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

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 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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160,000 community dwelling adults Agustin Tristán-López, Mahmoud Al-Ammouri, Austen El-Osta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6864317/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. Loneliness Social isolation Social connection Psychometric validation Rasch model Public health interventions Social capital COVID-19 Community cohesion Figures Figure 1 Figure 2 Figure 3 Background Loneliness and social isolation are critical public health issues globally, significantly affecting mental and physical health across diverse populations [ 1 , 2 ]. Loneliness is commonly defined as a subjective feeling arising from a discrepancy between desired and actual social interactions and relationships, potentially impacting psychological wellbeing, and contributing to poorer physical health outcomes [ 3 , 4 ]. In the United Kingdom (UK), loneliness is prevalent, with approximately half of adults experiencing feelings of loneliness at varying frequencies, particularly affecting certain vulnerable demographic groups including unmarried middle-aged adults, elderly individuals, widows, people with disabilities and women [ 1 , 5 ]. The associated health consequences of loneliness include unhealthy behaviours such as smoking, excessive alcohol consumption, poor diet, and physical inactivity, leading to increased healthcare costs, higher morbidity, and elevated mortality rates [ 2 , 6 ]. The economic implications are also substantial, with UK employers experiencing annual productivity losses estimated at approximately £3.7 billion[ 7 , 8 ]. Recognising these significant health and economic burdens, the UK government and various stakeholders have launched initiatives aimed at addressing loneliness and promoting social connection, targeting both the general population and specific vulnerable groups [ 6 , 9 ]. Effective targeting of such interventions depends critically on comprehensive and precise measurement tools. Existing instruments, notably the UCLA Loneliness Scale, while extensively validated and frequently used, have limitations, particularly their narrow focus on loneliness alone without adequately assessing broader social connectedness and community-level factors such as trust and cohesion [ 10 , 11 ] Measures of neighbourhood social capital which include trust, cohesion and willingness to assist neighbours have demonstrated predictive utility in understanding social wellbeing, providing context-specific insights beyond individual experiences of loneliness alone [ 12 – 14 ]. Incorporating these neighbourhood-level items offers a comprehensive assessment, enabling more targeted and effective community-based interventions. The Measuring Loneliness in the UK (INTERACT) Study is the largest global study on loneliness with a cohort exceeding 160,000 participants. The study profile and descriptive results are presented in the third paper in the series ( El-Osta et al., 2025a) , whereas the inferential analysis of the predictors of loneliness is presented in paper 2 ( El-Osta et al., 2025b ). The main objective of this manuscript (paper 3) is to explore the psychometric properties of the INTERACT data collection tool. The INTERACT data collection tool is a comprehensive survey instrument combining items from the UCLA Loneliness Scale [ 10 ], the Office for National Statistics (ONS) Direct Measure of Loneliness, neighbourhood social capital measures [ 12 ], and isolation items specific to the COVID-19 pandemic context [ 15 ]. This integrated approach allows for a more nuanced measurement of both loneliness and social connection as opposing but interrelated constructs, capturing broader dimensions relevant to effective intervention planning and public policy formulation. This paper is the first to report the psychometric evaluation of this novel 13-item scale using Rasch analysis, drawing on data from a large national sample of over 130,000 NHS patients in England. The rationale was to leverage the Rasch measurement model [ 16 ] an advanced psychometric technique capable of transforming ordinal data into interval-level measures, to enhance objective interpretability. Study aims The aim of this study was to evaluate the psychometric properties of the new 13-item instrument, ensuring its calibration as an objective, valid and reliable measurement tool. Specifically, we sought to confirm the scale's unidimensionality, validity and reliability, and to examine differential patterns of loneliness and social connection across distinct age and gender groups. Methods Study Design and Participants A cross-sectional online survey was conducted among NHS patients across England. A large, randomly selected sample was invited to participate, resulting in responses from over 160,000 individuals. The final sample consisted of 134,164 consenting respondents after excluding individuals who did not provide consent, participants under the age of 16, and incomplete responses. Full details, including the link to the INTERACT survey are already provided in Paper 1 of this three-part series ( El-Osta et al., 2025a ). The COnsensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) checklist [ 17 ], and the Strengthening the Reporting of Observational Studies in Epidemiology [ 18 ] guidelines [ 19 ] were used to improve the quality of reporting. Sampling Methods & Participant Recruitment Participants were randomly sampled from NHS patient registers across England. Stratified random sampling was employed at the General Practice Level and by the National Institute for Health and Care Research (NIHR) Be Part of Research Network. Invitations were distributed via email and/or Short Messaging Service (SMS) containing secure survey links. Participants were categorised into demographic groups by gender (Female, Male, and Other) and age groups, following standards from the UK ONS and STATISTA guidelines, validated by prior studies [ 20 , 21 ]. The age categories were Youth (16–24 years), Young Adults (25–39 years), Middle-aged Adults (40–64 years), and Seniors (≥ 65 years). Measurement Instrument The INTERACT data collection tool measures a variable with two opposing constructs. Loneliness, a subjective, negative experience, is primarily internally driven, while social connection, involving interaction and emotional closeness, has positive polarity and stems from internal and external factors. Though not strict antonyms, these terms interpret results in a variable with positive polarity. The questionnaire comprised 27 items, including 14 socio-demographic questions and 13 Likert-type scale items assessing loneliness and social connection. The 13-item scale included (i) three items from the UCLA Loneliness Scale (Version 3) [ 10 , 22 , 23 ] seven items addressing neighbourhood social capital and trust, adapted from established measures of community cohesion[ 12 ], (iii) two items specifically focused on loneliness and isolation experienced during the COVID-19 pandemic [ 15 ] and (iv) the ONS single Direct Measure of Loneliness context [ 24 ]. The socio-demographic items and the Social Capital Score items of the INTERACT tool are shown in Tables 1 and 2 , respectively. The questionnaire consisted of two response formats: (i) frequency-based scales (e.g., "Never" to "Always"), and [ 23 ] agreement-based scales ("Strongly Agree" to "Strongly Disagree"). Items measuring loneliness or having negative polarity were reverse coded to standardise responses for analysis. This paper focuses on age and gender, while the remaining items will be analysed in future studies. Table 1 Socio-demographic items ID Socio-demographic questions 1 Q1_Region/Q3_postcode. What is your Region? / What is your full postcode? 2 Q8_age. What is your age? (in years). 3 Q9_gender. Gender or sex of the person in three categories: Female, Male and Other. 4 Q10_relatives. How many RELATIVES did you see or hear from in the last month? 5 Q11_friends. How many FRIENDS did you see or hear from in the last month? 6 Q13_employment. What is your employment status? 7 Q14_ethnicity. What best describes your ethnic group or background? 8 Q15_marital_status. What is your marital status? 9 Q16_pets. Do you have any pets? 10 Q17_education. What is your highest level of education? 11 Q18_children. Do you have any children? 12 Q19_household. How many people other than you live in your household? 13 Q20_disability. Do you have a disability? 14 Q21_conditions. Do you have any long-term health conditions (e.g. mental health, diabetes, asthma)? Table 2 Survey items Group ID Short name Item in the survey Categories Coding 1 1 Q4LackSocial connection How often do you feel that you lack social connection? 3 (Frequency) Negative 2 Q5Left_out How often do you feel left out? 3 Q6FeelIsolated How often do you feel isolated from others? 2 4 Q7FeelLonely How often do you feel lonely? 5 (Frequency) Negative 3 5 Help People around here are willing to help their neighbours. 4 (Agreement) Positive 6 Know This is a close-knit, or ‘‘tight’’ neighbourhood where people generally know one another. Positive 7 Borrow If I had to borrow £30 in an emergency, I could borrow it from a neighbour. Positive 8 NotAlong People in this neighbourhood generally don’t get along with each other. Negative 9 Trust People in this neighbourhood can be trusted. Positive 10 Shopping If I were sick, I could count on my neighbours to shop for groceries for me. Positive 11 SameValues People in this neighbourhood do not share the same values. Negative 4 12 COVIDLonely The COVID-19 pandemic & lockdowns made me feel more LONELY 4 (Agreement) Negative 13 COVIDIsolated The COVID-19 pandemic & lockdown made me feel more ISOLATED Negative Rasch Model and Fit Analysis Rasch analysis was chosen due to its ability to convert ordinal Likert-type responses into interval-level measures, facilitating an objective assessment of latent constructs like loneliness and social connection [ 25 , 26 ]. The analysis was conducted using Winsteps software [ 27 ] applying the Grouped Rating Scale Model because the questionnaire incorporated distinct rating scales across item groups [ 28 ]. The critical assumptions concerning unidimensionality, scale validity, item fit and differential item functioning were verified. Unidimensionality was evaluated using Principal Component Analysis of Residuals (PCAR) and item-test correlations, ensuring that items measured a single latent trait [ 29 – 31 ]. Fit Statistics (INFIT and OUTFIT) mean-square (MNSQ) values between 0.7–1.3 were considered acceptable [ 27 ]. Items outside this range were flagged as potentially problematic [ 25 ]. Scale validity [ 32 , 33 ] was verified using the mean absolute difference of observed difficulties versus theoretical uniform distribution (MAD = 4.2%, below the critical 5% criterion). Differential Item Functioning (DIF) was assessed using comparisons of item characteristic curves and mean absolute differences (MAD) across demographic