Life Sciences Meets AI: Prediction Models for Opioid-Induced Gait Abnormalities in Aging Populations

Opioids are commonly prescribed to manage pain, but their effects on movement and coordination can pose serious risks, particularly for older adults. One major concern is their impact on gait. Changes in balance, muscle control, and reaction time can make walking unsteady, increasing the risk likelihood of dangerous falls. So, how do opioids increase the risk of falls in older adults?

Understanding Opioids and Their Effects on Gait

Opioids are a class of powerful pain-relieving medications. They work by interacting with the brain’s opioid receptors, blocking pain signals. Common examples include morphine, oxycodone, and hydrocodone. While these drugs are effective in treating acute and chronic pain, they come with serious risks. In particular, long-term use can lead to dependence and a range of side effects, some of which can directly affect physical movement.¹

One of the most significant impacts opioids have is on gait. Gait refers to the manner or pattern of walking, which can be affected by several factors, including muscle weakness, coordination, and balance. Opioids can disrupt the central nervous system, causing dizziness, sedation, and impaired motor skills. Because opioids affect balance and coordination, they can significantly increase fall risk, making it harder for you to walk steadily and maintain their posture.² Over time, these changes can become more pronounced, particularly in older adults already at a higher risk of mobility issues.

How Do Opioids Increase the Risk of Falls in Older Adults?

The aging population is especially vulnerable. As people age, natural changes in the musculoskeletal and nervous systems can contribute to a decline in gait. When combined with opioid use, the effects are often compounded. Seniors may already experience muscle stiffness, joint pain, or neurological degeneration, and opioids further increase the risk of falls in older adults.

Adding opioids into the mix can exacerbate these problems, increasing the likelihood of falls and injuries. At least 800,000 patients end up in hospitals each year due to falls, typically because of a hip or head injury.³ The risk of opioid-induced gait abnormalities is a growing concern as opioid use remains prevalent among seniors, often prescribed for chronic conditions like arthritis and back pain.

What Are Gait Abnormalities?

Gait abnormalities refer to any deviation from a normal walking pattern. In a healthy person, walking is a smooth, coordinated process involving multiple muscle groups, joints, and systems working in sync. When something disrupts this coordination, it can lead to noticeable changes in how you walk. These abnormalities can range from subtle differences, like a slight limp, to more severe issues, such as an unsteady or shuffled gait.⁴

Several factors contribute to gait abnormalities, including neurological disorders, musculoskeletal problems, and medication side effects — like opioids. For example, Parkinson’s disease can cause rigidity and tremors, leading to a shuffling gait, while arthritis can cause joint pain, affecting your stride length and walking speed.⁴

In the case of opioid use, the drugs interfere with the brain’s ability to control movement, leading to slower, more uncoordinated steps.² This lack of control can cause instability and increase the likelihood of falls, especially in older adults who may already face balance challenges.

Gait abnormalities aren’t always easy to detect right away. Sometimes, small changes in posture or movement go unnoticed, but over time, they can worsen. You might begin to feel less confident walking or notice that you are using assistive devices like a cane or walker more frequently. In severe cases, gait abnormalities can limit an individual’s independence, making simple tasks like getting out of bed or going for a walk much harder to accomplish.

The Role of AI in Predicting Gait Abnormalities

AI has the potential to revolutionize predicting and managing gait abnormalities, particularly in individuals at risk due to opioid use or other health conditions. By leveraging large amounts of data and advanced algorithms, AI can provide useful information about how gait changes over time, even before noticeable symptoms occur.⁵ This predictive capability can lead to earlier interventions and more personalized care.

Data Collection and Analysis

AI relies on data to make predictions. In the case of gait abnormalities, this information can come from various sources, such as wearable devices, motion sensors, and patient health records. These tools can track an individual’s walking pattern, including step length, speed, and posture, in real time.⁵

When AI analyzes this data, it can identify subtle shifts that may indicate a developing problem, even before it becomes visible to the human eye. By comparing these patterns with a large dataset of other patients, AI can learn to recognize what’s normal for a specific individual and what might signal an abnormality.⁵

Early Detection and Prediction

One of the most significant advantages of AI is its ability to predict future events based on past patterns. For example, by monitoring changes in gait over time, AI can predict when you might experience a more severe decline in mobility. This is particularly important for aging populations who are at higher risk of falls. Early detection allows for timely interventions, such as adjusting medication dosages, recommending physical therapy, or introducing assistive devices before the person’s mobility becomes significantly impaired.⁵

Personalized Care Plans

AI doesn’t just predict gait abnormalities — it can also help tailor care plans to your individual needs. By analyzing data specific to each patient, AI can suggest interventions that are most likely to be effective for that person.⁵

For instance, AI may recommend targeted physical therapy exercises, changes in medication, or even adjustments to the environment to improve balance. These personalized recommendations are based on a detailed understanding of how the individual’s body responds to different treatments, providing more accurate and effective care.

Continuous Monitoring and Adjustment

Unlike traditional methods of monitoring gait, which may require periodic checkups or assessments, AI enables continuous monitoring.⁵ Wearable devices or smartphone apps can track walking patterns 24/7, providing real-time feedback to patients and healthcare providers.

