Sharing Digital Self-Monitoring Data with Others to Enhance Long-Term Weight Loss: A Randomized Trial using a Factorial Design

摘要 成年人试图通过改变生活方式(LM)减肥，通常会发现行为变化保持不变 很难。如果向参与者提供持续的问责来源和 支持和持续的机会，与其他人一起反思目标的进展。这项研究提出，分享 数字数据(例如，来自无线秤的体重、来自可穿戴传感器的体力活动，以及饮食摄入量 来自智能手机应用程序)与其他各方的合作可能会改善长期减肥效果。的好处 设备数据共享尚未经过严格测试，传统的LM计划尚未纳入 以系统化的方式共享数字数据。建议的研究将招募超重的成年人(N=320)。 在24个月的LM计划中进行肥胖，并指导他们使用数字工具自我监测体重、身体状况 活动，以及每天的饮食。小组将在1-3个月内每周面对面会面，开始减肥。 在4-24个月内，干预接触将是远程的，将包括以下内容：每季度举行一次小组会议 通过视频会议；每季度与教练进行两次简短的电话交谈；每月发送短信 教练，与一小群同伴一起参加小组活动，并与朋友或家人一起参加 程序。2×2×2析因设计将检验三种数据共享类型的独立效应 合作伙伴关系：教练共享、小组共享和朋友/家人共享。一半的参与者将接受教练 份额和一半不会；一半将获得集团份额，一半不会；一半将获得朋友/家人份额 而一半的人不会。在Coach Share中，行为教练将查看整个 计划，并将直接处理干预接触期间观察到的数据。在组共享中，参与者 在给定的LM组中，群组将在各自的小组短信中查看彼此的自我监控数据。在……里面 朋友/家人共享，组外的朋友或家人将通过查看参与者的数据 自动文本消息。与之共享数据的每一方都将接受培训，通过引出 指标参与者对其目标进展的反映，这是自我调节的一个关键组成部分，以及 支持学员达到计划目标的动机。两人之间的干预接触量 参与者和每一方(教练、小组、朋友/家人)将在不同的治疗条件下具有可比性， 隔离数据共享组件的影响。结果将在0、6、12和24个月进行衡量。这个 研究将确定教练份额、小组份额和朋友/家人份额是否各自提高了长期体重 损失、PA和卡路里摄入量(即，将比较随机参与的参与者的结果 数据共享伙伴关系，而不是那些不是)。这项研究还将检查影响是否具有累加性 当参与者被指派参与一种以上类型的数据共享伙伴关系时。调解员和 将审查干预效果的主持人。随着数字技术使数据共享日益增多 在可行的情况下，关键是要确定如何优化这些伙伴关系，以提高长期合作伙伴关系的长期成果。

Abstract Adults attempting weight loss through lifestyle modification (LM) typically find maintenance of behavior change difficult. Outcomes might be improved if participants are provided with sustained sources of accountability and support and ongoing opportunities to reflect with others on goal progress. This study proposes that sharing digital data (i.e., body weight from wireless scale, physical activity from wearable sensor, and dietary intake from smartphone app) with other parties has the potential to improve long-term weight loss. The benefit of device data sharing has not yet been rigorously tested, and traditional LM programs do not yet incorporate digital data sharing in a systematic way. The proposed study will enroll adults (N = 320) with overweight/ obesity in a 24-month LM program and instruct them to use digital tools for self-monitoring of weight, physical activity, and eating on a daily basis. Groups will meet face-to-face weekly in months 1-3 to initiate weight loss. In months 4-24, intervention contact will be remote and will include the following: quarterly group meetings held via videoconference; brief phone calls with the coach held twice per quarter; and monthly text messages with the coach, with a small group of fellow group participants, and with a friend or family member outside of the program. A 2 x 2 x 2 factorial design will test the independent effects of three types of data sharing partnerships: Coach Share, Group Share, and Friend/Family Share. Half of the participants will receive Coach Share and half will not; half will receive Group Share and half will not; and half will receive Friend/Family Share and half will not. In Coach Share, the behavioral coach will view digital self-monitoring data throughout the program and will directly address data observations during intervention contacts. In Group Share, participants in a given LM group will view each other’s self-monitoring data in their small-group text messages. In Friend/Family Share, a friend or family member outside of the group will view the participant’s data via automated text message. Each party with whom data are shared will be trained to respond by eliciting reflection from the index participant on his/her goal progress, which is a key component of self-regulation, and supporting the participant’s motivation to meet program goals. Amount of intervention contact between the participant and each party (Coach, Group, Friend/Family) will be comparable across treatment conditions, isolating the effects of data sharing components. Outcomes will be measured at months 0, 6, 12, and 24. The study will determine if Coach Share, Group Share, and Friend/Family Share each improve long-term weight loss, PA, and calorie intake (i.e., outcomes will be compared for participants who are randomized to engage in that data sharing partnership, versus those who are not). The study also will examine if effects are additive when participants are assigned to engage in more than one type of data sharing partnership. Mediators and moderators of intervention effects will be examined. As digital technology makes data sharing increasingly feasible, it is critical to determine how to optimize these partnerships to improve long-term outcomes in LM.