groups. Significant DIF was defined by absolute differences exceeding 0.25 logits [ 34 , 35 ]. Reliability was assessed using separation indices and Cronbach’s alpha, aiming for values above 0.8 to indicate adequate internal consistency [ 36 , 37 ]. Supplementary File 2 presents the values of Standard Error of Measurement (SEM). This study assessed the instrument’s psychometric properties following fair analysis practices (including COSMIN guidelines), with a focus on validity and reliability. As the instrument was not newly developed, content and face validity were not explicitly assessed in this study primarily because all items were derived from pre-existing, widely used instruments. Given that the INTERACT tool was based on previously validated tools including the UCLA 3 Item Loneliness Scale, the ONS Direct Measure of Loneliness and the Social Capital Score, it was assumed that some level of face validity had been addressed during their original development [ 17 ]. Scale validity and internal structure Rasch modelling was used to evaluate scale validity, internal structure (via PCAR) as a basis for construct and structural validity, internal consistency (through person reliability and separation indices), model fit and group differences (using DIF). These properties are essential for assessing instrument quality in large-scale population studies. Score stability, inter-rater agreement, repeated administrations, and specific reliability indicators such as the Intraclass Correlation Coefficient (ICC) or Cohen’s kappa were beyond the scope of this study. Instead, the SEM from the Rasch measures provided in the supplementary File 2 offers a meaningful estimate of precision for this large, cross-sectional dataset collected at a single time point [ 25 ]. The SEM may also support future calculations of the Smallest Detectable Change (SDC) to compare individuals by levels of loneliness or social connection. Rationale for Choosing Grouped Rating Scale Model The Grouped Rating Scale Model was specifically chosen as the INTERACT questionnaire combined items with differing rating structures: frequency-based items from the UCLA Loneliness Scale and COVID-19 items and agreement-based items assessing neighbourhood social capital. Unlike the Rating Scale Model, which assumes identical response formats across all items, the Grouped Rating Scale Model allows the clustering of items by rating scale structure, ensuring accurate measurement and enhancing model fit by respecting the unique response characteristics across different item groups [ 27 , 38 , 39 ]. Handling Missing Data & Extreme Respondents Participants with incomplete surveys, those under 16, and individuals who did not provide consent were excluded, resulting in the final analytic sample. Missing item-level data did not require imputation for the Rasch analysis [ 40 ]. Further details on the full and analytic samples are provided in [ 41 ]. To minimise bias and maintain data integrity in Rasch modelling, extreme respondents (i.e. those consistently selecting the highest or lowest response options) were identified using Winsteps and analysed separately to ensure model robustness. Statistical Methods Analyses were performed using Winsteps software [ 27 ]. The Rasch model provides a linear scale (in log-odd ratio units or logits) indicating individuals' social connection and loneliness levels. Item measures were calibrated to produce a hierarchy from easier to harder items, facilitating interpretation and potential refinement of the scale. Person and item reliability indices, separation indices, and fit statistics (INFIT and OUTFIT) were calculated to evaluate the precision of the scale. INFIT and OUTFIT values within 0.7 to 1.3 logits indicated acceptable item fit [ 42 ]. Wright maps visually represented person-item distributions, facilitating comparisons of loneliness perceptions across age and gender groups. Administration occurred once for each participant; although no formal stability check was done, it was not expected due to the short response time and lack of interventions or major external changes. As no longitudinal or intervention data were collected, responsiveness could not be assessed. This limitation is acknowledged, highlighting the need for future research to determine the instrument’s sensitivity to change. Ethical Considerations The INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study received a favourable opinion from NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483). Survey respondents provided consent electronically prior to participation. Data confidentiality and anonymity were maintained throughout the study process. To protect participants' privacy, all responses were pseudonymized at the point of entry. No personal identifiers, such as IP addresses, were collected and all data were stored on secure, encrypted servers at Imperial College London, in compliance with GDPR and the Data Protection Act 2018. Data will be securely archived for a minimum of 10 years following the completion of the study. The COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) and the Checklist for reporting results of Internet E-Surveys (CHERRIES) checklists were used to improve the quality of reporting. Results Participant Characteristics The final dataset comprised n = 134,164 respondents after exclusions for non-consent, incomplete data, or individuals below 16 years old (Table 3 ). Participants were predominantly female (n = 82,805; 61.7%), followed by male (n = 50,088; 37.3%), with a smaller proportion identifying as 'Other' (n = 1,271; 0.95%). Most participants were middle-aged adults (40–64 years; n = 58,477; 43.5%) and seniors (≥ 65 years; n = 50,327; 37.5%), with fewer classified as young adults (25–39 years; n = 19,272; 14.4%) and youth (16–24 years; n = 6,088; 4.5%); Table 3 . Table 3 Number of study participants by age and gender Gender Age [-1] Female [-2] Male [-3] Other Total [1-] less than 16 Not included Not included Not included Not included [2-] 16–24 [ 21 ] 4032 [ 22 ] 1761 [ 23 ] 295 6088 [3-] 25–39 [ 31 ] 13182 [ 32 ] 5693 [ 33 ] 397 19272 [4-] 40–64 [ 41 ] 37683 [ 42 ] 20407 [ 43 ] 387 58477 [5-] 65 years or over [ 51 ] 27908 [ 52 ] 22227 [ 53 ] 192 50327 TOTAL 82805 50088 1271 134164 Rasch Analysis and Model Fit Of the 134,164 respondents, 131,662 provided responses suitable for Rasch measurement, with 1,253 and 1,249 classified as extreme respondents scoring the maximum and minimum possible scores, respectively. The mean person measure across respondents was 0.14 logits (SD = 1.06), suggesting an overall slight tendency towards social connection in the population sampled. The instrument demonstrated robust reliability, with a person Rasch separation of 2.37 equivalent to a reliability of 0.85 and Cronbach’s alpha of 0.91, reflecting excellent internal consistency and measurement accuracy. Item Calibration and Difficulty Items were calibrated on a Rasch linear scale ranging from − 0.67 to + 0.72 logits. The item hierarchy identified "I could borrow £30 from a neighbour (Borrow)" as the most difficult item (+ 0.72 logits), signifying a high level of social connection, while "People in this neighbourhood generally don’t get along (NotAlong)" was the easiest item (-0.67 logits), indicative of a low level of social connection or high perceived isolation. The three items derived from the UCLA Loneliness Scale showed close clustering, suggesting redundancy and potential for refinement to enhance the scale’s efficiency. Differential Item Functioning (DIF) DIF analysis using Student’s t-test across demographic groups and absolute DIF, applying the half-logit critical threshold (> 0.5 logits), indicated minimal overall bias[ 29 ]. To examine specific items more rigorously, a stricter absolute DIF threshold (> 0.25 logits) was adopted, as shown in Fig. 1 . For instance, younger age groups and certain gender categories exhibited differential response patterns on items related to neighbourhood trust and COVID-19 experiences, suggesting that perceptions of social connection and loneliness vary significantly by age and gender. The observed DIF supports the instrument's utility in detecting relevant subgroup differences, consistent with the construct's theoretical and practical understanding ( Fig. 1 ). Wright Maps: Demographic Variations Wright maps provide visual insights into the distributions of item difficulties and the distribution of respondents' loneliness and social connection measures alongside item difficulties, giving a clear visual representation of demographic differences. Wright Map for the measured age-gender groups are shown in Fig. 2 . Consistently across demographic groups, "Borrow" ranked as the most challenging item (high social connection), while "NotAlong" ranked as easiest (high loneliness), reflecting broad feelings of social disconnection. The analysis of the Wright Map in the context of age and gender groups follows criteria for psychometric evaluation, such as COSMIN’s, where the emphasis is placed on ensuring that the instrument remains valid and reliable across groups. By evaluating the mean item difficulty and person measures, it helps to analyse construct validity, confirming that the scale measures the intended social connection construct accurately across various demographics. Age and Gender Differences in Loneliness and Social Connection Person measures showed a distinct gradient of loneliness by age (Fig. 3 ). Younger respondents reported higher levels of loneliness (Youth: mean= -0.49 logits; Young Adults: mean= -0.37 logits), whereas older participants (Seniors: mean = + 0.51 logits) reported higher social connection. Gender differences, while less pronounced, indicated males generally reported slightly higher social connection compared to females within each age group. Differential Item Functioning (DIF) was assessed using the half-logit and quarter-logit rules[ 29 ], which are essential for maintaining the fairness and validity of the scale. Additional DIF analyses based on classical test theory and not using the Rasch model [ 17 ] support the validity of demonstrating that the resulting scale functions similarly and equitably across genders. This is further reinforced by the comparison shown in the Wright maps in Fig. 2 . Dimensionality and Reliability Principal Component Analysis of Residuals (PCAR) confirmed the unidimensionality of the INTERACT scale, revealing two strongly correlated residual clusters: one related to loneliness (UCLA and COVID-related items) and the other to social connection (neighbourhood trust items). Both clusters showed high correlations with the primary dimension (r = 0.81 and 0.82), supporting the interpretation of loneliness and social connection as opposite ends of a single continuum. Rasch analysis also demonstrated strong reliability, with a Cronbach’s alpha of 0.91 and a separation index of 2.37, indicating high internal consistency and measurement precision. Supplementary table 1 summarises output values, including reliability and standard error of measurement (SEM) for the full scale. Together, the