Background This scoping review summarized research on (a) seasonal differences in physical activity and sedentary behavior, and (b) specific weather indices associated with those behaviors. Methods PubMed, CINAHL, and SPORTDiscus were searched to identify relevant studies. After identifying and screening 1459 articles, data were extracted from 110 articles with 118,189 participants from 30 countries (almost exclusively high-income countries) on five continents. Results Both physical activity volume and moderate-to-vigorous physical activity (MVPA) were greater in summer than winter. Sedentary behavior was greater in winter than either spring or summer, and insufficient evidence existed to draw conclusions about seasonal differences in light physical activity. Physical activity volume and MVPA duration were positively associated with both the photoperiod and temperature, and negatively associated with precipitation. Sedentary behavior was negatively associated with photoperiod and positively associated with precipitation. Insufficient evidence existed to draw conclusions about light physical activity and specific weather indices. Many weather indices have been neglected in this literature (e.g., air quality, barometric pressure, cloud coverage, humidity, snow, visibility, windchill). Conclusions The natural environment can influence health by facilitating or inhibiting physical activity. Behavioral interventions should be sensitive to potential weather impacts. Extreme weather conditions brought about by climate change may compromise health-enhancing physical activity in the short term and, over longer periods of time, stimulate human migration in search of more suitable environmental niches.

The transition from adolescence into emerging adulthood is marked by changes in both physical activity and substance use. This systematic review characterized associations between movement behaviors (physical activity, sedentary behavior) and frequently used substances (alcohol, cannabis) among adolescents and emerging adults to inform lifestyle interventions that target multiple behavior change outcomes. This systematic review was guided by PRISMA. Electronic databases of PubMed, PsycINFO, and Web of Science were searched from inception through June 25, 2019. The search was designed to identify empirical studies reporting an association between physical activity or sedentary behavior and alcohol or cannabis, with search criteria determining eligibility based on several sampling characteristics (e.g., participants under 25 years of age). After identifying and screening 5,610 studies, data were extracted from 97 studies. Physical activity was positively associated with alcohol use among emerging adults, but the literature was mixed among adolescents. Sedentary behavior was positively associated with alcohol and cannabis use among adolescents, but evidence was limited among emerging adults. Self-report measures were used in all but one study to assess these behaviors. Physical activity is linked to greater alcohol use among emerging adults. Whereas existing studies demonstrate that sedentary behavior might serve as a risk marker for alcohol and cannabis use among adolescents, additional primary research is needed to explore these associations in emerging adults. Future work should also use device-based measures to account for timing of and contextual features surrounding activity and substance use in these populations.

Objective: Kidney stones are painful and costly. Prevention guidelines emphasize a simple behavior change: increasing fluid intake and urine output. Unfortunately, adherence to those prevention guidelines is limited, and patients report forgetting or not being thirsty enough. This study evaluated the acceptability of using semiautomated tracking of fluid consumption to trigger just-in-time reminders to drink and increase the experienced automaticity of fluid intake. Method: In a single-group trial, participants with a history of kidney stones (n ϭ 31) used the sip IT digital tools (H2OPal connected water bottle, H2OPal mobile app for self-tracking, Fitbit smartwatch app for gesture detection) for 3 months. Results: The semiautomated monitoring system detected 46,654 drinking events. From baseline to 1-month follow-up, the experienced automaticity of fluid intake increased significantly (d ϭ 0.50) and remained elevated at 3-month follow-up (d ϭ 0.64). A major barrier to adherence (lack of thirst) decreased from baseline to follow-ups. Retention rates and participant feedback indicated that this digital tool was acceptable to patients. Conclusion: Semiautomated tracking of fluid consumption can be used to trigger just-in-time reminders. Based on this demonstration, the sip IT tools are ready for testing in a rigorous Phase II trial to evaluate efficacy for increasing fluid consumption and urine output as recommended for preventing the recurrence of kidney stones.

