Technology for Chronic Pain

Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life

ABSTRACT

Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.

Quantitative Electrophysiological Evaluation of the Analgesic Efficacy of Two Lappaconitine Derivatives: A Window into Antinociceptive Drug Mechanisms

Quantitative evaluation of analgesic efficacy improves understanding of the antinociceptive mechanisms of new analgesics and provides important guidance for their development. Lappaconitine (LA), a potent analgesic drug extracted from the root of natural Aconitum species, has been clinically used for years because of its effective analgesic and non-addictive properties. However, being limited to ethological experiments, previous studies have mainly investigated the analgesic effect of LA at the behavioral level, and the associated antinociceptive mechanisms are still unclear. In this study, electrocorticogram (ECoG) technology was used to investigate the analgesic effects of two homologous derivatives of LA, Lappaconitine hydrobromide (LAH) and Lappaconitine trifluoroacetate (LAF), on Sprague-Dawley rats subjected to nociceptive laser stimuli, and to further explore their antinociceptive mechanisms. We found that both LAH and LAF were effective in reducing pain, as manifested in the remarkable reduction of nocifensive behaviors and laser-evoked potentials (LEPs) amplitudes (N2 and P2 waves, and gamma-band oscillations), and significantly prolonged latencies of the LEP-N2/P2. These changes in LEPs reflect the similar antinociceptive mechanism of LAF and LAH, i.e., inhibition of the fast signaling pathways. In addition, there were no changes in the auditory-evoked potential (AEP-N1 component) before and after LAF or LAH treatment, suggesting that neither drug had a central anesthetic effect. Importantly, compared with LAH, LAF was superior in its effects on the magnitudes of gamma-band oscillations and the resting-state spectra, which may be associated with their differences in the octanol/water partition coefficient, degree of dissociation, toxicity, and glycine receptor regulation. Altogether, jointly applying nociceptive laser stimuli and ECoG recordings in rats, we provide solid neural evidence for the analgesic efficacy and antinociceptive mechanisms of derivatives of LA.

The Use of Technology in the Management of Orthodontic Treatment-Related Pain

Orthodontic pain is one of the negatives associated with fixed orthodontic treatment that cannot be avoided. This pain usually comes around the wire placement period and gradually decreases once the endogenous analgesic mechanisms start functioning. Over the years, several treatment modalities have been utilized for relief from orthodontic pain, and these include mechanical, behavior modification, and pharmacological methods. However, in the last decade, there are several newer methods employing the use of technology that have come up and are being used for alleviating pain. From computerized indirect bonding to virtual treatment planning, technology has slowly become a vital part of an orthodontist's repertoire. The digital age is here, and orthodontics must embrace the use of technology to help improve the quality of life of patients.

Pain Control by Co-adaptive Learning in a Brain-Machine Interface

Innovation in the field of brain-machine interfacing offers a new approach to managing human pain. In principle, it should be possible to use brain activity to directly control a therapeutic intervention in an interactive, closed-loop manner. But this raises the question as to whether the brain activity changes as a function of this interaction. Here, we used real-time decoded functional MRI responses from the insula cortex as input into a closed-loop control system aimed at reducing pain and looked for co-adaptive neural and behavioral changes. As subjects engaged in active cognitive strategies orientated toward the control system, such as trying to enhance their brain activity, pain encoding in the insula was paradoxically degraded. From a mechanistic perspective, we found that cognitive engagement was accompanied by activation of the endogenous pain modulation system, manifested by the attentional modulation of pain ratings and enhanced pain responses in pregenual anterior cingulate cortex and periaqueductal gray. Further behavioral evidence of endogenous modulation was confirmed in a second experiment using an EEG-based closed-loop system. Overall, the results show that implementing brain-machine control systems for pain induces a parallel set of co-adaptive changes in the brain, and this can interfere with the brain signals and behavior under control. More generally, this illustrates a fundamental challenge of brain decoding applications-that the brain inherently adapts to being decoded, especially as a result of cognitive processes related to learning and cooperation. Understanding the nature of these co-adaptive processes informs strategies to mitigate or exploit them.