WB6Dim-LTSA: Can Workplace Well-Being Scores Predict Collective Absenteeism? (WB6Dim-LTSA)
Predictive Value of the Adaptive Load Index (ICA) Derived From the WB6Dim Instrument on Collective Absenteeism Rates at a 6-Month Horizon: A Prospective Multicenter Cohort Study
Studieoversigt
Status
Betingelser
Detaljeret beskrivelse
BACKGROUND: In France, sickness benefit expenditure reached 10.2 billion euros in 2023, up 28% since 2019 (DREES/CNAM, Études & Résultats n°1321, 2024). The cost distribution follows a Pareto pattern: 7% of sick leave episodes (those exceeding 6 months) generate 45% of total expenditure (Cour des Comptes, RALFSS 2024). For group disability insurers (prévoyance collective), the pressure is acute: charges for incapacity-disability-dependence rose +24.4% in 2024, long-duration indemnified days increased +31% since 2020, and the claims-to-premiums ratio deteriorated to 56.9% (France Assureurs 2025). These 7% of episodes are the primary cost driver, yet current identification relies on retrospective administrative data - the signal arrives after the damage is done. The literature identifies two pathways to long-duration sick leave: (1) escalation from repeated short absences (≥3 episodes/year, RR 1.5-2.5; Koopmans 2008, Hultin 2012, Roelen 2018, Sørensen et al. 2025), where administrative data detect the pattern but often too late for effective prevention; and (2) the 'cliff effect' - sudden onset without prior absence signal, driven by prolonged presenteeism masking progressive deterioration (Gustafsson & Marklund 2011, Ahola 2009, López-Bueno & Clausen 2021). Administrative data are entirely blind to the second pathway. Composite psychometric instruments can detect risk during the presenteeism window: they reach C-index 0.73-0.74 vs 0.65 for absence-only models (Airaksinen et al. 2018; Roelen 2013), and combining questionnaire + administrative data reaches C-index 0.79 (Nyberg et al. 2023).
The WB6Dim (Well-Being 6 Dimensions) is a 28-item digital psychometric instrument assessing 9 well-being dimensions, validated on 808 participants across 4 cohorts with 2 pre-registered protocols (NCT07301879, NCT07433764). All 19 convergent validity hypotheses were confirmed against 10 international gold-standard scales (PSS-10, WHO-5, CBI, ISI-7, RSES, SAS-SV, MSPSS, UCLA-3, CFQ-13, BPNS). The test-retest reliability (ICA) reached .904 (excellent). The Adaptive Load Index (ICA) classifies each respondent into 4 levels: low-to-moderate load, high load, very high load, and critical load. The Environmental Attentional Dysregulation (DAE) further characterizes the dominant source of strain (internal, digital, relational, or mixed). No published study has tested the predictive value of a composite well-being index, measured at the collective level, on sick leave ≥30 days - the threshold triggering group disability insurance benefits.
DESIGN: Prospective multicenter cohort study with 4 measurement waves over 6 months (June-November 2026). The unit of analysis is the company (collective level). No individual diagnosis or prognosis is delivered. Data sources include: (1) company-level HR data on absenteeism stratified by duration (aggregated, anonymized) for 2024, 2025, and 2026; (2) individual self-reported absence integrated into the WB6Dim at T0, T2, and T3. The study includes two analytical components: a retrospective analysis correlating T0 WB6Dim scores with 2024-2025 absenteeism, and a concurrent analysis testing whether T0-T2 trajectories predict T2-T3 absenteeism. Six pre-registered hypotheses are tested, including an exploratory hypothesis (H6) targeting the added value of ICA for companies with no elevated absence history but degraded well-being scores - directly testing whether the WB6Dim can identify the costly 7% before administrative data show any signal.
Undersøgelsestype
Tilmelding (Anslået)
Kontakter og lokationer
Studiekontakt
- Navn: Quentin ALITTA, MBA
- Telefonnummer: +33 686505361
- E-mail: quentin.alitta@gmail.com
Studiesteder
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Bandol, Frankrig, 83150
- Clover Link
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Kontakt:
- quentin ALITTA
- Telefonnummer: 0686505361
- E-mail: quentin.alitta@gmail.com
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Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Prøveudtagningsmetode
Studiebefolkning
Beskrivelse
Inclusion Criteria:
- Employee of a participating French company (≥ 50 employees)
- Age 18 years or older
- Access to a smartphone or computer to complete the digital questionnaire
- Electronic informed consent provided at baseline
Exclusion Criteria:
- Refusal to participate or withdrawal of consent
- Inability to complete the questionnaire in French
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
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Multi-company workforce cohort
Single-cohort design.
