AbsenteeismWB6Dim-LTSANCT07606261
WB6Dim-LTSA: Can Workplace Well-Being Scores Predict Collective Absenteeism?
An observational study of Absenteeism and Sick Leave, run by Clover Link at 1 site in France.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Absenteeism, Sick Leave, Workplace Well-Being
- How many people
- 2,000 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jun 2026to 30 Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- Refusal to participate or withdrawal of consent
- Inability to complete the questionnaire in French
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site
Clover Link
Bandol, 83150
quentin ALITTA, 0686505361, quentin.alitta@gmail.com
About this study
Official title: 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.
Read the 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.
- Conditions
- Absenteeism, Sick Leave, Workplace Well-Being, Occupational Stress
- Keywords
- WB6Dim, Adaptive Load Index, absenteeism prediction, cliff effect, ICA
- Medical subject headings
- Occupational Stress
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months post-enrollment
Secondary outcomes (6)
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.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 6 months post-enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 May 2026
- Study start1 Jun 2026 (estimated)
- Primary completion15 Nov 2026 (estimated)
- Study completion30 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for AbsenteeismStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Clover Link
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Frédérique RETORNAZ, MD, PhD, Study chair, European Hospital, Unit of Care and Research in Internal Medicine and Infectious Diseases.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07606261
- Sponsor's protocol ID
- WB6DIM-LTSA-2026-01
- Acronym
- WB6Dim-LTSA
- Status last verified
- May 2026
- First submitted
- 17 May 2026
- Last updated
- 26 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Study at a glance
From the registry record, last updated by the sponsor on 26 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07606261
- Acronym / sponsor ID
- WB6Dim-LTSA / WB6DIM-LTSA-2026-01
- Conditions
- Absenteeism, Sick Leave, Workplace Well-Being, Occupational Stress
- Keywords
- WB6Dim, Adaptive Load Index, absenteeism prediction, cliff effect, ICA
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Clover LinkIndustry (pharma, biotech, devices)
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 15 Nov 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 26 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (2 listed)
- Refusal to participate or withdrawal of consent
- Inability to complete the questionnaire in French
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Company-level incidence rate of sick leave episodes ≥ 30 days over 6 months, as measured from aggregated HR records
Time frame: 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 secondary outcomes
Company-level incidence rate of sick leave episodes ≥ 90 days over 6 months, as measured from aggregated HR records
Time frame: 6 months post-enrollment
Number of employees with ≥ 3 distinct absence episodes within 6 months per company, as measured from aggregated HR records
Time frame: 6 months post-enrollment
Self-reported cumulative absence duration and episode count, as measured by WB6Dim questionnaire items
Time frame: 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
Time frame: 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
Time frame: 6 months post-enrollment
Agreement (Cohen's kappa) between aggregated self-reported absence and company-level HR absence data, stratified by duration class
Time frame: 6 months post-enrollment
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 May 2026.
- WB6Dim validation protocol - Cohorts A+BLink provided by the sponsor.
- WB6Dim validation protocol - Cohorts C+DLink provided by the sponsor.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Frédérique RETORNAZ, MD, PhD study chair
European Hospital, Unit of Care and Research in Internal Medicine and Infectious Diseases.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Absenteeism and Sick Leave, run by Clover Link at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 26 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Quentin ALITTA, MBA (686505361 ext. +33, quentin.alitta@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Company-level incidence rate of sick leave episodes ≥ 30 days over 6 months, as measured from aggregated HR records (time frame: 6 months post-enrollment). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 May 2026; AzTrials last refreshed its copy on 7 Nov 2026.