James J. Yang

In spite of wide applications of the singular spectrum analysis (SSA) method, understanding how SSA reconstructs time series and eliminates noise remains challenging due to its complex process. This study provided a novel geometric perspective to elucidate the underlying mechanism of SSA. To address the key issue of conventional SSA that requires a fixed window length and a given threshold for determining the number of groups, we proposed a sequential reconstruction approach that averages reconstructed series from various window lengths with a stopping rule based on a symmetric test. Three main advantages of the proposed method were demonstrated by the simulations and real data analysis of 7-day heart rate data from an e-cigarette user: 1) requiring no prior knowledge of the window length or group number; 2) yielding smaller values of root mean square error (RMSE) than the conventional SSA; and 3) revealing both local features and sudden changes related to events of interest. While conventional SSA excels in extracting stable signal structures, the proposed method is tailored for time series with varying structures such as heart rate data from smartwatches, and thus will have even wider applications.

Introduction As the US continues to battle the opioid epidemic, recovery residences remain valuable services for people in recovery. While there is a growing body of literature describing positive outcomes experienced by people who live in recovery residences, little is known about the experience of people who live in these residences while taking medications for an opioid use disorder (MOUD) as part of their recovery. Thus, this study has three aims: (1) expand the availability of recovery residences that meet the National Alliance for Recovery Residences standards in Texas and serve individuals taking medications for an opioid use disorder as part of their recovery; (2) evaluate recovery residences for people taking MOUD as part of their recovery; and (3) compare the cost-effectiveness of recovery residences to treatment-as-usual. Methods and analysis In collaboration with community partners, we opened 15 recovery residences in the State of Texas to house people taking MOUD as part of their recovery. We are collecting quantitative and qualitative data to evaluate outcomes at the intrapersonal, interpersonal, organisational and community levels. At the intrapersonal level, we are assessing changes in behavioural and psychosocial constructs using a longitudinal survey, objectively measuring current substance use with a point-of-interview breathalyser and urinalysis, and examining changes in healthcare utilisation using data obtained from a healthcare information exchange. We are collecting interpersonal data using in-depth individual interviews with residents and staff. We are collecting organisational data using field observation and a cost-effectiveness study, and we are collecting community data using neighbourhood mapping. Ethics and dissemination The UTHealth institutional review board approved all protocols. We will disseminate study findings via conference presentations, peer-reviewed publications and brief community reports.

New-generation disposable e-cigarettes have become increasingly popular among young adults in the USA since the FDA’s partial flavor ban. This study aims to examine longitudinal changes in health risks among young adults who adopted these novel products, as well as the health effects of device types beyond the effects of other important e-cigarette characteristics. This study recruited e-cigarette users via voluntary response sampling from three college campuses in the USA to respond to four-wave online surveys conducted in four consecutive semesters. Among the participants who adopted disposables during the study, their health risks (dependence symptoms, respiratory symptoms, combustible tobacco use) and e-cigarette consumption characteristics (use frequency, nicotine concentration and flavors) before and after the adoption were compared using paired-sample t- or McNemar’s tests. Generalized linear mixed models with a random intercept were conducted on data from the entire sample to investigate the effects of device type (tank, cartridge/pod, disposable) on health risks, controlling for other e-cigarette consumption characteristics. The study sample of 650 e-cigarette users were, on average, 20 years old, with 49% being male, 70% being White, and 13% being Hispanic. Adopting disposables may increase secondary dependence motives (t = 2.42, p < 0.05) and the use of higher levels of nicotine concentration (t = 2.09, p < 0.05) and sweet flavors (x² = 22.53, p < 0.05) but decrease the number of times of vaping per day (t = −2.18, p < 0.05) and the use of menthol flavors (x² = 4.57, p < 0.05). Tank use is associated with a higher level of primary dependence motives (b = 0.1998, p < 0.05) and a greater odds of using combustible tobacco (b = 0.4772, p < 0.05). Although disposable use is not associated with the likelihood of using combustible tobacco, it is associated with higher levels of both primary (b = 0.2158, p < 0.05) and secondary (b = 0.2533, p < 0.05) dependence motives. It is not the device type, but rather the frequency of vaping, that affects respiratory symptoms (b = 0.0602, p < 0.05). The findings indicate that when young adults switch to disposables, their e-cigarette dependence and use of sweet-flavored e-liquids increase. Even after controlling for use frequency, nicotine concentration and flavors, using disposables is related to not only instrumental motives that are influenced by psychological and environmental contexts but also heavy, automatic use that can operate without environmental cues. Given the health risks associated with disposable e-cigarettes, more comprehensive tobacco product regulations that consider the impact of device types may be needed.

The prevalence of e‐cigarette use among young adults in the USA is high (14%). Although the majority of users plan to quit vaping, the motivation to make a quit attempt is low and available support during a quit attempt is limited. Using wearable sensors to collect physiological data (eg, heart rate) holds promise for capturing the right timing to deliver intervention messages. This study aims to fill the current knowledge gap by proposing statistical methods to (1) de‐noise beat‐to‐beat interval (BBI) data from smartwatches worn by 12 young adult regular e‐cigarette users for 7 days; and (2) summarize the de‐noised data by event and control segments. We also conducted a comprehensive review of conventional methods for summarizing heart rate variability (HRV) and compared their performance with the proposed method. The results show that the proposed singular spectrum analysis (SSA) can effectively de‐noise the highly variable BBI data, as well as quantify the proportion of total variation extracted. Compared to existing HRV methods, the proposed second order polynomial model yields the highest area under the curve (AUC) value of 0.76 and offers better interpretability. The findings also indicate that the average heart rate before vaping is higher and there is an increasing trend in the heart rate before the vaping event. Importantly, the development of increasing heart rate observed in this study implies that there may be time to intervene as this physiological signal emerges. This finding, if replicated in a larger scale study, may inform optimal timings for delivering messages in future intervention.

Background/aim: The polygenic risk score (PRS) shows promise as a potentially effective approach to summarize genetic risk for complex diseases such as alcohol use disorder that is influenced by a combination of multiple variants, each of which has a very small effect. Yet, conventional PRS methods tend to over-adjust confounding factors in the discovery sample and thus have low power to predict the phenotype in the target sample. This study aims to address this important methodological issue.Methods: This study proposed a new method to construct PRS by (1) approximating the polygenic model using a few principal components selected based on eigen-correlation in the discovery data; and (2) conducting principal component projection on the target data. Secondary data analysis was conducted on two large scale databases: the Study of Addiction: Genetics and Environment (SAGE; discovery data) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; target data) to compare performance of the conventional and proposed methods.Result and conclusion: The results show that the proposed method has higher prediction power and can handle participants from different ancestry backgrounds. We also provide practical recommendations for setting the linkage disequilibrium (LD) andpvalue thresholds.