{"doi":"10.1001/jamanetworkopen.2021.12528","title":"Automated Behavioral Workplace Intervention to Prevent Weight Gain and Improve Diet","abstract":"Importance: Personalized interventions that leverage workplace data and environments could improve effectiveness, sustainability, and scalability of employee wellness programs. Objective: To test an automated behavioral intervention to prevent weight gain and improve diet using employee cafeteria purchasing data. Design, Setting, and Participants: This individual-level randomized clinical trial of a 12-month intervention with 12 months of follow-up was conducted among employees of a hospital in Boston, Massachusetts, who purchased food at on-site cafeterias that used traffic-light labels (ie, green indicates healthy; yellow, less healthy; red, unhealthy). Participants were enrolled September 2016 to February 2018. Data were analyzed from May to September 2020. Interventions: For 12 months, participants in the intervention group received 2 emails per week with feedback on previous cafeteria purchases and personalized health and lifestyle tips and 1 letter per month with peer comparisons and financial incentives for healthier purchases. Emails and letters were automatically generated using survey, health, and cafeteria data. Control group participants received 1 letter per month with general healthy lifestyle information. Main Outcomes and Measures: The main outcome was change in weight from baseline to 12 months and 24 months of follow-up. Secondary outcomes included changes in cafeteria purchases, including proportion of green- and red-labeled purchases and calories purchased per day, from baseline (12 months preintervention) to the intervention (months 1-12) and follow-up (months 13-24) periods. Baseline Healthy Eating Index-15 (HEI-15) scores were compared to HEI-15 scores at 6, 12, and 24 months. Results: Among 602 employees enrolled (mean [SD] age, 43.6 [12.2] years; 478 [79.4%] women), 299 were randomized to the intervention group and 303 were randomized to the control group. Baseline mean (SD) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was 28.3 (6.6) and HEI-15 score was 60.4 (12.4). There were no between-group differences in weight change at 12 (0.2 [95% CI, -0.6 to 1.0] kg) or 24 (0.6 [95% CI, -0.3 to 1.4] kg) months. Compared with baseline, the intervention group increased green-labeled purchases by 7.3% (95% CI, 5.4% to 9.3%) and decreased red-labeled purchases by 3.9% (95% CI, -5.0% to -2.7%) and calories purchased per day by 49.5 (95% CI, -75.2 to -23.9) kcal more than the control group during the intervention period. In the intervention group, differences in changes in green (4.8% [95% CI, 2.9% to 6.8%]) and red purchases (-3.1% [95% CI, -4.3% to -2.0%]) were sustained at the 24-month follow-up. Differences in changes in HEI-15 scores were not significantly different in the intervention compared with the control group at 6 (2.2 [95% CI, 0 to 4.4]), 12 (1.8 [95% CI, -0.6 to 4.1]), and 24 (1.6, 95% CI, -0.7 to 3.8]) months. Conclusions and Relevance: The findings of this randomized clinical trial suggest that an automated behavioral intervention using workplace cafeteria data improved employees' food choices but did not prevent weight gain over 2 years. Trial Registration: ClinicalTrials.gov Identifier: NCT02660086.","journal":"JAMA Network Open","year":2021,"id":167932,"datarank":1.1060431283040586,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":0.5861827428840996,"self_endowment_contribution":0.519860385419959,"citer_contribution":0.5861827428840996,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":18,"citers_with_citation_signal":14,"citers_with_endowment":14,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9498,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT02660086"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":483590,"name":"Jessica L. McCurley","orcid":"0000-0003-0356-5608","position":1,"is_corresponding":false},{"id":676221,"name":"Emily D. Gelsomin","orcid":null,"position":2,"is_corresponding":false},{"id":360717,"name":"Emma M. Anderson","orcid":"0000-0002-8253-9549","position":3,"is_corresponding":false},{"id":385230,"name":"Yuchiao Chang","orcid":"0000-0003-1093-3755","position":4,"is_corresponding":false},{"id":514446,"name":"Bianca Porneala","orcid":"0009-0001-5762-379X","position":5,"is_corresponding":false},{"id":696315,"name":"Charles A. Johnson","orcid":"0000-0002-1975-2005","position":6,"is_corresponding":false},{"id":14733,"name":"Eric B. Rimm","orcid":"0000-0002-1402-7250","position":7,"is_corresponding":false},{"id":419956,"name":"Douglas E. Levy","orcid":"0000-0001-9446-7899","position":8,"is_corresponding":false},{"id":483591,"name":"Anne N. Thorndike","orcid":"0000-0003-1096-9221","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T23:46:02.603031Z","pmid":"34097048","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}