BACKGROUND: Physical activity engagement following a cancer diagnosis is positively associated with survival, reduced risk of disease recurrence, and reduced cancer-specific and all-cause mortality. However, rates of physical activity engagement are low among individuals diagnosed with and being treated for breast cancer or prostate cancer.
OBJECTIVE: The purpose of this study was to describe the systematic process of developing an e-cycling intervention aimed at increasing physical activity among individuals living with prostate cancer or breast cancer and outline the key components to be implemented.
METHODS: The Medical Research Council guidance for developing complex interventions and the Behaviour Change Wheel were used to guide intervention development. Information was gathered from the literature and through discussions with end users to understand factors influencing e-cycling. These factors were mapped onto the Theoretical Domains Framework to identify potential mechanisms of action. Behavior change techniques were selected from theory and evidence to develop intervention content. Interested parties, including cycling instructors, end users, and behavior change experts, reviewed and refined the intervention.
RESULTS: Anticipated barriers and facilitators to e-cycling engagement were mapped onto 11 of the 14 domains of the Theoretical Domains Framework. A total of 23 behavior change techniques were selected to target these domains over 4 one-to-one e-cycling sessions delivered by trained cycling instructors in the community. Cycling instructors were provided a 3-hour classroom training session on delivering the intervention and a 3-hour practical session with feedback. The outcome of this work is a theory and evidence-informed intervention aimed at promoting e-cycling behavior among individuals being treated for breast cancer or prostate cancer, which is currently being implemented and evaluated.
CONCLUSIONS: Transparent intervention development and reporting of content is important for comprehensively examining intervention implementation. The implementation of this intervention package is currently being evaluated in a pilot randomized controlled trial. If the intervention is found to be effective and the content and delivery are acceptable, this intervention will form a basis for the development of e-cycling interventions in other survivors of cancer.
BACKGROUND: ISRCTN Registry ISRCTN39112034 https://www.isrctn.com/ISRCTN39112034; and IRSCTN Registry ISRCTN42852156; https://www.isrctn.com/ISRCTN42852156.

Social recovery capital (SRC) is the combination of social resources that can be used to initiate and sustain addiction recovery through friends, family, and peers. Broadly, understanding one\'s SRC allows us to get a sense of where one has social support for recovery and where there may be social barriers to their recovery process. SRC is often a vital component of many people\'s recovery journey, yet our understanding of how best to use this concept in research and practice remains underdeveloped. To improve understanding of the role of social recovery capital and strategies to measure and increase it, we present a roadmap involving a five-pronged research agenda to: (1) Refine the measurement of social recovery capital; (2) Model the complexity of social recovery capital empirically; (3) Integrate personality science with social recovery capital research; (4) Optimize evidence-based behavior change techniques of social recovery capital; and (5) Incorporate an intersectional framework when examining or applying social recovery capital. Overall, this five-pronged research agenda seeks to enhance the clinical utility of SRC research to maximize the impact of SRC on one\'s recovery.

BACKGROUND: Worldwide, physical inactivity (PIA) and sedentary behavior (SB) are recognized as significant challenges hindering the achievement of the United Nations (UN) sustainable development goals (SDGs). PIA and SB are responsible for 1.6 million deaths attributed to non-communicable diseases (NCDs). The World Health Organization (WHO) has urged governments to implement interventions informed by behavioral theories aimed at reducing PIA and SB. However, limited attention has been given to the range of theories, techniques, and contextual conditions underlying the design of behavioral theories. To this end, we set out to map these interventions, their levels of action, their mode of delivery, and how extensively they apply behavioral theories, constructs, and techniques.
METHODS: Following the scoping review methodology of Arksey and O\'Malley (2005), we included peer-reviewed articles on behavioral theories interventions centered on PIA and SB, published between 2010 and 2023 in Arabic, French, and English in four databases (Scopus, Web of Science [WoS], PubMed, and Google Scholar). We adopted a framework thematic analysis based on the upper-level ontology of behavior theories interventions, Behavioral theories taxonomies, and the first version (V1) taxonomy of behavior change techniques(BCTs).
RESULTS: We included 29 studies out of 1,173 that were initially screened/searched. The majority of interventions were individually focused (n = 15). Few studies have addressed interpersonal levels (n = 6) or organizational levels (n = 6). Only two interventions can be described as systemic (i.e., addressing the individual, interpersonal, organizational, and institutional factors)(n = 2). Most behavior change interventions use four theories: The Social cognitive theory (SCT), the socioecological model (SEM), SDT, and the transtheoretical model (TTM). Most behavior change interventions (BCIS) involve goal setting, social support, and action planning with various degrees of theoretical use (intensive [n = 15], moderate [n = 11], or low [n = 3]).
CONCLUSIONS: Our review suggests the need to develop systemic and complementary interventions that entail the micro-, meso- and macro-level barriers to behavioral changes. Theory informed BCI need to integrate synergistic BCTs into models that use micro-, meso- and macro-level theories to determine behavioral change. Future interventions need to appropriately use a mix of behavioral theories and BCTs to address the systemic nature of behavioral change as well as the heterogeneity of contexts and targeted populations.

