Division of General Internal Medicine, Massachusetts General Hospital, Boston.
Harvard Medical School, Boston, Massachusetts.
JAMA Netw Open. 2021 Jun 1;4(6):e2112528. doi: 10.1001/jamanetworkopen.2021.12528.
Personalized interventions that leverage workplace data and environments could improve effectiveness, sustainability, and scalability of employee wellness programs.
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.
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.
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.
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.
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.
ClinicalTrials.gov Identifier: NCT02660086.
重要性：利用工作场所数据和环境的个性化干预措施可能会提高员工健康计划的效果、可持续性和可扩展性。
目的：使用员工自助餐厅购买数据，测试一种预防体重增加和改善饮食的自动化行为干预措施。
设计、地点和参与者：这是一项在马萨诸塞州波士顿一家医院进行的为期 12 个月的干预和 12 个月随访的个体水平随机临床试验，参与者在使用红绿灯标签（即绿色表示健康；黄色表示不太健康；红色表示不健康）的现场自助餐厅购买食物。参与者于 2016 年 9 月至 2018 年 2 月入组。数据于 2020 年 5 月至 9 月进行分析。
干预措施：在 12 个月的时间里，干预组的参与者每周收到 2 封电子邮件，其中包含对之前自助餐厅购买情况的反馈和个性化的健康和生活方式提示，每月收到 1 封电子邮件，其中包含同伴比较和对更健康购买的财务奖励。电子邮件是使用调查、健康和自助餐厅数据自动生成的。对照组的参与者每月收到 1 封电子邮件，其中包含一般健康的生活方式信息。
主要结果和措施：主要结果是从基线到 12 个月和 24 个月随访的体重变化。次要结果包括从基线（12 个月干预前）到干预（1-12 个月）和随访（13-24 个月）期间，自助餐厅购买情况的变化，包括绿色和红色标签购买的比例以及每天购买的卡路里量。比较基线健康饮食指数-15（HEI-15）评分与 6、12 和 24 个月的 HEI-15 评分。
结果：在 602 名入组的员工中（平均[标准差]年龄，43.6[12.2]岁；478[79.4%]名女性），299 名被随机分配到干预组，303 名被随机分配到对照组。基线平均（标准差）体重指数（BMI；体重以千克为单位除以身高以米为单位）为 28.3（6.6），HEI-15 评分为 60.4（12.4）。在 12 个月（0.2[95%CI，-0.6 至 1.0]kg）和 24 个月（0.6[95%CI，-0.3 至 1.4]kg）时，两组之间的体重变化没有差异。与基线相比，干预组增加了 7.3%（95%CI，5.4%至 9.3%）的绿色标签购买量，减少了 3.9%（95%CI，-5.0%至-2.7%）的红色标签购买量和每天购买的卡路里量减少了 49.5（95%CI，-75.2 至-23.9）千卡，比对照组在干预期间更多。在干预组中，绿色（4.8%[95%CI，2.9%至 6.8%]）和红色购买量的变化差异在 24 个月的随访中持续存在（-3.1%[95%CI，-4.3%至-2.0%]）。在 6（2.2[95%CI，0 至 4.4]）、12（1.8[95%CI，-0.6 至 4.1]）和 24（1.6，95%CI，-0.7 至 3.8]）个月时，干预组与对照组相比，HEI-15 评分的变化差异无统计学意义。
结论和相关性：这项随机临床试验的结果表明，使用工作场所自助餐厅数据的自动化行为干预措施改善了员工的食物选择，但在 2 年内并没有防止体重增加。
试验注册：ClinicalTrials.gov 标识符：NCT02660086。
