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Background Behavior change is essential for promoting health and preventing illness; yet, motivating individuals to adopt and maintain healthy behaviors remains a significant challenge. Multiple models have been developed in Psychology and Behavioral Sciences to understand and encourage behavior change, including the Capability, Opportunity, Motivation, Behavior model (COM-B model), the Health Belief Model, the Behavior Change Wheel, the Theoretical Domains Framework (TDF), nudge theory, and Behavior Change Techniques (BCTs). Among these, the Fogg Behavior Model (FBM), developed by Dr. BJ Fogg, offers a specific and valuable framework for facilitating behavior change. This scoping review aims to analyze studies that apply the FBM in health, particularly its impact on changing health-related behaviors. By synthesizing the evidence, this review provides valuable insights into the use and impact of the FBM in promoting behavior change in health, with implications for future research and practice. Objective To investigate the application of the FBM within the health domain, focusing on its role in behavior change interventions. Methods A comprehensive search of multiple databases, including MEDLINE/PubMed, Cochrane Library, Epistemonikos, and PsycINFO, was conducted to identify studies applying the FBM in health-related interventions. The search strategy incorporated terms related to the FBM, health behavior change, and relevant health outcomes, with no restrictions on publication date. Unpublished and grey literature was also searched. Eligibility criteria included studies that applied the FBM in health interventions and reported outcomes. The review followed PRISMA-ScR and SAGER guidelines to ensure comprehensive reporting and consideration of sex and gender variables. Data were synthesized using a narrative approach, summarizing findings descriptively and thematically. Results Six studies met the inclusion criteria, covering sexual and reproductive health, vaccination, chronic disease management, general wellness, and healthcare adherence. The FBM was applied through motivation (anticipation, sensation, belonging), ability enhancement (reducing effort, increasing accessibility, integrating behaviors into routines), and prompts (reminders, calls to action, environmental cues). Effectiveness varied across studies. Gestational weight management reduced gestational diabetes (10.34% vs. 34.48%, p = 0.028), hypertension (3.45% vs. 27.59%, p = 0.030), and cesarean rates (41.38% vs. 72.41%, p = 0.017). HPV vaccination interventions increased intent to vaccinate (63.3–96.7%, p < 0.001), with 30% receiving the first dose within three months. Diabetes self-management improved dietary adherence (p = 0.04), physical activity (p = 0.005), and glucose monitoring (p = 0.02). Parental nutrition interventions increased knowledge (d = 1.07), beliefs (d = 0.61), and behavior change (d = 0.59). A vaginal birth intervention raised intention by 29% (p < 0.05). Conclusions Our scoping review highlights the FBM as an effective framework for promoting health behavior change across various domains. By systematically applying motivation, ability, and prompts, FBM-based interventions demonstrated positive outcomes. However, limitations such as the lack of long-term follow-ups and sex- and gender-disaggregated data indicate areas for future research. Expanding its application to diverse populations, integrating digital health technologies, and addressing structural and cultural barriers will enhance its impact. Strengthening methodological rigor and comparative analyses with other behavior change models will further refine its effectiveness for broader public health applications. Despite its potential, the FBM remains underutilized in public health research. Trial registration Open Science Framework osf.io/jpwxg.

Large language models (LLMs) are a recent advancement in artificial intelligence that has the potential to revolutionize learning, teaching, and research. Still, there is room for improvement regarding how effectively LLMs could be incorporated into these environments. This study investigated the role of LLMs, specifically ChatGPT, in learning, teaching, and research contexts. To understand how motivation, ability, and triggers influence the behavior of undergraduate students, teachers, and research scholars toward LLMs, the Fogg Behavior Model (FBM) is adopted. The study revealed that the behavior of students and researchers to apply LLMs in their respective domains was greatly influenced by their motivation and ability. However, teachers exhibited little interest in incorporating any LLMs into their pedagogical strategies. In addition to these results, participants identified limitations of ChatGPT in learning, teaching, and research fields. These insights contribute valuable perspectives on the practical implementation and effectiveness of LLMs in diverse academic and research contexts. The study sheds light on the potential benefits and challenges of integrating LLMs into educational and research environments. The findings emphasize the importance of accounting for motivational factors and individual abilities when applying such models. The study's findings offer invaluable insights for educators and researchers to harness the potential of LLMs in educational and research environments while mitigating their limitations.

