Polygenic risk score for obesity and the quality, quantity, and timing of workplace food purchases: A secondary analysis from the ChooseWell 365 randomized trial

The utility of obesity polygenic risk scores from research to clinical practice: A review

Obesity represents a major public health emergency worldwide, and its etiology is shaped by a complex interplay of environmental and genetic factors. Over the last decade, polygenic risk scores (PRS) have emerged as a promising tool to quantify an individual's genetic risk of obesity. The field of PRS in obesity genetics is rapidly evolving, shedding new lights on obesity mechanisms and holding promise for contributing to personalized prevention and treatment. Challenges persist in terms of its clinical integration, including the need for further validation in large-scale prospective cohorts, ethical considerations, and implications for health disparities. In this review, we provide a comprehensive overview of PRS for studying the genetics of obesity, spanning from methodological nuances to clinical applications and challenges. We summarize the latest developments in the generation and refinement of PRS for obesity, including advances in methodologies for aggregating genome-wide association study data and improving PRS predictive accuracy, and discuss limitations that need to be overcome to fully realize its potential benefits of PRS in both medicine and public health.

Evidence of gene-nutrient interaction association with waist circumference, cross-sectional analysis

BACKGROUND AND AIMS

Waist circumference (WC) is a significant indicator of body adiposity and is associated with increased mortality and morbidity of cardiovascular diseases. Although, single nutrient intake and candidate genes were previously associated with WC. Little is known about WC association with overall diet quality, genetic risk score and gene-nutrient interaction. This study aims to investigate the influence of overall diet quality and multiple WC-associated single nucleotide polymorphisms on WC. In addition to investigating gene-nutrient interaction association with WC.

METHODS

This study explored cross-sectional data from two large sample-size studies, to provide reproducible results. As a representation of the UK population, the Airwave Health Monitoring Study (n = 6,502) and the UK-Biobank Cohort Study (n = 171,129) were explored for factors associated with WC. Diet quality was evaluated based on the Mellen Index for Dietary Approaches to Stop Hypertension (Mellen-DASH). The genetic risk score for WC (GRS-Waist) was calculated by screening the population genotype for WC-associated single nucleotide polymorphisms. Multivariate linear regression models were built to explore WC association with diet quality and genetic risk score. Gene-nutrient interaction was explored by introducing the interaction term (GRS-Waist X Mellen-DASH score) to multivariate linear regression analysis.

RESULTS

The prevalence of high WC (Female > 80 cm, Male > 94 cm) was 46.5% and 51.7% in both populations. Diet quality and genetic risk score of WC were significantly associated with WC. There was no evidence of interaction between GRS-Waist, DASH diet scores and nutrient intake on WC.

CONCLUSION

This study's findings provided reproducible results on waist circumference association with diet and genetics and tested the possibility of gene-nutrient interaction. These reproducible results are successful in building the foundation for using diet and genetics for early identification of those at risk of having high WC and WC-associated diseases. In addition, evidence on gene-diet interactions on WC is limited and lacks replication, therefore our findings may guide future research in investigating this interaction and investigating its application in precision nutrition.

Associations of a multidimensional polygenic sleep health score and a sleep lifestyle index on health outcomes and their interaction in a clinical biobank

Sleep is a complex behavior regulated by genetic and environmental factors, and is known to influence health outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on diseases has yet to be fully elucidated. Using the Mass General Brigham Biobank, we aimed to examine the association of multidimensional sleep with health outcomes and investigate whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health outcomes. First, we generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms for sleep health and constructed a Sleep Lifestyle Index using data from self-reported sleep questions and electronic health records; second, we performed phenome-wide association analyses between these indexes and clinical phenotypes; and third, we analyzed the interaction between the indexes on prevalent mental health outcomes. Fifteen thousand eight hundred and eighty-four participants were included in the analysis (mean age 54.4; 58.6% female). The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (β=0.050, 95%CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10-5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10-5). No interactions were found between the indexes on prevalent mental health outcomes. These findings suggest that favorable sleep behaviors and genetic predisposition to healthy sleep may independently be protective of disease outcomes. This work provides novel insights into the role of multidimensional sleep on population health and highlights the need to develop prevention strategies focused on healthy sleep habits.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.