multi-cluster structure, strong correlations with the primary trait, and evidence of scale validity support both structural and construct validity. While the UCLA scale is often treated as a “gold standard” for loneliness, COSMIN notes that true gold standards rarely exist for patient-reported outcome measures (PROMs), except when comparing short forms to their original versions. Thus, comparing the INTERACT scale with the UCLA scale contributes to construct validity and may also support criterion validity due to the widespread use and established reputation of the UCLA scale. Discussion Summary of principal findings This study presents one of the largest psychometric evaluations of loneliness and social connection conducted to date, leveraging a nationally representative sample of 134,164 NHS patients across England. The findings confirm the unidimensional structure of the 13-item INTERACT scale, demonstrating high internal consistency (Cronbach’s alpha = 0.91) and strong reliability (Rasch separation = 2.37; reliability = 0.85). Item calibration illustrated a clear hierarchy of social connection and loneliness, with 'I could borrow £30 from a neighbour' (+ 0.72 logits) emerging as the most challenging item (indicative of higher social connection), whereas 'People in this neighbourhood generally don’t get along' (-0.67 logits) was the easiest item to endorse (indicative of lower social connection). A key demographic pattern identified in this study was the age gradient in loneliness, with younger adults (16–39 years) reporting significantly higher loneliness levels, while older adults (≥ 65 years) demonstrated greater social connection. Conversely, older adults, particularly those embedded in stable community environments, may benefit from long-standing social ties and established support networks that mitigate loneliness. Although gender differences were less pronounced, males reported slightly higher levels of social connection compared to females. These results warrant further investigation into potential factors such as gendered social roles, caregiving responsibilities and differential socialization patterns, which may influence loneliness experiences across the lifespan. Importantly, DIF analysis highlighted minimal measurement bias across demographic groups, reinforcing the scale’s applicability across diverse populations. However, redundancy was observed among certain items derived from the UCLA Loneliness Scale, suggesting that future iterations of the INTERACT scale could benefit from item reduction strategies to enhance efficiency while maintaining content, scale, and construct validity. The scale’s psychometric robustness and applicability across different demographic groups highlight its potential for use in public health monitoring, targeted intervention development, and policy evaluation. Interpretation and comparison to existing literature This study significantly advances loneliness research by validating the INTERACT scale, which uniquely integrates loneliness, neighbourhood social capital and COVID-19-related isolation experiences using Rasch analysis. Content validity was supported by the original instrument developers, but unlike existing measures like the UCLA Loneliness Scale, the INTERACT scale provides a broader framework that captures both the subjective experience of loneliness and social connectedness within the community. The inclusion of social capital items such as trust in neighbours and perceived social cohesion, expands the measurement beyond individual loneliness to consider broader social determinants, whereas previous studies relying solely on the UCLA Loneliness Scale or its short-form adaptations have been limited in assessing these contextual factors. By contrast, the INTERACT scale allows for a multidimensional approach to loneliness and social connectedness, addressing an important gap in public health measurement. The findings that younger individuals report higher loneliness than older adults are consistent with recent evidence challenging the conventional perception that loneliness primarily affects the elderly ( El-Osta et al., 2025b) . The INTERACT scale's ability to detect these age-related differences highlights its practical utility in tailoring interventions that address the specific drivers of loneliness in different demographic groups. The redundancy of some items derived from the UCLA Loneliness Scale, with certain items showing clustering in the Rasch model, aligns with prior psychometric evaluations [ 10 , 22 , 43 ] that highlight the potential for redundancy in standard loneliness measures. Specifically, our analysis suggests that multiple items measuring similar constructs (e.g., feeling isolated, feeling left out) could be streamlined to improve scale efficiency without compromising content and scale validity. Future iterations of the INTERACT scale may benefit from item reduction strategies, such as removing highly correlated items or consolidating questions with overlapping content, to enhance respondent burden and improve practicality for large-scale implementation. The inclusion of COVID-19-related loneliness items further differentiates the INTERACT scale from traditional instruments. Consistent with studies [ 44 , 45 ] our findings confirm that pandemic-induced isolation is a distinct but closely related construct to general loneliness, suggesting that pandemic loneliness is not transient but indicates that COVID-19 experiences had lingering effects on social cohesion, particularly among younger respondents. The close Rasch measures among COVID-19-related items in our analysis suggests that future refinements may consider reducing their number or integrating them into broader social isolation metrics. Lastly, DIF analysis reinforced demographic differences in loneliness and social cohesion, aligning with prior research [ 9 , 46 ], emphasising the importance of community social capital in buffering against loneliness, particularly for socioeconomically disadvantaged groups. The DIF findings highlight the need for culturally sensitive interventions tailored to community-specific dynamics [ 46 ]. Implications for policy and practice The findings from this study have significant implications for public health interventions, emphasizing the necessity of tailored, age- and gender-specific strategies to effectively mitigate loneliness and strengthen social cohesion across diverse populations. Policymakers and public health practitioners can leverage these insights to design evidence-based interventions that target specific demographic groups, ensuring efficient resource allocation and maximizing intervention impact. The INTERACT scale serves as a validated, psychometrically robust tool for community assessments, guiding policymakers in identifying at-risk populations and tailoring interventions accordingly. The practical utility of the INTERACT scale in public health planning offers several key benefits including (i) enhancing precision in loneliness assessments, [ 23 ] guiding resource allocation and intervention targeting, (iii) monitoring and evaluating intervention effectiveness, and (iv) integrating into national and local policy frameworks. For example, by capturing both individual and community-level determinants, the INTERACT scale facilitates a nuanced understanding of social connections across diverse demographic groups. This dual focus aligns with the comprehensive approach of instruments like the Patient-Reported Outcomes Measurement Information System (PROMIS), which assesses physical, mental, and social health domains to provide a holistic view of patient wellbeing [ 47 ]. The demographic insights derived from the INTERACT scale also enables the development of evidence-based interventions tailored to the specific needs of various age and gender groups. This targeted approach ensures the efficient use of public health resources, addressing the unique loneliness patterns identified in different populations. For example, the English Longitudinal Study of Ageing (ELSA) demonstrated that loneliness and social isolation are distinct constructs, each requiring specific intervention strategies [ 48 ]. By leveraging age- and gender-specific patterns, health authorities can prioritize and deploy interventions that foster social connection in ways that resonate with each demographic. For example, community-based programs should focus on engaging younger adults through digital platforms and social initiatives that reflect their communication preferences [ 49 , 50 ], whereas interventions for older populations should prioritize maintaining and enhancing existing social networks, reinforcing intergenerational connections, and improving access to community resources [ 51 – 54 ]. The INTERACT scale can also serve as both a baseline and follow-up measurement tool, allowing for the assessment of changes in loneliness and social connection following intervention implementation. Pertinently, given its ability to measure loneliness within a community context, the INTERACT scale can be incorporated into public health surveillance systems to identify at-risk populations, inform targeted interventions tailored to distinct demographic groups, and to help track progress on loneliness reduction initiatives and the effectiveness of social prescribing over time. Strengths and limitations The strengths and limitations of the study were addressed in Papers 1 and 2 ( El-Osta et al., 2025a, and El-Osta et al., 2025b ). Briefly, key limitations include (i) the cross-sectional design which restricts causal inferences and prevents the examination of changes in loneliness over time, [ 23 ] the online survey administration may introduce selection bias, as individuals with digital literacy and internet access are more likely to participate, and (iii) that no repeated administrations to the same participants were conducted; although this was mitigated somewhat as measurement independence was preserved by the administration protocol, and the anonymous conditions were explicitly stated and deemed appropriate. Pertinently, we acknowledge that although the INTERACT scale demonstrated strong psychometric properties, some item redundancy was identified suggesting opportunities for refinement in future iterations. Additionally, COVID-19-related items, while relevant at the time of data collection, may diminish in applicability over time. Future research should evaluate whether these items remain valid indicators of social isolation in a post-pandemic context. We also acknowledge that self-reported data may be subject to social desirability and recall biases potentially leading to potential under- or over-reporting of loneliness and social connection. Another limitation is the lack of control for unmeasured confounders, such as socioeconomic status, ethnicity, and pre-existing mental health conditions, all of which are known to influence loneliness. The study also does not account for intersectional effects, such as the combined impact of gender, socioeconomic status, and ethnicity on loneliness experiences. Addressing these