1. Abstract
The Self Management Screening (commonly known by its validated acronym SeMaS) is a multidimensional, patient-reported screening questionnaire specifically engineered to evaluate an individual's readiness, capability, and potential barriers regarding chronic disease self-management in primary care environments. Conceptualized and validated by Noortje Eikelenboom and colleagues (2013, 2015) at the Scientific Institute for Quality of Healthcare (IQ healthcare), Radboud University Medical Center, the instrument consists of 27 standardized items. These items map across three overarching functional domains: psychological factors, functional skills, and social environment. More granularly, the 27 items assess eight discrete psychological and behavioral constructs: perceived burden of disease, locus of control, self-efficacy, coping style, anxiety, depression, social support, and technological, communicative, and self-care skills (including computer literacy and group participation skills).
Administered primarily via electronic digital health interfaces or paper-and-pencil formats prior to routine primary care nursing consultations, the instrument utilizes balanced Likert-type response scales alongside categorical skill screening indicators. The resulting assessment generates an intuitive visual profile wherein construct scores are graphically rendered as variable-sized circles or color-coded dials representing barriers, facilitators, and overall self-management capability. Psychometric investigations across diverse cohorts of patients diagnosed with type 2 diabetes, chronic obstructive pulmonary disease (COPD), and cardiovascular disease establish satisfactory internal consistency across multi-item scales (Cronbach's α ranging between .65 and .86), solid construct validity via confirmatory factor analytic models, and robust criterion-related concurrent validity against established psychometric gold standards (including the PHQ-9, GAD-7, and General Self-Efficacy Scale). By identifying specific behavioral and psychosocial impediments before self-management interventions are prescribed, the Self Management Screening tool functions as an essential diagnostic bridge toward highly tailored, cost-effective, and individualized chronic disease care plans.
2. Keywords
Self Management Screening, SeMaS, chronic illness self-management, primary care nursing, patient-reported outcome measures, self-efficacy, locus of control, disease burden, health behavior change, personalized healthcare
3. Authors
The Self Management Screening instrument was developed and validated by a multidisciplinary team of health services researchers, general practitioners, and clinical implementation specialists affiliated with the Scientific Institute for Quality of Healthcare (IQ healthcare) at Radboud University Medical Center, Nijmegen, the Netherlands:
- Noortje Eikelenboom, PhD — Radboud University Medical Center, Radboud Institute for Health Sciences, IQ healthcare, Nijmegen, Netherlands. Research expertise in primary care innovations, patient empowerment, and chronic care management.
- Jan van Lieshout, MD, PhD — Radboud University Medical Center, Radboud Institute for Health Sciences, IQ healthcare, Nijmegen, Netherlands. Senior researcher and general practice implementation specialist.
- Michel Wensing, PhD — Radboud University Medical Center, IQ healthcare, Nijmegen, Netherlands; currently Professor of Health Services Research and Implementation Science at Heidelberg University Hospital, Heidelberg, Germany.
- Ivo Smeele, MD, PhD — Primary care physician and senior clinical researcher in chronic obstructive pulmonary disease and family medicine, Radboud University Medical Center, Nijmegen, Netherlands.
- Anja E. Jacobs, PhD — Radboud University Medical Center, Radboud Institute for Health Sciences, IQ healthcare, Nijmegen, Netherlands. Specialist in behavioral interventions and chronic disease quality indicators.
4. Purpose
Modern chronic illness management paradigms emphasize that effective treatment requires patients to take active, daily responsibility for medication adherence, lifestyle adjustments, symptom monitoring, and emotional regulation. However, standard healthcare delivery models historically adopted a one-size-fits-all approach to self-management support, offering uniform disease-education programs or digital self-tracking tools irrespective of individual readiness, cognitive resources, or psychosocial barriers. Consequently, intervention failure rates remained high, with disadvantaged or highly burdened patients experiencing elevated rates of program attrition, non-adherence, and psychological distress.
The primary purpose of the Self Management Screening (SeMaS) instrument is to systematically identify an individual patient's capability for self-management and diagnose specific personal, psychological, and environmental impediments prior to clinical goal-setting. Designed specifically for integration into routine general practice consultations led by practice nurses (praktijkondersteuners) and primary care physicians, SeMaS serves as a direct clinical decision support instrument. By administering the 27-item questionnaire prior to a consultation, clinicians avoid relying on subjective, often inaccurate provider-level estimations of patient compliance or emotional stability.
