MétaCan
Menu
Back to cohort
Record W4409255214 · doi:10.2196/59405

Development of Digital Strategies for Reducing Sedentary Behavior in a Hybrid Office Environment: Modified Delphi Study

2025· article· en· W4409255214 on OpenAlexvenueno aff
Iris Parés-Salomón, Cristina Vaqué-Crusellas, Alan Coffey, Bette Loef, Karin I. Proper, Anna M. Señé-Mir, Anna Puig‐Ribera, Kieran Dowd, Judit Bort‐Roig

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
FundersEuropean Commission
KeywordsDelphi methodRanking (information retrieval)Psychological interventionLikert scaleDelphiContext (archaeology)Focus groupWork (physics)Computer scienceKnowledge managementApplied psychologyPsychologyEngineeringMedicineMarketingBusinessArtificial intelligenceNursingGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Hybrid work is the new modus operandi for many office workers, leading to more sedentary behavior than office-only working. Given the potential of digital interventions to reduce sedentary behavior and the current lack of studies evaluating these interventions for home office settings, it is crucial to develop digital interventions for such contexts involving all stakeholders. OBJECTIVE: This study aimed to reach expert consensus on the most feasible work strategies and the most usable digital elements as a delivery method to reduce sedentary behavior in the home office context. METHODS: A modified Delphi study including 3 survey rounds and focus groups was conducted to achieve consensus. The first Delphi round consisted of two 9-point Likert scales for assessing the feasibility of work strategies and the potential usefulness of digital elements to deliver the strategies. The work strategies were identified and selected from a scoping review, a systematic review, and 2 qualitative studies involving managers and employees. The median and mean absolute deviation from the median for each item are reported. The second round involved 2 ranking lists with the highly feasible strategies and highly useful digital elements based on round 1 responses to order the list according to experts' preferences. The weighted average ranking for each item was calculated to determine the most highly ranked work strategies and digital elements. The third round encompassed work strategies with a weight above the median from round 2 to be matched with the most useful digital elements to implement each strategy. In total, 4 focus groups were additionally conducted to gain a greater understanding of the findings from the Delphi phase. Focus groups were analyzed using the principles of reflexive thematic analysis. RESULTS: A total of 27 international experts in the field of occupational health participated in the first round, with response rates of 86% (25/29) and 66% (19/29) in rounds 2 and 3, respectively, and 52% (15/29) in the focus groups. Consensus was achieved on 18 work strategies and 16 digital elements. Feedback on activity progress and goal achievement; creating an action plan; and standing while reading, answering phone calls, or conducting videoconferences were the most feasible work strategies, whereas wrist-based activity trackers, a combination of media, and app interfaces in smartphones were the most useful digital elements. Moreover, experts highlighted the requirement of combining multiple levels of strategies, such as social support, physical environment, and individual strategies, to enhance their implementation and effectiveness in reducing sedentary behavior when working from home. CONCLUSIONS: This expert consensus provided a foundation for developing digital interventions for sedentary behavior in home office workers. Ongoing interventions should enable the evaluation of feasible strategies delivered via useful digital elements in home office or hybrid contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0040.002
Scholarly communication0.0020.003
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.081
GPT teacher head0.363
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Human FactorsSame topicPhysical Activity and HealthFrench-language works237,207