factors in future research would provide a more comprehensive understanding of disparities in social connection. Future research should adopt longitudinal designs to examine loneliness trends and intervention effects over time. Additionally, a mixed methods approach combining quantitative analysis with qualitative insights is needed to capture the complexity of loneliness and social cohesion (this work is ongoing, and the results will be published by the authors in 2026). Addressing these methodological challenges will further strengthen the INTERACT scale’s applicability, ensuring its effectiveness as a tool for intervention planning and policy development. Conclusions The INTERACT study provides a rigorous, psychometrically validated tool for measuring loneliness and social connection on a national scale. The findings highlight important demographic differences in loneliness and social connectivity, offering crucial insights for public health policy and practice. By refining measurement instruments and targeting interventions based on age- and gender-specific patterns, stakeholders can more effectively mitigate loneliness and enhance social wellbeing across diverse populations. Our findings provide an evidence-based foundation for policymakers and healthcare authorities to better understand, monitor, and address loneliness and social isolation, enhancing individual and community wellbeing. The INTERACT scale provides a practical, scalable, and policy-relevant tool to support loneliness surveillance, intervention refinement and policy evaluation, ensuring data-driven approaches to promoting social connection at individual and community levels. Declarations Consent for publication Not applicable. Funding This research received no funding. Austen El-Osta is grateful for support by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Author Contribution All authors provided substantial contributions to the conception, design, acquisition (AEO) and interpretation (AT, MA and AEO) of study data. AEO took the lead in planning the study with support from co-authors. AT carried out the data analysis with support from AEO. AT developed the manuscript with support from co-authors. AEO is the guarantor. Acknowledgement The authors also appreciate the insightful comments by Dr John Michael Linacre regarding the unidimensionality of the variable reported in the Winsteps® software. The authors also thank Mr Aos Alaa (INTERACT Study Coordinator), Mrs Sandra O’Sullivan, Dr Arti Sharma, the NIHR Research Delivery Networks, and the NIHR Be Part of Research (BPoR) Network for their support in recruiting study participants. Data Availability No new data were created during this study and data sharing is not applicable to this article. 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Separation, Reliability and Skewed Distributions Statistically Different Sample-independent Levels of Performance . Rasch Measurement Transactions. 2001. 14(4): p.786; Available from: https://rasch.org/rmt/rmt144k.htm . Wright BD. Model selection: Rating Scale Model (RSM) or Partial Credit Model (PCM)? 1998. 12(3): p. 641-2; Available from: https://www.rasch.org/rmt/rmt1231.htm . Linacre JM. The Partial Credit Model and the One-Item Rating Scale Model . Rasch Measurement Transactions. 2005. 19(1): p.1000-2; Available from: https://www.rasch.org/rmt/rmt191e.htm . Granger CV. Rasch analysis is important to understand and use for measurement . Rasch Measurement Transactions. 2008. 21(3): p. 1122-3; Available from: https://www.rasch.org/rmt/rmt213d.htm . Bond TG, Yan Z, Heene M. Applying the Rasch model: Fundamental measurement in the human sciences . 2021: Routledge. ISBN 9780367141424 Prinsen CAC, Mokkink LB, Bouter LM, Alonso J, Patrick DL, de Vet HCW, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures . Qual Life Res, 2018. 27(5): p. 1147–1157. doi: 10.1007/s11136-018-1798-3 Faustino B, Santos S, Marôco J. Psychometric and Rasch analysis of the UCLA Loneliness Scale-16 in a Portuguese sample of older adults . Psychological Studies, 2019. 64: p. 140–146. doi: 10.1007/s12646-019-00483-5 Wu, B., Social isolation and loneliness among older adults in the context of COVID-19: a global challenge . Global Health Research and Policy, 2020. 5(1): p. 27. doi: 10.1186/s41256-020-00154-3 Hwang TJ, Rabheru K, Peisah C, Reichman W, Ikeda M. Loneliness and social isolation during the COVID-19 pandemic . International Psychogeriatrics, 2020. 32(10): p. 1217–1220. doi: 10.1017/S1041610220000988 . Welch V, Ghogomu E, Shea B, Tugwell P, Kristjansson E, Petkovic J, et al. PROTOCOL: In-person interventions to reduce social isolation and loneliness: An evidence and gap map . Campbell Systematic Reviews, 2023. 19(3): p. e1340. https://doi.org/10.1002/cl2.1340 Hahn EA, DeWalt DA, Bode RK, Garcia SF, DeVellis RF, Correia H, et al. Measuring social health in the patient-reported outcomes measurement information system (PROMIS): item bank development and testing . Qual Life Res, 2010. 19(7): p. 1035–44. doi: 10.1007/s11136-010-9654-0 Davies K, Collerton J, Jagger C, Kingston A, Robinson L, Hanratty B. The longitudinal relationship between loneliness, social isolation, and frailty in older adults in England: a prospective analysis . The Lancet Healthy Longevity, 2021. 2(2): p. e70-e77. doi: 10.1016/S2666-7568(20)30038-6 Shah HA, Househ M. Understanding loneliness in younger people: Review of the opportunities and challenges for loneliness interventions . Interactive Journal of Medical Research, 2023. 12(1): p. e45197. doi: 10.2196/45197 Eager S, Grant R, Jones S, McAuley L, McGowan J, McKenzie J, et al. Young people’s views on the acceptability and feasibility of loneliness interventions for their age group . BMC Psychiatry, 2024. 24(1): p. 308. doi: 10.1186/s12888-024-05751-x Abel J, Wood TR. Compassionate communities as the foundation of the next healthcare revolution . Lifestyle Medicine, 2023. 4(4): p. e89. doi: 10.1002/lim2.89 Suragarn U, Hain D, Pfaff G. Approaches to enhance social connection in older adults: an integrative review of literature . Aging and Health Research, 2021. 1(3): p. 100029. doi: 10.1016/j.ahr.2021.100029 National Academies of Sciences, Engineering, and Medicine. Social isolation and loneliness in older adults: Opportunities for the health care system . Washington, DC: The National Academies Press. 2020. https://doi.org/10.17226/25663 Wilcox EM. The silver line helpline: A “ChildLine” for older people . Working with Older People, 2014. 18(4): p. 197–204. doi: 10.1108/WWOP-08-2014-0023 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1Methods.docx SupplementaryFile2Rasch.docx SupplementaryFile3COSMINCHECKLIST1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":364699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWright Map for the measured age-gender groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure002.png","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/2c39287ef3b156e850c1e18c.png"},{"id":85614932,"identity":"f06985d5-12bd-4903-ab21-f538a8c0e11c","added_by":"auto","created_at":"2025-06-29 14:22:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean measures by age-gender groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure003.png","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/4cdf76a2235e163b17d0ef35.png"},{"id":91161216,"identity":"7a74bcb3-3ad8-4496-92b1-d2d69746df5d","added_by":"auto","created_at":"2025-09-12 09:16:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562627,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/ed49d6e3-4210-48dc-a186-2bbc78461d7d.pdf"},{"id":85614926,"identity":"5e438df9-a5fd-4306-bf51-e522f1590735","added_by":"auto","created_at":"2025-06-29 14:22:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":45898,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1Methods.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/f6252fe77393a3f085f8048f.docx"},{"id":85614925,"identity":"ea0c211a-78f5-4377-94a1-cc1e72cb543d","added_by":"auto","created_at":"2025-06-29 14:22:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54998,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2Rasch.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/04fe7719b6d0dbe43bd07c07.docx"},{"id":85615456,"identity":"28ded602-fb62-474b-86e9-54627e843614","added_by":"auto","created_at":"2025-06-29 14:30:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23688,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile3COSMINCHECKLIST1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864317/v1/d58c8fe6ac8979b1f5ed7a89.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"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","fulltext":[{"header":"Background","content":"\u003cp\u003eLoneliness and social isolation are critical public health issues globally, significantly affecting mental and physical health across diverse populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Loneliness is commonly defined as a subjective feeling arising from a discrepancy between desired and actual social interactions and relationships, potentially impacting psychological wellbeing, and contributing to poorer physical health outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the United Kingdom (UK), loneliness is prevalent, with approximately half of adults experiencing feelings of loneliness at varying frequencies, particularly affecting certain vulnerable demographic groups including unmarried middle-aged adults, elderly individuals, widows, people with disabilities and women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The associated health consequences of loneliness include unhealthy behaviours such as smoking, excessive alcohol consumption, poor diet, and physical inactivity, leading to increased healthcare costs, higher morbidity, and elevated mortality rates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The economic implications are also substantial, with UK employers experiencing annual productivity losses estimated at approximately \u0026pound;3.7 billion[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecognising these significant health and economic burdens, the UK government and various stakeholders have launched initiatives aimed at addressing loneliness and promoting social connection, targeting both the general population and specific vulnerable groups [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Effective targeting of such interventions depends critically on comprehensive and precise measurement tools. Existing instruments, notably the UCLA Loneliness Scale, while extensively validated and frequently used, have limitations, particularly their narrow focus on loneliness alone without adequately assessing broader social connectedness and community-level factors such as trust and cohesion [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Measures of neighbourhood social capital which include trust, cohesion and willingness to assist neighbours have demonstrated predictive utility in understanding social wellbeing, providing context-specific insights beyond individual experiences of loneliness alone [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Incorporating these neighbourhood-level items offers a comprehensive assessment, enabling more targeted and effective community-based interventions.