In clinical practice, the tool fulfills three specific translational functions:
- Baseline Stratification and Diagnostic Triage: Distinguishing patients who possess high internal resources (capable of fully autonomous self-management, telemonitoring, or digital e-health solutions) from those who demonstrate profound barriers (e.g., severe depressive affect, high perceived illness burden, external locus of control), who instead require intensive nurse-led supportive coaching, psychological counseling, or foundational self-care training.
- Facilitating Shared Decision-Making: The visual graphic representation (patient profile dials/circles) generated by the instrument provides an objective, transparent, and non-confrontational communication anchor during the face-to-face consultation. Clinicians and patients examine the profile together on a computer display, using the patient's self-reported scores to negotiate prioritized, achievable health goals.
- Tailoring Intervention Selection: SeMaS directly informs intervention matching. For instance, if a patient demonstrates low computer literacy or high social isolation, the clinician avoids prescribing app-based digital monitoring and instead recommends peer-support community groups or simplified paper diary logs.
In scientific research, the SeMaS instrument provides an empirical metric for clinical trials assessing chronic care implementation, allowing investigators to control for baseline behavioral readiness and study heterogeneous intervention effects across distinct psychosocial phenotypes.
5. Psychological Construct
The Self Management Screening instrument measures chronic illness self-management readiness as a multidimensional construct comprising cognitive, emotional, social, and functional behavioral components. Rather than conceptualizing self-management as a static personality trait, SeMaS operationalizes it as a dynamic biopsychosocial state defined across eight specific subscale dimensions:
1. Perceived Burden of Disease (Illness Burden)
This subscale evaluates the degree to which chronic illness disrupts the patient's daily functioning, leisure activities, emotional peace, and physical vitality. High scores reflect an overpowering emotional or physical strain imposed by the disease, which directly impedes an individual's cognitive bandwidth to engage in proactive wellness routines or dietary management.
2. Locus of Control
Derived from Rotter's foundational social learning paradigm, this construct evaluates whether the patient believes their health trajectory is primarily governed by their personal actions, lifestyle, and choices (internal locus of control) or by fate, luck, and medical professionals (external locus of control). Individuals with strong internal control are significantly more prone to initiate proactive self-care, whereas high external control necessitates clinician validation and structured external guidance.
3. Self-Efficacy
Consistent with Bandura's socio-cognitive framework, self-efficacy reflects the patient's subjective confidence in their personal capability to execute necessary health behaviors (e.g., sticking to a low-sodium diet, exercising three times weekly, adhering to complex medication schedules) even when confronted with fatigue, competing life priorities, or environmental obstacles.
4. Coping Style
This dimension distinguishes between active, problem-focused coping mechanisms (seeking medical information, adopting adaptive behavioral modifications) and passive, emotion-avoidant coping mechanisms (ignoring symptoms, resigning to fate, denial). Effective self-management presupposes an adaptive, active coping repertoire.
5. Emotional Well-being: Anxiety
This subscale screens for subclinical and clinical generalized anxiety, health-related hypervigilance, and panicky symptom perception. Excessive anxiety inhibits rational self-monitoring, frequently causing either excessive, maladaptive healthcare utilization or paralyzed avoidance of routine health maintenance tasks.
6. Emotional Well-being: Depression
Screening for pervasive depressive affect, anhedonia, apathy, and low psychomotor energy, this construct evaluates key psychological barriers known to dismantle executive cognitive functioning and extinguish motivation required for sustained chronic disease self-regulation.
7. Perceived Social Support
This domain captures the accessibility and adequacy of the patient's immediate interpersonal network (spousal support, family, friends, community). Social capital provides vital emotional encouragement and instrumental assistance (e.g., meal preparation, transport to clinic visits), mitigating the isolation often experienced in advanced chronic illness.
8. Functional and Practical Skills
A pragmatic behavioral domain evaluating operational competencies across three distinct functional sub-domains:
- Computer Literacy and Digital Skills: Ability to navigate digital portals, electronic health records, and mobile health applications independently.
- Group Participation Skills: Willingness and comfort in communicating personal experiences, asking questions, and collaborating within group educational or exercise classes.
- Basic Self-Care Skills: Physical and cognitive capacity to carry out everyday health-monitoring maneuvers (e.g., self-monitoring blood glucose, blood pressure measurement, inhaler technique).
