Empirical Evidence for a Process-Based Model of Health-Related Quality of Life Using Network Analysis

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Peiris, Aleksandra Stanimirovic, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5278979/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 Health-related quality of life (HRQL) is the perceived impact of a medical condition on one’s overall well-being. While contemporary assessments are structured to evaluate an individual’s HRQL state, we propose a complementary process-based model, which is defined as an appraisal that evolves over time as it reflects and informs a self-regulatory process of adapting to dynamic changes in bio-psycho-social life domains. In support of this approach, we developed a novel HRQL assessment tool called the EUROIA: E val U ation of goal-di R ected activities to pr O mote well-be I ng and he A lth, which uses self-report data to assess the frequency with which individuals engage in a sample of goal-directed activities in pursuit of living well. Methods We conducted a network analysis to evaluate the hypothesis that the EUROIA subscales would demonstrate a meaningful pattern of associations with an established HRQL measure and associated indices of psychosocial functioning and efficacy in self-managing a chronic medical condition. Results The EUROIA is associated with established indices of HRQL in a manner that is theoretically consistent with our process-based model. Stability coefficients (i.e., betweenness, closeness, and strength) of the analysis revealed high reliability for the network. Conclusion This analysis provides support for the validation of a process-based approach to HRQL assessment, which is represented, in part, by the EUROIA. A process-based approach complements and expands conventional measures of HRQL by focusing on how a patient’s capacity to engage in goal-directed activities for living well is affected by their medical condition. Health-Related Quality of Life Network Analysis Patient-Reported Outcomes Process-Based Approach Chronic Heart Failure Chronic Kidney Disease Figures Figure 2 Figure 3 Figure 4 Figure 5 Introduction Health-related quality of life (HRQL) is defined as the perceived impact of a medical condition or therapy on physical, psychological, or social domains of one’s well-being, assessed via patient-reported outcome measures [ 1 ]. Patients living with chronic illnesses often exhibit impaired HRQL, which is assessed using single ratings [ 2 ] and multidimensional profiles [ 3 , 4 ]. There is an extensive range of HRQL instruments, with demonstrated validity and reliability, which serve as prognostic indicators for clinical outcomes [ 5 , 6 ]. Accordingly, policy statements advocate the assessment of HRQL as a primary endpoint in the evaluation of the effects of disease progression or the benefit of clinical interventions [ 7 ]. Previous work by our team identified three distinct models of well-being that are embedded in current HRQL assessments: a) eudaimonic well-being (i.e., the perception of flourishing in personal growth and happiness as sampled by self-ratings for happiness or purpose in life [ 8 , 9 ]), b) hedonic well-being (i.e., the absence of symptoms of physical discomfort or emotional distress and the presence of elevated positive feelings and life satisfaction [ 10 , 11 ]), and c) desire-satisfaction (i.e., a state marked by fulfillment vs. frustration in attaining objects or experiences to which we are attracted [ 12 , 13 ]). Therefore, it is common to observe that individual HRQL assessments use an eclectic mix of items that reflect multiple dimensions of well-being across bio-psycho-social life domains. At the same time, these assessments are not necessarily designed to evaluate how an individual’s self-reported HRQL state compares with a theoretically coherent model of well-being. The process-based model for living well. In contrast to contemporary assessments that aim to evaluate an individual’s HRQL state, Nolan and Sharpe (2023) introduced a process-based model which is an appraisal that evolves over time as it reflects and informs a self-regulatory process of adapting to dynamic changes in health status [ 14 – 17 ]. Figure 1 illustrates key features of the process-based model of HRQL. Briefly, HRQL appraisals express the perceived impact of an illness or clinical treatment on one’s well-being at a given point in time. These appraisals are associated with new or re-newed priorities for living well, which are expressed in terms of life goals or aspirations [ 17 ]. In turn, one’s aspirations evoke goal-directed activities that affect changes in bio-psycho-social life domains. Performance-based feedback from these activities is reviewed in terms of efficacy appraisals and outcome expectations. These then influence our re-appraisal of HRQL and re-evaluation of HRQL priorities. This iterative process is self-regulatory in nature. We adapt to life events across bio-pscyho-social domains while appraising our progress in fulfilling our aspirations for living well, which informs our recalibration of HRQL priorities and engagement in subsequent goal-directed activities. The conceptualization of a process-based approach is based on four considerations. First, qualitative research with individuals diagnosed with a chronic progressive medical condition has shown that self-reported HRQL is part of a dynamic process of adapting to acute changes in their health status [ 18 , 19 ]. These changes are often unanticipated or recurrent, which can, in cases of progressive illness, evoke a sense of existential threat that challenges the individual to learn how to accept a life that feels fundamentally tenuous. Second, current assessments evaluate an individual’s HRQL profile against a state of idealized well-being where ratings for items are anchored by optimal levels of personal flourishing (eudaimonia), pleasure and satisfaction (hedonia), or fulfillment of desire. It is challenging to see how these ideals for well-being provide a meaningful standard of comparison for evaluating HRQL in individuals with pathophysiologic conditions that involve chronic impairment or suffering (e.g. chronic pain), or progressive functional limitations with premature morbidity and mortality (e.g. heart failure). Instead, the process-based approach to HRQL utilizes a concept from ancient Stoic philosophy in which the goal of attaining eudaimonic well-being (i.e. personal flourishing, mastery, or excellence) was reconceptualized by the goal of learning to live well with adversities or positive events in daily life —i.e., “good flow of life” ( euroia biou ). HRQL was presented as a process of responding to life events in a manner that affirmed our personal agency and dignity, as well as our ability to connect with our environment [ 20 ]. Third, systematic reviews and meta-analyses have established that a response shift is commonly observed in the way that individuals respond over time to HRQL assessments. This shift is expressed as (i) recalibrating how self-rating scales are used when gauging the severity of a given symptom or function, (ii) reprioritizing different features of HRQL to better reflect new insights or experiences about living well that have become personally salient, or (iii) reconceptualizing their understanding of the HRQL construct to reflect their evolving priorities for living well [ 21 ]. This evidence indicates that the HRQL construct may be routinely undergoing change in its content and structure, which suggests that it may reflect an ongoing adaptive process. The last point of consideration is the structure of the well-being construct that is embedded in HRQL assessments. From an empirical and theoretical standpoint, there is consensus that well-being is a multi-dimensional construct. At the same time, there are diverse accounts of its hierarchical structure, even though the core components are essentially the same across studies. The components usually include hedonic, eudaimonic, and social indices of well-being [ 22 – 24 ]. In more recent years, proposals have been made for empirically integrating these separate models into a single unified structure [ 25 , 26 ]. Gallagher et al. (2009) asserts that well-being is integrated within a primary hierarchical structure that contains three second-order latent factors of hedonic, eudaimonic and social dimensions of well-being [ 27 ]. Other studies have also reported a primary hierarchical structure, though the specific components of well-being have varied. Bjørndal et al. 2023 proposed a structure where six distinct factors of well-being loaded onto a single higher-order factor that may represent a general index of happiness [ 28 ]. On the other hand, Linton et al. (2016) reported that the dimensions of well-being clustered around 6 core themes, but without a primary hierarchical model [ 29 ]. Similarly, Ruggeri et al. (2020) reported that 10 dimensions of well-being identified in their population survey could not be meaningfully aggregated into a single composite index of happiness or life-satisfaction, although they acknowledged that composite scores can be useful for summarizing change over time and for capturing its variation in social circumstances on a macro-level [ 30 ]. Finally, Van Woerkom et al. (2022) conducted a network analysis which showed that the nodes (or areas of interaction in their communities of variables) did not passively reflect a higher order causal agent of well-being or HRQL. Rather, the nodes were viewed as active agents in a causal system in which well-being was an emergent property of the interactions [ 31 ]. Given the diversity of state-based models for the structure of well-being that have emerged in the literature, two main interpretations of the findings are possible. Either only one of the proposed models on the structure of well-being/HRQL is correct, or each may present a profile that is valid. The latter case would suggest that the structure of HRQL/well-being is not fixed, and rather that it is dynamic and changes over time. We suggest that the reported structure of well-being would be expected to fluctuate over time according to an individual’s ongoing adaptation to dynamic changes in bio-psycho-social domains of their personal environment. Arguably, it is a priority to evaluate the potential validity of this account. Our team recently introduced a novel HRQL assessment, the EUROIA: E val U ation of goal-di R ected activities to pr O mote well-be I ng and