Habituation to pain : a motivational-ethological perspective

Habituation to pain is mainly studied using external pain stimuli in healthy volunteers, often to identify the underlying brain mechanisms, or to investigate problems in habituation in specific forms of pain (eg, migraine). Although these studies provide insight, they do not address one pertinent question: Why do we habituate to pain? Pain is a warning signal that urges us to react. Habituation to pain may thus be dysfunctional: It could make us unresponsive in situations where sensitivity and swift response to bodily damage are essential. Early theories of habituation were well aware of this argument. Sokolov argued that responding to pain should not decrease, but rather increase with repeated exposure, a phenomenon he called “sensitization.” His position makes intuitive sense: Why would individuals respond less to pain that inherently signals bodily harm? In this topical review, we address this question from a motivational ethological perspective. First, we describe some core characteristics of habituation. Second, we discuss theories that explain how and when habituation occurs. Third, we introduce a motivational-ethological perspective on habituation and explain why habituation occurs. Finally, we discuss how a focus on habituation to pain introduces important methodological, theoretical, and clinical implications, otherwise overlooked

Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances.Comment: 7 pages, 3 figures, 2 tables, code available, accepted in ACII 201

Chronic-Pain Protective Behavior Detection with Deep Learning

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.Comment: 24 pages, 12 figures, 7 tables. Accepted by ACM Transactions on Computing for Healthcar

Guarding and flow in the movements of people with chronic pain: A qualitative study of physiotherapists' observations

BACKGROUND: Among the adaptations of movement consistently associated with disability in chronic pain, guarding is common. Based on previous work, we sought to understand better the constituents of guarding; we also used the concept of flow to explore the description of un/naturalness that emerged from physiotherapists' descriptions of movement in chronic pain. The aim was to inform the design of technical systems to support people with chronic pain in everyday activities. METHODS: Sixteen physiotherapists, experts in chronic pain, were interviewed while repeatedly watching short video clips of people with chronic low back pain doing simple movements; physiotherapists described the movements, particularly in relation to guarding and flow. The transcribed interviews were analysed thematically to elaborate these constructs. RESULTS: Moderate agreement emerged on the extent of guarding in the videos, with good agreement that guarding conveyed caution about movement, distinct from biomechanical variables of stiffness or slow speed. Physiotherapists' comments on flow showed slightly better agreement, and described the overall movement in terms of restriction (where there was no flow or only some flow), of tempo of the entire movement, and as naturalness (distinguished from normality of movement). CONCLUSIONS: These qualities of movement may be useful in designing technical systems to support self-management of chronic pain. SIGNIFICANCE: Drawing on the descriptions of movements of people with chronic low back pain provided by expert physiotherapists to standard stimuli, two key concepts were elaborated. Guarding was distinguished from stiffness (a physical limitation) or slowness as motivated by fear or worry about movement. Flow served to describe harmonious and continuous movement, even when adapted around restrictions of pain. Movement behaviours associated with pain are better understood in terms of their particular function than aggregated without reference to function