A genomics perspective of personalized prevention and management of obesity

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

Invited Expert Opinion- Bioinformatic and Limitation Directives to Help Adopt Genetic Addiction Risk Screening and Identify Preaddictive Reward Dysregulation: Required Analytic Evidence to Induce Dopamine Homeostatsis

Addiction, albeit some disbelievers like Mark Lewis [1], is a chronic, relapsing brain disease, resulting in unwanted loss of control over both substance and non- substance behavioral addictions leading to serious adverse consequences [2]. Addiction scientists and clinicians face an incredible challenge in combatting the current opioid and alcohol use disorder (AUD) pandemic throughout the world. Provisional data from the Centers for Disease Control and Prevention (CDC) shows that from July 2021-2022, over 100,000 individuals living in the United States (US) died from a drug overdose, and 77,237 of those deaths were related to opioid use [3]. This number is expected to rise, and according to the US Surgeon General it is highly conceivable that by 2025 approximately 165,000 Americans will die from an opioid overdose. Alcohol abuse, according to data from the World Health Organization (WHO), results in 3 million deaths worldwide every year, which represents 5.3% of all deaths globally [4].

Genetic predisposition to macronutrient preference and workplace food choices

Prior research identified genetic variants influencing macronutrient preference, but whether genetic differences underlying nutrient preference affect long-term food choices is unknown. Here we examined the associations of polygenic scores for carbohydrate, fat, and protein preference with 12 months' workplace food purchases among 397 hospital employees from the ChooseWell 365 study. Food purchases were obtained retrospectively from the hospital's cafeteria sales data for the 12 months before participants were enrolled in the ChooseWell 365 study. Traffic light labels, visible to employees when making purchases, measured the quality of workplace purchases. During the 12-month study period, there were 215,692 cafeteria purchases. Each SD increase in the polygenic score for carbohydrate preference was associated with 2.3 additional purchases/month (95%CI, 0.2 to 4.3; p = 0.03) and a higher number of green-labeled purchases (β = 1.9, 95%CI, 0.5-3.3; p = 0.01). These associations were consistent in subgroup and sensitivity analyses accounting for additional sources of bias. There was no evidence of associations between fat and protein polygenic scores and cafeteria purchases. Findings from this study suggest that genetic differences in carbohydrate preference could influence long-term workplace food purchases and may inform follow-up experiments to enhance our understanding of the molecular mechanisms underlying food choice behavior.

Chronotype Polygenic Score and the Timing and Quality of Workplace Cafeteria Purchases: Secondary Analysis of the ChooseWell 365 Randomized Controlled Trial

Background

Studies on the link between chronotype (i.e., propensity for morning or evening preference) and dietary intake have relied on self-reported data, estimating consumption, and chronotype from questionnaires.

Objectives

This study examined the associations between genetically proxied evening chronotype, objectively estimated workplace dietary choices, and the effectiveness of a behavioral intervention in hospital employees enrolled in the ChooseWell 365 study.

Methods

ChooseWell 365 was a randomized trial of a 12-mo automated, personalized intervention to prevent weight gain and improve diet. Cafeteria sales data were used to measure the timing and healthfulness of workplace food purchases of employees during the 12-mo-long baseline, intervention, and postintervention follow-up periods. A genome-wide polygenic score for evening chronotype was calculated for all participants and the population was divided into quartiles; the highest quartile indicated the most evening chronotype. Associations between polygenic score quartiles and workplace purchases at baseline, 12 mo, and 24 mo and changes from baseline at 12 and 24 mo were tested using adjusted multivariable linear regression models.

