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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Occupational Stress; Absenteeism; Sick Leave; Workplace Well-Being

Detailed Description

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.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Quentin ALITTA, MBA; Phone Number: +33 686505361; Email: quentin.alitta@gmail.com

Study Locations

• France
  • Bandol, France, 83150
    • Clover Link
    • Contact: quentin ALITTA; Phone Number: 0686505361; Email: quentin.alitta@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Company-level incidence rate of sick leave episodes ≥ 30 days over 6 months, as measured from aggregated HR records	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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Company-level incidence rate of sick leave episodes ≥ 90 days over 6 months, as measured from aggregated HR records	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	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	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	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	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	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

Collaborators and Investigators

• Sponsor: Clover Link
• Investigators: Study Chair: Frédérique RETORNAZ, MD, PhD, European Hospital, Unit of Care and Research in Internal Medicine and Infectious Diseases.

Publications and helpful links

Helpful Links

• WB6Dim validation protocol - Cohorts A+B
• WB6Dim validation protocol - Cohorts C+D

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): November 15, 2026
• Study Completion (Estimated): November 30, 2026

Study Registration Dates

• First Submitted: May 17, 2026
• First Submitted That Met QC Criteria: May 17, 2026
• First Posted (Actual): May 26, 2026

Study Record Updates

• Last Update Posted (Actual): May 26, 2026
• Last Update Submitted That Met QC Criteria: May 17, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: ICA; WB6Dim; Adaptive Load Index; absenteeism prediction; cliff effect
• Additional Relevant MeSH Terms: Behavioral Symptoms; Stress, Psychological; Occupational Diseases; Behavior; Occupational Stress
• Other Study ID Numbers: WB6DIM-LTSA-2026-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No