\u003c/p\u003e \u003cp\u003eThe Measuring Loneliness in the UK (INTERACT) Study is the largest global study on loneliness with a cohort exceeding 160,000 participants. The study profile and descriptive results are presented in the third paper in the series (\u003cb\u003eEl-Osta et al., 2025a)\u003c/b\u003e, whereas the inferential analysis of the predictors of loneliness is presented in paper 2 (\u003cb\u003eEl-Osta et al., 2025b\u003c/b\u003e). The main objective of this manuscript (paper 3) is to explore the psychometric properties of the INTERACT data collection tool.\u003c/p\u003e \u003cp\u003eThe INTERACT data collection tool is a comprehensive survey instrument combining items from the UCLA Loneliness Scale [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], the Office for National Statistics (ONS) Direct Measure of Loneliness, neighbourhood social capital measures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and isolation items specific to the COVID-19 pandemic context [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This integrated approach allows for a more nuanced measurement of both loneliness and social connection as opposing but interrelated constructs, capturing broader dimensions relevant to effective intervention planning and public policy formulation. This paper is the first to report the psychometric evaluation of this novel 13-item scale using Rasch analysis, drawing on data from a large national sample of over 130,000 NHS patients in England. The rationale was to leverage the Rasch measurement model [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] an advanced psychometric technique capable of transforming ordinal data into interval-level measures, to enhance objective interpretability.\u003c/p\u003e\n\u003ch3\u003eStudy aims\u003c/h3\u003e\n\u003cp\u003eThe aim of this study was to evaluate the psychometric properties of the new 13-item instrument, ensuring its calibration as an objective, valid and reliable measurement tool. Specifically, we sought to confirm the scale's unidimensionality, validity and reliability, and to examine differential patterns of loneliness and social connection across distinct age and gender groups.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eA cross-sectional online survey was conducted among NHS patients across England. A large, randomly selected sample was invited to participate, resulting in responses from over 160,000 individuals. The final sample consisted of 134,164 consenting respondents after excluding individuals who did not provide consent, participants under the age of 16, and incomplete responses. Full details, including the link to the INTERACT survey are already provided in Paper 1 of this three-part series (\u003cb\u003eEl-Osta et al., 2025a\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe COnsensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) checklist [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and the Strengthening the Reporting of Observational Studies in Epidemiology [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] guidelines [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] were used to improve the quality of reporting.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Methods \u0026 Participant Recruitment\u003c/h3\u003e\n\u003cp\u003eParticipants were randomly sampled from NHS patient registers across England. Stratified random sampling was employed at the General Practice Level and by the National Institute for Health and Care Research (NIHR) Be Part of Research Network. Invitations were distributed via email and/or Short Messaging Service (SMS) containing secure survey links. Participants were categorised into demographic groups by gender (Female, Male, and Other) and age groups, following standards from the UK ONS and STATISTA guidelines, validated by prior studies [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The age categories were Youth (16\u0026ndash;24 years), Young Adults (25\u0026ndash;39 years), Middle-aged Adults (40\u0026ndash;64 years), and Seniors (\u0026ge;\u0026thinsp;65 years).\u003c/p\u003e\n\u003ch3\u003eMeasurement Instrument\u003c/h3\u003e\n\u003cp\u003eThe INTERACT data collection tool measures a variable with two opposing constructs. Loneliness, a subjective, negative experience, is primarily internally driven, while social connection, involving interaction and emotional closeness, has positive polarity and stems from internal and external factors. Though not strict antonyms, these terms interpret results in a variable with positive polarity.\u003c/p\u003e \u003cp\u003eThe questionnaire comprised 27 items, including 14 socio-demographic questions and 13 Likert-type scale items assessing loneliness and social connection. The 13-item scale included (i) three items from the UCLA Loneliness Scale (Version 3) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] seven items addressing neighbourhood social capital and trust, adapted from established measures of community cohesion[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], (iii) two items specifically focused on loneliness and isolation experienced during the COVID-19 pandemic [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and (iv) the ONS single Direct Measure of Loneliness context [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe socio-demographic items and the Social Capital Score items of the INTERACT tool are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003eThe questionnaire consisted of two response formats: (i) frequency-based scales (e.g., \"Never\" to \"Always\"), and [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] agreement-based scales (\"Strongly Agree\" to \"Strongly Disagree\"). Items measuring loneliness or having negative polarity were reverse coded to standardise responses for analysis. This paper focuses on age and gender, while the remaining items will be analysed in future studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocio-demographic questions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1_Region/Q3_postcode. What is your Region? / What is your full postcode?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ8_age. What is your age? (in years).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9_gender. Gender or sex of the person in three categories: Female, Male and Other.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ10_relatives. How many RELATIVES did you see or hear from in the last month?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ11_friends. How many FRIENDS did you see or hear from in the last month?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ13_employment. What is your employment status?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ14_ethnicity. What best describes your ethnic group or background?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ15_marital_status. What is your marital status?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ16_pets. Do you have any pets?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ17_education. What is your highest level of education?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ18_children. Do you have any children?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ19_household. How many people other than you live in your household?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ20_disability. Do you have a disability?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ21_conditions. Do you have any long-term health conditions (e.g. mental health, diabetes, asthma)?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurvey items\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShort name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eItem in the survey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ4LackSocial connection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHow often do you feel that you lack social connection?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e(Frequency)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ5Left_out\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHow often do you feel left out?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ6FeelIsolated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHow often do you feel\u003c/p\u003e \u003cp\u003eisolated from others?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ7FeelLonely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHow often do you feel lonely?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e(Frequency)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHelp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople around here are willing to help their neighbours.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(Agreement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKnow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis is a close-knit, or \u0026lsquo;\u0026lsquo;tight\u0026rsquo;\u0026rsquo; neighbourhood where people generally know one another.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBorrow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIf I had to borrow \u0026pound;30 in an emergency, I could borrow it from a neighbour.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNotAlong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople in this neighbourhood generally don\u0026rsquo;t get along with each other.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople in this neighbourhood can be trusted.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShopping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIf I were sick, I could count on my neighbours to shop for groceries for me.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSameValues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople in this neighbourhood do not share the same values.