Results

At baseline, the highest chronotype quartile was associated with self-reported breakfast skipping. Over the 24-mo study, the highest quartile was associated with later timing of the first workplace purchase, but not with the healthfulness of purchases. There were no differences by the chronotype quartile in the effectiveness of the ChooseWell 365 intervention in improving employees' healthy food choices at work.

Conclusions

A chronotype polygenic score was associated with breakfast skipping and later workplace mealtimes of hospital employees, but not with the nutritional quality of objectively measured workplace food purchases. In addition, employees across the chronotype spectrum benefited from the workplace healthy eating intervention.This trial was registered at clinicaltrials.gov as NCT02660086 (https://clinicaltrials.gov/ct2/show/NCT02660086?cond=NCT02660086&draw=2&rank=1).

Robust Bioinformatics Approaches Result in the First Polygenic Risk Score for BMI in Greek Adults

Quantifying the role of genetics via construction of polygenic risk scores (PRSs) is deemed a resourceful tool to enable and promote effective obesity prevention strategies. The present paper proposes a novel methodology for PRS extraction and presents the first PRS for body mass index (BMI) in a Greek population. A novel pipeline for PRS derivation was used to analyze genetic data from a unified database of three cohorts of Greek adults. The pipeline spans various steps of the process, from iterative dataset splitting to training and test partitions, calculation of summary statistics and PRS extraction, up to PRS aggregation and stabilization, achieving higher evaluation metrics. Using data from 2185 participants, implementation of the pipeline enabled consecutive repetitions in splitting training and testing samples and resulted in a 343-single nucleotide polymorphism PRS yielding an R² = 0.3241 (beta = 1.011, p-value = 4 × 10^-193) for BMI. PRS-included variants displayed a variety of associations with known traits (i.e., blood cell count, gut microbiome, lifestyle parameters). The proposed methodology led to creation of the first-ever PRS for BMI in Greek adults and aims at promoting a facilitating approach to reliable PRS development and integration in healthcare practice.

Dissecting the clinical relevance of polygenic risk score for obesity-a cross-sectional, longitudinal analysis

BACKGROUND

Obesity is a global pandemic disease whose prevalence is increasing worldwide. The clinical relevance of a polygenic risk score (PRS) for obesity has not been fully elucidated in Asian populations.

METHOD

We utilized a comprehensive health check-up database from the Korean population in conjunction with genotyping to generate PRS for BMI (PRS-BMI). We conducted a phenome-wide association (PheWAS) analysis and observed the longitudinal association of BMI with PRS-BMI.

RESULTS

PRS-BMI was generated by PRS-CS. Adding PRS-BMI to a model predicting ten-year BMI based on age, sex, and baseline BMI improved the model's accuracy (p = 0.003). In a linear mixed model of longitudinal change in BMI with aging, higher deciles of PRS were directly associated with changes in BMI. In the PheWAS, significant associations were observed for metabolic syndrome, bone density, and fatty liver. In the lean body population, those having the top 20% PRS-BMI had higher BMI and body fat mass along with better metabolic trait profiles compared to the bottom 20%. A bottom-20% PRS-BMI was a risk factor for metabolically unhealthy lean body (odds ratio 3.092, 95% confidence interval 1.707-6.018, p < 0.001), with adjustment for age, sex and BMI.

CONCLUSIONS

Genetic predisposition to obesity as defined by PRS-BMI was significantly associated with obesity-related disease or trajectory of obesity. Low PRS-BMI might be a risk factor associated with a metabolically unhealthy lean body. Better understanding the mechanisms of these relationships may allow tailored intervention in obesity or early selection of populations at risk of metabolic disease.

