration with Access Health Care Physicians & Vedere University
