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）来减肥， 难如果向参与者提供持续的问责制来源， 支持和持续的机会，与其他人一起反思目标的进展情况。这项研究表明，共享 数字数据（即，体重来自无线秤，身体活动来自可穿戴传感器，饮食摄入量 从智能手机应用程序）与其他各方有可能改善长期减肥。的利益 设备数据共享尚未经过严格的测试，传统的LM计划尚未纳入 以系统的方式共享数字数据。拟议的研究将招募超重/肥胖的成人（N = 320）。 在一个为期24个月的LM项目中，指导他们使用数字化工具来自我监测体重、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，以开始减肥。 在第4-24个月，干预联系将是远程的，并将包括以下内容： 通过视频会议;每季度与教练进行两次简短的电话交谈;每月与 教练，与一小群小组参与者，以及一个朋友或家人在外面， 程序. 2 × 2 × 2析因设计将测试三种类型的数据共享的独立效应 合作伙伴关系：教练共享、小组共享和朋友/家人共享。一半的参与者将获得教练 分享，一半不会;一半将获得团体分享，一半不会;一半将获得朋友/家人分享 一半不会。在Coach Share中，行为教练将查看整个过程中的数字自我监控数据。 计划，并将直接解决干预接触期间的数据观察。在集团份额中，参与者 将在其小组文本消息中查看彼此的自我监控数据。在 朋友/家人共享，组外的朋友或家庭成员将通过以下方式查看参与者的数据 自动短信与之共享数据的每一方都将接受培训， 指数参与者对其目标进展的反映，这是自我调节的关键组成部分，以及 支持参与者实现计划目标的动机。干预接触量 参与者和每一方（教练、小组、朋友/家人）在治疗条件下都是可比的， 隔离数据共享组件的影响。将在第0、6、12和24个月测量结局。的 研究将确定教练分享、团体分享和朋友/家人分享是否都能长期改善体重 损失、PA和卡路里摄入（即，将比较随机参与以下研究的受试者的结果： 这种数据共享伙伴关系，与那些谁不是）。这项研究还将检查影响是否是累加的 当参与者被分配参与一种以上类型的数据共享伙伴关系时。调解人和 将对干预效果的调节因素进行研究。随着数字技术使数据共享越来越 因此，确定如何优化这些伙伴关系以改善LM的长期成果至关重要。

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.