Development of a Neonatal Pain Assessment Tool with Family Involvement and Telehealth Evaluation

Thesis Abstract

Effective assessment of neonatal pain remains challenging due to developmental variability and reliance on physiological cues, with limited integration of family input and remote telehealth modalities in routine clinical practice. This study aims to develop a valid, reliable neonatal pain assessment tool that integrates parental observations and telehealth-enabled clinician assessment to improve accuracy and responsiveness to analgesic needs in neonatal intensive care units (NICUs). Specific objectives are (1) to identify core pain indicators observable by families and clinicians in the NICU, (2) to design a composite tool combining behavioral, physiological, and family-reported cues suitable for telehealth administration, (3) to validate the tool’s psychometric properties (reliability, construct validity, and sensitivity to analgesia), (4) to evaluate feasibility, acceptability, and impact on analgesia management through a telehealth workflow, and (5) to examine potential disparities in pain assessment accuracy across gestational age and parental literacy levels. A sequential, mixed-methods design will be employed. In Phase I, qualitative interviews with 40 families (parents of preterm and term newborns) and 20 NICU clinicians will elucidate observable pain cues, parental involvement preferences, and telehealth communication barriers. Thematic analysis guided by the Theory of Parent-Student-Clinician Partnership and the Biopsychosocial Model of Pain will generate candidate indicators. Phase II will pilot a draft tool in 6 NICU wards, enrolling 180 neonates (birth to 28 days postnatal age) across three sites, with stratified sampling by gestational age (very preterm <32 weeks, moderate to late preterm 32–37 weeks, term). Data collection will pair conventional standardized pain assessments (e.g., PIPP-R) with the newly proposed tool administered via in-person and telehealth sessions. Instruments will include clinician-rated scales, caregiver checklists, and objective physiological data (heart rate variability, skin photoplethysmography). Reliability will be assessed using inter-rater reliability (Cohen’s kappa for categorical indicators, intraclass correlation for continuous scores) and internal consistency (Cronbach’s alpha). Construct validity will be examined through convergent validity (correlations with PIPP-R and the FLACC scale) and discriminant validity across analgesic status. Responsiveness will be evaluated by calculating effect sizes (Cohen’s d) pre- and post-analgesia administration. A multivariable regression model will quantify the contribution of behavioral, physiological, and family-reported indicators to the composite pain score, controlling for medical confounders. A mixed-methods integration will triangulate quantitative findings with qualitative insights to refine the tool. Ethical approval will be obtained from the National Health Research Ethics Committee, with parental informed consent and adherence to data protection standards. Data analysis will utilize R for quantitative analyses (regression modeling, reliability statistics, ROC analysis for cut-off thresholds) and NVivo for qualitative coding. A diagnostic accuracy assessment will determine optimal cut-off points for telehealth administration. The study hypothesizes that incorporating family-reported cues alongside clinician-observed indicators within a telehealth framework will yield higher sensitivity and specificity for neonatal pain detection than standard tools alone. Expected findings include a validated composite score with demonstrated reliability (ICC > 0.80) and validity (r > 0.70 with established measures), improved analgesia-aligned decision-making in telehealth-mediated care, and evidence of user acceptability among families and clinicians. The research contributes to knowledge by bridging family engagement and telehealth in neonatal pain assessment, extending the Ecological Theory of Pain to neonatal care, and offering a scalable, cross-context tool adaptable to resource-limited settings. It will inform guidelines for telehealth-enabled pain management in NICUs, promote family-centered care, and potentially reduce undertreatment or overtreatment of analgesia. Recommendations include implementation protocols for bedside and remote use, training modules for families and clinicians, and further research to test longitudinal outcomes on neurodevelopmental trajectories and analgesia-related adverse events.