The development of online learning environments has facilitated changes in educational interactions. This study focuses on an in-depth exploration of learners’ online interaction behaviors based on the theory of the Fogg Behavior Model, all in the context of accurately depicting and optimizing these interactions. Considering the complexity and richness of online education platform data, this paper proposes an improved analytical methodology to accurately model and predict learner behaviors, expecting to provide a scientific basis for the development of personalized teaching strategies and the enhancement of learning experience This methodology is based on the Fractional Brownian Motion and BAS algorithms to analyze the data of online learning platforms. The study collected data such as login, learning status, and course grades, and used the combination algorithm to optimize the Hurst index estimation and improve prediction accuracy. The number of view notifications is significantly correlated with the interactive learning behavior of resources at the 0.01 level, and the correlation coefficients are 0.297, 0.557 and 0.360. The algorithm accurately reflects learner behavior, provides optimization strategies for online learning platforms, and enhances learning efficiency and interaction quality.

Inhaled medication is the preferred route of administration for patients with chronic obstructive pulmonary disease (COPD). The compliance rate of inhaled medication in patients with COPD is <50%, which increases the risk of acute exacerbations. Considering the complex steps of inhaled medication, improving inhaled medication compliance not only requires consistent medication frequency and medical advice but also an evaluation of whether the patient has mastered the inhaler technique to achieve the correct dose. This study aimed to explore the effectiveness of an inhaled medication education program (Inhaling-Health website) based on the Fogg Behavior Model and gamification design on inhaled medication compliance and other health-related outcomes in patients with COPD. In a randomized, 2-arm, concurrent parallel design, we enrolled 102 patients with COPD from respiratory medicine clinics at 2 hospitals in Zhejiang Province, China, between April and May 2023. Participants were randomly assigned to either the control group (51/102, 50%) or the experimental group (51/102, 50%). All participants completed the intervention, with 94 participants completing 6 months of follow-up. Two independent-sample 2-tailed t tests and the Mann-Whitney U test were used to analyze group differences, and generalized estimating equations were used to analyze repeated measurements. After the intervention, all outcome measures improved compared to baseline. The generalized estimating equation showed that, compared to the control group, the Inhaling-Health website led to a gradual improvement in total adherence-to-inhaler scores starting from 2 months after the intervention (median 51.00, IQR 49.00-52.25 vs median 50.00, IQR 47.00-51.00; Z=-2.014; P=.04). It had a separate positive effect on inhaler technique. In addition, the website was more effective in reducing the modified Medical Research Council score than routine inhaled medication education, starting from 4 months after intervention (median 1.00, IQR 1.00-2.00 vs median 1.00, IQR 0.00-2.00; Z=-2.260; P=.02). The website was also more effective than the conventional intervention in improving Chronic Obstructive Pulmonary Disease Knowledge Questionnaire scores, beginning at the end of the intervention (mean 6.14, SD 1.83 vs mean 7.06, SD 1.82; t =-2.551; P=.01). The gamified inhaled medication education program based on the Fogg Behavior Model can improve inhaled medication adherence, inhaler technique accuracy, health literacy, lung function, and health-related quality of life; help reduce the severity of dyspnea and COPD physical symptoms; and reduce the risk of acute exacerbations in patients with COPD. This study can provide a reference for inhaled medication education in patients with COPD. China Clinical Trials Registry (ChiCTR) ChiCTR2300070213; https://www.chictr.org.cn/showprojEN.html?proj=194829.

Taxonomies and models are useful tools for defining eHealth content and intervention features, enabling comparison and analysis of research across studies and disciplines. The Behavior Change Technique Taxonomy version 1 (BCTTv1) was developed to decrease ambiguity in defining specific characteristics inherent in health interventions, but it was developed outside the context of digital technology. In contrast, the Persuasive System Design Model (PSDM) was developed to define and evaluate the persuasive content in software solutions but did not have a specific focus on health. Both the BCTTv1 and PSDM have been used to define eHealth interventions in the literature, with some researchers combining or reducing the taxonomies to simplify their application. It is unclear how well the taxonomies accurately define eHealth and whether they should be used alone or in combination. This scoping review explored how the BCTTv1 and PSDM capture the content and intervention features of parent-focused eHealth as part of a program of studies investigating the use of technology to support parents with therapy home programs for children with special health care needs. It explored the active ingredients and persuasive technology features commonly found in parent-focused eHealth interventions for children with special health care needs and how the descriptions overlap and interact with respect to the BCTTv1 and PSDM taxonomies. A scoping review was used to clarify concepts in the literature related to these taxonomies. Keywords related to parent-focused eHealth were defined and used to systematically search several electronic databases for parent-focused eHealth publications. Publications referencing the same intervention were combined to provide comprehensive intervention details. The data set was coded using codebooks developed from the taxonomies in NVivo (version 12; QSR International) and qualitatively analyzed using matrix queries. The systematic search found 23 parent-focused eHealth interventions described in 42 articles from various countries; delivered to parents with children aged 1 to 18 years; and covering medical, behavioral, and developmental issues. The predominant active ingredients and intervention features in parent-focused eHealth were concerned with teaching parents behavioral skills, encouraging them to practice and monitor the new skills, and tracking the outcomes of performing the new skills. No category had a complete set of active ingredients or intervention features coded. The two taxonomies conceptually captured different constructs even when their labels appeared to overlap in meaning. In addition, coding by category missed important active ingredients and intervention features. The taxonomies were found to code different constructs related to behavior change and persuasive technology, discouraging the merging or reduction of the taxonomies. This scoping review highlighted the benefit of using both taxonomies in their entirety to capture active ingredients and intervention features important for comparing and analyzing eHealth across different studies and disciplines. RR2-doi.org/10.15619/nzjp/47.1.05.

