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Smartband/smartphone-based automatic smoking detection and real time mindfulness intervention

基于智能手环/智能手机的自动吸烟检测和实时正念干预

基本信息

批准号：
9925202
负责人：
Kathleen A. GARRISON
金额：
$ 37.69万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-15 至 2022-10-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9925202
关键词：
Adherence Attention Awareness Cellular Phone Cessation of life Clinical Trials Data Detection Development Devices Disease Ecological momentary assessment Exercise Feedback Future Goals Growth Guidelines Hand Individual Internet Intervention Learning Machine Learning Measures Memory Mindfulness Training Monitor Motivation Notification Participant Patient Self-Report Pattern Persons Population Protocols documentation Randomized Controlled Trials Reporting Smoke Smoker Smoking Smoking Behavior Smoking Cessation Intervention Standardization Surveys Technology Testing Time Work base cost effective craving disability effective therapy efficacy clinical trial evidence base experience innovation innovative technologies mindfulness mindfulness intervention mobile computing multimodal data novel pilot trial preventable death response sensor smartphone Application smoking cessation smoking intervention tool wearable device

项目摘要

PROJECT SUMMARY Smoking is the leading cause of preventable death in the US. Effective smoking cessation interventions are available but underutilized. Smoking cessation interventions delivered by smartphone apps are a promising tool for helping smokers quit. Delivery of treatments via smartphone apps may maximize the likelihood of use by smokers and the potential impact on smoking behavior. However, currently available smartphone apps for smoking cessation have not exploited their unique potential advantages to aid quitting. Notably, no available apps utilize wearable technologies; all current apps require users to self-report their smoking; and no apps deliver treatment automatically contingent upon smoking. Therefore, this pilot trial will test the feasibility of using a smartband to detect and track smoking and deliver brief smoking cessation interventions by smartphone app in real time. The interventions to be delivered will be brief mindfulness exercises that have been previously shown to reduce craving and smoking. This trial uses SmokeBeat, a novel mobile technology platform that uses multimodal data from wristband sensors to monitor and detect smoking, notify smokers about their smoking in real time and deliver real time interventions triggered by detected smoking episodes. SmokeBeat also applies machine learning to smoking tracking data to identify individual smoking patterns and deliver real time interventions targeted at predicted smoking episodes. This trial tests a three-step intervention to reduce smoking, in which smokers first become aware of their smoking and triggers by tracking smoking; then gain a clear recognition of the actual effects of smoking by “mindful smoking”; and finally learn to work mindfully with cravings rather than smoke. Briefly, daily smokers (N=200, ≥5 cig/day) will wear a smartband to detect and notify them of smoking for 21 days and obtain individual smoking profiles; detected smoking will then trigger a “mindful smoking” exercise for the next 7 days leading up to their quit date at 30 days; after which another mindfulness exercise (“RAIN”: recognize, accept, investigate and note cravings rather than smoke) will be delivered prior to each predicted smoking episode according to their individual smoking profile for 30 days post-quit. Aim 1 will be to determine treatment fidelity. Fidelity measures will be: (1) percent of smoking episodes correctly detected; (2) percent of “mindful smoking” exercises correctly triggered by smoking; and (3) users’ real time ratings of how timely “RAIN” was delivered to predicted smoking episodes. Aim 2 will be to determine adherence to treatment. Adherence measures will be: (1) percent of time spent wearing the smartband; (2) percent of smoking notifications answered; (3) percent of ecological momentary assessment (EMA) ratings (e.g., timeliness and others) answered; and (4) percent of mindfulness exercises completed. Aim 3 will be to determine the acceptability of treatment. Acceptability measures will be: (1) average helpfulness ratings after each mindfulness exercise; (2) feedback on user experience surveys. Overall: this project tests a highly innovative technology-based mindfulness intervention for smoking cessation.

项目摘要吸烟是美国可预防死亡的主要原因。有效的戒烟干预措施是可用但未充分利用。通过智能手机应用程序提供的戒烟干预是一种有前途的方法。帮助吸烟者戒烟的工具。通过智能手机应用程序提供治疗可以最大限度地提高使用的可能性以及对吸烟行为的潜在影响。然而，目前可用的智能手机应用程序，戒烟没有利用其独特的潜在优势来帮助戒烟。值得注意的是，没有可用的应用程序利用可穿戴技术;所有当前的应用程序都要求用户自我报告吸烟情况;没有应用程序根据吸烟情况自动提供治疗。因此，这次试点试验将测试的可行性，使用智能手环检测和跟踪吸烟，并提供简短的戒烟干预措施，真实的智能手机应用程序。要提供的干预措施将是简短的正念练习，以前被证明可以减少渴望和吸烟。该试验使用了一种新颖的移动的技术SmokeBeat 该平台使用来自腕带传感器的多模态数据来监测和检测吸烟，关于他们的吸烟的真实的时间和交付由检测到的吸烟事件触发的真实的时间干预。 SmokeBeat还将机器学习应用于吸烟跟踪数据，以识别个人吸烟模式，提供针对预测吸烟事件的真实的实时干预。这个试验测试了一个三步干预减少吸烟，其中吸烟者首先意识到他们吸烟，并通过跟踪吸烟触发; 然后通过“正念吸烟”来明确认识吸烟的实际影响;最后学会工作而不是吸烟。简而言之，每日吸烟者（N=200，≥5支/天）将佩戴智能手环，检测并通知他们吸烟21天，并获得个人吸烟档案;检测到的吸烟将然后在接下来的7天内触发“正念吸烟”练习，直到他们在30天内戒烟; 另一个正念练习（“雨”：识别，接受，调查和注意渴望，而不是吸烟者）将根据其个人吸烟特征在每次预测的吸烟事件之前递送戒烟后30天目标1是确定治疗保真度。忠诚度指标将是：（1）百分比正确检测到的吸烟事件;（2）正确触发的“正念吸烟”练习的百分比吸烟;和（3）用户对“RAIN”如何及时地递送到预测的吸烟发作的真实的时间评级。目标2是确定治疗依从性。遵守措施将是：（1）花费的时间百分比戴着智能手环;（2）回答吸烟通知的百分比;（3）评估（EMA）评级（例如，及时性和其他）回答;（4）正念练习的百分比完成目标3是确定治疗的可接受性。可接受性措施将是：（1）每次正念练习后的平均帮助评级;（2）用户体验调查的反馈。总体而言：该项目测试了一种高度创新的基于技术的戒烟正念干预。