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVIDLonely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe COVID-19 pandemic \u0026amp; lockdowns made me feel more LONELY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e(Agreement)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVIDIsolated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe COVID-19 pandemic \u0026amp; lockdown made me feel more ISOLATED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eRasch Model and Fit Analysis\u003c/h3\u003e\n\u003cp\u003eRasch analysis was chosen due to its ability to convert ordinal Likert-type responses into interval-level measures, facilitating an objective assessment of latent constructs like loneliness and social connection [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The analysis was conducted using Winsteps software [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] applying the Grouped Rating Scale Model because the questionnaire incorporated distinct rating scales across item groups [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The critical assumptions concerning unidimensionality, scale validity, item fit and differential item functioning were verified. Unidimensionality was evaluated using Principal Component Analysis of Residuals (PCAR) and item-test correlations, ensuring that items measured a single latent trait [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFit Statistics (INFIT and OUTFIT) mean-square (MNSQ) values between 0.7\u0026ndash;1.3 were considered acceptable [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Items outside this range were flagged as potentially problematic [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Scale validity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was verified using the mean absolute difference of observed difficulties versus theoretical uniform distribution (MAD\u0026thinsp;=\u0026thinsp;4.2%, below the critical 5% criterion). Differential Item Functioning (DIF) was assessed using comparisons of item characteristic curves and mean absolute differences (MAD) across demographic groups. Significant DIF was defined by absolute differences exceeding 0.25 logits [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Reliability was assessed using separation indices and Cronbach\u0026rsquo;s alpha, aiming for values above 0.8 to indicate adequate internal consistency [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cb\u003eSupplementary File 2\u003c/b\u003e presents the values of Standard Error of Measurement (SEM).\u003c/p\u003e \u003cp\u003e This study assessed the instrument\u0026rsquo;s psychometric properties following fair analysis practices (including COSMIN guidelines), with a focus on validity and reliability. As the instrument was not newly developed, content and face validity were not explicitly assessed in this study primarily because all items were derived from pre-existing, widely used instruments. Given that the INTERACT tool was based on previously validated tools including the UCLA 3 Item Loneliness Scale, the ONS Direct Measure of Loneliness and the Social Capital Score, it was assumed that some level of face validity had been addressed during their original development [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eScale validity and internal structure\u003c/h2\u003e \u003cp\u003eRasch modelling was used to evaluate scale validity, internal structure (via PCAR) as a basis for construct and structural validity, internal consistency (through person reliability and separation indices), model fit and group differences (using DIF). These properties are essential for assessing instrument quality in large-scale population studies.\u003c/p\u003e \u003cp\u003eScore stability, inter-rater agreement, repeated administrations, and specific reliability indicators such as the Intraclass Correlation Coefficient (ICC) or Cohen\u0026rsquo;s kappa were beyond the scope of this study. Instead, the SEM from the Rasch measures provided in the \u003cb\u003esupplementary File 2\u003c/b\u003e offers a meaningful estimate of precision for this large, cross-sectional dataset collected at a single time point [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The SEM may also support future calculations of the Smallest Detectable Change (SDC) to compare individuals by levels of loneliness or social connection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRationale for Choosing Grouped Rating Scale Model\u003c/h3\u003e\n\u003cp\u003eThe Grouped Rating Scale Model was specifically chosen as the INTERACT questionnaire combined items with differing rating structures: frequency-based items from the UCLA Loneliness Scale and COVID-19 items and agreement-based items assessing neighbourhood social capital. Unlike the Rating Scale Model, which assumes identical response formats across all items, the Grouped Rating Scale Model allows the clustering of items by rating scale structure, ensuring accurate measurement and enhancing model fit by respecting the unique response characteristics across different item groups [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eHandling Missing Data \u0026 Extreme Respondents\u003c/h3\u003e\n\u003cp\u003eParticipants with incomplete surveys, those under 16, and individuals who did not provide consent were excluded, resulting in the final analytic sample. Missing item-level data did not require imputation for the Rasch analysis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Further details on the full and analytic samples are provided in [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. To minimise bias and maintain data integrity in Rasch modelling, extreme respondents (i.e. those consistently selecting the highest or lowest response options) were identified using Winsteps and analysed separately to ensure model robustness.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eAnalyses were performed using Winsteps software [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Rasch model provides a linear scale (in log-odd ratio units or logits) indicating individuals' social connection and loneliness levels. Item measures were calibrated to produce a hierarchy from easier to harder items, facilitating interpretation and potential refinement of the scale.\u003c/p\u003e \u003cp\u003ePerson and item reliability indices, separation indices, and fit statistics (INFIT and OUTFIT) were calculated to evaluate the precision of the scale. INFIT and OUTFIT values within 0.7 to 1.3 logits indicated acceptable item fit [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Wright maps visually represented person-item distributions, facilitating comparisons of loneliness perceptions across age and gender groups.\u003c/p\u003e \u003cp\u003eAdministration occurred once for each participant; although no formal stability check was done, it was not expected due to the short response time and lack of interventions or major external changes. As no longitudinal or intervention data were collected, responsiveness could not be assessed. This limitation is acknowledged, highlighting the need for future research to determine the instrument\u0026rsquo;s sensitivity to change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eThe INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study received a favourable opinion from NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483).\u003c/p\u003e \u003cp\u003e Survey respondents provided consent electronically prior to participation. Data confidentiality and anonymity were maintained throughout the study process. To protect participants' privacy, all responses were pseudonymized at the point of entry. No personal identifiers, such as IP addresses, were collected and all data were stored on secure, encrypted servers at Imperial College London, in compliance with GDPR and the Data Protection Act 2018. Data will be securely archived for a minimum of 10 years following the completion of the study.\u003c/p\u003e \u003cp\u003eThe COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) and the Checklist for reporting results of Internet E-Surveys (CHERRIES) checklists were used to improve the quality of reporting.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eThe final dataset comprised n\u0026thinsp;=\u0026thinsp;134,164 respondents after exclusions for non-consent, incomplete data, or individuals below 16 years old (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Participants were predominantly female (n\u0026thinsp;=\u0026thinsp;82,805; 61.7%), followed by male (n\u0026thinsp;=\u0026thinsp;50,088; 37.3%), with a smaller proportion identifying as 'Other' (n\u0026thinsp;=\u0026thinsp;1,271; 0.95%). Most participants were middle-aged adults (40\u0026ndash;64 years; n\u0026thinsp;=\u0026thinsp;58,477; 43.5%) and seniors (\u0026ge;\u0026thinsp;65 years; n\u0026thinsp;=\u0026thinsp;50,327; 37.5%), with fewer classified as young adults (25\u0026ndash;39 years; n\u0026thinsp;=\u0026thinsp;19,272; 14.4%) and youth (16\u0026ndash;24 years; n\u0026thinsp;=\u0026thinsp;6,088; 4.5%); Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of study participants by age and gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-1] Female\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-2] Male\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-3] Other\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[1-] less than 16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot included\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot included\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot included\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot included\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[2-] 16\u0026ndash;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] 4032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] 1761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] 295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[3-] 25\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] 13182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] 5693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] 397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[4-] 40\u0026ndash;64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] 37683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] 20407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] 387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e[5-] 65 years or over\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] 27908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] 22227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] 192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRasch Analysis and Model Fit\u003c/h2\u003e \u003cp\u003eOf the 134,164 respondents, 131,662 provided responses suitable for Rasch measurement, with 1,253 and 1,249 classified as extreme respondents scoring the maximum and minimum possible scores, respectively. The mean person measure across respondents was 0.14 logits (SD\u0026thinsp;=\u0026thinsp;1.06), suggesting an overall slight tendency towards social connection in the population sampled. The instrument demonstrated robust reliability, with a person Rasch separation of 2.37 equivalent to a reliability of 0.85 and Cronbach\u0026rsquo;s alpha of 0.91, reflecting excellent internal consistency and measurement accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eItem Calibration and Difficulty\u003c/h2\u003e \u003cp\u003eItems were calibrated on a Rasch linear scale ranging from \u0026minus;\u0026thinsp;0.67 to +\u0026thinsp;0.72 logits. The item hierarchy identified \"I could borrow \u0026pound;30 from a neighbour (Borrow)\" as the most difficult item (+\u0026thinsp;0.72 logits), signifying a high level of social connection, while \"People in this neighbourhood generally don\u0026rsquo;t get along (NotAlong)\" was the easiest item (-0.67 logits), indicative of a low level of social connection or high perceived isolation. The three items derived from the UCLA Loneliness Scale showed close clustering, suggesting redundancy and potential for