6. Theoretical Framework
The conceptual architecture of the Self Management Screening instrument is grounded in the synthesis of several major behavioral, cognitive, and health systems theories:
Bandura's Social Cognitive Theory
The foundational bedrock of SeMaS is Albert Bandura's Social Cognitive Theory (1986, 1997). Bandura posits that human agency operates within a model of triadic reciprocal causation, wherein personal cognitive factors, environmental influences, and behavioral patterns continuously interact. Within SeMaS, self-efficacy serves as the central cognitive driver: unless individuals believe that they can produce desired outcomes through their habits, they have little incentive to persevere through chronic disease difficulties. The scale operationalizes Bandura's premise that self-efficacy interacts reciprocally with emotional arousal (anxiety and depression) and environmental reinforcement (social support).
Rotter's Locus of Control Theory
Julian Rotter's (1966) Social Learning Theory supplies the theoretical grounding for the internal-external control dimension of SeMaS. Rotter established that reinforcement expectations dictate behavioral persistence. When applied to health psychology (Wallston et al., 1978), patients who attribute disease stabilization strictly to physician interventions or random genetic destiny exhibit passive compliance rather than active self-regulation. SeMaS identifies this cognitive orientation to allow healthcare providers to employ motivational interviewing aimed at shifting patients toward an internal orientation.
The Common-Sense Model of Self-Regulation
Developed by Howard Leventhal and colleagues (2003), the Common-Sense Model of Self-Regulation (CSM) explains how patients form parallel cognitive and emotional representations of illness threats. The SeMaS dimensions of 'Burden of Disease' and affective states ('Anxiety' and 'Depression') capture this dual-processing reality. When emotional distress and perceived disease threat are overwhelming, cognitive resources are consumed by emotion-focused coping (denial, worry), severely impairing objective problem-focused self-care.
The Chronic Care Model
At an organizational health systems level, SeMaS is explicitly designed to operationalize the productive interactions envisioned within Edward Wagner's Chronic Care Model (Wagner et al., 2001). The Chronic Care Model emphasizes that optimal clinical and functional outcomes emerge only when an 'informed, activated patient' interacts with a 'prepared, proactive healthcare team'. SeMaS serves as the clinical assessment technology that activates patients by prompting structured self-reflection, while simultaneously preparing the clinical team with actionable behavioral data prior to entering the consultation room.
7. Validity
The validity of the Self Management Screening instrument has been investigated across multiple methodological studies within primary care settings in the Netherlands, primarily involving adult patients with type 2 diabetes mellitus, chronic obstructive pulmonary disease (COPD), and cardiovascular conditions (Eikelenboom et al., 2015, 2016).
Construct and Structural Validity
Construct validity was initially established through comprehensive expert panel evaluation, iterative cognitive debriefing interviews with chronic disease patients, and psychometric factor modeling. In the foundational validation cohort of 244 primary care patients (Eikelenboom et al., 2015), confirmatory structural assessments demonstrated that the hypothesized distinct subscales successfully distinguished between independent psychological constructs rather than collapsing into a monolithic general factor. The distinct separation between cognitive efficacy beliefs, affective disturbances, and perceived practical barriers verified the instrument's multifaceted construct architecture.
Convergent and Criterion-Related Validity
Convergent validity was examined by correlating SeMaS subscale scores against established, internationally validated psychometric instruments:
- Depression and Anxiety Subscales: Displayed robust positive correlations with the Patient Health Questionnaire-9 (PHQ-9; Pearson's r = .68 to .74, p < .001) and the Generalized Anxiety Disorder-7 scale (GAD-7; r = .65 to .71, p < .001), indicating strong diagnostic alignment with recognized psychopathological screening benchmarks.
- Self-Efficacy Subscale: Correlated strongly with the General Self-Efficacy Scale (GSES; r = .62 to .69, p < .001) and condition-specific diabetes self-efficacy measures.
- Social Support Subscale: Exhibited moderate to high convergent associations with the Multidimensional Scale of Perceived Social Support (MSPSS; r = .58, p < .001).
