ArticleJMIR formative research2026
Digital Phenotyping of Pain Modulation and Associations Among Personality, Attachment, and Behavioral Signatures: Cross-Sectional Study.
Article in JMIR formative research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe transition from acute to chronic pain often reflects a persistent dissociation between physical tissue damage and subjective reports. In alignment with the 2020 International Association for the Study of Pain definition, pain is a personal experience filtered through a latent "susceptibility architecture." While clinical assessment currently relies on static, text-based questionnaires, these are often confounded by linguistic interpretation bias and cognitive literacy. We hypothesized that an individual's internal psychological substrate-traditionally captured via text-can be characterized through real-time behavioral signatures during physical challenge.
objectiveThis study aimed to demonstrate that the "pain-prone" phenotype can be identified through high-frequency digital assessment of pain ratings. By correlating established psychometric traits with dynamic behavioral signatures, we sought to establish a foundation for "digital phenotyping" that moves beyond the limitations of linguistic self-reports.
methodsA cohort of 534 healthy volunteers (mean age 38.62, SD 22.35 years; n=336, 62.9% male and n=198, 37.1% female) underwent a controlled thermal stimulation protocol (36 °C, 44 °C, 46 °C, and 48 °C). Continuous pain intensity was recorded via a high-frequency (1000 Hz) digital visual analog scale (VAS). To establish a psychological baseline, participants were profiled using the Revised NEO Personality Inventory (NEO PI-R) and the Relationship Questionnaire. Two behavioral indexes were then derived from the digital VAS: the temporal augmentation index (TAI), reflecting within-stimulus physiological sensitization, and the cognitive contrast effect (evaluative instability). Statistical significance was adjusted using the false discovery rate.
resultsRepeated-measure multivariate ANOVA confirmed a highly significant main effect of time for all noxious conditions (P<.001; 46 °C: t
conclusionsSubjective pain evaluation is governed by a stable internal psychological substrate. By shifting the assessment modality from linguistic self-reports to dynamic behavioral signatures, we provide a framework for "digital phenotyping." These evaluation patterns serve as an objective behavioral marker, enabling the identification of latent susceptibility before chronification and offering a novel foundation for personalized precision pain management.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.