Part 1: Examining heterogeneity of treatment effects in transcranial direct current stimulation for knee osteoarthritis pain and symptoms
Lee, C.; Sun, X.; Park, J.; Chen, C. X.; Pellegrini, C.; Chen, N.-k.; Garcia, D. O.; Kim, H.; Kwoh, C. K.; Ahn, H.
Show abstract
BackgroundAlthough heterogeneity of treatment effects (HTE) is commonly observed in clinical trials, it has received little attention in studies on transcranial direct current stimulation (tDCS). This study aimed to identify the presence of HTE in tDCS treatment among participants with symptomatic knee osteoarthritis (KOA) and to explore participant characteristics associated with this heterogeneity. MethodsThis exploratory secondary analysis of a randomized clinical trial included 120 participants with symptomatic KOA who received 15 daily sessions of home-based 2-mA active or sham tDCS (20 minutes per session) over three weeks. First, we used a multi-trajectory latent class growth analysis to identify distinct subgroups based on the longitudinal trajectories of KOA pain and symptoms from baseline to three months postintervention, capturing differential responses to tDCS. We then performed bivariate analyses to examine associations between trajectory groups and baseline demographic, clinical, and quantitative sensory testing characteristics. ResultsIn the active tDCS group, two distinct trajectories emerged: "low initial symptoms with significant improvement" (high responders; n = 28) and "high initial symptoms with minimal improvement" (low responders; n = 32). Compared to high responders, low responders had a higher body mass index (p = .040), lower educational attainment (p = .013), and greater pain catastrophizing (p < .000). Low responders also exhibited lower pressure pain thresholds at both the medial knee (p = .009) and trapezius (p = .002), higher punctate mechanical pain at both the patella (p = .013) and hand (p = .016), lower conditioned pain modulation at 30 seconds (p = .008) and 60 seconds (p < .000), and higher cold pain intensity (p = .003) at baseline. No notable HTE was observed in the sham tDCS group. ConclusionParticipants exhibited varying responses to active tDCS. The characteristics associated with HTE may inform the development of personalized stimulation protocols. Further research is needed to investigate potential HTE in the sham tDCS group and refine strategies to address placebo-related effects.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Sensory profiling in classical Ehlers-Danlos syndrome: a case-control study revealing pain characteristics, somatosensory changes, and impaired pain modulation 96%
- Temporal changes in mechanical pin prick sensitivity following high frequency induced sensitisation of central nociceptive pathways: a test re-test reliability study 94%
- Low somatosensory cortex excitability in the acute stage of low back pain causes chronic pain 94%
Similar papers in this journal
Similar papers in this journal
- Can a specific biobehavioral based therapeutic education program lead to changes in pain perception and brain plasticity biomarkers in chronic pain patients? A study protocol for a randomized clinical trial 95%
- Restoration of normal central pain processing following manual therapy in nonspecific chronic neck pain 95%
- Spatial summation of pain is associated with pain expectations: Results from a home-based paradigm 95%
Similar papers in this journal
- Surgical interventions targeting the nucleus caudalis for craniofacial pain: a systematic and historical review 94%
- Towards individualized deep brain stimulation: A stereoelectroencephalography-based workflow for neurostimulation target identification 91%
- Transcutaneous auricular vagus nerve stimulation enhances emotional bias towards happiness in healthy young adults: A comparative study of electrical and ultrasound stimulation 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.