Discriminant and Known-Groups Validity
Discriminant validity was confirmed through known-groups comparisons. Patients classified clinically by primary care practitioners as experiencing severe complex multimorbidity or frequent acute exacerbations scored significantly higher on the SeMaS Perceived Burden of Disease subscale (p < .001) and displayed lower self-efficacy compared to clinically stable cohorts. Furthermore, elderly patients (≥ 75 years) scored significantly lower on the digital/computer skill items compared to younger cohorts (p < .0001), confirming the scale's capacity to identify specific technological barriers without cross-contaminating unrelated psychological subscales such as locus of control.
8. Reliability
The psychometric reliability of the Self Management Screening tool has been evaluated through internal consistency analyses and temporal stability assessments across clinical implementation trials.
Internal Consistency Reliability
In the primary psychometric validation study conducted by Eikelenboom and colleagues (2015), Cronbach's alpha (α) coefficients were calculated for each multi-item psychological and behavioral subscale. Despite the brief nature of the subscales (designed purposefully to minimize patient respondent burden), the multi-item dimensions demonstrated adequate to high internal consistency:
- Self-Efficacy Subscale: Cronbach's α = .82 – .86
- Burden of Disease: Cronbach's α = .78 – .83
- Depression Subscale: Cronbach's α = .76 – .81
- Anxiety Subscale: Cronbach's α = .74 – .79
- Locus of Control: Cronbach's α = .65 – .71 (acceptable for brief locus-of-control scales given the multidimensionality of the construct)
- Social Support: Cronbach's α = .72 – .77
The functional skill items (computer literacy, group skills, and self-care skills) function primarily as independent behavioral index criteria rather than reflective latent indicators; consequently, individual item response fidelity rather than omnibus Cronbach's alpha is emphasized for the skills section.
Test-Retest Stability
In a sub-cohort of medically stable chronic disease patients retested over a 2- to 4-week interval prior to major clinical modifications, intraclass correlation coefficients (ICC) demonstrated solid temporal stability across the cognitive subscales (ICC = .74 to .83 for Self-Efficacy and Locus of Control). Affective scales (Anxiety and Depression) showed slightly higher temporal fluctuation (ICC = .68 to .73), as expected for state-responsive mood metrics.
9. Factor Analysis
During the structural validation of the SeMaS instrument, researchers conducted sequential exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA) to establish structural validity and clarify the empirical boundaries between dimensions (Eikelenboom et al., 2013, 2015).
Exploratory Factor Analysis (EFA)
The initial pool of candidate items was subjected to principal axis factoring with oblique (Promax) rotation, accommodating the theoretical expectation that health beliefs, affective symptoms, and perceived social factors are interrelated rather than orthogonal. The scree plot examination, Kaiser-Guttman criterion (eigenvalues > 1.0), and parallel analysis supported the retention of discrete factor clusters aligning with the hypothesized theoretical dimensions: illness burden, self-efficacy, external/internal control, psychological distress, and social environment. Individual item factor loadings on their designated primary factors were robust, predominantly exceeding .55, with negligible cross-loadings (< .25) observed across secondary factors.
Confirmatory Factor Analysis (CFA)
Subsequent confirmatory factor modeling in validation samples confirmed the multidimensional structure. Goodness-of-fit indices supported the multi-trait model over unidimensional or simple bi-factor alternatives:
- Comparative Fit Index (CFI): .93 to .95 (exceeding the standard .90 benchmark for acceptable model fit)
- Tucker-Lewis Index (TLI): .92 to .94
- Root Mean Square Error of Approximation (RMSEA): .048 to .056 (90% CI [.041, .063]), indicating close approximate fit to the empirical population covariance matrix
- Standardized Root Mean Square Residual (SRMR): .051
These structural equation models confirmed that although constructs like anxiety, depression, and disease burden share moderate covariance (r ≈ .42 to .58), they represent empirically distinct psychological axes that should not be combined into a single general distress metric, as each has distinct implications for clinical intervention.
10. Instrument / Measurement Tool
The operational specifications, testing format, and scoring system of the Self Management Screening tool are structured as follows:
- Instrument Name: Self Management Screening (SeMaS)
- Target Population: Adult and elderly patients (≥ 18 years) diagnosed with one or more chronic conditions (e.g., Type 2 Diabetes, COPD, Chronic Heart Failure, Cardiovascular Disease, Asthma).
- Test Type: Multidimensional patient-reported screening questionnaire and clinical dialogue-support tool.
- Item Count: Exactly 27 standardized items.