he A lth. Psychometric properties of the EUROIA were previously described in terms of reliability, content validity, and clinical utility [ 32 – 34 ]. This scale uses self-report data to assess the frequency with which individuals engage in a sample of goal-directed activities that are identified with their pursuit of living well (well-being). These goal-directed activities represent a key feature of our process-based model of HRQL. In this study, we conducted a network analysis to evaluate whether EUROIA subscales would demonstrate a meaningful pattern of associations with established HRQL measures and with associated indices of psychosocial functioning and efficacy in self-managing a chronic medical condition. Methods Participants and Data Sources This investigation was a sub-study of the Open Access Digital Community Promoting Self-Care, Peer Support, and Health Literacy – A Virtual Community Promoting Mental Health, Psychosocial Adjustment, and Peer Support (ODYSSEE-vCHAT) projects: a single group, open label, pre-post study [ 35 ] and a double-arm, parallel group, randomized controlled trial [ 36 ]. The ODYSSEE-vCHAT [ 35 ] study recruited participants aged 18 years and older with a proficiency in English and a diagnosis of chronic heart failure (CHF; NYHA Class II to IV for at least 3 months prior to enrolment) or advanced chronic kidney disease (CKD; greater than 10% risk of requiring dialysis within 2 years or end-stage renal disease and receiving dialysis). Complete study details can be found elsewhere [ 35 ]. The present network analysis was based on complete case data from the baseline assessments of the ODYSSEE-vCHAT study and trial. The sample estimate for the ODYSSEE-vCHAT study accounted for changes in the Mental Component Summary (MCS) of the Short Form 36 (SF-36) Health Survey [ 37 ] over a period of 3 to 12 months, while the sample estimate for the ODYSSEE-vCHAT trial was based on change over 12 months in a composite index of all-cause mortality and hospitalization. The trial was estimated to have a sample size of N = 162, while the study had a sample size of N = 188, which included oversampling. Both the study and trial were adjusted for a potential 12-month withdrawal or attrition rate of 14.7%, a type 1 error of 5%, and a power of 80% [ 38 ]. Measures The EUROIA measure includes dimensions related to eudaimonic well-being (EUD-WB), self-affirmation (SLF-AFF), social affiliation (SOC-AFF), and social roles and responsibilities (SOC-RR). Eudaimonia is composed of behaviors that promote flourishing and self-actualization aimed at personal growth, connection to a greater purpose, and life satisfaction [ 39 , 40 ]. SOC-AFF is comprised of behaviors such as participation in social activities, maintaining close relationships, and helping others. SOC-RR assess behaviors related to maintaining roles and obligations to significant others and being productive in a work-related setting. SLF-AFF focuses on behaviors related to physical activity and exercise and includes activities that promote feelings of being healthy and attractive and maintaining positive affect. Established indices of HRQL used to validate the EUROIA subscales are summarized in Table 1 . Table 1 HRQL scales used in network analysis. Measure of HRQL Description Short Form 36 (SF-36) Health Survey Mental Component Summary (MCS) [ 37 ] A questionnaire of 36 items that assess patients’ health status and its impact on their lives. Consists of various multi-item scales: Role Limitation due to Emotional Problems (RE), Emotional Well-Being (EWB), Vitality (VT), and Social Functioning (SF). Revised UCLA Loneliness Scale (RULS-6) [ 54 ] A short version of the 20-item scale measuring subjective feelings of loneliness and social isolation. Flourishing Scale (FS) [ 56 ] An 8-item summary measure of self-perceived success in areas such as relationships, self-esteem, purpose, and optimism. Self-Efficacy for Managing Chronic Diseases 6-item Scale (SEMCD-6) [ 65 ] A 6-item scale that covers several common domains across many chronic diseases, including symptom control, role function, emotional functioning and communicating with physicians. ENRICHD Social Support Index (ESSI) [ 55 ] A 7-item self-report questionnaire to assess social support. Godin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [ 53 ] A 4-item questionnaire used to assess leisure-time physical activity (any leisurely activity undertaken by the individual that increases their total energy expenditure). Network Analysis Network analysis was used to complement and extend the results of the EUROIA and its association with established HRQL indices, as summarized in Table 1 . Graphical least absolute shrinkage and selection operator (glasso) were performed to estimate the network structure of the EUROIA and HRQL indices using the extended Bayesian information criterion (EBIC). A polychloric correlation matrix was computed, which provided the foundation of the network. This allowed for the examination of partial correlations between each subscale while controlling for all the other variables in the network [ 41 ]. We constructed a network wherein each subscale was represented as a node and the partial correlations between the items as edges. Networks were displayed using a Fruchterman-Reingold algorithm [ 42 ] whereby nodes with stronger connections are placed at the center of the network and weaker connections more peripherally. Analyses were performed using R software v4.1.0 [ 43 ], qgraph [ 44 ], glasso [ 45 ], bootnet [ 46 ], psych [ 47 ], and igraph [ 48 ]. Network Centrality Measures Centrality refers to several metrics that determine a node's relative importance compared to other nodes in the network [ 49 ]. Strength centrality reflects a node being central through having strong connections to other nodes based on the absolute sum of the weighted number and strength of its connections relative to all other nodes. Betweenness centrality determines how important a node is in connecting other nodes by identifying the frequency with which a node lies on the shortest path between two other nodes. Closeness centrality measures the distance between nodes and quantifies the node’s relationship to all other nodes in the network [ 50 ]. Bootstrapping with 2500 permutations was done to assess the stability of the centrality metrics. The generated stability coefficients demonstrated values greater than 0.5, indicating high reliability, while values of 0.25 indicated the minimum evidence. We used the Correlation Stability coefficient (CS-coefficient) for correlation values equal to or above r = 0.7 to measure the stability of centrality indices [ 46 ]. The CS-coefficient indicates the percentage of our sample that can be dropped to maintain, with a 95% confidence interval, correlation values equal to or above r = 0.7 between our sample's centrality indices and our bootstrapped samples' centrality indices. Non-parametric bootstrap (resampling rows with replacement) was employed to create 1000 samples to estimate edge weight stability [ 46 ]. Network Sub-Community Identification Network sub-communities or clusters were identified through exploratory graph analysis (EGA), which uses random walk algorithms to identify dimensions in psychometric data [ 51 ]. Clusters seek to identify areas in the network with nodes have many connections within, and few connections between, clusters [ 41 ]. The walktrap method was selected as communities identified using this method are shown to be consistent with latent factors of factor models [ 51 ]. Item stability statistics were calculated to evaluate the strength of the placement of each item within the derived dimensions. The proportion of times that each item is placed in each dimension was calculated to estimate item stability. This calculation is useful in identifying which items contribute to the consistency of the structure of the dimensions by frequently replicating in the same dimension, and which items lead to inconsistency by frequently replicating in other dimensions. Variables at or below the range of 0.65 to 0.75 are considered to be less stable [ 52 ]. Results A total of 276 participants with complete baseline data were included in the network analysis. Patients included in the sample had a primary diagnosis of either CHF (50.5%) or CKD (49.5%). The mean age was 56.6 years (range: 20.0 to 91.0, SD = 16.0) and 59.6% were male. The majority of the sample (65.0%) had completed a college or university degree and another 23.0% completed graduate or post-graduate degrees. Respondents were primarily identified as having a White or Caucasian racial background (58.2%), living with a partner (59.1%), and had a combined household income ≥ $ 100 000 CAD. Network Analysis The glasso network of the EUROIA and HRQL indices is shown in Fig. 2 . The network was comprised of 13 nodes, 40 edges (31 positive and 9 negative) out of a possible 78 connections, a mean edge weight of 0.039, and a network density of 0.51 (51% of nodes were directly connected). A green connection indicates a positive link while red connections signify negative links. The width (thickness) of the connection depicts the strength of the partial correlations. Network centrality values are presented in Fig. 3 and Table S1 . MCS-Emotional Wellbeing (EWB) was shown to be the most influential node in the network, with a strength centrality of 1.64. The Godin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [ 53 ] demonstrated the weakest centrality (-2.08). The Revised UCLA Loneliness Scale (RULS-6) [ 54 ] (1.75) and ENRICHD Social Support Index (ESSI) [ 55 ] (1.42) exhibited the highest betweenness centralities and were shown to mediate the connections between other nodes, primarily the EUROIA and MCS subscales. Additionally, the RULS-6, MCS-EWB, and ESSI were the most closely connected subscales. The EUROIA subscales were linked to MCS subscales via positive connections with social support (ESSI) and psychological flourishing (Flourishing Scale (FS) [ 56 ]), and through an inverse association with loneliness (RULS-6). Additional pathways to the MCS involved self-efficacy and leisure activity (GSLTPAQ). Network Accuracy and Stability The tests for network accuracy and stability are presented in the online supplementary materials (S2-S3). Bootstrapping was used to compute robust centrality estimates represented as correlation stability coefficients. The correlation stability coefficients were 0.28 (ranging from 0.21 to 0.36) for betweenness, 0.44 (ranging from 0.36 to 0.52) for closeness, and 0.67 (ranging from 0.59 to 0.75) for strength. The minimum 0.25 cut-off was met, indicating that the centrality metrics can be interpreted with confidence and the network can be considered reliable [ 46 ]. Community Detection Figure 4 shows the glasso network map with the EGA sub-communities highlighted. Notably, three sub-communities were identified, of which two matched a priori theme groupings reflecting the EUROIA scale (SOC-AFF, SOC-RR, EUD-WB, SLF-AFF) and the MCS Role Limitation due to Emotional Problems (RE), Emotional Well-Being (EWB), Vitality (VT), and Social Functioning (SF) subscales [ 37 ]. The themes identified EUROIA and leisure activities, MCS and self-efficacy, and social support, loneliness, and flourishing. Item stability statistics ( Fig S4 ) for each dimension were close to 1, demonstrating the robustness of the communities estimated using EGA. Discussion In this empirical study, we present a process-based, meta-theoretical model of HRQL that complements established measures of an HRQL state, while also introducing a novel dynamic feature of this construct. Using network analysis, we demonstrate that the EUROIA, an assessment of goal-directed activities for living well, is associated with established indices of HRQL in a manner that is theoretically consistent with our process-based model. More specifically, EUROIA subscales were linked to components of HRQL as measured by MCS subscales, via social support, psychological flourishing, and through an inverse association with loneliness. Additional pathways to the MCS involved self-efficacy and leisure activity. Importantly, stability coefficients (i.e., betweenness, closeness, and strength) of the analysis revealed high reliability for the network. Meta-Theoretical Underpinnings of a Process-Based Approach to HRQL The process-based approach presented by Nolan and Sharpe (2023) (Fig. 1 ) is best defined as a meta-theoretical model informed by relevant theories of behaviour change, including Self-Determination Theory [ 17 ], Motivational Interviewing [ 57 ], the Transtheortical Model of Health Behaviour Change [ 58 ], and the social-cognitive theory of agentic change [ 16 ]. Our model posits the following: HRQL is an appraisal that evolves as it reflects our effort to adapt to dynamic changes in health status [ 59 ] and to challenges or facilitating factors in our biopsychosocial environment. HRQL appraisals are associated with new or revised aspirations that are personally salient and meaningful [ 60 , 61 ]. These aspirations subsequently inform and evoke goal-directed activities for living well, which are expressed in our self-regulated or self-determined effort to promote change associated with improved well-being [ 14 – 17 ]. Performance-based feedback from these goal-directed activities is reviewed and expressed as efficacy and outcome expectations [ 16 ]; efficacy expectations are organized along a continuum that ranges from intrinsic to extrinsic sources of motivation/causality, and this locus of causality influences the degree to which a goal-directed activity for living well may be sustained [ 17 ]. The evaluation of outcomes from our goal-direced activities, in turn, influences our re-appraisal of HRQL, which carries forward in this complex system to re-shape subsequent aspirations and goal-directed actions that ultimately promote our adaptation across bio-psycho-social life domains. The EUROIA is an instrument that evaluates a key component of a process-based model of HRQL/well-being and, as such, has the potential to contribute meaningfully to recent theoretical/philosophical and empirical initiatives in this area. For example, use of the EUROIA in applied research complements the call for “mid-level theories” of well-being, as expressed by Alexandrova [ 62 ]. She has asserted that abstract (normative) theories of HRQL or well-being (e.g., eudaimonic and hedonic) are only obscurely associated with real-life sitatuations, and fail to promote our understanding of how a specific individual might appraise well-being in a given life circumstance. In keeping with a process-based model, the EUROIA offers a mid-level approach to identifying prototypical categories of goal-directed activities through which individuals pursue their aspirations for living well within the parameters of daily life. Both qualitative and quantitative studies of these self-reported goal-directed activities should provide novel information about how HRQL appraisals are relativized to an individual’s daily life and their pursuit of well-being [ 59 ]. Furthermore, our use of the EUROIA within a process-based approach to HRQL and well-being builds on the concept of response shift that was coined by Sprangers and Schwartz (1999). Their research highlights that individuals living with a chronic pathophysiologic condition or illness are inclined to change their internal standards, values, and personal conceptualization of HRQL [ 63 ]. In response, it is challenging on theoretical grounds for state-based models of HRQL assessment to assimilate findings of response shift. Assessments used in that approach are premised on the theory that items, subscales, and summary scores depicting an individual’s HRQL state are organized according to a fixed psychometric structure, and dynamic changes in the organization of an individual’s self-reported HRQL profile are attributed to “noise” or error variance [ 64 ]. Moreover, these assessments evaluate an individual’s HRQL profile according to its departure from a score that represents an idealized state whereby one has abundant energy and emotional tranquility all or most of the time, there are no symptoms of emotional stress or physical discomfort, there is no limitation on activities of daily living or social or work activities, and one’s health is viewed as being excellent at present and into the future: c.f. SF-36 [ 37 ]. It is unclear how this clinical standard for evaluating HRQL or well-being is relevant to the common experience of individuals who are interacting with natural challenges and demands of life that naturally evolve over time. Empirical Evidence from the EUROIA to Support a Process-Based Approach to HRQL The EUORIA provides a multi-dimensional profile of prototypical categories of goal-directed activities for living well that we have described previously [ 32 – 34 ]. Categories that have emerged to date are identified by the following EUROIA subscales: eudaimonic well-being (EUD-WB), self-affirmation (SLF-AFF), social affiliation (SOC-AFF), and social roles and responsibilities (SOC-RR). The network analysis presented in this study demonstrated that the EUROIA subscales were associated with the MCS community of variables (RE, EWB, VT, SF subscales) via pathways that were consistent with our theoretical model (Fig. 5 ) [ 37 ]. Indices such as the GSLTAS [ 53 ], FS [ 56 ], ESSI [ 55 ], and RULS6 [ 54 ] reflect psychosocial interactions or stressors in one’s bio-psycho-social environment, and the Self-Efficacy for Managing Chronic Diseases 6-item Scale (SEMCD-6) [ 65 ] reflects perceived efficacy in self-managing one’s medical condition. The directionality of the associations between the EUROIA and the MCS via the intermediary communities of variables noted above was aligned with our expectations. Higher scores on the ESSI, FS, GSLTPAQ, and SEMCD-6 were associated with greater HRQL [ 53 , 55 , 66 ], while a higher score for loneliness (RULS6) was associated with decreased HRQL [ 67 ]. Methodological Considerations and Limitations This study utilized network analysis to re-examine data and variables that were previously identified using latent variable methods [ 41 , 60 , 68 ]. The subcommunities we identified for the EUROIA closely match the latent variables identified in Francis, Peiris et al.. [ 34 ], which provides confirmatory evidence for its general underlying structure. Nevertheless, there remain a number of limitations to the present study. Our use of network analysis was exploratory and should be treated with caution as the connections between nodes cannot be treated as true causal relationships and may be impacted by unobserved factors. Our subset of data was cross-sectional and does not reflect the dynamic nature of the networks. To improve our confidence in the underlying structure of the EUROIA and our process-based approach to HRQL, this study must be replicated using longitudinal time series analyses with an adequate sample size. Despite these limitations, we believe this study makes a strong contribution to the development of the EUROIA instrument and brings credence to our meta-theoretical approach to HRQL. It should also be noted that our previous work with the EUROIA included a subscale that assesses the personal salience or meaning attributed to each goal-directed activity [ 69 ]. A priority for future studies with the EUROIA is to re-introduce subscales where each item is self-rated according to its frequency (F), perceived priority/importance (P), and F*P cross-product. Conclusions This study presents a process-based model of HRQL that highlights the dynamic nature of this construct. Use of the EUROIA in applied research represents a mid-level theoretical strategy that contributes to current efforts to clarify how HRQL appraisals are applied in daily efforts to maintain or improve our personal well-being. Moreover, the EUROIA introduces a preliminary summary of prototypical categories of goal-directed activities associated with HRQL/well-being. The process-based approach to HRQL assessment, which is partially represented by the EUROIA, complements and extends conventional measures by addressing how this patient-reported appraisal fits within a complex system of self-determined or self-regulated adaptation to one’s medical condition. Declarations Conflicts of Interest The authors have no relevant financial or non-financial interests to disclose. Ethics Statement The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. This study only involved secondary analyses of anonymized data from the ODYSSEE v-CHAT study; therefore, additional ethics approval was not required. Informed consent was obtained from all individuals prior to participation in the study. Funding The following grants supported the trials reported in the manuscript: Canadian Institutes of Health Research (CIHR) Grant # PJT173222 Canadian Institutes of Health Research (CIHR) Grant # MS2173076 Author Contribution All authors contributed to the study conception, design, or writing of this paper. NS and TF analyzed and interpreted the patient data included in this study. RPG and AS contributed to the writing and editing of this paper. VR provided statistical expertise and oversight related to the network analysis. RPN provided expertise related to the theoretical underpinnings of the process-based model to health-related quality of life. All authors read and approved the final manuscript. Acknowledgement We are grateful to the following individuals who assisted with proofreading and formatting of successive versions of this manuscript: Karly Gunson, HBA, BHSc; Jiesi Zhang; and Grace Taylor Armstrong. Data Availability Data from this work are available upon reasonable request. References World Health Organization. Strengthening mental health (Fact sheet, No. 220). Geneva: World Health Organization; 2001. Yin S, et al. Summarizing health-related quality of life (HRQOL): development and testing of a one-factor model. Popul Health Metr. 2016;14:22. Ware JE Jr., Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473–83. Green CP, et al. 