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WB6Dim-LTSA: Can Workplace Well-Being Scores Predict Collective Absenteeism? (WB6Dim-LTSA)

17. Mai 2026 aktualisiert von: Clover Link

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

This prospective multicenter cohort study evaluates the predictive value of the Adaptive Load Index (ICA), a composite indicator derived from the WB6Dim well-being instrument, on long-duration sick leave (≥ 30 days) in French companies at a 6-month horizon. In France, 7% of sick leave episodes (those exceeding 6 months) account for 45% of total sickness benefit expenditure (Cour des Comptes 2024). Group disability insurance charges rose +24.4% in 2024 (France Assureurs 2025). Critically, a substantial proportion of long-duration sick leave occurs without prior escalation in administrative absence data - the 'cliff effect' - where presenteeism masks progressive deterioration (Gustafsson & Marklund 2011). Prediction models based solely on absence history plateau at AUC 0.65 for cumulative days (Roelen 2013), while composite psychometric instruments reach C-index 0.73-0.74 (Airaksinen et al. 2018, SJWEH). The WB6Dim is a validated 28-item psychometric tool measuring 9 dimensions of workplace well-being (NCT07301879, NCT07433764; test-retest ICA .904). The ICA classifies respondents into 4 adaptive load levels. Aggregated at the company level, the ICA distribution may detect deterioration during the presenteeism window, before costly sick leave materializes. The study collects 4 WB6Dim assessments over 6 months alongside company-level absence data stratified by duration (2024-2026) and individual self-reported absence data (duration and episode count). Six pre-registered hypotheses test whether ICA predicts long-duration leave, including an exploratory hypothesis targeting companies with no prior absence signal but degraded well-being scores.

Studienübersicht

Status

Noch keine Rekrutierung

Bedingungen

Detaillierte Beschreibung

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.

Studientyp

Beobachtungs

Einschreibung (Geschätzt)

2000

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienkontakt

Studienorte

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

  • Erwachsene
  • Älterer Erwachsener

Akzeptiert gesunde Freiwillige

Ja

Probenahmeverfahren

Nicht-Wahrscheinlichkeitsprobe

Studienpopulation

Employees of French companies with 50 or more employees, recruited through employer participation agreements. Companies are sourced through occupational health networks and direct outreach. All employees meeting inclusion criteria within participating companies are eligible regardless of job type, contract status, or health condition.

Beschreibung

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

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

Kohorten und Interventionen

Gruppe / Kohorte
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.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Company-level incidence rate of sick leave episodes ≥ 30 days over 6 months, as measured from aggregated HR records
Zeitfenster: 6 months post-enrollment
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.
6 months post-enrollment

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Company-level incidence rate of sick leave episodes ≥ 90 days over 6 months, as measured from aggregated HR records
Zeitfenster: 6 months post-enrollment
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.
6 months post-enrollment
Number of employees with ≥ 3 distinct absence episodes within 6 months per company, as measured from aggregated HR records
Zeitfenster: 6 months post-enrollment
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).
6 months post-enrollment
Self-reported cumulative absence duration and episode count, as measured by WB6Dim questionnaire items
Zeitfenster: Baseline, 3 months, and 6 months post-enrollment
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.
Baseline, 3 months, and 6 months post-enrollment
Change in collective ICA distribution from baseline to 3 months as a predictor of sick leave ≥ 30 days between 3 and 6 months
Zeitfenster: Baseline and 3 months (predictor); 3 to 6 months post-enrollment (outcome)
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.
Baseline and 3 months (predictor); 3 to 6 months post-enrollment (outcome)
Change in predictive model AUC when adding DAE profile distribution to the ICA-based model for sick leave ≥ 30 days
Zeitfenster: 6 months post-enrollment
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.
6 months post-enrollment
Agreement (Cohen's kappa) between aggregated self-reported absence and company-level HR absence data, stratified by duration class
Zeitfenster: 6 months post-enrollment
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.
6 months post-enrollment

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Sponsor

Clover Link

Ermittler

  • Studienstuhl: Frédérique RETORNAZ, MD, PhD, European Hospital, Unit of Care and Research in Internal Medicine and Infectious Diseases.

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Nützliche Links

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn (Geschätzt)

1. Juni 2026

Primärer Abschluss (Geschätzt)

15. November 2026

Studienabschluss (Geschätzt)

30. November 2026

Studienanmeldedaten

Zuerst eingereicht

17. Mai 2026

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

17. Mai 2026

Zuerst gepostet (Tatsächlich)

26. Mai 2026

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

26. Mai 2026

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

17. Mai 2026

Zuletzt verifiziert

1. Mai 2026

Mehr Informationen

Begriffe im Zusammenhang mit dieser Studie

Schlüsselwörter

Zusätzliche relevante MeSH-Bedingungen

Andere Studien-ID-Nummern

  • WB6DIM-LTSA-2026-01

Plan für individuelle Teilnehmerdaten (IPD)

Planen Sie, individuelle Teilnehmerdaten (IPD) zu teilen?

NEIN

Beschreibung des IPD-Plans

Individual participant data will not be shared. The study analyzes company-level aggregated indicators only. No individual diagnosis or prognosis is delivered. Sharing individual-level data would conflict with GDPR requirements and the anonymization commitments made to participants and employers in the informed consent. De-identified, aggregated company-level datasets may be made available to qualified researchers upon reasonable request and approval by the data protection officer.

Arzneimittel- und Geräteinformationen, Studienunterlagen

Studiert ein von der US-amerikanischen FDA reguliertes Arzneimittelprodukt

Nein

Studiert ein von der US-amerikanischen FDA reguliertes Geräteprodukt

Nein

Diese Informationen wurden ohne Änderungen direkt von der Website clinicaltrials.gov abgerufen. Wenn Sie Ihre Studiendaten ändern, entfernen oder aktualisieren möchten, wenden Sie sich bitte an register@clinicaltrials.gov. Sobald eine Änderung auf clinicaltrials.gov implementiert wird, wird diese automatisch auch auf unserer Website aktualisiert .

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