Genetic variation in satiety signaling and hypothalamic inflammation: merging fields for the study of obesity

Although obesity has been a longstanding health crisis, the genetic architecture of the disease remains poorly understood. Genome-wide association studies have identified many genomic loci associated with obesity, with genes being enriched in the brain, particularly in the hypothalamus. This points to the role of the central nervous system (CNS) in predisposition to obesity, and we emphasize here several key genes along the satiety signaling pathway involved in genetic susceptibility. Interest has also risen regarding the chronic, low-grade obesity-associated inflammation, with a growing concern toward inflammation in the hypothalamus as a precursor to obesity. Recent studies have found that genetic variation in inflammatory genes play a role in obesity susceptibility, and we highlight here several key genes. Despite the interest in the genetic variants of these pathways individually, there is a lack of research that investigates the relationship between the two. Understanding the interplay between genetic variation in obesity genes enriched in the CNS and inflammation genes will advance our understanding of obesity etiology and heterogeneity, improve genetic risk prediction analyses, and highlight new drug targets for the treatment of obesity. Additionally, this increased knowledge will assist in physician's ability to develop personalized nutrition and medication strategies for combating the obesity epidemic. Though it often seems to present universally, obesity is a highly individual disease, and there remains a need in the field to develop methods to treat at the individual level.

Application of P4 (Predictive, Preventive, Personalized, Participatory) Approach to Occupational Medicine

In recent years there has been a growth in the role of prevention in controlling the disease burden. Increasing efforts have been conveyed in the screening implementation and public health policies, and the spreading knowledge on risk factors reflects on major attention to health checks. Despite this, lifestyle changes are difficult to be adopted and the adherence to current public health services like screening and vaccinations remains suboptimal. Additionally, the prevalence and outcome of different chronic diseases and cancers is burdened by social disparities. P4 [predictive, preventive, personalized, participatory] medicine is the conceptualization of a new health care model, based on multidimensional data and machine-learning algorithms in order to develop public health intervention and monitoring the health status of the population with focus on wellbeing and healthy ageing. Each of the characteristics of P4 medicine is relevant to occupational medicine, and indeed the P4 approach appears to be particularly relevant to this discipline. In this review, we discuss the potential applications of P4 to occupational medicine, showing examples of its introduction on workplaces and hypothesizing its further implementation at the occupational level.

Nutrigenetic variants and response to diet/lifestyle intervention in obese subjects: a pilot study

AIMS

Nutritional and lifestyle interventions can contribute to prevent and treat obesity and its complications; however, genetic background may influence the success of a therapy. The aim of this pilot study is to evaluate the effects of the interaction between nutrigenetic variants and nutritional intervention, as well as the changes in clinical parameters and the adherence to Mediterranean diet (MedDiet) and to physical activity, of 18 overweight or obese subjects affected by T2D or dysglycemia included in a nutritional program.

METHODS

The subjects' clinical parameters as well as their PREDIMED score and physical activity levels were recorded and compared at baseline, at 6 months and at the end of the intervention. Rs9939609 in FTO, rs17782313 near MC4R, rs326 in LPL, rs16147 in NPY, rs2943641 near IRS-1 were genotyped.

RESULTS

The subjects carrying the A allele in FTO lost less weight (p = 0.022) and had a lower BMI decrease from baseline to 12 months (p-interaction = 0.047) than TT carriers. In addition, there was a significant PREDIMED score modification over time, according to genotypes for FTO rs9939609 (p = 0.025) and NPY rs16147 (p = 0.039), respectively.

CONCLUSIONS

These preliminary findings show a significant interaction between genetic variants and the PREDIMED score, suggesting that individuals carrying the FTO variant may lose less weight than non-carriers through diet/lifestyle intervention.

