A Mobile-Integrated Behavioral Activation Program Using Wearable Sensor Support and Optional Decentralized Data Verification to Enhance Daily Productivity in Healthy Adults
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 227,000
- 试验地点
- 1
- 主要终点
- Change in Average Daily Active Minutes
研究概览
简要总结
This study evaluates a mobile-integrated behavioral activation program designed to help adults improve their daily productivity, motivation, and task engagement. The program combines a smartphone application, wearable sensor data, and a decentralized data-verification layer to support participants as they build healthier routines and increase consistent daily activity.
Behavioral activation is a well-established psychological approach that encourages individuals to take small, structured actions that align with their goals and values. In this study, participants receive daily prompts, activity suggestions, and personalized behavioral tasks through a mobile app. The app uses information from a wearable sensor-such as movement patterns, activity levels, and environmental cues-to help participants track progress and stay engaged with the program.
A unique feature of this study is the use of decentralized data verification. Participants' activity logs and task completions are recorded in a secure, tamper-resistant system that allows them to maintain control over their own data while ensuring accuracy and transparency. This approach supports participant autonomy and strengthens the reliability of the study's outcome measures.
The study aims to understand whether combining behavioral activation with real-time sensor feedback and decentralized data verification can improve daily productivity, increase follow-through on planned tasks, and support healthier routines. Participants will use the mobile app and wearable device for the duration of the study and will complete periodic check-ins to share their experiences, challenges, and overall satisfaction with the program.
The research team will evaluate changes in daily activity patterns, task completion rates, self-reported productivity, and engagement with the behavioral activation tasks. Findings from this study may help inform future digital health tools that support motivation, routine-building, and personal productivity in everyday life.
详细描述
Daily functioning in modern environments is often affected by high cognitive load, fragmented attention, and inconsistent routines. Many adults experience difficulty initiating tasks, sustaining engagement, and maintaining productive habits. Behavioral Activation (BA), originally developed for mood-related conditions, provides a structured approach for increasing engagement in meaningful activities and reducing avoidance patterns. BA emphasizes that action precedes motivation and that small, achievable behaviors can create reinforcement cycles that improve daily functioning.
Mobile technology and wearable sensors now allow BA principles to be delivered in real time. Wearable devices can detect movement patterns, activity levels, sleep-related rhythms, and environmental cues. When paired with a mobile application, these data streams support adaptive prompts, personalized task suggestions, and real-time feedback. This integration enables interventions to occur in natural environments rather than relying solely on retrospective self-report.
To enhance transparency and participant autonomy, the study incorporates a decentralized verification layer. Traditional digital-health systems rely on centralized data storage controlled by the research team. In contrast, decentralized verification provides tamper-resistant logs, participant-controlled data review, and transparent audit trails. This approach supports data integrity and participant trust.
The study evaluates a mobile-integrated behavioral activation program supported by wearable sensors and a decentralized verification layer. The objective is to determine whether this combined approach can improve daily functioning, increase follow-through on planned tasks, and support healthier routines in adults seeking to enhance their productivity.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Adults age 18 or older
- •Owns a compatible smartphone capable of running the study app
- •Willing to wear a lightweight activity sensor during waking hours (for applicable arms)
- •Able to read and understand study instructions in English
- •Willing to complete daily tasks and weekly check-ins through the mobile app
- •Provides informed consent
排除标准
- •Any condition that would prevent use of a mobile app or wearable sensor (e.g., inability to operate a smartphone)
- •Any condition that would interfere with participation in daily behavioral tasks as determined by the research team
- •Current participation in another digital-behavioral intervention study
- •Inability or unwillingness to comply with study procedures
- •Plans to travel or relocate in a way that would prevent consistent participation during the study period
研究组 & 干预措施
Experimental - Mobile App Only
Participants use a mobile behavioral activation application that delivers daily tasks, reminders, and routine-building prompts. No wearable sensor or secure activity-verification layer is used in this arm.
干预措施: Behavioral: Mobile Behavioral Activation App (Behavioral)
Experimental - Mobile App + Wearable Sensor
Participants use the same mobile behavioral activation app as Arm 1 and also wear a lightweight activity sensor that passively tracks movement and activity patterns. Sensor-derived activity data may inform the timing or intensity of app-delivered prompts.
干预措施: Mobile App + Wearable Activity Sensor (Behavioral)
Experimental - Mobile App + Wearable Sensor + Secure Activity Verification
Participants use the mobile behavioral activation app and wearable activity sensor. Activity logs are additionally processed through a secure verification layer that provides participant-controlled, tamper-evident activity records used for engagement tracking and outcome analysis.
干预措施: Mobile App + Wearable Sensor + Secure Activity Verification Layer (Behavioral)
结局指标
主要结局
Change in Average Daily Active Minutes
时间窗: 8 weeks from baseline enrollment
Average number of minutes per day spent in non-sedentary activity, as captured by the wearable activity sensor (for applicable arms) and inferred from app interaction logs (for all arms). Daily active minutes will be averaged over the assessment period and compared across intervention arms to evaluate whether adding a wearable sensor and decentralized verification layer increases real-world physical activation associated with behavioral tasks.
Change in Daily Task Completion Rate
时间窗: 8 Weeks from baseline enrollment
Proportion of assigned behavioral activation tasks completed per day, as recorded by the mobile app, averaged over the assessment period. Daily task completion rate will be compared across intervention arms (mobile app only; mobile app + wearable sensor; mobile app + wearable sensor + decentralized verification layer) to evaluate the effect of each intervention tier on real-world task follow-through and productivity.
次要结局
- Change in Self-Reported Daily Productivity(8 weeks from baseline enrollment)
- Change in Routine Stability Index(8 weeks from baseline enrollment)