Loneliness and Social Connection Across the Lifespan in the UK: A Rasch Analysis of Age and Gender Differences Among a Sample of 160,000 community dwelling adults

Background The Measuring Loneliness in the UK (INTERACT) Study is the largest global study on loneliness. This study aimed to validate the INTERACT scale, a novel 13-item instrument integrating loneliness, social capital, and COVID-19-related isolation measures to enhance the assessment of loneliness and social connection across diverse populations. Methods A cross-sectional online survey was conducted among 160,000 NHS patients across England, yielding 134,164 consenting respondents. Rasch analysis was employed to evaluate the psychometric properties of the INTERACT scale, assessing scale validity, unidimensionality, reliability and differential item functioning (DIF) across demographic groups. Results The INTERACT scale demonstrated excellent psychometric properties, confirming its unidimensionality with high internal consistency (Cronbach’s alpha = 0.91) and strong person reliability (Rasch separation = 2.37; reliability = 0.85). Item calibration ranged from − 0.67 to + 0.72 logits, with "I could borrow £30 from a neighbour" (+ 0.72 logits) representing high social connection, whereas "People in this neighbourhood generally don’t get along" (-0.67 logits) indicated low social connection. Younger adults (16–39 years) exhibited significantly higher loneliness scores (mean measures − 0.70 to -0.40 logits), while older adults (≥ 65 years) reported greater social connection (+ 0.15 to + 0.56 logits). DIF analysis indicated minimal bias across gender and age groups. Conclusions The INTERACT scale is a valid and reliable tool for assessing loneliness and social connection, overcoming limitations of existing measures by integrating social capital and contextual factors. These findings highlight the importance of targeted public health interventions addressing age and gender-specific loneliness patterns. The INTERACT scale has strong potential for application in community health monitoring, policy evaluation and intervention design, ensuring a data-driven approach to reducing loneliness and enhancing social cohesion.