Topic Significance & Study Purpose/Background/ Rationale: The organisms of the human flora are the most common cause of bacteremia and sepsis in immunocompromised and bone marrow transplant (BMT) patients. Activities of daily living (ADL) may potentially lower the risk of infection by decreasing pathogenic bacteria on the skin and mouth. A literature review was inconclusive for ADLs lowering the risk of infections in BMT patients. To address ADL compliance, a multidisciplinary team of both oncology and BMT units consisting of PCAs, RNs, nursing leadership and physicians was created. Methods, Intervention, & Analysis: To improve compliance, the team adopted the ADL 1-2-3 initiative: Daily bath/ chlorhexidine (CHG) bath and linen change, at least 2 activities per day, and oral care 3 times per day. Using the Model of Improvement and through Plan Do Study Act (PDSA) testing, we created a standardized ADL process that involved all providers. Interventions included addressing 1-2-3 compliance during rounds, creating accountability in care delivery, creation of an algorithm and order set for oral care, daily text message reminders, and physician intervention with non-compliant and high-risk patients. Findings & Interpretation: Our baseline compliance with the 1-2-3 initiative was 25%. With our interventions we increased our median compliance to 66% in 90 days. The greatest impact on compliance was seen with text message reminders to staff to complete the 1-2-3 components, designated roles and responsibilities, and physician discussion with noncompliant and high-risk patients. Our current compliance, as of September 3 rd, 2015, is 78.7%. Discussion & Implications: The 53% increase in ADL compliance for BMT validates the importance of a multidisciplinary team collaborating with the patient and family. Multiple factors were found to disrupt the flow of care being provided to the patient. Utilizing the oral care algorithm and order set, daily text message reminders, and physician intervention with non-compliant and high-risk patients were found to offset these disruptions. Units where compliance with ADL participation is low would be an ideal environment to incorporate the ADL 1-2-3 initiative.

Introduction: Text-message interventions with universal decision rules (treatment algorithms) demonstrate modest and inconsistent effects on physical activity. Efforts to increase effect sizes by targeting, tailoring or generically customizing content have largely been unsuccessful. In this study, system identification methods from control systems engineering were applied to develop person-specific dynamical models of individual responses to motivational text messages. Methods: Emerging adults not meeting aerobic physical activity guidelines (verified by accelerometer during a 1-week run-in period) received 0-6 messages/day while wearing a Fitbit Versa watch for 3 months. Experimental messages targeted cognitive or affective determinants of physical activity (move more) or sedentary behavior (sit less); comparator messages were inspirational quotes. For each participant, two dynamical models were estimated using difference equations: one to model the effects of move more and sit less messages and the other to model the effects of affectively- and cognitively-targeted messages. Both models included comparator messages. Effects represent the expected change in behavior immediately after message receipt (vs expected behavior without a message). Model order was determined individually to balance uncertainty and overfitting (range = 2-9). Results: The sample (n = 20) was mostly female (60%), White (65%), and not Hispanic or Latino (95%) with a mean age of 24.4 years (SD = 3.3; range = 19-29). Idiographic patterns of behavior change were readily observed from the personalized dynamical models. Most participants’ step counts increased after messages to move more (60% of participants’ responses exceeded the 95% error interval [responders], M = 94.3 steps; 83% of responders increased steps) and sit less (50% responders, M = 114.6 steps; 90% of responders increased steps), as well as control messages (75% responders, M = 59.5 steps; 60% of responders increased steps). Step counts also increased after messages targeting cognitive determinants (80% responders, M = 97.1 steps; 75% of responders increased steps) and affective determinants (75% responders, M = 52.9 steps, 67% of responders increased steps), as well as control messages (75% responders, M = 57.8 steps; 60% of responders increased steps). Conclusions: Motivational text messages can alter physical activity dynamics, and responses to different message types are highly personalized. Among responders, most message types increased expected step counts but some messages were iatrogenic and decreased expected step counts. Computational models of those dynamics provide a foundation for personalizing decision rules to select the type and time the delivery of messages to promote physical activity and improve cardiovascular health.