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Social psychology quarterly, 1998: pp. 121–140. Ryff CD. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J Personal Soc Psychol. 1989;57(6):1069. Keyes CL. Mental illness and/or mental health? Investigating axioms of the complete state model of health. J Consult Clin Psychol. 2005;73(3):539. Keyes CL. Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. Am Psychol. 2007;62(2):95. Gallagher MW, Lopez SJ, Preacher KJ. The hierarchical structure of well-being. J Pers. 2009;77(4):1025–50. Bjørndal LD, et al. The structure of well-being: a single underlying factor with genetic and environmental influences. Qual Life Res. 2023;32(10):2805–16. Linton M-J, Dieppe P, Medina-Lara A. Review of 99 self-report measures for assessing well-being in adults: exploring dimensions of well-being and developments over time. BMJ open. 2016;6(7):e010641. Ruggeri K, et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes. 2020;18:1–16. Woerkom Mv, et al. Networks of happiness: applying a Network Approach to Well-Being in the General Population. J Happiness Stud. 2022;23(7):3215–31. Syed F et al. Goal-directed behaviours for living well promote health-related quality of life and health status: A proof of concept study. , in Canadian Cardiovascular Congress . 2022 (October): Ottawa, ON, Canada. Nolan RP et al. The evaluation of goal-directed activities to promote well-being and health-related quality of life: EUROIA study . Manuscript available upon request., Under Review. Francis T et al. The EvalUation of goal-diRected behaviors to Promote well-being and heAlth scale (EUROIA): Psychometric testing with confirmatory factor analysis . Manuscript available upon request., Under Review. ClinicaTrials.gov. ODYSSEE-vCHAT mental health program for heart failure and kidney disease patients . https://clinicaltrials.gov/ct2/show/NCT05560737 Peiris RG, et al. Automated digital counselling with social network support as a novel intervention for patients with heart failure: protocol for randomised controlled trial. BMJ Open. 2022;12(9):e059635. Suzukamo Y, et al. Validation testing of a three-component model of Short Form-36 scores. J Clin Epidemiol. 2011;64(3):301–8. Nolan RP, et al. Automated E-Counseling for Chronic Heart Failure: CHF-CePPORT Trial. Circ Heart Fail. 2021;14(1):e007073. Kaufman SB. Self-Actualizing People in the 21st Century: Integration With Contemporary Theory and Research on Personality and Well-Being. J Humanistic Psychol. 2023;61(1):51–83. Ryff CD. Psychological well-being revisited: advances in the science and practice of eudaimonia. Psychother Psychosom. 2014;83(1):10–28. Bell V, O'Driscoll C. The network structure of paranoia in the general population. 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Social Networks. 2010;32(3):245–51. Isvoranu AM, et al. Network Psychometrics with R: A Guide for Behavioral and Social Scientists. Taylor & Francis; 2022. Golino HF, Epskamp S. Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE. 2017;12(6):e0174035. Christensen AP, Golino H. Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial. Psych. 2021;3(3):479–500. Amireault S, Godin G. The Godin-Shephard leisure-time physical activity questionnaire: validity evidence supporting its use for classifying healthy adults into active and insufficiently active categories. Percept Mot Skills. 2015;120(2):604–22. Wongpakaran N, et al. Development and validation of a 6-item Revised UCLA Loneliness Scale (RULS-6) using Rasch analysis. Br J Health Psychol. 2020;25(2):233–56. Vaglio J Jr., et al. Testing the performance of the ENRICHD Social Support Instrument in cardiac patients. Health Qual Life Outcomes. 2004;2:24. Diener E, et al. New Measures of Well-Being. Springer Netherlands; 2009. pp. 247–66. Miller WR, Rollnick S. Motivational interviewing: helping people change . 3rd ed. 2012, New York: Guilford Press. xx, 428 p. Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38–48. Nolan RP, Sharpe MJ. A process-based approach to health-related quality of life as a way of living. Qual Life Res, 2023. Martela F, Bradshaw EL, Ryan RM. Expanding the Map of Intrinsic and Extrinsic Aspirations Using Network Analysis and Multidimensional Scaling: Examining Four New Aspirations. Front Psychol. 2019;10:2174. Sheldon KM, et al. The independent effects of goal contents and motives on well-being: it's both what you pursue and why you pursue it. Pers Soc Psychol Bull. 2004;30(4):475–86. Alexandrova A. A philosophy for the science of well-being. New York, NY: Oxford University Press; 2017. xlv, 196 pages. Sprangers MA, Schwartz CE. Integrating response shift into health-related quality of life research: a theoretical model. Soc Sci Med. 1999;48(11):1507–15. Schwartz CE, et al. If it's information, it's not bias: a scoping review and proposed nomenclature for future response-shift research. Qual Life Res. 2022;31(8):2247–57. Ritter PL, Lorig K. The English and Spanish Self-Efficacy to Manage Chronic Disease Scale measures were validated using multiple studies. J Clin Epidemiol. 2014;67(11):1265–73. Hone L, Jarden A, Schofield G. Psychometric Properties of the Flourishing Scale in a New Zealand Sample. Soc Indic Res. 2014;119(2):1031–45. Beridze G et al. Are Loneliness and Social Isolation Associated with Quality of Life in Older Adults? Insights from Northern and Southern Europe. Int J Environ Res Public Health, 2020. 17(22). Oreel TH, et al. The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis. J Clin Epidemiol. 2019;107:116–23. Nolan RP, et al. The evaluation of goal-directed activities to promote well-being and health in heart failure: EUROIA scale. J Patient Rep Outcomes. 2024;8(1):47. Additional Declarations No competing interests reported. Supplementary Files STROBEchecklistEUROIA4Jul24.doc SupplementalAppendix.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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5278979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367612367,"identity":"0a8c49d3-ce2a-4c70-a2bf-b7fdabcf8eb9","order_by":0,"name":"Nicolette Stogios","email":"","orcid":"","institution":"University of Toronto Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nicolette","middleName":"","lastName":"Stogios","suffix":""},{"id":367612368,"identity":"15a48bca-6c7b-4553-9a30-1755a70c9f6b","order_by":1,"name":"Troy Francis","email":"","orcid":"","institution":"University Health Network","correspondingAuthor":false,"prefix":"","firstName":"Troy","middleName":"","lastName":"Francis","suffix":""},{"id":367612369,"identity":"0a9b463f-fc17-4e64-a3d5-f7cbea65ea26","order_by":2,"name":"Rachel G. Peiris","email":"","orcid":"","institution":"Institute for Mental Health Policy Research, Centre for Addiction and Mental Health","correspondingAuthor":false,"prefix":"","firstName":"Rachel","middleName":"G.","lastName":"Peiris","suffix":""},{"id":367612370,"identity":"7088e61a-7071-47ad-84ab-5328d961b174","order_by":3,"name":"Aleksandra Stanimirovic","email":"","orcid":"","institution":"University Health Network","correspondingAuthor":false,"prefix":"","firstName":"Aleksandra","middleName":"","lastName":"Stanimirovic","suffix":""},{"id":367612378,"identity":"d6a8ac1d-7984-4601-9078-30fd8b4a5048","order_by":4,"name":"Valeria Rac","email":"","orcid":"","institution":"University Health Network","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Rac","suffix":""},{"id":367612380,"identity":"3611556c-9050-4a5c-afd5-cb171aed2596","order_by":5,"name":"Robert P. Nolan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYFACHhBhwcDA3sBGkhYJIH2AZC0SCURqMW/vPcBcUCEhby75xuxxAYOdPEEtMmfOJTDPOCNhuHN2jrnxDIZkwwZCWiQkcgyYedskGDfczjGT5mE4wEi0FvsNN8+AtdgTrSVxww0esJZEwlp4ziUc5jkjkbzhTFqZNI9BcjJhLey9Bx/zVNjYbjh+eJs0T4WdLUEtIHAAwTQgRv0oGAWjYBSMAoIAAJuBMOARn06PAAAAAElFTkSuQmCC","orcid":"","institution":"University of Toronto Faculty of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"P.","lastName":"Nolan","suffix":""}],"badges":[],"createdAt":"2024-10-17 02:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5278979/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5278979/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67291174,"identity":"58cc61d2-1d26-4639-9e8b-2648080aa3d4","added_by":"auto","created_at":"2024-10-23 10:14:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175623,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Structure of the EUROIA and associated HRQL indices\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/7aa1eda3df6923d6c407251f.png"},{"id":67290368,"identity":"0738ec08-93a8-4c89-b1d4-303920d400e3","added_by":"auto","created_at":"2024-10-23 10:06:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103787,"visible":true,"origin":"","legend":"\u003cp\u003eStrength centrality, closeness centrality, and betweenness centrality of each EUROIA subscale.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/636c0984ac84c2695d03a1b1.png"},{"id":67290055,"identity":"4b831657-8ac1-42b2-8b1d-0286e22fc46c","added_by":"auto","created_at":"2024-10-23 09:58:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":229842,"visible":true,"origin":"","legend":"\u003cp\u003eHRQL Network sub-community identified using exploratory graph analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/f298d5a8e5547fe91c5a8722.png"},{"id":67290369,"identity":"643b4d78-c0eb-46e3-955d-c1037a78132a","added_by":"auto","created_at":"2024-10-23 10:06:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":141317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation of sub-comnunities identified in network analysis with the theoretical model.