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临床试验/NCT04440553
NCT04440553已完成不适用

An mHealth App Using Adaptive Learning to Increase Physical Activity in University Students

University of California, Berkeley2 个研究点 分布在 1 个国家目标入组 103 人开始时间: 2019年9月12日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
103
试验地点
2
主要终点
Steps (measured by phone pedometer)

研究概览

简要总结

Background: Insufficient physical activity is one of the leading risk factors of death worldwide. Behavioral treatments delivered via smartphone apps, hold great promise for helping people engage in healthy behaviors including becoming more physically active. However, similar to 'face-to-face' treatments, effects typically do not seem to be sustained over longer periods of time.

Methods: the investigators developed a smartphone application that uses different types of motivational and feedback text-messaging to motivate individuals to increase physical activity. Here, participants are randomized to either receive messages by a uniform random distribution (n=50), or chosen by a reinforcement learning algorithm (n=50), which learns from daily participant data to personalize the frequency and type of motivation of messages.

Objectives: In the current study, the investigators examine this application in undergraduate and graduate students at the University of California, Berkeley. The investigators compare whether participants in the uniform random or adaptive group have higher increases in steps during the study. The investigators also examine the effect of the different types of messages on step counts. Further the investigators assess the influence of patient characteristics, such as socio-demographic, psychological questionnaire scores and baseline physical activity on the effect of the adaptive arm and effectiveness of the messages. Finally, the investigators assess participant qualitative feedback on the text-messaging program, through feedback provided via questionnaires, text-message and phone interviews.

详细描述

The investigators developed a smartphone application, the DIAMANTE app, that uses machine learning to generate adaptive text messages, learning from daily participant data to personalize the frequency and type of motivation of messages. In the current study, the investigators will compare this application in undergraduate and graduate students at the University of Berkeley, to text-messaging chosen randomly. This study will provide insight into the effectiveness of this smartphone application for increasing physical activity in university students. Further, it will provide preliminary knowledge on the working mechanisms and variables that moderate the effectiveness of the intervention.

This study is characterized by a factorial design with a total of 3 factors representing Motivational Messages (M), Feedback Messages (F) and the Time Frame (T) when the message was sent, of 4, 5 and 4 levels each, respectively. One level of M and F corresponded to a control treatment, i.e., no message sent. Each participant received one different combination of M, F and T every day.

Both the adaptive and uniform random group will receive the same types of messages: feedback (4 active categories plus no message) and motivation (3 active categories plus no message). However, the message categories, timing and frequency will be optimized by a reinforcement learning algorithm in the adaptive group, and will be delivered with equal probabilities in the uniform random group (following a uniform random distribution).

For the reinforcement learner group, the algorithm training data consists of the historical data of all participants (contextual variables), which include which messages were sent previously and within which time periods, and select clinical/demographic data (such as age, day of the week and depression scores) to improve prediction abilities. Subsequently, the message is chosen based on the predicted effectiveness of messages, combined with a sampling method. As such, it frequently picks out from the most rewarding messages and occasionally explores the messages with uncertainty in their reward.

The aims of this study are:

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Prevention
盲法
Single (Participant)

盲法说明

Participants were unaware of their group membership. Investigators were not blinded to group membership.

入排标准

年龄范围
18 Years 至 65 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • We will include currently enrolled undergraduate and graduate students ages 18 to

排除标准

  • Students that do not have a smartphone, are not able to exercise due to disability, or have plans to leave the country during the 6 week study will be excluded.

结局指标

主要结局

Steps (measured by phone pedometer)

时间窗: Change from baseline to 6 week follow-up

Mean change in daily step counts during the course of the study

次要结局

  • Depression scores(Change from baseline to 6 week follow-up)
  • Anxiety scores(Change from baseline to 6 week follow-up)
  • Behavioral Activation(Change from baseline to 6 week follow-up)

研究者

申办方类型
Other
责任方
Sponsor

研究点 (2)

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