Sleep and circadian rhythms: pillars of health-a Keystone Symposia report

The human circadian system consists of the master clock in the suprachiasmatic nuclei of the hypothalamus as well as in peripheral molecular clocks located in organs throughout the body. This system plays a major role in the temporal organization of biological and physiological processes, such as body temperature, blood pressure, hormone secretion, gene expression, and immune functions, which all manifest consistent diurnal patterns. Many facets of modern life, such as work schedules, travel, and social activities, can lead to sleep/wake and eating schedules that are misaligned relative to the biological clock. This misalignment can disrupt and impair physiological and psychological parameters that may ultimately put people at higher risk for chronic diseases like cancer, cardiovascular disease, and other metabolic disorders. Understanding the mechanisms that regulate sleep circadian rhythms may ultimately lead to insights on behavioral interventions that can lower the risk of these diseases. On February 25, 2021, experts in sleep, circadian rhythms, and chronobiology met virtually for the Keystone eSymposium "Sleep & Circadian Rhythms: Pillars of Health" to discuss the latest research for understanding the bidirectional relationships between sleep, circadian rhythms, and health and disease.

Improving the Dietary Intake of Healthcare Workers Through Workplace Dietary Interventions: A Systematic Review and Meta-Analysis

The workplace has been identified as a potential location for dietary intervention delivery due to the amount of time spent and the meals eaten in this setting. It is recommended that interventions are tailored to specific occupational groups, and to date, there is limited synthesis of the evidence relating to healthcare workers. This review characterises and evaluates the effectiveness of dietary interventions in healthcare workers to aid the design and implementation of interventions. The MEDLINE database was searched to September 2020. The reference list of an umbrella review was hand searched for additional titles against inclusion criteria. The search included i) population, ii) intervention and iii) work environment. Studies were assessed for risk of bias. Harvest plots and Forest plots were created to display study quality, direction and size of effect of selected primary (energy, fruit and vegetable and fat intake) and secondary outcomes (weight, body mass index, blood pressure and serum cholesterol levels). Thirty-nine articles assessing thirty-four interventions were eligible for inclusion. Intervention types most commonly used were environmental, educational, educational plus behavioural, and behavioural. Due to the heterogeneity in study design and intervention type, results were largely inconclusive. For dietary outcomes, interventions produced small-moderate favorable changes in fruit, vegetable and fat intake. Decreased fat intake was mainly observed in environmental interventions and increases in fruit and vegetable intake were observed when an educational or/and behavioral component was present. Interventions producing weight loss were mostly non-randomised trials involving education and physical activity. Total and low-density lipoprotein cholesterol decreased in interventions involving physical activity. Meta-analyses revealed significant decreases in energy intake, weight, blood pressure, total cholesterol, and LDL cholesterol in non-randomised trials where data were available. Much more research is needed into strategies to promote diet quality improvement in healthcare workers. Statement of significance: It is recommended that workplace dietary interventions are tailored to specific occupational groups. To our knowledge, this is the first review to examine the effects of dietary workplace interventions in healthcare professionals. Small-moderate favourable changes in fruit and vegetable intake can be achieved when an educational or/and behavioural component is included in the intervention. For weight loss, interventions involving nutrition education and physical activity in addition to a dietary component show benefit. In the studies reviewed, a high level of heterogeneity was evident and insufficient information reported to ascertain potential bias.

Dietary Mediators of the Genetic Susceptibility to Obesity-Results from the Quebec Family Study

BACKGROUND

Recent studies showed that eating behaviors such as disinhibition, emotional and external eating, and snacking mediate genetic susceptibility to obesity. It remains unknown if diet quality and intake of specific food groups also mediate the genetic susceptibility to obesity.

OBJECTIVE

This study aimed to assess if diet quality and intakes of specific food groups mediate the association between a polygenic risk score (PRS) for BMI and BMI and waist circumference (WC). We hypothesized that poor diet quality, high intakes of energy-dense food groups, and low intakes of nutrient-dense food groups mediate the genetic susceptibility to obesity.