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/59b5fd8daf3e04c42f295737.png"},{"id":67746234,"identity":"a09fd723-3cfa-41bd-9e6c-050c7aed5cb7","added_by":"auto","created_at":"2024-10-29 09:40:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1116025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/547a924d-a957-4b2c-98c6-3a0158f50cd5.pdf"},{"id":67290051,"identity":"75634925-2804-4760-8c82-309bf90221ff","added_by":"auto","created_at":"2024-10-23 09:58:06","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":84480,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistEUROIA4Jul24.doc","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/57a834e0310fd7ae3bdd0686.doc"},{"id":67290053,"identity":"4111229d-9440-4ce3-b97f-7e68d7bfc467","added_by":"auto","created_at":"2024-10-23 09:58:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":319221,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalAppendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-5278979/v1/2f33dd902bf798d45c9c594e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empirical Evidence for a Process-Based Model of Health-Related Quality of Life Using Network Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth-related quality of life (HRQL) is defined as the perceived impact of a medical condition or therapy on physical, psychological, or social domains of one\u0026rsquo;s well-being, assessed via patient-reported outcome measures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Patients living with chronic illnesses often exhibit impaired HRQL, which is assessed using single ratings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and multidimensional profiles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. There is an extensive range of HRQL instruments, with demonstrated validity and reliability, which serve as prognostic indicators for clinical outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accordingly, policy statements advocate the assessment of HRQL as a primary endpoint in the evaluation of the effects of disease progression or the benefit of clinical interventions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious work by our team identified three distinct models of well-being that are embedded in current HRQL assessments: a) eudaimonic well-being (i.e., the perception of flourishing in personal growth and happiness as sampled by self-ratings for happiness or purpose in life [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]), b) hedonic well-being (i.e., the absence of symptoms of physical discomfort or emotional distress and the presence of elevated positive feelings and life satisfaction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]), and c) desire-satisfaction (i.e., a state marked by fulfillment vs. frustration in attaining objects or experiences to which we are attracted [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]). Therefore, it is common to observe that individual HRQL assessments use an eclectic mix of items that reflect multiple dimensions of well-being across bio-psycho-social life domains. At the same time, these assessments are not necessarily designed to evaluate how an individual\u0026rsquo;s self-reported HRQL state compares with a theoretically coherent model of well-being.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe process-based model for living well.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn contrast to contemporary assessments that aim to evaluate an individual\u0026rsquo;s HRQL state, Nolan and Sharpe (2023) introduced a process-based model which is an appraisal that evolves over time as it reflects and informs a self-regulatory process of adapting to dynamic changes in health status [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates key features of the process-based model of HRQL. Briefly, HRQL appraisals express the perceived impact of an illness or clinical treatment on one\u0026rsquo;s well-being at a given point in time. These appraisals are associated with new or re-newed priorities for living well, which are expressed in terms of life goals or aspirations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In turn, one\u0026rsquo;s aspirations evoke goal-directed activities that affect changes in bio-psycho-social life domains. Performance-based feedback from these activities is reviewed in terms of efficacy appraisals and outcome expectations. These then influence our re-appraisal of HRQL and re-evaluation of HRQL priorities. This iterative process is self-regulatory in nature. We adapt to life events across bio-pscyho-social domains while appraising our progress in fulfilling our aspirations for living well, which informs our recalibration of HRQL priorities and engagement in subsequent goal-directed activities.\u003c/p\u003e \u003cp\u003eThe conceptualization of a process-based approach is based on four considerations. First, qualitative research with individuals diagnosed with a chronic progressive medical condition has shown that self-reported HRQL is part of a dynamic process of adapting to acute changes in their health status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These changes are often unanticipated or recurrent, which can, in cases of progressive illness, evoke a sense of existential threat that challenges the individual to learn how to accept a life that feels fundamentally tenuous.\u003c/p\u003e \u003cp\u003eSecond, current assessments evaluate an individual\u0026rsquo;s HRQL profile against a state of idealized well-being where ratings for items are anchored by optimal levels of personal flourishing (eudaimonia), pleasure and satisfaction (hedonia), or fulfillment of desire. It is challenging to see how these ideals for well-being provide a meaningful standard of comparison for evaluating HRQL in individuals with pathophysiologic conditions that involve chronic impairment or suffering (e.g. chronic pain), or progressive functional limitations with premature morbidity and mortality (e.g. heart failure). Instead, the process-based approach to HRQL utilizes a concept from ancient Stoic philosophy in which the goal of attaining eudaimonic well-being (i.e. personal flourishing, mastery, or excellence) was reconceptualized by the goal of learning to live well with adversities or positive events in daily life \u0026mdash;i.e., \u0026ldquo;good flow of life\u0026rdquo; (\u003cem\u003eeuroia biou\u003c/em\u003e). HRQL was presented as a process of responding to life events in a manner that affirmed our personal agency and dignity, as well as our ability to connect with our environment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, systematic reviews and meta-analyses have established that a response shift is commonly observed in the way that individuals respond over time to HRQL assessments. This shift is expressed as (i) recalibrating how self-rating scales are used when gauging the severity of a given symptom or function, (ii) reprioritizing different features of HRQL to better reflect new insights or experiences about living well that have become personally salient, or (iii) reconceptualizing their understanding of the HRQL construct to reflect their evolving priorities for living well [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This evidence indicates that the HRQL construct may be routinely undergoing change in its content and structure, which suggests that it may reflect an ongoing adaptive process.\u003c/p\u003e \u003cp\u003eThe last point of consideration is the structure of the well-being construct that is embedded in HRQL assessments. From an empirical and theoretical standpoint, there is consensus that well-being is a multi-dimensional construct. At the same time, there are diverse accounts of its hierarchical structure, even though the core components are essentially the same across studies. The components usually include hedonic, eudaimonic, and social indices of well-being [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In more recent years, proposals have been made for empirically integrating these separate models into a single unified structure [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Gallagher et al. (2009) asserts that well-being is integrated within a primary hierarchical structure that contains three second-order latent factors of hedonic, eudaimonic and social dimensions of well-being [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Other studies have also reported a primary hierarchical structure, though the specific components of well-being have varied. Bj\u0026oslash;rndal et al. 2023 proposed a structure where six distinct factors of well-being loaded onto a single higher-order factor that may represent a general index of happiness [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. On the other hand, Linton et al. (2016) reported that the dimensions of well-being clustered around 6 core themes, but without a primary hierarchical model [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, Ruggeri et al. (2020) reported that 10 dimensions of well-being identified in their population survey could not be meaningfully aggregated into a single composite index of happiness or life-satisfaction, although they acknowledged that composite scores can be useful for summarizing change over time and for capturing its variation in social circumstances on a macro-level [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Finally, Van Woerkom et al. (2022) conducted a network analysis which showed that the nodes (or areas of interaction in their communities of variables) did not passively reflect a higher order causal agent of well-being or HRQL. Rather, the nodes were viewed as active agents in a causal system in which well-being was an emergent property of the interactions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the diversity of state-based models for the structure of well-being that have emerged in the literature, two main interpretations of the findings are possible. Either only one of the proposed models on the structure of well-being/HRQL is correct, or each may present a profile that is valid. The latter case would suggest that the structure of HRQL/well-being is not fixed, and rather that it is dynamic and changes over time. We suggest that the reported structure of well-being would be expected to fluctuate over time according to an individual\u0026rsquo;s ongoing adaptation to dynamic changes in bio-psycho-social domains of their personal environment. Arguably, it is a priority to evaluate the potential validity of this account.