The COVID-19 pandemic, and resultant “Stay-at-Home” orders, may have impacted adults’ positive health behaviors (sleep, physical activity) and negative health behaviors (alcohol consumption, drug use, and tobacco use). The purpose of this study was to investigate how these health behaviors changed (increased/improved or decreased/worsened) at the early stages of the pandemic, what participant characteristics were associated with health behavior changes, and why these behavioral changes may have occurred. A convenience sample of 1809 adults residing in the United States completed a 15-min self-report questionnaire in April and May 2020. Multinomial logistic regressions and descriptive statistics were used to evaluate how, for whom, and why these health behaviors changed. Participants were primarily female (67.4%), aged 35–49 years (39.8%), college graduates (83.3%), non-tobacco users (74.7%), and had previously used marijuana (48.6%). Overall, participants primarily reported a decrease in physical activity, while sleep and all of the negative health behaviors remained the same. Changes in negative health behaviors were related (p < 0.05) to sex, age, parental status, educational status, job status, BMI, and depression scores. Changes in positive health behaviors were related (p < 0.05) to sex, parental status, job status, and depression scores. Having more time available during the pandemic was the most commonly cited reason for changing health behaviors (negative and positive). Public health efforts should address the potential for long-term health consequences due to behavior change during COVID-19.

BackgroundCancer survivors are at an elevated risk for several negative health outcomes, but physical activity (PA) can decrease those risks. Unfortunately, adherence to PA recommendations among survivors is low. Fitness mobile apps have been shown to facilitate the adoption of PA in the general population, but there are limited apps specifically designed for cancer survivors. This population has unique needs and barriers to PA, and most existing PA apps do not address these issues. Moreover, incorporating user preferences has been identified as an important priority for technology-based PA interventions, but at present there is limited literature that serves to establish these preferences in cancer survivors. This is especially problematic given the high cost of app development and because the majority of downloaded apps fail to engage users over the long term.ObjectiveThe aim of this study was to take a qualitative approach to provide practical insight regarding this population’s preferences for the features and messages of an app to increase PA.MethodsA total of 35 cancer survivors each attended 2 focus groups; a moderator presented slide shows on potential app features and messages and asked open-ended questions to elicit participant preferences. All sessions were audio recorded and transcribed verbatim. Three reviewers independently conducted thematic content analysis on all transcripts, then organized and consolidated findings to identify salient themes.ResultsParticipants (mean age 63.7, SD 10.8, years) were mostly female (24/35, 69%) and mostly white (25/35, 71%). Participants generally had access to technology and were receptive to engaging with an app to increase PA. Themes identified included preferences for (1) a casual, concise, and positive tone, (2) tools for personal goal attainment, (3) a prescription for PA, and (4) an experience that is tailored to the user. Participants reported wanting extensive background data collection with low data entry burden and to have a trustworthy source translate their personal data into individualized PA recommendations. They expressed a desire for app functions that could facilitate goal achievement and articulated a preference for a more private social experience. Finally, results indicated that PA goals might be best established in the context of personally held priorities and values.ConclusionsMany of the desired features identified are compatible with both empirically supported methods of behavior change and the relative strengths of an app as a delivery vehicle for behavioral intervention. Participating cancer survivors’ preferences contrasted with many current standard practices for mobile app development, including value-based rather than numeric goals, private socialization in small groups rather than sharing with broader social networks, and interpretation of PA data rather than merely providing numerical data. Taken together, these insights may help increase the acceptability of theory-based mHealth PA interventions in cancer survivors.

BackgroundThe integration of body-worn sensors with mobile devices presents a tremendous opportunity to improve just-in-time behavioral interventions by enhancing bidirectional communication between investigators and their participants. This approach can be used to deliver supportive feedback at critical moments to optimize the attainment of health behavior goals.ObjectiveThe goals of this systematic review were to summarize data on the content characteristics of feedback messaging used in diet and physical activity (PA) interventions and to develop a practical framework for designing just-in-time feedback for behavioral interventions.MethodsInterventions that included just-in-time feedback on PA, sedentary behavior, or dietary intake were eligible for inclusion. Feedback content and efficacy data were synthesized descriptively.ResultsThe review included 31 studies (15/31, 48%, targeting PA or sedentary behavior only; 13/31, 42%, targeting diet and PA; and 3/31, 10%, targeting diet only). All studies used just-in-time feedback, 30 (97%, 30/31) used personalized feedback, and 24 (78%, 24/31) used goal-oriented feedback, but only 5 (16%, 5/31) used actionable feedback. Of the 9 studies that tested the efficacy of providing feedback to promote behavior change, 4 reported significant improvements in health behavior. In 3 of these 4 studies, feedback was continuously available, goal-oriented, or actionable.ConclusionsFeedback that was continuously available, personalized, and actionable relative to a known behavioral objective was prominent in intervention studies with significant behavior change outcomes. Future research should determine whether all or some of these characteristics are needed to optimize the effect of feedback in just-in-time interventions.

BackgroundUse of mass transit has been proposed as a way to incorporate regular physical activity into daily life because transit use typically requires additional travel to access and depart the stop or station. If this additional travel is active, a small but potentially important amount of physical activity can be achieved daily. Although prior research has shown that transit use is associated with physical activity, important questions remain unanswered. Utilizing a major expansion of the Houston, TX, USA light-rail system as a natural experiment, the Houston Travel-Related Activity in Neighborhoods (TRAIN) Study was developed to address these unanswered questions.PurposeThe purpose of the TRAIN Study is to determine if the development of light-rail lines in Houston, TX, USA will prospectively affect both transit use and physical activity over 4 years. We also aim to understand how contextual effects (i.e., moderators or interaction effects), such as the neighborhood built environment and socioeconomic factors, affect the primary relations under study.MethodsThe TRAIN Study is a longitudinal cohort design, in which participants are recruited at baseline from a 3-mile buffer around each of the three new lines and measured annually four times. Recruitment is accomplished via telephone contact, ads in newspapers and advertising circulars, and targeted community outreach. Data are collected via mail and include questionnaire-assessed factors, such as perceived neighborhood characteristics, attitudes about transportation, demographics, and reported physical activity; a travel diary; and accelerometry. Additionally, field-based neighborhood audits are conducted to capture micro-scale environmental features. To assess macro-scale environmental characteristics, we utilize GIS mapping and spatial analyses. Statistical analyses will be conducted using latent growth curve modeling and discrete choice models, with a focus on identifying moderating factors (i.e., statistical interaction effects). Selection bias will be controlled via propensity score analysis.ConclusionThe TRAIN study is a unique opportunity to study how a multi-billion dollar investment in mass transit can simultaneously affect transportation needs and physical activity behavior. This comprehensive evaluation will provide needed evidence for policy makers, and can inform health impact assessments of future transportation projects around the world.