METHODS

This cross-sectional study included 750 participants (56.3% women, aged 41.5 ± 14.9 y, BMI 27.8 ± 7.5 kg/m2) from the Quebec Family Study. A PRSBMI based on >500,000 genetic variants was calculated using LDpred2. Dietary intakes were assessed with a 3-d food record from which a diet quality score (i.e. Nutrient Rich Food Index 6.3) and food groups were derived. Mediation analyses were conducted using a regression-based and bootstrapping approach.

RESULTS

The PRSBMI explained 25.7% and 19.8% of the variance in BMI and WC, respectively. The association between PRSBMI and BMI was partly mediated by poor diet quality (β = 0.33 ± 0.12; 95% CI: 0.13, 0.60), high intakes of fat and high-fat foods (β = 0.46 ± 0.16; 95% CI: 0.19, 0.79) and sugar-sweetened beverages (β = 0.25 ± 0.14; 95% CI: 0.05, 0.60), and low intakes of vegetables (β = 0.15 ± 0.08; 95% CI: 0.03, 0.32), fruits (β = 0.37 ± 0.12; 95% CI: 0.17, 0.64), and dairy products (β = 0.17 ± 0.09; 95% CI: 0.02, 0.37). The same trends were observed for WC.

CONCLUSIONS

The genetic susceptibility to obesity was partly mediated by poor diet quality and intakes of specific food groups. These results suggest that improvement in diet quality may reduce obesity risk among individuals with high genetic susceptibility and emphasize the need to intervene on diet quality among these individuals.

Genetic risk for obesity and the effectiveness of the ChooseWell 365 workplace intervention to prevent weight gain and improve dietary choices

BACKGROUND

It is unknown whether behavioral interventions to improve diet are effective in people with a genetic predisposition to obesity.

OBJECTIVES

To examine associations between BMI genetic risk and changes in weight and workplace purchases by employees participating in a randomized controlled trial of an automated behavioral workplace intervention to promote healthy food choices.

METHODS

Participants were hospital employees enrolled in a 12-mo intervention followed by a 12-mo follow-up. Hospital cafeterias utilized a traffic-light labeling system (e.g., green = healthy, red = unhealthy) that was used to calculate a validated Healthy Purchasing Score (HPS; higher = healthier). A weighted genome-wide BMI genetic score was generated by summing BMI-increasing alleles.

RESULTS

The study included 397 adults of European ancestry (mean age, 44.9 y; 80.9% female). Participants in the highest genetic quartile (Q4) had a lower HPS and higher purchases of red-labeled items relative to participants in the lowest quartile (Q1) at baseline [Q4-Q1 Beta HPS, -4.66 (95% CI, -8.01 to -1.32); red-labeled items, 4.26% (95% CI, 1.45%-7.07%)] and at the 12-mo [HPS, -3.96 (95% CI, -7.5 to -0.41); red-labeled items, 3.20% (95% CI, 0.31%-6.09%)] and 24-mo [HPS, -3.70 (95% CI, -7.40 to 0.00); red-labeled items, 3.48% (95% CI, 0.54%-6.41%)] follow-up periods. In the intervention group, increases in HPS were similar in Q4 and Q1 at 12 mo (Q4-Q1 Beta, 1.04; 95% CI, -2.42 to 4.50). At the 24-mo follow-up, the change in BMI from baseline was similar between Q4 and Q1 (0.17 kg/m2; 95% CI, -0.55 to 0.89 kg/m2) in the intervention group, but higher in Q4 than Q1 (1.20 kg/m2; 95% CI, 0.26-2.13 kg/m2) in the control group. No interaction was evident between the treatment arm and genetic score for BMI or HPS.

CONCLUSIONS

Having a high BMI genetic risk was associated with greater increases in BMI and lower quality purchases over 2 y. The 12-mo behavioral intervention improved employees' food choices, regardless of the genetic burden, and may have attenuated weight gain conferred by having the genetic risk.

Automated Behavioral Workplace Intervention to Prevent Weight Gain and Improve Diet: The ChooseWell 365 Randomized Clinical Trial

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.