\u003c/p\u003e \u003cp\u003eOur team recently introduced a novel HRQL assessment, the EUROIA: \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eE\u003c/span\u003eval\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eU\u003c/span\u003eation of goal-di\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eR\u003c/span\u003eected activities to pr\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eO\u003c/span\u003emote well-be\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eI\u003c/span\u003eng and he\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eA\u003c/span\u003elth. Psychometric properties of the EUROIA were previously described in terms of reliability, content validity, and clinical utility [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This scale uses self-report data to assess the frequency with which individuals engage in a sample of goal-directed activities that are identified with their pursuit of living well (well-being). These goal-directed activities represent a key feature of our process-based model of HRQL. In this study, we conducted a network analysis to evaluate whether EUROIA subscales would demonstrate a meaningful pattern of associations with established HRQL measures and with associated indices of psychosocial functioning and efficacy in self-managing a chronic medical condition.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Data Sources\u003c/h2\u003e \u003cp\u003eThis investigation was a sub-study of the Open Access Digital Community Promoting Self-Care, Peer Support, and Health Literacy \u0026ndash; A Virtual Community Promoting Mental Health, Psychosocial Adjustment, and Peer Support (ODYSSEE-vCHAT) projects: a single group, open label, pre-post study [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] and a double-arm, parallel group, randomized controlled trial [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The ODYSSEE-vCHAT [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] study recruited participants aged 18 years and older with a proficiency in English and a diagnosis of chronic heart failure (CHF; NYHA Class II to IV for at least 3 months prior to enrolment) or advanced chronic kidney disease (CKD; greater than 10% risk of requiring dialysis within 2 years or end-stage renal disease and receiving dialysis). Complete study details can be found elsewhere [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The present network analysis was based on complete case data from the baseline assessments of the ODYSSEE-vCHAT study and trial.\u003c/p\u003e \u003cp\u003eThe sample estimate for the ODYSSEE-vCHAT study accounted for changes in the Mental Component Summary (MCS) of the Short Form 36 (SF-36) Health Survey [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] over a period of 3 to 12 months, while the sample estimate for the ODYSSEE-vCHAT trial was based on change over 12 months in a composite index of all-cause mortality and hospitalization. The trial was estimated to have a sample size \u003cem\u003eof N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;162, while the study had a sample size \u003cem\u003eof N\u003c/em\u003e\u0026thinsp;=\u0026thinsp;188, which included oversampling. Both the study and trial were adjusted for a potential 12-month withdrawal or attrition rate of 14.7%, a type 1 error of 5%, and a power of 80% [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe EUROIA measure includes dimensions related to eudaimonic well-being (EUD-WB), self-affirmation (SLF-AFF), social affiliation (SOC-AFF), and social roles and responsibilities (SOC-RR). \u003cem\u003eEudaimonia\u003c/em\u003e is composed of behaviors that promote flourishing and self-actualization aimed at personal growth, connection to a greater purpose, and life satisfaction [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. \u003cem\u003eSOC-AFF\u003c/em\u003e is comprised of behaviors such as participation in social activities, maintaining close relationships, and helping others. \u003cem\u003eSOC-RR\u003c/em\u003e assess behaviors related to maintaining roles and obligations to significant others and being productive in a work-related setting. \u003cem\u003eSLF-AFF\u003c/em\u003e focuses on behaviors related to physical activity and exercise and includes activities that promote feelings of being healthy and attractive and maintaining positive affect. Established indices of HRQL used to validate the EUROIA subscales are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eHRQL scales used in network analysis.\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\u003eMeasure of HRQL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\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\u003eShort Form 36 (SF-36) Health Survey Mental Component Summary (MCS)\u003c/b\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA questionnaire of 36 items that assess patients\u0026rsquo; health status and its impact on their lives. Consists of various multi-item scales: Role Limitation due to Emotional Problems (RE), Emotional Well-Being (EWB), Vitality (VT), and Social Functioning (SF).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRevised UCLA Loneliness Scale (RULS-6)\u003c/b\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA short version of the 20-item scale measuring subjective feelings of loneliness and social isolation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlourishing Scale (FS)\u003c/b\u003e [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn 8-item summary measure of self-perceived success in areas such as relationships, self-esteem, purpose, and optimism.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-Efficacy for Managing Chronic Diseases 6-item Scale (SEMCD-6)\u003c/b\u003e [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 6-item scale that covers several common domains across many chronic diseases, including symptom control, role function, emotional functioning and communicating with physicians.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eENRICHD Social Support Index (ESSI)\u003c/b\u003e [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 7-item self-report questionnaire to assess social support.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGodin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ)\u003c/b\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA 4-item questionnaire used to assess leisure-time physical activity (any leisurely activity undertaken by the individual that increases their total energy expenditure).\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\u003eNetwork Analysis\u003c/h3\u003e\n\u003cp\u003eNetwork analysis was used to complement and extend the results of the EUROIA and its association with established HRQL indices, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Graphical least absolute shrinkage and selection operator (glasso) were performed to estimate the network structure of the EUROIA and HRQL indices using the extended Bayesian information criterion (EBIC). A polychloric correlation matrix was computed, which provided the foundation of the network. This allowed for the examination of partial correlations between each subscale while controlling for all the other variables in the network [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. We constructed a network wherein each subscale was represented as a node and the partial correlations between the items as edges. Networks were displayed using a Fruchterman-Reingold algorithm [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] whereby nodes with stronger connections are placed at the center of the network and weaker connections more peripherally. Analyses were performed using R software v4.1.0 [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], qgraph [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], glasso [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], bootnet [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], psych [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and igraph [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eNetwork Centrality Measures\u003c/h3\u003e\n\u003cp\u003eCentrality refers to several metrics that determine a node's relative importance compared to other nodes in the network [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Strength centrality reflects a node being central through having strong connections to other nodes based on the absolute sum of the weighted number and strength of its connections relative to all other nodes. Betweenness centrality determines how important a node is in connecting other nodes by identifying the frequency with which a node lies on the shortest path between two other nodes. Closeness centrality measures the distance between nodes and quantifies the node\u0026rsquo;s relationship to all other nodes in the network [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Bootstrapping with 2500 permutations was done to assess the stability of the centrality metrics. The generated stability coefficients demonstrated values greater than 0.5, indicating high reliability, while values of 0.25 indicated the minimum evidence. We used the Correlation Stability coefficient (CS-coefficient) for correlation values equal to or above r\u0026thinsp;=\u0026thinsp;0.7 to measure the stability of centrality indices [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The CS-coefficient indicates the percentage of our sample that can be dropped to maintain, with a 95% confidence interval, correlation values equal to or above r\u0026thinsp;=\u0026thinsp;0.7 between our sample's centrality indices and our bootstrapped samples' centrality indices. Non-parametric bootstrap (resampling rows with replacement) was employed to create 1000 samples to estimate edge weight stability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eNetwork Sub-Community Identification\u003c/h3\u003e\n\u003cp\u003eNetwork sub-communities or clusters were identified through exploratory graph analysis (EGA), which uses random walk algorithms to identify dimensions in psychometric data [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Clusters seek to identify areas in the network with nodes have many connections within, and few connections between, clusters [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The walktrap method was selected as communities identified using this method are shown to be consistent with latent factors of factor models [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Item stability statistics were calculated to evaluate the strength of the placement of each item within the derived dimensions. The proportion of times that each item is placed in each dimension was calculated to estimate item stability. This calculation is useful in identifying which items contribute to the consistency of the structure of the dimensions by frequently replicating in the same dimension, and which items lead to inconsistency by frequently replicating in other dimensions. Variables at or below the range of 0.65 to 0.75 are considered to be less stable [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 276 participants with complete baseline data were included in the network analysis. Patients included in the sample had a primary diagnosis of either CHF (50.5%) or CKD (49.5%). The mean age was 56.6 years (range: 20.0 to 91.0, SD\u0026thinsp;=\u0026thinsp;16.0) and 59.6% were male. The majority of the sample (65.0%) had completed a college or university degree and another 23.0% completed graduate or post-graduate degrees. Respondents were primarily identified as having a White or Caucasian racial background (58.2%), living with a partner (59.1%), and had a combined household income \u0026ge; \u003cspan\u003e$\u003c/span\u003e100 000 CAD.