UNASSIGNED: Overweight and obesity are among the most prevalent health problems worldwide leading to various diseases and having a significant impact on the healthcare system. In Germany, the prevalence of obesity among adults is 19%. Mobile health applications offer a new approach to treatment and prevention and have been proven effective in previous studies. However, it is essential to investigate the availability and quality of these digital applications. The aim of this systematic assessment is to evaluate the accessibility and quality of digital health applications in German language designed to treat obesity.
UNASSIGNED: In January 2024, a systematic search for mobile health applications was conducted on both the Google Play Store and Apple App Store. Just those apps available in German for both iOS and Android were considered acceptable. The German Mobile Application Rating Scale (MARS-G) was used to assess the quality of the apps. The content of mobile health applications was evaluated using the guideline from the German Obesity Society for the treatment of obesity. The characteristics of the apps were summarized and presented, and the results were analyzed using descriptive statistics and presented in tables.
UNASSIGNED: After screening, ten apps were included in the review. The apps varied in terms of calorie tracking, individual workout plans, educational aspects, nutritional plans, and exercises for behavioral change. On average, 6.4 out of 12 items of the German Obesity guideline recommendations were fulfilled. The MARS score (possible range from 1-5) reached a mean of 3.39 (SD = 0.39). The section \"Engagement\" had the lowest quality score with a mean of 3.14 (SD = 0.57), while the section \"Aesthetics\" achieved the highest mean of 3.57 (SD = 0.52).
UNASSIGNED: Most German mobile health applications for managing obesity meet some guideline recommendations. They demonstrate adequate to good quality according to the MARS score. Assessing the quality of mobile health applications can be challenging for patients, despite being easily accessible and low-threshold. However, such digital health applications, reimbursed by the German SHI, offer evidence-based information, even if access can be associated with higher hurdles.

BACKGROUND: Behavior change techniques (BCTs) are considered as active components of implementation strategies, influencing determinants and, ultimately, implementation performance. In our previous Delphi study, experts formulated \'implementation hypotheses\', detailing how specific combinations of BCTs and strategies (referred to as BCT-strategy combinations) might influence determinants and guideline implementation within youth care. For example, educational meetings providing instructions on guideline use were hypothesized to enhance practitioners\' knowledge and, consequently, guideline implementation. However, these hypotheses have not been verified in practice yet.
METHODS: We conducted a cross-sectional study involving practitioners and management professionals from youth (health)care organizations. Using questionnaires, we obtained data on the presence of BCT-strategy combinations and their perceived influence on determinants and implementation performance. Chi-squared tests and regression analyses were employed to determine the influence of specific BCT-strategy combinations on determinants and implementation performance.
RESULTS: Our analyses included data from 104 practitioners and 34 management professionals. Most of the management professionals indicated that the BCT-strategy combinations positively influenced or had the potential to influence their implementation performance. At the practitioner level, half of the combinations were perceived to have a positive influence on determinants and implementation performance. Furthermore, practitioners who reported the absence of BCT-strategy combinations were more skeptical about their potential influence on determinants and implementation performance.
CONCLUSIONS: Several BCT-strategy combinations were perceived to improve or potentially improve implementation performance of both practitioners and management professionals. In the development and evaluation of implementation efforts, we advocate for clearly describing the implementation effort\'s objective and using frameworks that detail the BCTs inducing behavior change, the strategy employed, and the processes driving the observed changes. Understanding these interconnected processes is important in designing targeted, evidence-based behavior change interventions. This understanding optimizes resource allocation and contributes to the overall success of implementation efforts in youth care.

The development of neuroscientific techniques enabling the recording of brain and peripheral nervous system activity has fueled research in cognitive science. Recent technological advancements offer new possibilities for inducing behavioral change, particularly through cost-effective Internet-based interventions. However, limitations in laboratory equipment volume have hindered the generalization of results to real-life contexts. The advent of Internet of Things (IoT) devices, such as wearables, equipped with sensors and microchips, has ushered in a new era in behavior change techniques. Wearables, including smartwatches, electronic tattoos, and more, are poised for massive adoption, with an expected annual growth rate of 55% over the next five years. These devices enable personalized instructions, leading to increased productivity and efficiency, particularly in industrial production. Additionally, the healthcare sector has seen a significant demand for wearables, with over 80% of global consumers willing to use them for health monitoring. This research explores the primary biometric applications of wearables and their impact on users\' well-being, focusing on the integration of behavior change techniques facilitated by IoT devices. Wearables have revolutionized health monitoring by providing real-time feedback, personalized interventions, and gamification. They encourage positive behavior changes by delivering immediate feedback, tailored recommendations, and gamified experiences, leading to sustained improvements in health. Furthermore, wearables seamlessly integrate with digital platforms, enhancing their impact through social support and connectivity. However, privacy and data security concerns must be addressed to maintain users\' trust. As technology continues to advance, the refinement of IoT devices\' design and functionality is crucial for promoting behavior change and improving health outcomes. This study aims to investigate the effects of behavior change techniques facilitated by wearables on individuals\' health outcomes and the role of wearables in promoting a healthier lifestyle.

