Background: Social media can be used to quickly disseminate focused public health messages, increasing message reach and interaction with the public. Social media can also be an indicator of people’s emotions and concerns. Social media data text mining can be used for disease forecasting and understanding public awareness of health-related concerns. Limited studies explore the impact of type, sentiment and source of tweets on engagement. Thus, it is crucial to research how the general public reacts to various kinds of messages from different sources. Objective: The objective of this paper was to determine the association between message type, user (source) and sentiment of tweets and public engagement during the COVID-19 pandemic. Methods: For this study, 867,485 tweets were extracted from January 1, 2020 to March 31, 2022 from Ireland and the United Kingdom. A 4-step analytical process was undertaken, encompassing sentiment analysis, bio-classification (user), message classification and statistical analysis. A combination of manual content analysis with abductive coding and machine learning models were used to categorize sentiment, user category and message type for every tweet. A zero-inflated negative binomial model was applied to explore the most engaging content mix. Results: Our analysis resulted in 12 user categories, 6 message categories, and 3 sentiment classes. Personal stories and positive messages have the most engagement, even though not for every user group; known persons and influencers have the most engagement with humorous tweets. Health professionals receive more engagement with advocacy, personal stories/statements and humor-based tweets. Health institutes observe higher engagement with advocacy, personal stories/statements, and tweets with a positive sentiment. Personal stories/statements are not the most often tweeted category (22%) but have the highest engagement (27%). Messages centered on shock/disgust/fear-based (32%) have a 21% engagement. The frequency of informative/educational communications is high (33%) and their engagement is 16%. Advocacy message (8%) receive 9% engagement. Humor and opportunistic messages have engagements of 4% and 0.5% and low frequenciesof 5% and 1%, respectively. This study suggests the optimum mix of message type and sentiment that each user category should use to get more engagement. Conclusions: This study provides comprehensive insight into Twitter (rebranded as X in 2023) users’ responses toward various message type and sources. Our study shows that audience engages with personal stories and positive messages the most. Our findings provide valuable guidance for social media-based public health campaigns in developing messages for maximum engagement.

Tractors and quad bikes pose a significant risk of fatal injuries among farmers, particularly affecting older farmers. This study aimed to explore the barriers and facilitators to the adoption of machine related safety behaviors among older farmers in Irish farm settings. Four focus groups were conducted via Zoom in February 2021. Nineteen Irish farmers from four farm types participated. The discussions were audio-recorded, transcribed verbatim, and analyzed using an inductive, reflexive thematic analysis approach. The themes identified were then mapped to the COM-B (Capability-Opportunity-Motivation) model, providing a systematic theoretical basis for designing a future intervention to reduce machine-related accidents. The analysis identified five inductive themes that encompassed both barriers and facilitators in farm safety practices: (1) Capability to manage competing responsibilities; (2) Characteristics of the farm and its work environment; (3) Availability and affordability of resources; (4) Prevailing sociocultural opportunities; and (5) Perceived likelihood and cost-benefit analysis in safety decision-making. These themes captured the complex interplay of capability, opportunity, and motivation in farmers' decision-making processes. The study also revealed limitations in existing interventions, such as voluntary guidelines and educational methods, in effectively addressing these barriers. Farmers' abilities (capability), prevailing sociocultural factors, resource availability (opportunity), and their perceived consequences and benefits (motivation) affect how safely they work with machines. The study emphasizes the need for comprehensive, theory-driven approaches that consider the interplay of capability, opportunity, and motivational factors that may support or impede machine safety. Understanding the challenges faced by Irish farmers highlights potential strategies for safety intervention, and these strategies should be co-designed with farmers and attentive to the local context. The study provides a template for understanding farmers' perspectives using the COM-B model. The findings can inform the development of theoretically informed intervention strategies based on the Behavior Change Wheel framework.

