背景:数字即时自适应干预(JITAI)可以减少年轻人的暴饮暴食事件(BDE:女性/男性每次饮用4/5以上饮料),但需要针对时间和内容进行优化。在BDE之前的几个小时内及时提供支持信息可以改善干预效果。
目标:我们确定了开发机器学习模型以准确预测未来的可行性。也就是说,同一天,使用智能手机传感器数据的BDE前1至6小时。我们旨在确定周末和工作日与BDE相关的信息最丰富的电话传感器功能,分别,来确定解释预测模型性能的关键特征。
方法:我们收集了75名具有危险饮酒行为的年轻人(21-25岁;平均值=22.4,SD=1.9)的电话传感器数据,他们报告了超过14周的饮酒行为。这项二级分析的参与者参加了一项临床试验。我们开发了测试不同算法的机器学习模型(例如,XGBoost,决策树)使用智能手机传感器数据(例如,加速度计,GPS)。我们测试了从饮酒开始的各种“预测距离”时间窗口(更接近:1小时;到远处:6小时)。我们还测试了各种分析时间窗口(即,要分析的数据量),饮酒前1至12小时,因为这决定了计算模型需要存储在手机上的数据量。可解释的AI(XAI)用于探索对BDE有贡献的信息最多的电话传感器功能之间的相互作用。
结果:XGBoost模型在预测即将到来的当天BDE方面表现最好,周末准确率为95.0%,工作日准确率为94.3%(F1评分分别为0.95和0.94)。这个XGBoost模型需要12-和9-小时的电话传感器数据在3-和6-小时的预测距离从饮酒开始,在周末和工作日,分别,在预测当天的BDE之前。用于BDE预测的信息最多的电话传感器功能是时间(例如,一天中的时间)和GPS派生的,如回转半径(行程指标)。关键特征之间的交互(例如,一天的时间,GPS派生的功能)有助于当天BDE的预测。
结论:我们证明了智能手机传感器数据和机器学习的可行性和潜在用途,可以准确预测年轻人中即将发生的(当天)BDE。预测模型提供了“机会窗口”,并采用了XAI,我们确定了“关键贡献特征”以在BDE发作之前触发JITAI,有可能降低年轻人患BDE的可能性。
BACKGROUND: Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact.
OBJECTIVE: We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance.
METHODS: We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical
trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various \"prediction distance\" time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs.
RESULTS: The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs.
CONCLUSIONS: We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides \"windows of opportunity,\" and with the adoption of explainable artificial intelligence, we identified \"key contributing features\" to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults.
BACKGROUND: ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565.