\u003c/p\u003e\n\u003ch3\u003eNetwork Analysis\u003c/h3\u003e\n\u003cp\u003eThe glasso network of the EUROIA and HRQL indices is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The network was comprised of 13 nodes, 40 edges (31 positive and 9 negative) out of a possible 78 connections, a mean edge weight of 0.039, and a network density of 0.51 (51% of nodes were directly connected). A green connection indicates a positive link while red connections signify negative links. The width (thickness) of the connection depicts the strength of the partial correlations. Network centrality values are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. MCS-Emotional Wellbeing (EWB) was shown to be the most influential node in the network, with a strength centrality of 1.64. The Godin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] demonstrated the weakest centrality (-2.08). The Revised UCLA Loneliness Scale (RULS-6) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] (1.75) and ENRICHD Social Support Index (ESSI) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] (1.42) exhibited the highest betweenness centralities and were shown to mediate the connections between other nodes, primarily the EUROIA and MCS subscales. Additionally, the RULS-6, MCS-EWB, and ESSI were the most closely connected subscales. The EUROIA subscales were linked to MCS subscales via positive connections with social support (ESSI) and psychological flourishing (Flourishing Scale (FS) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]), and through an inverse association with loneliness (RULS-6). Additional pathways to the MCS involved self-efficacy and leisure activity (GSLTPAQ).\u003c/p\u003e\n\u003ch3\u003eNetwork Accuracy and Stability\u003c/h3\u003e\n\u003cp\u003eThe tests for network accuracy and stability are presented in the online supplementary materials (S2-S3). Bootstrapping was used to compute robust centrality estimates represented as correlation stability coefficients. The correlation stability coefficients were 0.28 (ranging from 0.21 to 0.36) for betweenness, 0.44 (ranging from 0.36 to 0.52) for closeness, and 0.67 (ranging from 0.59 to 0.75) for strength. The minimum 0.25 cut-off was met, indicating that the centrality metrics can be interpreted with confidence and the network can be considered reliable [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Detection\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the glasso network map with the EGA sub-communities highlighted. Notably, three sub-communities were identified, of which two matched \u003cem\u003ea priori\u003c/em\u003e theme groupings reflecting the EUROIA scale (SOC-AFF, SOC-RR, EUD-WB, SLF-AFF) and the MCS Role Limitation due to Emotional Problems (RE), Emotional Well-Being (EWB), Vitality (VT), and Social Functioning (SF) subscales [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The themes identified EUROIA and leisure activities, MCS and self-efficacy, and social support, loneliness, and flourishing. Item stability statistics (\u003cb\u003eFig S4\u003c/b\u003e) for each dimension were close to 1, demonstrating the robustness of the communities estimated using EGA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this empirical study, we present a process-based, meta-theoretical model of HRQL that complements established measures of an HRQL state, while also introducing a novel dynamic feature of this construct. Using network analysis, we demonstrate that the EUROIA, an assessment of goal-directed activities for living well, is associated with established indices of HRQL in a manner that is theoretically consistent with our process-based model. More specifically, EUROIA subscales were linked to components of HRQL as measured by MCS subscales, via social support, psychological flourishing, and through an inverse association with loneliness. Additional pathways to the MCS involved self-efficacy and leisure activity. Importantly, stability coefficients (i.e., betweenness, closeness, and strength) of the analysis revealed high reliability for the network.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeta-Theoretical Underpinnings of a Process-Based Approach to HRQL\u003c/h2\u003e \u003cp\u003eThe process-based approach presented by Nolan and Sharpe (2023) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is best defined as a meta-theoretical model informed by relevant theories of behaviour change, including Self-Determination Theory [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], Motivational Interviewing [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], the Transtheortical Model of Health Behaviour Change [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], and the social-cognitive theory of agentic change [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our model posits the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHRQL is an appraisal that evolves as it reflects our effort to adapt to dynamic changes in health status [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and to challenges or facilitating factors in our biopsychosocial environment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHRQL appraisals are associated with new or revised aspirations that are personally salient and meaningful [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThese aspirations subsequently inform and evoke goal-directed activities for living well, which are expressed in our self-regulated or self-determined effort to promote change associated with improved well-being [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePerformance-based feedback from these goal-directed activities is reviewed and expressed as efficacy and outcome expectations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; efficacy expectations are organized along a continuum that ranges from intrinsic to extrinsic sources of motivation/causality, and this locus of causality influences the degree to which a goal-directed activity for living well may be sustained [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe evaluation of outcomes from our goal-direced activities, in turn, influences our re-appraisal of HRQL, which carries forward in this complex system to re-shape subsequent aspirations and goal-directed actions that ultimately promote our adaptation across bio-psycho-social life domains.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe EUROIA is an instrument that evaluates a key component of a process-based model of HRQL/well-being and, as such, has the potential to contribute meaningfully to recent theoretical/philosophical and empirical initiatives in this area. For example, use of the EUROIA in applied research complements the call for \u0026ldquo;mid-level theories\u0026rdquo; of well-being, as expressed by Alexandrova [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. She has asserted that abstract (normative) theories of HRQL or well-being (e.g., eudaimonic and hedonic) are only obscurely associated with real-life sitatuations, and fail to promote our understanding of how a specific individual might appraise well-being in a given life circumstance. In keeping with a process-based model, the EUROIA offers a mid-level approach to identifying prototypical categories of goal-directed activities through which individuals pursue their aspirations for living well within the parameters of daily life. Both qualitative and quantitative studies of these self-reported goal-directed activities should provide novel information about how HRQL appraisals are \u003cem\u003erelativized\u003c/em\u003e to an individual\u0026rsquo;s daily life and their pursuit of well-being [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, our use of the EUROIA within a process-based approach to HRQL and well-being builds on the concept of response shift that was coined by Sprangers and Schwartz (1999). Their research highlights that individuals living with a chronic pathophysiologic condition or illness are inclined to change their internal standards, values, and personal conceptualization of HRQL [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. In response, it is challenging on theoretical grounds for state-based models of HRQL assessment to assimilate findings of response shift. Assessments used in that approach are premised on the theory that items, subscales, and summary scores depicting an individual\u0026rsquo;s HRQL state are organized according to a fixed psychometric structure, and dynamic changes in the organization of an individual\u0026rsquo;s self-reported HRQL profile are attributed to \u0026ldquo;noise\u0026rdquo; or error variance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Moreover, these assessments evaluate an individual\u0026rsquo;s HRQL profile according to its departure from a score that represents an idealized state whereby one has abundant energy and emotional tranquility all or most of the time, there are no symptoms of emotional stress