Pain Management in the Most Vulnerable Intellectual Disability: A Review

Abstract

This review is made up of two parts; the first part discussing intellectual disability (ID) in general, while the second part covers the pain associated with intellectual disability and the challenges and practical tips for the management of pain associated with (ID). Intellectual disability is characterized by deficits in general mental abilities, such as reasoning, problem solving, planning, abstract thinking, judgment, academic learning, and learning from experience. ID is a disorder with no definite cause but has multiple risk factors, including genetic, medical, and acquired. Vulnerable populations such as individuals with intellectual disability may experience more pain than the general population due to additional comorbidities and secondary conditions, or at least the same frequency of pain as in the general population. Pain in patients with ID remains largely unrecognized and untreated due to barriers to verbal and non-verbal communication. It is important to identify patients at risk to promptly prevent or minimize those risk factors. As pain is multifactorial, thus, a multimodal approach using both pharmacotherapy and non-pharmacological management is often the most beneficial. Parents and caregivers should be oriented to this disorder, given adequate training and education, and be actively involved with the treatment program. Significant work to create new pain assessment tools to improve pain practices for individuals with ID has taken place, including neuroimaging and electrophysiological studies. Recent advances in technology-based interventions such as virtual reality and artificial intelligence are rapidly growing to help give patients with ID promising results to develop pain coping skills with effective reduction of pain and anxiety. Therefore, this narrative review highlights the different aspects regarding the current status of the pain associated with intellectual disability, with more emphasis on the recent pieces of evidence for the assessment and management of pain among populations with intellectual disability.

Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review

“…These technologies can help improve the reliability of pain assessment tools. For example, ML algorithms can be used to assess pain by establishing a baseline and then looking at the patient’s changing facial expressions and analyzing and assessing their vocal sounds [ 5 , 10 ]. Following are some examples of different AI techniques used for pain assessment and how they are beneficial in improving patient outcomes (see Fig.…”

“…Moreover, computer vision techniques can be applied to automatically identify pain-related facial expressions in specific populations, such as patients with dementia, non-verbal expressions, or impaired communication. Computer vision algorithms have also been utilized in pain assessment during movement analysis based on movement patterns of the patients [ 5 , 48 ] .…”

“…These tools collect data on pain levels, physical activity, sleep patterns, and medication adherence. Wearable devices include, for example: wristbands, smart watches, wearable mobile sensors, and smart wearable shirts [ 5 ]. Recent literature reviews on the clinical impact of wearable devices and behavior change have shown promising effectiveness for digital technology [ 59 ].…”

“…“Digital bio-markers” is a term recently defined as the “objective, quantifiable, physiological, and behavioral measures collected using digital devices that are portable, wearable, implantable, or digestible” [ 60 ]. Digital devices can capture vital signs such as heart rate, respiration, blood volume pulse, and skin temperature; physiological data such as skin conductance, brain activity, muscle activity, and pattern of body movements; and behavioral signals such as speech, facial expressions, and body movements [ 4 , 5 ]. Numerous biosignal methods have been used to assess pain: electrocardiography (ECG), electromyography (EMG), electrodermal activity (EDA), photoplethysmography (PPG), blood oxygen saturation (SpO 2 ), and near-infrared spectroscopy (NIRS) [ 61 ].…”

“…Traditional methods for pain assessment rely mainly on patient self-report scores, which can be influenced by various factors such as cultural, psychological, and social biases. Moreover, the self-report scores are unfeasible for patients who cannot communicate or verbalize their pain experience properly [ 5 ]. AI-based approaches have emerged to improve pain assessment by providing automated objective, personalized, and standardized measures [ 6 ].…”