This ongoing data stream allows for more dynamic and responsive care, as adjustments can be made quickly if someone’s gait begins to deteriorate. With continuous monitoring, AI can help ensure that interventions are always aligned with your current condition, preventing further complications and improving overall outcomes.

How Prediction Models Work

AI-driven prediction models use machine learning algorithms to analyze vast amounts of information and identify patterns that indicate potential gait abnormalities. These models rely on inputs like motion sensor data, wearable device tracking, and medical histories to assess how a person’s walking pattern changes over time.

According to a study conducted by Sarah Markham of King’s College London, predictive tools are vital for ensuring quality patient care. “These tools are based on models which have been trained on healthcare data to predict various potential harms such as the possibility of a patient, as an individual or as someone possessing certain characteristics, developing a certain health condition, or experiencing certain treatment outcomes.” [SOURCE: https://pmc.ncbi.nlm.nih.gov/articles/PMC11751774/ ]⁶

By comparing your gait data with established patterns, AI can detect subtle deviations that might indicate instability, slowed movement, or increased fall risk. The more information the model processes, the better it becomes at recognizing early warning signs.

Once AI detects a potential gait abnormality, it can generate risk assessments and recommend interventions.⁵ These might include medication adjustments, physical therapy, or lifestyle modifications to improve mobility. Some models even provide real-time alerts, helping healthcare providers take action before a minor imbalance turns into a serious fall.

Possible Challenges in Prediction Models for Opioid-Induced Gait Abnormalities in Aging Populations

While AI-driven prediction models hold great promise for identifying and preventing gait abnormalities, several challenges must be addressed to ensure their effectiveness and reliability.

Data Quality and Availability

AI models rely on large datasets to make accurate predictions. However, collecting high-quality, standardized gait data across diverse aging populations can be difficult. Factors such as inconsistent collection methods, limited access to wearable devices, and variations in patient health records can reduce the accuracy of predictions. Additionally, aging individuals with mobility issues may struggle to use technology, further limiting information availability.

Individual Variable in Gait

Gait patterns are highly individualized, influenced by factors such as genetics, preexisting conditions, and lifestyle habits.⁴ AI models must differentiate between normal age-related gait changes and abnormalities caused by opioid use. This complexity makes it challenging to develop a one-size-fits-all model that accurately predicts gait abnormalities across diverse populations.

Ethical and Privacy Concerns

The use of AI in healthcare requires collecting and analyzing sensitive personal data. Ensuring patient privacy, securing information against breaches, and complying with regulations like HIPAA and GDPR are critical challenges.⁷ Patients may also be hesitant to share their movement data due to concerns about surveillance or misuse.

Difficulty in Accounting for Polypharmacy

Older adults often take multiple medications, and drug interactions can significantly impact gait.¹ Opioids alone may not be the sole cause of gait abnormalities, making it difficult for AI models to isolate their effects. Without accounting for the full range of medications a patient takes, predictions may be inaccurate or misleading.

Limited Access to AI-Based Interventions

Even if AI accurately predicts gait abnormalities, access to interventions may be a barrier. Many older adults — particularly those in rural or low-income areas — may not have access to physical therapy, specialized medical care, or wearable devices that track gait. Without proper follow-up, AI predictions may not translate into meaningful improvements in patient outcomes.

Balancing Innovation and Practicality in Fall Prevention

Falls in older adults are a growing public health concern, and opioid use adds another layer of complexity. Opioids increase the risk of falls in older adults by affecting balance, coordination, and muscle strength, making walking less stable and increasing the likelihood of serious injuries. AI-driven prediction models offer a promising solution, helping detect gait abnormalities before they lead to falls. With continuous monitoring and personalized interventions, these technologies can enhance mobility and improve overall safety.

References

1. Opioids. National Institute on Drug Abuse. Published December 3, 2024. https://nida.nih.gov/research-topics/opioids
2. Virnes RE, Tiihonen M, Karttunen N, van Poelgeest EP, van der Velde N, Hartikainen S. Opioids and Falls Risk in Older Adults: A Narrative Review. Drugs Aging. 2022 Mar;39(3):199-207. doi: 10.1007/s40266-022-00929-y. Epub 2022 Mar 15. PMID: 35288864; PMCID: PMC8934763. 
3. Fall costs, risks and prevention. Medical Guardian. 2018. https://www.medicalguardian.com/medical-alert-blog/senior-safety-/fall-costs-risks-and-prevention
4. Ataullah AHM, De Jesus O. Gait Disturbances. [Updated 2024 Apr 20]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2025 Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK560610/
5. Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci. 2023 Jul 12;15:1191378. doi: 10.3389/fnagi.2023.1191378. PMID: 37502426; PMCID: PMC10368956.
6. Markham S. Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations? BMJ Health Care Inform. 2025 Jan 16;32(1):e101153. doi: 10.1136/bmjhci-2024-101153. PMID: 39824519; PMCID: PMC11751774.
7. Naik N, Hameed BMZ, Shetty DK, et al. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Frontiers in Surgery. 2022;9. doi:10.3389/fsurg.2022.862322