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Abstract Introduction Mindfulness training may reduce smoking rates and lessen the association between craving and smoking. This trial tested the efficacy of mindfulness training via smartphone app to reduce smoking. Experience sampling (ES) was used to measure real-time craving, smoking, and mindfulness. Methods A researcher-blind, parallel randomized controlled trial compared the efficacy of mobile mindfulness training with experience sampling (MMT-ES; Craving to Quit) versus experience sampling only (ES) to (1) increase 1-week point-prevalence abstinence rates at 6 months, and (2) lessen the association between craving and smoking. A modified intent-to-treat approach was used for treatment starters (MMT-ES n = 143; ES n = 182; 72% female, 81% white, age 41 ± 12 year). Results No group difference was found in smoking abstinence at 6 months (overall, 11.1%; MMT-ES, 9.8%; ES, 12.1%; χ2(1) = 0.43, p = .51). From baseline to 6 months, both groups showed a reduction in cigarettes per day (p < .0001), craving strength (p < .0001) and frequency (p < .0001), and an increase in mindfulness (p < .05). Using ES data, a craving by group interaction was observed (F(1,3785) = 3.71, p = .05) driven by a stronger positive association between craving and cigarettes per day for ES (t = 4.96, p < .0001) versus MMT-ES (t = 2.03, p = .04). Within MMT-ES, the relationship between craving and cigarettes per day decreased as treatment completion increased (F(1,104) = 4.44, p = .04). Conclusions Although mindfulness training via smartphone app did not lead to reduced smoking rates compared with control, our findings provide preliminary evidence that mindfulness training via smartphone app may help lessen the association between craving and smoking, an effect that may be meaningful to support quitting in the longer term. Implications This is the first reported full-scale randomized controlled trial of any smartphone app for smoking cessation. Findings provide preliminary evidence that smartphone app-based MMT-ES may lessen the association between craving and smoking. Trial registration Clinicaltrials.gov NCT02134509.

Ecological momentary assessment (EMA) is increasingly recognized as a vital tool for tracking the fluctuating nature of mental states and symptoms in psychiatric research. However, determining the optimal sampling rate - that is, deciding how often participants should be queried to report their symptoms - remains a significant challenge. To address this issue, our study utilizes the Nyquist-Shannon theorem from signal processing, which establishes that any sampling rate more than twice the highest frequency component of a signal is adequate. We applied the Nyquist-Shannon theorem to analyze two EMA datasets on depressive symptoms, encompassing a combined total of 35,452 data points collected over periods ranging from 30 to 90 days per individual. Our analysis of both datasets suggests that the most effective sampling strategy involves measurements at least every other week. We find that measurements at higher frequencies provide valuable and consistent information across both datasets, with significant peaks at weekly and daily intervals. Ideal frequency for measurements remains largely consistent, regardless of the specific symptoms used to estimate depression severity. For conditions in which abrupt or transient symptom dynamics are expected, such as during treatment, more frequent data collection is recommended. However, for regular monitoring, weekly assessments of depressive symptoms may be sufficient. We discuss the implications of our findings for EMA study optimization, address our study's limitations, and outline directions for future research.

Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Eurasia comprises about 36% of the world's total area and about 70% of the world population. Eurasia comprises Asia and Europe, although, geographically, it is a single continent with arbitrary geological borders. Eurasia has been a home to the world's oldest civilizations and plays an important part in the mainstream history of the world. Eurasian countries have many common characteristics and forecasting of this region can prove to be of major help in integrated resources management, leading to sustainable development, optimum decision making of international world organizations and achieving goals of world peace. This book deals with the various aspects of social and environmental importance in this region, especially climate change and hydrological modelling and flood forecasting.

Mobile ecological momentary assessment (mEMA) permits real-time capture of self-reported participant behaviors and perceptual experiences. Reporting of mEMA protocols and compliance has been identified as problematic within systematic reviews of children, youth, and specific clinical populations of adults. This study aimed to describe the use of mEMA for self-reported behaviors and psychological constructs, mEMA protocol and compliance reporting, and associations between key components of mEMA protocols and compliance in studies of nonclinical and clinical samples of adults. In total, 9 electronic databases were searched (2006-2016) for observational studies reporting compliance to mEMA for health-related data from adults (>18 years) in nonclinical and clinical settings. Screening and data extraction were undertaken by independent reviewers, with discrepancies resolved by consensus. Narrative synthesis described participants, mEMA target, protocol, and compliance. Random effects meta-analysis explored factors associated with cohort compliance (monitoring duration, daily prompt frequency or schedule, device type, training, incentives, and burden score). Random effects analysis of variance (P≤.05) assessed differences between nonclinical and clinical data sets. Of the 168 eligible studies, 97/105 (57.7%) reported compliance in unique data sets (nonclinical=64/105 [61%], clinical=41/105 [39%]). The most common self-reported mEMA target was affect (primary target: 31/105, 29.5% data sets; secondary target: 50/105, 47.6% data sets). The median duration of the mEMA protocol was 7 days (nonclinical=7, clinical=12). Most protocols used a single time-based (random or interval) prompt type (69/105, 65.7%); median prompt frequency was 5 per day. The median number of items per prompt was similar for nonclinical (8) and clinical data sets (10). More than half of the data sets reported mEMA training (84/105, 80%) and provision of participant incentives (66/105, 62.9%). Less than half of the data sets reported number of prompts delivered (22/105, 21%), answered (43/105, 41%), criterion for valid mEMA data (37/105, 35.2%), or response latency (38/105, 36.2%). Meta-analysis (nonclinical=41, clinical=27) estimated an overall compliance of 81.9% (95% CI 79.1-84.4), with no significant difference between nonclinical and clinical data sets or estimates before or after data exclusions. Compliance was associated with prompts per day and items per prompt for nonclinical data sets. Although widespread heterogeneity existed across analysis (I >90%), no compelling relationship was identified between key features of mEMA protocols representing burden and mEMA compliance. In this 10-year sample of studies using the mEMA of self-reported health-related behaviors and psychological constructs in adult nonclinical and clinical populations, mEMA was applied across contexts and health conditions and to collect a range of health-related data. There was inconsistent reporting of compliance and key features within protocols, which limited the ability to confidently identify components of mEMA schedules likely to have a specific impact on compliance.

This study applies the DPSIR (Driving Forces-Pressure-State-Impact-Response) framework to evaluate the ecological safety of Anping Port, Taiwan, from 2015 to 2021. Using time series data, the study identifies key indicators, including water transparency, nitrogen oxides, and fuel oil consumption, that significantly affect the port's ecological health. The DPSIR model highlights the dominance of ‘Environmental policy,' ‘Environmental quality,' and ‘Emergency improvement' as critical factors, with pollution control policies contributing 19.79% to the overall weight of the evaluation. Of the total weight, ‘Environmental quality' accounts for 14.9%, ‘Emergency improvement' accounts for 14.58%. The research findings reveal that Anping Port's ecological safety exhibited a ‘U'-shaped trend, declining from 2015 to 2017, but gradually improving through 2021, largely due to emergency governmental interventions. The entropy method further ranks ‘Advice for Improvement' and ‘Number of notifications' as the most influential indicators, underscoring the importance of timely management actions. The weight of ‘Advice for Improvement' was 7.36%, and the weight for ‘Number of notifications’ was 7.22%. This model provides a comprehensive foundation for future marine protection policies, particularly in designing conservation areas and strengthening ecological safety protocols.