Background Cervical cancer imposes a significant burden on women in low- and middle-income countries (LMICs), where participation rates in screening programs are suboptimal. Identifying effective intervention strategies to increase screening participation is critical, particularly in these communities. This study aims to determine the effect of Fogg model-based virtual education on cervical cancer health literacy and stages of behavior change related to cervical cancer screening. Methods This is a two-arm, parallel-group, randomized controlled trial with blinded outcome assessor and data analyst. Eighty women aged 21–65 years will be recruited from one-fifth of Tabriz’s health centers, selected on the basis of socioeconomic diversity, and randomized at a 1:1 ratio via restricted block randomization and sealed opaque envelopes for allocation concealment. The intervention group will receive virtual education based on the Fogg behavior model for 4 weeks (twice weekly) via the Iranian messaging platform Eitaa, which uses multimedia content (videos, audio, infographics, text messages, and quizzes) about cervical cancer and its screening. The content includes spark messages (to increase motivation and reduce psychological barriers), facilitator messages (to simplify practical barriers), and trigger messages (to prompt action). Data analysis will use ANCOVA for continuous variables and ordinal regression for stages of change. Discussion If the results of this study demonstrate positive effects, the intervention could increase screening participation, reduce the disease burden, and be scalable and adaptable for other LMICs, serving as a foundation for national digital prevention programs for diseases such as cervical cancer. Trial registration This trial was prospectively registered on the Iranian Registry of Clinical Trials. Iranian Registry of Clinical Trials, IRCT20171007036615N14, 21/10/2025. It was updated once due to an error in registering the expected date for recruitment, with the update dated 23/10/2025. https://irct.behdasht.gov.ir/trial/86293

Objective: This study aims to extend the Theory of Planned Behavior with Triggers (TPBT) to improve the prediction of physical activity (PA) behavior using the TPB model. Methods: Questionnaires, including the TPB scale, PA rating scale (PARS-3), and triggers scale, were administered to 596 Chinese college students, and the data were analyzed using SPSS 23.0 and AMOS 24.0. Results: Subjective norm (SN), attitude (AT), and perceived behavioral control (PBC) all significantly and positively affected behavioral intention (BI). The path coefficient of PA behavior was significantly influenced by the interaction term of three types of triggers and BI, and the TPB with Triggers (TPBT) model improved the explanation rate of PA behavior. Conclusion: Triggers have a moderating effect on the relationship between BI and PA behavior, and the TPBT model better explains college students’ PA behavior. Among the three dimensions of triggers, people are more receptive to facilitator and signal triggers than spark triggers. This has practical implications for practitioners designing interventions to promote PA among college students.

Against intensifying climate risks, a paradox persists in green finance: high corporate awareness yet low green bond issuance. This study examines the impact of climate risk on green bond issuance using a final sample of 5958 bond issuances, which were issued by 469 unique A-share non-financial listed companies in China between 2016 and 2023. By integrating the Fogg Behavior Model (FBM) into a Motivation–Capability–Triggers framework and employing Logit and Karlson–Holm–Breen (KHB) methods, we investigate the underlying mechanisms. The baseline results show a positive link between climate risk and issuance likelihood, confirming a motivation-incentive effect. Mechanism analyses reveal significant negative mediation through financing constraints, green innovation, and environmental reputation, highlighting a capability-constraint effect. Heterogeneity analysis finds a stronger effect in non-state-owned firms, non-heavily polluting industries, and firms in pilot zones or central/western China, indicating that policy signals and resource endowments act as key triggers to synergize motivation and capability. Our findings offer valuable insights for policymakers in designing motivation-stimulating and capability-compensating intervention strategies to help firms balance economic and environmental objectives.