BACKGROUND: With the development of emerging technologies, digital behavior change interventions (DBCIs) help to maintain regular physical activity in daily life.
OBJECTIVE: To comprehensively understand the design implementations of habit formation techniques in current DBCIs, a systematic review was conducted to investigate the implementations of behavior change techniques, types of habit formation techniques, and design strategies in current DBCIs.
METHODS: The process of this review followed the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. A total of 4 databases were systematically searched from 2012 to 2022, which included Web of Science, Scopus, ACM Digital Library, and PubMed. The inclusion criteria encompassed studies that used digital tools for physical activity, examined behavior change intervention techniques, and were written in English.
RESULTS: A total of 41 identified research articles were included in this review. The results show that the most applied behavior change techniques were the self-monitoring of behavior, goal setting, and prompts and cues. Moreover, habit formation techniques were identified and developed based on intentions, cues, and positive reinforcement. Commonly used methods included automatic monitoring, descriptive feedback, general guidelines, self-set goals, time-based cues, and virtual rewards.
CONCLUSIONS: A total of 32 commonly design strategies of habit formation techniques were summarized and mapped to the proposed conceptual framework, which was categorized into target-mediated (generalization and personalization) and technology-mediated interactions (explicitness and implicitness). Most of the existing studies use the explicit interaction, aligning with the personalized habit formation techniques in the design strategies of DBCIs. However, implicit interaction design strategies are lacking in the reviewed studies. The proposed conceptual framework and potential solutions can serve as guidelines for designing strategies aimed at habit formation within DBCIs.

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals\' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit.

UNASSIGNED: Cardiac rehabilitation (CR) reduces recurrent cardiac events and mortality in patients with cardiovascular diseases (CVD). Innovative eHealth methods can facilitate CR uptake and effectiveness by addressing barriers associated with clinic-based rehabilitation. Tailoring eHealth-based CR to patient preferences is needed to further enhance CR.
UNASSIGNED: To identify preferred behavior change techniques (BCTs) as well as barriers and facilitators for the different health behaviors targeted in eHealth-based CR among patients who have been referred to CR.
UNASSIGNED: Thirty-nine patients were interviewed in nine focus groups in The Netherlands, Germany, and Spain. A thematic analysis, using a combined deductive and inductive approach to coding, was conducted to identify BCTs and barriers and facilitators to behavior change. Behaviors under investigation included physical activity, medication adherence, eating a cardiac healthy-diet, stress reduction and smoking cessation.
UNASSIGNED: The perceived helpfulness of BCTs depended on the specific behavior targeted. Common barriers were negative emotional state and physical limitations. A desire to feel physically or mentally well and having experienced a cardiac life event were the most common facilitators across health behaviors. Specific BCTs, barriers and facilitators were found for each of the health behavior.
UNASSIGNED: Behavior change techniques that patients preferred for each health behavior targeted in eHealth-based CR were identified. A negative emotional state, experiencing a life event, and improving physical functioning are important barriers and facilitators in multiple behaviors targeted in eHealth-based CR programs. Additional tailoring of interventions to patient preferences for BCTs and patient-specific barriers and facilitators per health behavior could lead to further improvement of eHealth-based CR.

UNASSIGNED: Risky health behaviors place an enormous toll on public health systems. While relapse prevention support is integrated with most behavior modification programs, the results are suboptimal. Recent advances in artificial intelligence (AI) applications provide us with unique opportunities to develop just-in-time adaptive behavior change solutions.
UNASSIGNED: In this study, we present an innovative framework, grounded in behavioral theory, and enhanced with social media sequencing and communications scenario builder to architect a conversational agent (CA) specialized in the prevention of relapses in the context of tobacco cessation. We modeled peer interaction data (n = 1000) using the taxonomy of behavior change techniques (BCTs) and speech act (SA) theory to uncover the socio-behavioral and linguistic context embedded within the online social discourse. Further, we uncovered the sequential patterns of BCTs and SAs from social conversations (n = 339,067). We utilized grounded theory-based techniques for extracting the scenarios that best describe individuals\' needs and mapped them into the architecture of the virtual CA.
UNASSIGNED: The frequently occurring sequential patterns for BCTs were comparison of behavior and feedback and monitoring; for SAs were directive and assertion. Five cravings-related scenarios describing users\' needs as they deal with nicotine cravings were identified along with the kinds of behavior change constructs that are being elicited within those scenarios.
UNASSIGNED: AI-led virtual CAs focusing on behavior change need to employ data-driven and theory-linked approaches to address issues related to engagement, sustainability, and acceptance. The sequential patterns of theory and intent manifestations need to be considered when developing effective behavior change CAs.