refinement to enhance the scale\u0026rsquo;s efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Item Functioning (DIF)\u003c/h2\u003e \u003cp\u003eDIF analysis using Student\u0026rsquo;s t-test across demographic groups and absolute DIF, applying the half-logit critical threshold (\u0026gt;\u0026thinsp;0.5 logits), indicated minimal overall bias[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To examine specific items more rigorously, a stricter absolute DIF threshold (\u0026gt;\u0026thinsp;0.25 logits) was adopted, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For instance, younger age groups and certain gender categories exhibited differential response patterns on items related to neighbourhood trust and COVID-19 experiences, suggesting that perceptions of social connection and loneliness vary significantly by age and gender. The observed DIF supports the instrument's utility in detecting relevant subgroup differences, consistent with the construct's theoretical and practical understanding \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWright Maps: Demographic Variations\u003c/h2\u003e \u003cp\u003eWright maps provide visual insights into the distributions of item difficulties and the distribution of respondents' loneliness and social connection measures alongside item difficulties, giving a clear visual representation of demographic differences. Wright Map for the measured age-gender groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Consistently across demographic groups, \"Borrow\" ranked as the most challenging item (high social connection), while \"NotAlong\" ranked as easiest (high loneliness), reflecting broad feelings of social disconnection.\u003c/p\u003e \u003cp\u003eThe analysis of the Wright Map in the context of age and gender groups follows criteria for psychometric evaluation, such as COSMIN\u0026rsquo;s, where the emphasis is placed on ensuring that the instrument remains valid and reliable across groups. By evaluating the mean item difficulty and person measures, it helps to analyse construct validity, confirming that the scale measures the intended social connection construct accurately across various demographics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAge and Gender Differences in Loneliness and Social Connection\u003c/h2\u003e \u003cp\u003ePerson measures showed a distinct gradient of loneliness by age (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Younger respondents reported higher levels of loneliness (Youth: mean= -0.49 logits; Young Adults: mean= -0.37 logits), whereas older participants (Seniors: mean\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.51 logits) reported higher social connection. Gender differences, while less pronounced, indicated males generally reported slightly higher social connection compared to females within each age group. Differential Item Functioning (DIF) was assessed using the half-logit and quarter-logit rules[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which are essential for maintaining the fairness and validity of the scale. Additional DIF analyses based on classical test theory and not using the Rasch model [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] support the validity of demonstrating that the resulting scale functions similarly and equitably across genders. This is further reinforced by the comparison shown in the Wright maps in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDimensionality and Reliability\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis of Residuals (PCAR) confirmed the unidimensionality of the INTERACT scale, revealing two strongly correlated residual clusters: one related to loneliness (UCLA and COVID-related items) and the other to social connection (neighbourhood trust items). Both clusters showed high correlations with the primary dimension (r\u0026thinsp;=\u0026thinsp;0.81 and 0.82), supporting the interpretation of loneliness and social connection as opposite ends of a single continuum.\u003c/p\u003e \u003cp\u003eRasch analysis also demonstrated strong reliability, with a Cronbach\u0026rsquo;s alpha of 0.91 and a separation index of 2.37, indicating high internal consistency and measurement precision. \u003cb\u003eSupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/b\u003e summarises output values, including reliability and standard error of measurement (SEM) for the full scale.\u003c/p\u003e \u003cp\u003eTogether, the multi-cluster structure, strong correlations with the primary trait, and evidence of scale validity support both structural and construct validity. While the UCLA scale is often treated as a \u0026ldquo;gold standard\u0026rdquo; for loneliness, COSMIN notes that true gold standards rarely exist for patient-reported outcome measures (PROMs), except when comparing short forms to their original versions. Thus, comparing the INTERACT scale with the UCLA scale contributes to construct validity and may also support criterion validity due to the widespread use and established reputation of the UCLA scale.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eSummary of principal findings\u003c/h2\u003e \u003cp\u003eThis study presents one of the largest psychometric evaluations of loneliness and social connection conducted to date, leveraging a nationally representative sample of 134,164 NHS patients across England. The findings confirm the unidimensional structure of the 13-item INTERACT scale, demonstrating high internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.91) and strong reliability (Rasch separation\u0026thinsp;=\u0026thinsp;2.37; reliability\u0026thinsp;=\u0026thinsp;0.85). Item calibration illustrated a clear hierarchy of social connection and loneliness, with 'I could borrow \u0026pound;30 from a neighbour' (+\u0026thinsp;0.72 logits) emerging as the most challenging item (indicative of higher social connection), whereas 'People in this neighbourhood generally don\u0026rsquo;t get along' (-0.67 logits) was the easiest item to endorse (indicative of lower social connection).\u003c/p\u003e \u003cp\u003eA key demographic pattern identified in this study was the age gradient in loneliness, with younger adults (16\u0026ndash;39 years) reporting significantly higher loneliness levels, while older adults (\u0026ge;\u0026thinsp;65 years) demonstrated greater social connection. Conversely, older adults, particularly those embedded in stable community environments, may benefit from long-standing social ties and established support networks that mitigate loneliness. Although gender differences were less pronounced, males reported slightly higher levels of social connection compared to females. These results warrant further investigation into potential factors such as gendered social roles, caregiving responsibilities and differential socialization patterns, which may influence loneliness experiences across the lifespan.\u003c/p\u003e \u003cp\u003eImportantly, DIF analysis highlighted minimal measurement bias across demographic groups, reinforcing the scale\u0026rsquo;s applicability across diverse populations. However, redundancy was observed among certain items derived from the UCLA Loneliness Scale, suggesting that future iterations of the INTERACT scale could benefit from item reduction strategies to enhance efficiency while maintaining content, scale, and construct validity. The scale\u0026rsquo;s psychometric robustness and applicability across different demographic groups highlight its potential for use in public health monitoring, targeted intervention development, and policy evaluation.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eInterpretation and comparison to existing literature\u003c/h2\u003e \u003cp\u003eThis study significantly advances loneliness research by validating the INTERACT scale, which uniquely integrates loneliness, neighbourhood social capital and COVID-19-related isolation experiences using Rasch analysis. Content validity was supported by the original instrument developers, but unlike existing measures like the UCLA Loneliness Scale, the INTERACT scale provides a broader framework that captures both the subjective experience of loneliness and social connectedness within the community.\u003c/p\u003e \u003cp\u003eThe inclusion of social capital items such as trust in neighbours and perceived social cohesion, expands the measurement beyond individual loneliness to consider broader social determinants, whereas previous studies relying solely on the UCLA Loneliness Scale or its short-form adaptations have been limited in assessing these contextual factors. By contrast, the INTERACT scale allows for a multidimensional approach to loneliness and social connectedness, addressing an important gap in public health measurement.\u003c/p\u003e \u003cp\u003eThe findings that younger individuals report higher loneliness than older adults are consistent with recent evidence challenging the conventional perception that loneliness primarily affects the elderly (\u003cb\u003eEl-Osta et al., 2025b)\u003c/b\u003e. The INTERACT scale's ability to detect these age-related differences highlights its practical utility in tailoring interventions that address the specific drivers of loneliness in different demographic groups.\u003c/p\u003e \u003cp\u003eThe redundancy of some items derived from the UCLA Loneliness Scale, with certain items showing clustering in the Rasch model, aligns with prior psychometric evaluations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] that highlight the potential for redundancy in standard loneliness measures. Specifically, our analysis suggests that multiple items measuring similar constructs (e.g., feeling isolated, feeling left out) could be streamlined to improve scale efficiency without compromising content and scale validity. Future iterations of the INTERACT scale may benefit from item reduction strategies, such as removing highly correlated items or consolidating questions with overlapping content, to enhance respondent burden and improve practicality for large-scale implementation.\u003c/p\u003e \u003cp\u003eThe inclusion of COVID-19-related loneliness items further differentiates the INTERACT scale from traditional instruments. Consistent with studies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] our findings confirm that pandemic-induced isolation is a distinct but closely related construct to general loneliness, suggesting that pandemic loneliness is not transient but indicates that COVID-19 experiences had lingering effects on social cohesion, particularly among younger respondents. The close Rasch measures among COVID-19-related items in our analysis suggests that future refinements may consider reducing their number or integrating them into broader social isolation metrics.