Background Temporomandibular disorders (TMD) have a relatively high prevalence and in many patients pain and masticatory dysfunction persist despite a range of treatments. Non-invasive brain neuromodulatory methods, namely transcranial direct current stimulation (tDCS), can provide relatively long-lasting pain relief in chronic pain patients. Objective To define the neuromodulatory effect of five daily 2×2 motor cortex high-definition tDCS (HD-tDCS) sessions on clinical pain and motor measures in chronic TMD patients. It is predicted that M1 HD-tDCS will selectively modulate clinical measures, by showing greater analgesic after-effects compared to placebo, and active treatment will increase pain free jaw movement more than placebo. Methods Twenty-four females with chronic myofascial TMD pain underwent five daily, 20-minute sessions of active or sham 2 milliamps (mA) HD-tDCS. Measurable outcomes included pain-free mouth opening, visual analog scale (VAS), sectional sensory-discriminative pain measures tracked by a mobile application, short form of the McGill Pain Questionnaire, and the Positive and Negative Affect Schedule. Follow-up occurred at one-week and four-weeks post treatment. Results There were significant improvements for clinical pain and motor measurements in the active HD-tDCS group compared to the placebo group for: responders with pain relief above 50% in the VAS at four-week follow-up (p=0.04); pain-free mouth opening at one-week follow-up (p<0.01); and sectional pain area, intensity and their sum measures contralateral to putative M1 stimulation during the treatment week (p<0.01). No changes in emotional values were shown between groups. Conclusion Putative M1 stimulation by HD-tDCS selectively improved meaningful clinical sensory-discriminative pain and motor measures during stimulation, and up to four weeks post-treatment in chronic myofascial TMD pain patients.

Background To quantify pain severity in patients and the efficacy treatments, researchers and clinicians apply tools such as the traditional visual analog scale (VAS) that leads to inaccurate interpretation of the main sensory pain. Objective This study aimed to validate the pain measurements of a neuroscience-based 3D body pain mobile app called GeoPain. Methods Patients with temporomandibular disorder (TMD) were assessed using GeoPain measures in comparison to VAS and positive and negative affect schedule (PANAS), pain and mood scales, respectively. Principal component analysis (PCA), scatter score analysis, Pearson methods, and effect size were used to determine the correlation between GeoPain and VAS measures. Results The PCA resulted in two main orthogonal components: first principal component (PC1) and second principal component (PC2). PC1 comprises a combination score of all GeoPain measures, which had a high internal consistency and clustered together in TMD pain. PC2 included VAS and PANAS. All loading coefficients for GeoPain measures in PC1 were above 0.70, with low loadings for VAS and PANAS. Meanwhile, PC2 was dominated by a VAS and PANAS coefficient >0.4. Repeated measure analysis revealed a strong correlation between the VAS and mood scores from PANAS over time, which might be related to the subjectivity of the VAS measure, whereas sensory-discriminative GeoPain measures, not VAS, demonstrated an association between chronicity and TMD pain in locations spread away from the most commonly reported area or pain epicenter (P=.01). Analysis using VAS did not detect an association at baseline between TMD and chronic pain. The long-term reliability (lag >1 day) was consistently high for the pain area and intensity number summation (PAINS) with lag autocorrelations averaging between 0.7 and 0.8, and greater than the autocorrelations for VAS averaging between 0.3 and 0.6. The combination of higher reliability for PAINS and its objectivity, displayed by the lack of association with PANAS as compared with VAS, indicated that PAINS has better sensitivity and reliability for measuring treatment effect over time for sensory-discriminative pain. The effect sizes for PAINS were larger than those for VAS, consequently requiring smaller sample sizes to assess the analgesic efficacy of treatment if PAINS was used versus VAS. The PAINS effect size was 0.51 SD for both facial sides and 0.60 SD for the right side versus 0.35 SD for VAS. Therefore, the sample size required to detect such effect sizes with 80% power would be n=125 per group for VAS, but as low as n=44 per group for PAINS, which is almost a third of the sample size needed by VAS. Conclusions GeoPain demonstrates precision and reliability as a 3D mobile interface for measuring and analyzing sensory-discriminative aspects of subregional pain in terms of its severity and response to treatment, without being influenced by mood variations from patients.

BACKGROUND Chronic pain is a significant health care problem. To quantify the pain severity in patients and the efficacy of new or current treatments, researchers and clinicians apply tools such as the traditional visual analogue scale (VAS), that lead to inaccurate and subjective interpretation related to the main sensory pain. OBJECTIVE To validate pain measurements of a neuroscience-based 3D body mobile application called GeoPain. METHODS Temporomandibular disorder (TMD) patients were assessed using GeoPain measures in comparison to traditional pain and mood scales, respectively VAS and Positive and Negative Affects (PANAS). Principal Component Analysis (PCA), scatter score analysis, Pearson’s methods and effect size were used to determine the correlation among GeoPain and VAS measures. RESULTS The PCA analysis resulted in two main orthogonal components: PC1 and PC2. PC1 comprises a combination score of all GeoPain measures, which had a high internally consistent, and clustered together in TMD pain. PC2 included VAS and both positive and negative effects (PANAS). All loading coefficient for GeoPain measures in PC1 were above .70, with low loadings for VAS and PANAS. Meanwhile, PC2 were dominated by VAS and PANAS > 0.4. Repeated measure analysis revealed a strong correlation between the VAS and mood scores from PANAS over time that might be related with the subjectivity of VAS measure, whereas sensory-discriminative GeoPain measures, not VAS, demonstrated an association between chronicity and TMD pain in locations spread away from the most commonly reported area, or pain epicenter (P = .01). Analysis using VAS did not detect an association at baseline between TMD pain and chronicity. The long-term reliability (lag > 1 day) was consistently high for P.A.I.N.S. with lag autocorrelations averaging between 0.7 and 0.8, and greater than the autocorrelations for VAS averaging between 0.3 to 0.6. The combination of higher reliability for P.A.I.N.S. and its objectivity displayed by the lack of association with PANAS as compared to VAS indicated that P.A.I.N.S. has a better sensitivity and reliability for measuring treatment effect over time for sensory-discriminative pain. The effect sizes for P.A.I.N.S. were larger compared to VAS, consequently requiring smaller sample sizes to assess the analgesic efficacy of treatment if P.A.I.N.S. is used versus VAS. P.A.I.N.S. effect size was 0.51SD for both facial sides and 0.60SD for the right side versus 0.35SD for VAS. Therefore, the sample size required to detect such effect sizes with 80% power would be n = 125/group for VAS, but for P.A.I.N.S. as low as o n = 44/group, almost 1/3 of the sample size needed by VAS. CONCLUSIONS GeoPain demonstrated precision and reliability as a 3D mobile interface for measuring and analyzing sensory-discriminative aspects of (sub)regional pain regarding its severity and response to treatment, without been influenced by mood variations from patients as noticed in the more traditional VAS. CLINICALTRIAL Registration: Clinicaltrials.gov, NCT02247063, https://clinicaltrials.gov/ct2/show/NCT02247063.