Background: There is an observable, growing trend toward tailoring support programs – in addition to medical treatment – more closely to individuals to help improve patients’ health status. The segmentation model developed by Bloem & Stalpers [Nyenrode Research Papers Series 12:1–22, 2012] may serve as a solid basis for such an approach. The model is focused on individuals’ ‘health experience’ and is therefore a ‘cross-disease’ model. The model is based on the main psychological determinants of subjective health: acceptance and perceived control. The model identifies four segments of health-care consumers, based on high or low values on these determinants. The goal of the present study is twofold: the identification of criteria for differentiating between segments, and profiling of the segments in terms of socio-demographic and socio-economic variables. Methods: The data (acceptance, perceived control, socio-economic, and socio-demographic variables) for this study were obtained by using an online survey (a questionnaire design), that was given (random sample N = 2500) to a large panel of Dutch citizens. The final sample consisted of 2465 participants – age distribution and education level distribution in the sample were similar to those in the Dutch population; there was an overrepresentation of females. To analyze the data factor analyses, reliability tests, descriptive statistics and t-tests were used. Results: Cut-off scores, criteria to differentiate between the segments, were defined as the medians of the distributions of control and acceptance. Based on the outcomes, unique profiles have been formed for the four segments: 1. ‘Importance of self-management’ – relatively young, high social class, support programs: high-quality information. 2. ‘Importance of personal control’ – relatively old, living in rural areas, high in homeownership; supportive programs: developing personal control skills. 3. ‘Importance of acceptance’ – relatively young male; supportive programs: help by physicians and nurses. 4. ‘Importance of perspective and direction’ – female, low social class, receiving informal care; support programs: counseling and personal care. Conclusions: The profiles describe four segments of individuals/patients that are clearly distinct from each other, each with its own description. The enriched descriptions provide a better basis for the allocation and developing of supportive programs and interventions across individuals.

Vitality and health-related quality of life are often assessed in older adults. However, these assessments do not provide guidance on support for older adults with different levels of vitality and health-related quality of life. This guidance can be established through segmentation. The Subjective Health Experience model segments individuals and indicates support for each segment. By examining how older adults with different levels of vitality and health-related quality of life correspond with each segment and by specifying the indicated support to older adults, guidance can be established. This was examined by administering a questionnaire to 904 older adults and interviewing 8. Analysis was performed using one-way ANOVA and the matrix method. In segment 1, older adults sustained higher levels of vitality and health-related quality of life relative to other segments. They need information and certainty. In segment 2, older adults sustained lower levels of vitality and health-related quality of life relative to segment 1, and higher levels relative to segment 3 or 4. They need planning and structure. In segment 3, older adults sustained lower levels of vitality and health-related quality of life relative to segment 1 or 2, and higher levels relative to segment 4. They need emotive assistance. In segment 4, older adults sustained lower levels of vitality and health-related quality of life relative to other segments. They need personal coaching. As levels of vitality and health-related quality of life correspond with the segments, deploying vitality and health-related quality of life measures together with the model might be beneficial.

Background: The Netherlands faces 60% prevalence of chronic conditions by 2040, impacting societal participation and quality of life. Current clinical care inadequately addresses these consequences, and most hospitals do not integrate occupational health in their care. Objectives: To develop a generic person- and work-oriented medical care model (WMCM) based on real life experiences with work-oriented care and supporting the chronically ill in active societal participation. Methods: A qualitative research project with a participative approach in one hospital (November 2019 until March 2020). In an expert meeting, a schematic representation of a work-oriented care model was developed. Subsequent discussion rounds, with professionals from different patient groups, iteratively refined the model to a WMCM. Results: Consensus was reached after seven rounds of discussion, defining the model’s core elements (1) a combination of biomedical and biopsychosocial approaches, (2) involvement of a clinical occupational physician in the treatment team, (3) a coordinating role for nursing specialists, and (4) incorporation of a work-oriented intervention plan (WoIP) into the treatment plan. Advocating early attention to societal participation, the model emphasises the WoIP and consensus on monitoring indicators. The final goal is a sustainable return to societal participation, considering both quality of life and work. Conclusion: It is feasible to develop a generic person- and work-oriented care model for patients with chronic illness within a hospital care setting. Collaboration between healthcare professionals and a specialised occupational physician, with a central role for nurses, is deemed crucial.

Background/Objectives: The multifactorial nature of immune-mediated inflammatory diseases (IMIDs) requires integrating pathophysiological understanding with subjective patient experiences. Patient-reported outcome measures (PROMs) are a useful tool for incorporating this in routine clinical practice. However, current PROMs do not fully encompass the complete subjective health experiences (SHE) of patients and are thus insufficient for guiding truly personalised care. The SHE model provides insights into SHE through the determinants of disease acceptance and perceived control. While validated across various demographics, its predictive power in IMIDs cohorts remains unexplored. This study aims to assess whether acceptance and perceived control in the SHE model predict health experiences in patients with IMID and how these immunological conditions compare. Methods: Questionnaires regarding health perception, acceptance, control, and subjective health experiences were distributed to 450 Dutch citizens. Descriptive statistics, reliability checks, and partial least squares structural equation modelling were used to examine relationships between variables. Results: Health perception strongly predicts SHE through acceptance and control. Across all conditions, the pathway moves from health perception to control, then to acceptance, and finally to SHE. However, the roles of acceptance and control differ by condition. In burdensome diseases like inflammatory bowel disease and rheumatoid arthritis, acceptance plays a greater role, while control has a stronger influence in conditions like psoriasis. Conclusions: This study supports the predictive validity of the SHE model for IMIDs, showing that disease acceptance and control affect health experiences differently across conditions. These insights improve the understanding of psychological factors in health experiences and call for tailored interventions for patients with IMID.

Background and Aims: The patient segmentation model based on disease acceptance and perceived control may guide personalized care in inflammatory bowel disease (IBD). We aimed to investigate the external validity of the segmentation model and its consistency over time. Methods: This is a multicenter longitudinal cohort study of adult IBD patients with questionnaires on disease acceptance and perceived control (6-items, 7-point Likert scale) and health-related quality of life (HRQoL) (Short IBD questionnaire, range 10-70). Segments were created based on mean scores (cut-off>5): (I) high acceptance, high control; (II) high acceptance, low control; (III) low acceptance, high control and; (IV) low acceptance, low control. Results: The external validation cohort included 921 IBD patients. The acceptance and control scale were unidimensional and internally consistent. Segments differed significantly in gender, disease duration, IBD medication and clinical disease activity. High acceptance and/or high control were significantly associated with a higher HRQoL compared with low acceptance and low control (i.e., segment IV) (Beta (95%CI) segment I=11.7 (10.4-13.1), segment II=9.3 (7.7-10.9) and segment III=3.8 (1.6-6.0), p≤0.001). The follow-up cohort included 783 patients: 58% remained in the same segment while 42% differed in segment over time. Changes in segment were positively correlated with changes in HRQoL over time (Spearman rho 0.38, p<0.001). Conclusions: The patient segmentation model based on disease acceptance and perceived control was externally valid and showed consistency over time. The different segments were independently associated with HRQoL. Future interventions should aim to personalize care based on segments and improve disease acceptance and perceived control of IBD patients.