or physical discomfort, there is no limitation on activities of daily living or social or work activities, and one\u0026rsquo;s health is viewed as being excellent at present and into the future: c.f. SF-36 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. It is unclear how this clinical standard for evaluating HRQL or well-being is relevant to the common experience of individuals who are interacting with natural challenges and demands of life that naturally evolve over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEmpirical Evidence from the EUROIA to Support a Process-Based Approach to HRQL\u003c/h2\u003e \u003cp\u003eThe EUORIA provides a multi-dimensional profile of prototypical categories of goal-directed activities for living well that we have described previously [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Categories that have emerged to date are identified by the following EUROIA subscales: eudaimonic well-being (EUD-WB), self-affirmation (SLF-AFF), social affiliation (SOC-AFF), and social roles and responsibilities (SOC-RR). The network analysis presented in this study demonstrated that the EUROIA subscales were associated with the MCS community of variables (RE, EWB, VT, SF subscales) via pathways that were consistent with our theoretical model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Indices such as the GSLTAS [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], FS [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], ESSI [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and RULS6 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] reflect psychosocial interactions or stressors in one\u0026rsquo;s bio-psycho-social environment, and the Self-Efficacy for Managing Chronic Diseases 6-item Scale (SEMCD-6) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] reflects perceived efficacy in self-managing one\u0026rsquo;s medical condition. The directionality of the associations between the EUROIA and the MCS via the intermediary communities of variables noted above was aligned with our expectations. Higher scores on the ESSI, FS, GSLTPAQ, and SEMCD-6 were associated with greater HRQL [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], while a higher score for loneliness (RULS6) was associated with decreased HRQL [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Considerations and Limitations\u003c/h2\u003e \u003cp\u003eThis study utilized network analysis to re-examine data and variables that were previously identified using latent variable methods [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The subcommunities we identified for the EUROIA closely match the latent variables identified in Francis, Peiris et al.. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which provides confirmatory evidence for its general underlying structure. Nevertheless, there remain a number of limitations to the present study. Our use of network analysis was exploratory and should be treated with caution as the connections between nodes cannot be treated as true causal relationships and may be impacted by unobserved factors. Our subset of data was cross-sectional and does not reflect the dynamic nature of the networks. To improve our confidence in the underlying structure of the EUROIA and our process-based approach to HRQL, this study must be replicated using longitudinal time series analyses with an adequate sample size. Despite these limitations, we believe this study makes a strong contribution to the development of the EUROIA instrument and brings credence to our meta-theoretical approach to HRQL. It should also be noted that our previous work with the EUROIA included a subscale that assesses the personal salience or meaning attributed to each goal-directed activity [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. A priority for future studies with the EUROIA is to re-introduce subscales where each item is self-rated according to its frequency (F), perceived priority/importance (P), and F*P cross-product.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study presents a process-based model of HRQL that highlights the dynamic nature of this construct. Use of the EUROIA in applied research represents a mid-level theoretical strategy that contributes to current efforts to clarify how HRQL appraisals are applied in daily efforts to maintain or improve our personal well-being. Moreover, the EUROIA introduces a preliminary summary of prototypical categories of goal-directed activities associated with HRQL/well-being. The process-based approach to HRQL assessment, which is partially represented by the EUROIA, complements and extends conventional measures by addressing how this patient-reported appraisal fits within a complex system of self-determined or self-regulated adaptation to one\u0026rsquo;s medical condition.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003e The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. This study only involved secondary analyses of anonymized data from the ODYSSEE v-CHAT study; therefore, additional ethics approval was not required. Informed consent was obtained from all individuals prior to participation in the study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe following grants supported the trials reported in the manuscript:\u003c/p\u003e\u003cul\u003e\n \u003cli\u003eCanadian Institutes of Health Research (CIHR) Grant # PJT173222\u003c/li\u003e\n \u003cli\u003eCanadian Institutes of Health Research (CIHR) Grant # MS2173076\u003c/li\u003e\n\u003c/ul\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception, design, or writing of this paper. NS and TF analyzed and interpreted the patient data included in this study. RPG and AS contributed to the writing and editing of this paper. VR provided statistical expertise and oversight related to the network analysis. RPN provided expertise related to the theoretical underpinnings of the process-based model to health-related quality of life. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are grateful to the following individuals who assisted with proofreading and formatting of successive versions of this manuscript: Karly Gunson, HBA, BHSc; Jiesi Zhang; and Grace Taylor Armstrong.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData from this work are available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Strengthening mental health (Fact sheet, No. 220). Geneva: World Health Organization; 2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin S, et al. Summarizing health-related quality of life (HRQOL): development and testing of a one-factor model. Popul Health Metr. 2016;14:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWare JE Jr., Sherbourne CD. 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Int J Environ Res Public Health, 2020. 17(22).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOreel TH, et al. The dynamics in health-related quality of life of patients with stable coronary artery disease were revealed: a network analysis. J Clin Epidemiol. 2019;107:116\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNolan RP, et al. The evaluation of goal-directed activities to promote well-being and health in heart failure: EUROIA scale. J Patient Rep Outcomes. 2024;8(1):47.\u003c/span\u003e\u003c/li\u003e\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":"Health-Related Quality of Life, Network Analysis, Patient-Reported Outcomes, Process-Based Approach, Chronic Heart Failure, Chronic Kidney Disease","lastPublishedDoi":"10.21203/rs.3.rs-5278979/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5278979/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHealth-related quality of life (HRQL) is the perceived impact of a medical condition on one\u0026rsquo;s overall well-being. While contemporary assessments are structured to evaluate an individual\u0026rsquo;s HRQL state, we propose a complementary process-based model, which is defined as an appraisal that evolves over time as it reflects and informs a self-regulatory process of adapting to dynamic changes in bio-psycho-social life domains. In support of this approach, we developed a novel HRQL assessment tool called the EUROIA: \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eE\u003c/span\u003eval\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eU\u003c/span\u003eation of goal-di\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eR\u003c/span\u003eected activities to pr\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eO\u003c/span\u003emote well-be\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eI\u003c/span\u003eng and he\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eA\u003c/span\u003elth, which uses self-report data to assess the frequency with which individuals engage in a sample of goal-directed activities in pursuit of living well.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a network analysis to evaluate the hypothesis that the EUROIA subscales would demonstrate a meaningful pattern of associations with an established HRQL measure and associated indices of psychosocial functioning and efficacy in self-managing a chronic medical condition.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe EUROIA is associated with established indices of HRQL in a manner that is theoretically consistent with our process-based model. Stability coefficients (i.e., betweenness, closeness, and strength) of the analysis revealed high reliability for the network.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis analysis provides support for the validation of a process-based approach to HRQL assessment, which is represented, in part, by the EUROIA. A process-based approach complements and expands conventional measures of HRQL by focusing on how a patient\u0026rsquo;s capacity to engage in goal-directed activities for living well is affected by their medical condition.\u003c/p\u003e","manuscriptTitle":"Empirical Evidence for a Process-Based Model of Health-Related Quality of Life Using Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 09:58:01","doi":"10.21203/rs.3.rs-5278979/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":"0d71c63c-df78-4597-8165-aab1a0ce7d2c","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-29T09:39:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 09:58:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5278979","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5278979","identity":"rs-5278979","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}