Farming is essential work, but it suffers from very high injury and fatality rates. Machinery, including tractors, are a leading cause of serious injuries and fatalities to farmers and farm workers in many countries. Herein, we document the systematic development of an evidence-based, theory-informed behaviour change intervention to increase machine-related safety on farms. Intervention development progressed through four phases. Phase 1 defined the problem in behavioural terms based a review of the literature, Phase 2 identified candidate intervention targets through a series of focus groups guided by the Capability–Opportunity–Motivation–Behaviour (COM-B) model and Phase 3 employed expert and stakeholder consultation guided by the Behaviour Change Wheel (BCW) to consider potential target behaviours and intervention components and finalise the intervention content. Phase 4 finalised the evaluation strategies with a team of agricultural advisors who supported the rollout and identified outcome measures for the first trial. The target intervention was the identification of blind spots of farm tractors, and three priority target behaviours (farm safety practices) were identified. Following Phase 3, the intervention comprised four components that are delivered in a group-based, face-to-face session with farmers. In Phase 4, the acceptability, feasibility, and fidelity of these components were identified as the outcome measures for the first trial of the intervention. The four-phase systematic method detailed here constitutes an initial template for developing theory-based, stakeholder-driven, behaviour-change-based interventions targeting farmers and reporting such developments.

Introduction: Agricultural workplaces have a high number of incidents and fatalities, with the majority occurring from machinery use. Farmers’ behaviour plays a critical role in maintaining safety, as improper or unsafe practices often lead to injuries and fatalities. This review categorises interventions targeting farm machine safety, examining both the behaviour change techniques (BCTs) used and their reported outcomes to understand how the techniques influence safety practices and outcomes on farms. Methods: The systematic review is reported in accordance with the Preferred reporting items for systematic reviews and meta-analysis guidelines. Seven electronic databases were searched for relevant studies published before June 2024, and the quality of included studies was assessed using Cochrane risk of bias assessment tools. Analysis of intervention behavioural components was guided by the behaviour change wheel framework and BCT taxonomy (v1). The findings were synthesised using a narrative review. Results: Nine studies were included and a total of 21 BCTs were identified. The most frequently coded BCTs were 4.1 (instruction on how to perform the behaviour), 10.8 (incentive [outcome]), and 16.3 (vicarious consequences) (each n = 6). Reported outcomes included reductions in injury rates, improved adoption of safety devices, implementation of safety measures, and positive shifts in safety norms and perceptions. However, due to variations in intervention design and reporting, assessing the direct impact of specific BCTs on these outcomes proved challenging. Discussion: The use of BCT taxonomy provided a common language for describing intervention components and enabled the standardisation of intervention content analysis. While patterns were observed regarding the commonly used BCTs, their implementation and outcomes, the heterogeneity and limited details provided by studies limited our ability to discern their effectiveness. Providing (i) greater transparency in reporting active intervention components and (ii) clearer connections between components and specific outcomes, will enable enhanced comparisons of future studies, and facilitate a greater understanding of how to support safe machine-related behaviours on farms.

Background: In Ireland, the agriculture sector reports the highest number of fatalities even though farmers constitute only 6% of the working population. Tractor-related behaviours are implicated in 55% of all vehicle work-related fatalities and 25% of reported injuries, and many of these occur in farmyards. There is limited research on the feasibility and acceptability of behaviour change interventions to improve tractor safety. Target behaviours that promote safe operation in farmyards, determining and addressing blind spots of tractors, were identified, and an intervention was developed following the Behaviour Change Wheel Approach. The objective of the study is to examine the feasibility, fidelity and acceptability of a behaviour change intervention to enhance the safe operation of tractors in farmyards with a particular focus on tractor blind spots.Method: A single group feasibility study will be undertaken. Approximately 16 farmers from four major farm types will be recruited for the study between August and September 2022. The intervention involves an in-person demo session, facilitated discussion and personalised safety training procedure with safety goals. The study will collect data from participants at three time points: baseline (3–10 days prior to the intervention), during the intervention and at the follow-up session (7–30 days post-intervention). Quantitative data will be collected through a pre-intervention interview and feedback surveys. A pre- and post-intervention qualitative interview will also be conducted with the participants and will be supplemented with qualitative data from recruitment logs, observational memos and logs and feedback from recruiters. Evaluation of the feasibility, acceptability and fidelity of the intervention will be guided by a pre-determined feasibility checklist, fidelity framework and theoretical framework of acceptability, respectively. Interviews will be analysed using the content analysis.Discussion: The current study can determine the feasibility and fidelity of delivering a systematic, theoretically driven, tailored behaviour change intervention. It will also assess whether the intervention, its ingredients and delivery are acceptable to the farming population. This study will also inform the development of a future larger trial to test the effectiveness of the intervention.Trial registration: ISRCTN Identifier: ISRCTN22219089. Date applied 29 July 2022