背景:吸烟的年轻人对戒烟的兴趣很普遍,但它可以证明具有挑战性。尽管基于证据的戒烟干预措施存在并且是有效的,缺乏专门为年轻人设计的这些干预措施仍然是这一人群成功戒烟的主要障碍.因此,研究人员已经开始开发现代,基于智能手机的干预措施,在适当的地点和时间为个人提供戒烟信息。一种有前途的方法是使用吸烟高风险位置周围的地理围栏空间缓冲区提供干预措施,当个人电话进入周边时,这些缓冲区会触发干预信息。尽管个性化和无处不在的戒烟干预措施有所增加,很少有研究使用空间方法来优化使用地点和时间信息的干预措施。
目的:本研究通过结合自我报告的基于智能手机的调查和被动跟踪的位置数据,提出了4个案例研究,展示了一种在吸烟高风险区域周围生成特定于人的地理围栏的探索性方法。该研究还研究了哪种地理围栏构建方法可以为后续研究设计提供信息,该设计将在年轻人进入地理围栏边界时自动部署应对信息的过程。
方法:数据来自2016年至2017年在旧金山湾地区对年轻成年吸烟者进行的生态瞬时评估研究。参与者通过智能手机应用程序报告了30天的吸烟和非吸烟事件,GPS数据由应用程序记录。我们沿着生态瞬时评估合规性四分位数对4个案例进行了采样,并在每个3小时的时间间隔内使用归一化平均核密度估计超过0.7的区域,在具有自我报告的吸烟事件的位置周围构建了特定于人的地理围栏。我们评估了为3种类型的区域(人口普查区,500ft2渔网网格,和1000平方英尺的渔网网格)。在4个案例中进行了描述性比较,以更好地了解每种地理围栏构建方法的优势和局限性。
结果:4例报告的过去30天吸烟事件的数量为12至177。4例病例中的3例的每个3小时地理围栏捕获了超过50%的吸烟事件。与4例人口普查相比,1000ft2渔网网格捕获的吸烟事件百分比最高。在3小时的时间段内,除了1起案件的3:00AM-5:59AM,地理围栏平均包含36.4%-100%的吸烟事件。研究结果表明,与人口普查区相比,渔网网格地理围栏可以捕获更多的吸烟事件。
结论:我们的研究结果表明,这种地理围栏构建方法可以按时间和地点识别高风险吸烟情况,并有可能为戒烟干预提供个性化的地理围栏。在随后的一项基于智能手机的戒烟干预研究中,我们计划使用渔网网格地理围栏来通知干预消息的传递。
Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking
cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking
cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences-spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual\'s phone enters the perimeter. Despite growth in personalized and ubiquitous smoking
cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information.
This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4
case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries.
Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method.
The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1
case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks.
Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages.