Alexis Kuerbis

Mobile messaging interventions provide a scalable, low-cost channel to support health behavior change, yet optimal content design remains uncertain, particularly regarding gain- versus loss-framed messages. Guided by the Multiphase Optimization Strategy (MOST), this study examined framing preferences among older adults to inform user-centered digital intervention design for alcohol reduction. In a choice experiment, N=1,348 participants aged 50 years and older evaluated 174 messages (86 gain- and 86 loss-framed) across 6,740 pairwise comparisons. Participants selected which message they would prefer to receive as part of a health messaging program. Gain-framed messages were preferred in 57.8% of direct gain–loss comparisons (95% CI: 0.561–0.594; χ²(1)=83.66, p<.001), independent of message placement or trial order. Substantial variability was observed across individual message content. Results indicate a consistent preference for gain framing but emphasize that framing alone does not determine preference, underscoring the need to study other behavioral mechanisms that can be optimized.

Inertia, widely studied in psychological research for its implications for psychologicalhealth and emotion regulation, is typically operationalized as the lag-1 autoregressiveeffect in time series linear models. The recent proliferation of intensivelongitudinal designs, such as ecological momentary assessment (EMA) and daily diarymethods facilitated by mobile technology, enables researchers to capture repeated,real-time information about contextual, physical, and emotional factors, allowing forthe study of inertia across diverse psychological contexts. However, the utilizationof raw count data—a common practice in recording time-sensitive events or behaviorssuch as drinking episodes or mood fluctuations—may introduce unintended bias,yielding suboptimal estimates. Moreover, the conceptualization of inertia with countintensive longitudinal data (ILD) remains understudied in existing methodological literature.Building upon time series methodology, we provide a novel framework for exploringthis concept in detail, demonstrating how the relationship between inertia andautocorrelation in count data can deviate from their relationship in linear models, andexamining connections with commonly used temporal indices in psychology. Throughcomprehensive simulation studies and an empirical alcohol use disorder study, we: (1)evaluate the validity of existing methods for estimating autoregressive processes withcount ILD; (2) propose an alternative approach for conceptualizing and quantifyinginertia in count ILD; and (3) offer practical guidance for applied researchers on interpretinginertia in count data contexts. Finally, we discuss broader implications ofthe proposed methodology, including how it can inform more effective interventiondesign in psychological studies, and outline future research directions.

Background: Knowledge on age-specific patient characteristics and treatment outcome predictors from routine-care is needed to understand the treatment needs of older adults (OA; age 60+) with alcohol use disorder (AUD). Aims: To explore age-specific patient characteristics and age-specific predictors of AUD-treatment outcomes. Methods: Routine-care data from n = 125 young adults (18–39 years), n = 216 middle-aged adults (40–59 years), and n = 80 OA who began outpatient AUD-treatment in a Danish municipality between 2019 and 2022. Patients underwent a clinical interview, including the Addiction Severity Index (ASI), at intake and three months after intake. Treatment outcome predictors were investigated with Generalized Linear Models with and without interactions with age group. Results: At intake, OA had greater health and financial problems than younger patients. OA more often had a higher secondary education and were not working. Being an OA (vs. young adults) predicted a lower ASI alcohol score at three months (p<0.001). In the whole sample, more days with health problems, greater financial problems, and previous AUD treatment predicted less favorable treatment outcomes (p<0.05). Most interactions with age were non-significant (p≥0.05). Conclusions: No set of age-specific predictors of treatment outcomes among OA was identified. Predictors of negative treatment outcomes are very prevalent among OA (e. g., health problems) and deserve attention in AUD interventions with OA.

Key TakeawaysOlder adults born from 1924 to 1945 (The Silent Generation) had strong responses to primary, secondary, and tertiary alcohol prevention interventions, particularly those that involved a physician (i.e., a physician delivered the intervention or followed up regarding alcohol use), with multiple contacts over time, and across decades of life.

Alcohol research aims to understand consumption patterns to develop interventions to mitigate harm. Adoption of Ecological Momentary Assessment (EMA) in alcohol research marked a critical shift in the ability to understand consumption patterns over time. Facilitated by mobile technology, EMA allows repeated, real-time data collection, offering insights into the immediate contextual, physical, and emotional factors impacting alcohol consumption. Despite EMA's utility, direct inquiries into the relationship between the repeated measures are rarely explored, limiting our understanding of key aspects of consumption patterns. Furthermore, utilization of raw count data, a common practice, may introduce unintended bias, generating suboptimal estimates. This study explores the validity of existing methods to estimate an autoregressive process with raw count data through data simulation and proposes an alternative approach to reduce bias. Both approaches are then applied to EMA data collected from a randomized controlled trial testing brief interventions to reduce drinking among participants who drink hazardously. Findings reveal that the proposed method has greater validity, aligns more precisely with behavioral health theories, and outperforms the existing method in estimating the autoregressive effect; however, it also requires more power to identify moderating effects than the existing method. Advantages and limitations of each method are reviewed.

This manuscript aims to contribute to the next phase of mechanisms of behavior change (MOBC) science on alcohol or other drug use. Specifically, we encourage the transition from a basic science orientation (i.e., knowledge generation) to a translational science orientation (i.e., knowledge application or Translational MOBC Science). To inform that transition, we examine MOBC science and implementation science and consider how these two research areas can intersect to capitalize on the goals, strengths, and key methodologies of each. First, we define MOBC science and implementation science and offer a brief historical rationale for these two areas of clinical research. Second, we summarize similarities in rationale and discuss two scenarios where one draws from the other—MOBC science on implementation strategy outcomes and implementation science on MOBC. We then focus on the latter scenario, and briefly review the MOBC knowledge base to consider its readiness for knowledge translation. Finally, we provide a series of research recommendations to facilitate the translation of MOBC science. These recommendations include: (1) identifying and targeting MOBC that are well suited for implementation, (2) use of MOBC research results to inform broader health behavior change theory, and (3) triangulation of a more diverse set of research methodologies to build a translational MOBC knowledge base. Ultimately, it is important for gains borne from MOBC science to affect direct patient care, while basic MOBC research continues to be developed and refined over time. Potential implications of these developments include greater clinical significance for MOBC science, an efficient feedback loop between clinical research methodologies, a multi‐level approach to understanding behavioral change, and reduced or eliminated siloes between MOBC science and implementation science.

Background: Motivational interviewing (MI) is a widely used intervention applied to a host of health behaviors, including alcohol consumption among individuals with alcohol use disorder (AUD). Age is an underexplored moderator of MI for treating AUD, with the impact of comparing older individuals with their younger counterparts virtually unexplored. Also unexplored is whether age is associated with distinct mechanisms of change (e.g., motivation and self‐efficacy) within treatment.Methods: This secondary data analysis utilizes combined data from two previous studies (total N = 228) that both aimed to test MI's mechanisms of action in the context of a goal for moderated drinking. Both studies had three conditions: MI, nondirective listening (NDL), and a self‐change condition (SC). In the current analyses, the moderating impact of continuous age and age group, <51 (younger adults, YA) versus ≥51 (older adults, OA), on the impact of MI on alcohol use compared to NDL and SC were tested using generalized linear models. Age differences in confidence and commitment to reduce heavy drinking during treatment were also explored.Results: Age group by condition differences emerged, where NDL significantly reduced drinking among YA but not OA (mean −12 vs. −3 standard drinks, respectively). Among OA, MI outperformed NDL but not SC, though the effect was weak. Confidence and commitment during treatment were not significantly different across age‐by‐condition groups.Conclusion: Findings underscore the importance of understanding the impact of age on treatment effectiveness, as providing a nondirective intervention for OA with AUD could provide suboptimal treatment. Further research is needed to explore these differential effects.

Ecological momentary assessment (EMA) has been broadly used to collect real-time longitudinal data in behavioral research. Several analytic methods have been applied to EMA data to understand the changes of motivation, behavior, and emotions on a daily or within-day basis. One challenge when utilizing those methods on intensive datasets in the behavioral field is to understand when and why the methods are appropriate to investigate particular research questions. In this manuscript, we compared two widely used methods (generalized estimating equations and generalized linear mixed models) in behavioral research with three other less frequently used methods (Markov models, generalized linear mixed-effects Markov models, and differential equations) in behavioral research but widely used in other fields. The purpose of this manuscript is to illustrate the application of five distinct analytic methods to one dataset of intensive longitudinal data on drinking behavior, highlighting the utility of each method.