All participants receive the same observational protocol: 4 WB6Dim assessments over 6 months.
The predictive analysis is conducted at the company level, comparing companies above versus below the sample median of collective critical ICA proportion at T0.
No group assignment is made at the individual level.
Stratification is performed post-hoc based on observed ICA distributions.
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Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Company-level incidence rate of sick leave episodes ≥ 30 days over 6 months, as measured from aggregated HR records
Tidsramme: 6 months post-enrollment
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Company-level incidence of sick leave episodes lasting 30 days or more, measured from aggregated HR data provided by each participating company for the period June-November 2026.
This threshold marks the transition from short-term to long-term sickness absence in the French social security system and is associated with sharply reduced return-to-work probability.
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6 months post-enrollment
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Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
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Company-level incidence rate of sick leave episodes ≥ 90 days over 6 months, as measured from aggregated HR records
Tidsramme: 6 months post-enrollment
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Company-level incidence of sick leave episodes lasting 90 days or more, corresponding to long-term illness (affection de longue durée) classification and elevated risk of permanent disability transition.
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6 months post-enrollment
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Number of employees with ≥ 3 distinct absence episodes within 6 months per company, as measured from aggregated HR records
Tidsramme: 6 months post-enrollment
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Company-level count of employees with 3 or more distinct absence episodes within a 6-month period.
Repeated short absences are an established early marker of subsequent long-duration leave (Koopmans 2008, RR=1.9;
Hultin 2012, OR=2.0).
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6 months post-enrollment
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Self-reported cumulative absence duration and episode count, as measured by WB6Dim questionnaire items
Tidsramme: Baseline, 3 months, and 6 months post-enrollment
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Self-reported work absence collected via two items in the WB6Dim questionnaire.
Item 1: cumulative duration (0 / 1-7 days / 8-30 days / 31-90 days / >90 days).
Item 2: number of separate episodes (0 / 1 / 2 / 3+).
Each assessment covers the period since the previous measurement.
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Baseline, 3 months, and 6 months post-enrollment
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Change in collective ICA distribution from baseline to 3 months as a predictor of sick leave ≥ 30 days between 3 and 6 months
Tidsramme: Baseline and 3 months (predictor); 3 to 6 months post-enrollment (outcome)
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Change in collective ICA distribution between baseline and 3 months (slope of degradation) as a predictor of sick leave ≥ 30 days observed between 3 and 6 months post-enrollment.
Tests whether longitudinal worsening of collective well-being adds predictive value beyond static baseline measurement.
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Baseline and 3 months (predictor); 3 to 6 months post-enrollment (outcome)
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Change in predictive model AUC when adding DAE profile distribution to the ICA-based model for sick leave ≥ 30 days
Tidsramme: 6 months post-enrollment
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Improvement in predictive model discrimination (AUC) when adding DAE profile distribution (internal, digital, relational, mixed) to the ICA-based model.
Tests whether characterizing the dominant source of strain improves identification of at-risk companies beyond overall load level.
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6 months post-enrollment
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Agreement (Cohen's kappa) between aggregated self-reported absence and company-level HR absence data, stratified by duration class
Tidsramme: 6 months post-enrollment
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Agreement between aggregated individual self-reports and company-level HR data, assessed using Cohen's kappa at the company level.
Stratified by duration class.
Validates the use of self-reported absence as a complementary data source when HR records are unavailable.
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6 months post-enrollment
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Samarbejdspartnere og efterforskere
Sponsor
Efterforskere
- Studiestol: Frédérique RETORNAZ, MD, PhD, European Hospital, Unit of Care and Research in Internal Medicine and Infectious Diseases.
Publikationer og nyttige links
Datoer for undersøgelser
Studer store datoer
Studiestart (Anslået)
Primær færdiggørelse (Anslået)
Studieafslutning (Anslået)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- WB6DIM-LTSA-2026-01
Plan for individuelle deltagerdata (IPD)
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