With the increasing adoption of electric vehicles (EV), congestion at charging stations during peak hours has become a prominent issue, imposing significant pressure on station scheduling. Furthermore, the large-scale integration of photovoltaics (PV) introduces dual uncertainties in both generation and load, negatively impacting grid voltage. To tackle the above problems, a strategy for demand response balancing and congestion alleviation of charging stations under grid–road network partition mapping is proposed in this paper. Firstly, a user demand response capability assessment method based on the Fogg Behavior Model is proposed to evaluate the demand response potential of individual users in each zone. The results are aggregated to obtain the demand response participation capability of each zone, thereby realizing capability-based allocation and achieving demand response balancing. Secondly, the road network is divided into several zones and mapped to the power grid, and a two-layer cross-zone collaborative autonomy model is established. The upper layer aims to alleviate inter-zone congestion and balance inter-station power, taking into account the grid voltage level. A tripartite benefit model involving the power grid, charging stations and users is constructed, and an inter-zone mutual-aid model for the upper layer is established and solved optimally. The lower layer establishes an intra-zone self-consistency model, which subdivides different functional zone types within the road network zone, allocates and accommodates the cross-zone power from the upper-layer output inside the zone, and synchronously performs intra-zone cross-zone judgment to avoid congestion at charging stations. Simulation verification is carried out on the IEEE 33-bus system. The results show that the proposed method can effectively alleviate the congestion of charging stations, the balance degree among all zones is increased by 43.58%, and the power grid voltage quality is improved by about 38%. This study offers feasible guidance for exploring large-scale planned participation of electric vehicles in power system demand response.

Background: Cervical cancer remains a leading cause of cancer-related deaths among women in Nigeria. In 2023, the Government of Nigeria, with support from Gavi and partners, introduced the single-dose human papillomavirus (HPV) vaccine through a phased, school-based campaign. The first phase was launched in October 2023 across 16 states, followed by a second phase in May 2024 that expanded coverage to the remaining states and the Federal Capital Territory. This study evaluates the additional impact of a behavioral insights–informed digital intervention, comprising a social media campaign amplified by trained pharmacists serving as local influencers, implemented in 2025 to increase acceptance and uptake of HPV vaccination during routine immunization. Methods: A pre-test/post-test quasi-experimental design with a control group was implemented in three Nigerian states in 2025 to assess the additional impact of a behavioral insights–informed social media campaign designed to strengthen social approval for HPV vaccination, increase awareness of vaccination locations, and reinforce caregivers’ recognition of their adolescent daughters’ desire to be vaccinated. Messages were amplified by trained pharmacists who served as local influencers. Caregivers of adolescent girls aged 9–17 years were recruited online through targeted Facebook and Instagram advertisements during Nigeria’s transition from school-based HPV vaccination campaigns to routine immunization. Caregivers in treatment areas were exposed to geofenced social media advertisements on Facebook and Instagram and pharmacist counseling, while those in control areas were not. Logistic regression models using a difference-in-difference approach estimated the campaign’s effect on HPV vaccination, controlling for caregiver and adolescent characteristics. Additional statistical models assessed the campaign’s impact on caregivers’ motivation and ability—key drivers of behavior according to the Fogg Behavior Model. Results: HPV vaccination increased at a significantly higher rate in the treatment compared to the control area. The adjusted odds of an adolescent girl being vaccinated were 1.48 times higher in the treatment area at follow-up (95% CI: 1.14–1.92). Adjusted marginal effects indicated that exposure to the campaign increased the probability of vaccination by 8.9 percentage points relative to the control group. The rate at which caregivers’ motivation (aOR = 1.31, 95% CI: 1.00–1.70) and ability (knowing where to get vaccinated: aOR = 1.38, 95% CI: 1.07–1.79; ease of vaccination: aOR = 1.59, 95% CI: 1.22–2.06) increased was also higher in the treatment area. There was no relative increase in intervention versus control groups in factual knowledge regarding HPV vaccination. Conclusions: A behavioral insights–informed social media campaign in which pharmacists served as influencers was associated with higher HPV vaccine uptake during routine immunization. The higher rate of vaccination observed in intervention areas was associated with higher rates of caregiver motivation and ability but not with higher rates of caregiver knowledge. These findings are consistent with the potential of behavioral insights–informed digital campaigns to complement routine immunization efforts and improve vaccine uptake in low- and middle-income countries.