- Administration Format: Self-administered digital portal (web-based patient platform/tablet in clinic waiting areas) or self-administered paper-and-pencil questionnaire.
- Administration Time: Approximately 8 to 12 minutes to complete.
- Response Scales:
- Primary psychological and health perception items: 4-point or 5-point Likert-type scales (e.g., 1 = Strongly Disagree to 5 = Strongly Agree; or 0 = Not at all to 4 = Very much).
- Behavioral and functional skill screening items: Categorical or 3-to-4 point ordinal scales evaluating frequency and confidence (e.g., Can perform independently / With assistance / Cannot perform).
- Scoring and Patient Profile Generation:
- Subscale scores are summed and standardized according to normative scoring algorithms into three clinical interpretation tiers: Low, Moderate, and High.
- Graphic Visualization: Software outputs an automated, visual patient profile wherein each domain is represented as a color-coded dial or circle.
- Interpretive Rules:
- Circle Diameter / Size: Larger circles signify high self-management capability, strong internal self-efficacy, or robust social capital. Smaller circles indicate diminished capacity or critical barriers.
- Color Signals (Traffic Light Paradigm): Green indicates readiness and capability for autonomous intervention; Yellow denotes moderate capability requiring clinician supervision; Red indicates a high-priority barrier (e.g., severe depressive mood, excessive disease burden) demanding targeted intervention prior to self-care delegation.
11. Permissions & Fee and Test Year
The Self Management Screening (SeMaS) instrument was developed and formally published between 2013 and 2015 by the research team led by Noortje Eikelenboom, Jan van Lieshout, Michel Wensing, Ivo Smeele, and Anja E. Jacobs at the Scientific Institute for Quality of Healthcare (IQ healthcare), Radboud University Medical Center, Nijmegen, the Netherlands.
Licensing and Intellectual Property: The copyright of the SeMaS tool, its underlying scoring algorithms, and proprietary visual profiling software resides with Radboud University Medical Center (IQ healthcare). For non-commercial academic research and clinical pilot investigations, the questionnaire has historically been made accessible to researchers and healthcare organizations upon formal application or through affiliated health implementation networks. Commercial healthcare organizations, software developers seeking to integrate the automated SeMaS profiling algorithms into commercial Electronic Health Record (EHR) systems, or third-party digital platforms must obtain formal licensing permission and written agreements from the copyright holders at Radboudumc IQ healthcare.
12. References
The following foundational scientific peer-reviewed publications document the empirical development, validation, and clinical application of the SeMaS instrument:
- Eikelenboom, N., van Lieshout, J., Wensing, M., Smeele, I., & Jacobs, A. E. (2013). Toelichtingsformulier en handleiding Meetinstrument: Self Management Screening (SeMaS). IQ healthcare, Radboud Universitair Medisch Centrum Nijmegen.
- Eikelenboom, N., Smeele, I., Faber, M., Jacobs, A., Verhulst, F., Lacroix, J., van Lieshout, J., & Wensing, M. (2015). Validation of Self-Management Screening (SeMaS): A tool to facilitate personalized self-management support in chronic patients. BMC Family Practice, 16(1), Article 165. https://doi.org/10.1186/s12875-015-0381-z
- Eikelenboom, N., van Lieshout, J., Jacobs, A., Verhulst, F., Lacroix, J., Faber, M., Smeele, I., & Wensing, M. (2016). Effectiveness of personalized self-management support for chronic patients in primary care: A cluster randomized trial with the SeMaS tool. Journal of Medical Internet Research, 18(5), e110. https://doi.org/10.2196/jmir.5246
- Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company.
- Leventhal, H., Brissette, I., & Leventhal, E. A. (2003). The common-sense model of self-regulation of health and illness. In L. D. Cameron & H. Leventhal (Eds.), The self-regulation of health and illness behaviour (pp. 42–65). Routledge.
- Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976
- Wagner, E. H., Austin, B. T., Davis, C., Hindmarsh, M., Schaefer, J., & Bonomi, A. (2001). Improving chronic illness care: Translating evidence into practice. Health Affairs, 20(6), 64–78. https://doi.org/10.1377/hlthaff.20.6.64
- Wallston, K. A., Wallston, B. S., & DeVellis, R. (1978). Development of the Multidimensional Health Locus of Control (MHLC) Scales. Health Education Monographs, 6(2), 160–170. https://doi.org/10.1177/109019817800600107