\u003c/p\u003e \u003cp\u003eLastly, DIF analysis reinforced demographic differences in loneliness and social cohesion, aligning with prior research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], emphasising the importance of community social capital in buffering against loneliness, particularly for socioeconomically disadvantaged groups. The DIF findings highlight the need for culturally sensitive interventions tailored to community-specific dynamics [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImplications for policy and practice\u003c/h2\u003e \u003cp\u003eThe findings from this study have significant implications for public health interventions, emphasizing the necessity of tailored, age- and gender-specific strategies to effectively mitigate loneliness and strengthen social cohesion across diverse populations. Policymakers and public health practitioners can leverage these insights to design evidence-based interventions that target specific demographic groups, ensuring efficient resource allocation and maximizing intervention impact.\u003c/p\u003e \u003cp\u003eThe INTERACT scale serves as a validated, psychometrically robust tool for community assessments, guiding policymakers in identifying at-risk populations and tailoring interventions accordingly. The practical utility of the INTERACT scale in public health planning offers several key benefits including (i) enhancing precision in loneliness assessments, [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] guiding resource allocation and intervention targeting, (iii) monitoring and evaluating intervention effectiveness, and (iv) integrating into national and local policy frameworks. For example, by capturing both individual and community-level determinants, the INTERACT scale facilitates a nuanced understanding of social connections across diverse demographic groups. This dual focus aligns with the comprehensive approach of instruments like the Patient-Reported Outcomes Measurement Information System (PROMIS), which assesses physical, mental, and social health domains to provide a holistic view of patient wellbeing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe demographic insights derived from the INTERACT scale also enables the development of evidence-based interventions tailored to the specific needs of various age and gender groups. This targeted approach ensures the efficient use of public health resources, addressing the unique loneliness patterns identified in different populations. For example, the English Longitudinal Study of Ageing (ELSA) demonstrated that loneliness and social isolation are distinct constructs, each requiring specific intervention strategies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. By leveraging age- and gender-specific patterns, health authorities can prioritize and deploy interventions that foster social connection in ways that resonate with each demographic. For example, community-based programs should focus on engaging younger adults through digital platforms and social initiatives that reflect their communication preferences [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], whereas interventions for older populations should prioritize maintaining and enhancing existing social networks, reinforcing intergenerational connections, and improving access to community resources [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe INTERACT scale can also serve as both a baseline and follow-up measurement tool, allowing for the assessment of changes in loneliness and social connection following intervention implementation. Pertinently, given its ability to measure loneliness within a community context, the INTERACT scale can be incorporated into public health surveillance systems to identify at-risk populations, inform targeted interventions tailored to distinct demographic groups, and to help track progress on loneliness reduction initiatives and the effectiveness of social prescribing over time.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe strengths and limitations of the study were addressed in Papers 1 and 2 (\u003cb\u003eEl-Osta et al., 2025a, and El-Osta et al., 2025b\u003c/b\u003e). Briefly, key limitations include (i) the cross-sectional design which restricts causal inferences and prevents the examination of changes in loneliness over time, [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] the online survey administration may introduce selection bias, as individuals with digital literacy and internet access are more likely to participate, and (iii) that no repeated administrations to the same participants were conducted; although this was mitigated somewhat as measurement independence was preserved by the administration protocol, and the anonymous conditions were explicitly stated and deemed appropriate.\u003c/p\u003e \u003cp\u003ePertinently, we acknowledge that although the INTERACT scale demonstrated strong psychometric properties, some item redundancy was identified suggesting opportunities for refinement in future iterations. Additionally, COVID-19-related items, while relevant at the time of data collection, may diminish in applicability over time. Future research should evaluate whether these items remain valid indicators of social isolation in a post-pandemic context. We also acknowledge that self-reported data may be subject to social desirability and recall biases potentially leading to potential under- or over-reporting of loneliness and social connection. Another limitation is the lack of control for unmeasured confounders, such as socioeconomic status, ethnicity, and pre-existing mental health conditions, all of which are known to influence loneliness. The study also does not account for intersectional effects, such as the combined impact of gender, socioeconomic status, and ethnicity on loneliness experiences. Addressing these factors in future research would provide a more comprehensive understanding of disparities in social connection.\u003c/p\u003e \u003cp\u003eFuture research should adopt longitudinal designs to examine loneliness trends and intervention effects over time. Additionally, a mixed methods approach combining quantitative analysis with qualitative insights is needed to capture the complexity of loneliness and social cohesion (this work is ongoing, and the results will be published by the authors in 2026). Addressing these methodological challenges will further strengthen the INTERACT scale\u0026rsquo;s applicability, ensuring its effectiveness as a tool for intervention planning and policy development.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe INTERACT study provides a rigorous, psychometrically validated tool for measuring loneliness and social connection on a national scale. The findings highlight important demographic differences in loneliness and social connectivity, offering crucial insights for public health policy and practice. By refining measurement instruments and targeting interventions based on age- and gender-specific patterns, stakeholders can more effectively mitigate loneliness and enhance social wellbeing across diverse populations. Our findings provide an evidence-based foundation for policymakers and healthcare authorities to better understand, monitor, and address loneliness and social isolation, enhancing individual and community wellbeing. The INTERACT scale provides a practical, scalable, and policy-relevant tool to support loneliness surveillance, intervention refinement and policy evaluation, ensuring data-driven approaches to promoting social connection at individual and community levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no funding. Austen El-Osta is grateful for support by the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors provided substantial contributions to the conception, design, acquisition (AEO) and interpretation (AT, MA and AEO) of study data. AEO took the lead in planning the study with support from co-authors. AT carried out the data analysis with support from AEO. AT developed the manuscript with support from co-authors. AEO is the guarantor.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors also appreciate the insightful comments by Dr John Michael Linacre regarding the unidimensionality of the variable reported in the Winsteps\u0026reg; software. The authors also thank Mr Aos Alaa (INTERACT Study Coordinator), Mrs Sandra O\u0026rsquo;Sullivan, Dr Arti Sharma, the NIHR Research Delivery Networks, and the NIHR Be Part of Research (BPoR) Network for their support in recruiting study participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eNo new data were created during this study and data sharing is not applicable to this article. Supplementary file 3 provides a summary table of COSMIN definitions of domains, measurement properties and aspects of measurement properties, alongside corresponding Rasch and classical test theory results, with references to their location within the sections of the main text, including a rationale for those that were not directly evaluated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePyle E, Evans D. Loneliness: what characteristics and circumstances are associated with feeling lonely. Newport: Office for National Statistics; 2018. 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Working with Older People, 2014. 18(4): p. 197\u0026ndash;204. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/WWOP-08-2014-0023\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Loneliness, Social isolation, Social connection, Psychometric validation, Rasch model, Public health interventions, Social capital, COVID-19, Community cohesion","lastPublishedDoi":"10.21203/rs.3.rs-6864317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6864317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe INTERACT scale demonstrated excellent psychometric properties, confirming its unidimensionality with high internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.91) and strong person reliability (Rasch separation\u0026thinsp;=\u0026thinsp;2.37; reliability\u0026thinsp;=\u0026thinsp;0.85). Item calibration ranged from \u0026minus;\u0026thinsp;0.67 to +\u0026thinsp;0.72 logits, with \"I could borrow \u0026pound;30 from a neighbour\" (+\u0026thinsp;0.72 logits) representing high social connection, whereas \"People in this neighbourhood generally don\u0026rsquo;t get along\" (-0.67 logits) indicated low social connection. Younger adults (16\u0026ndash;39 years) exhibited significantly higher loneliness scores (mean measures \u0026minus;\u0026thinsp;0.70 to -0.40 logits), while older adults (\u0026ge;\u0026thinsp;65 years) reported greater social connection (+\u0026thinsp;0.15 to +\u0026thinsp;0.56 logits). DIF analysis indicated minimal bias across gender and age groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e","manuscriptTitle":"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","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:22:45","doi":"10.21203/rs.3.rs-6864317/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c21953ee-b873-41ab-99d8-e037407d5342","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-12T09:08:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:22:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6864317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6864317","identity":"rs-6864317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}