Factors Influencing the Intention of MSM to Use the PrEP Intelligent Reminder System

Ma, Yingjie; Zhong, Xiaoni; Lin, Bingqing; He, Wei

doi:10.2147/rmhp.s337287

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…Therefore, MSMs’ behavior for HIV prevention determines whether they will receive medication reminders or information. This finding is consistent with similar studies [ 42 ]. According to the knowledge, attitude, and practice theory, knowledge and attitude are crucial determinants of an individual’s ability to form certain health behaviors [ 52 ].…”

Section: Discussionsupporting

confidence: 94%

“…The data collection process is shown in Figure 2 . The method of self-administered questionnaire was adopted, and the questionnaires were collected on the spot and checked for completeness and logic [ 42 , 43 ]. Data were obtained in the first follow-up period for the cohort because HBM-related questions were only involved during this period.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The Influencing Factors of HIV-Preventive Behavior Based on Health Belief Model among HIV-Negative MSMs in Western China: A Structural Equation Modeling Analysis

Liu

Lai

Shi

et al. 2022

IJERPH

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(1) Background: Men who have sex with other men (MSMs) are at high risk of being infected by the human immunodeficiency virus (HIV) in western China. Pre-exposure prophylaxis (PrEP) is an efficient way to prevent HIV transmission. However, adherence is the most vital determinant factor affecting PrEP effectiveness. We conducted a study based on the Health Belief Model to explore factors that predict adherence to PrEP among a cohort of 689 MSMs in western China. (2) Methods: We assessed perceived susceptibility, severity, benefits, barriers, self-efficacy, cues to action, and HIV-preventive behavior through a cross-sectional survey. (3) Results: PrEP self-efficacy was directly associated with PrEP behaviors (β = 0.221, p < 0.001), cues to action were directly associated with PrEP behaviors (β = 0.112, p < 0.001), perceived benefits were directly associated with PrEP behaviors (β = 0.101, p < 0.001), and perceived susceptibility was directly associated with PrEP behaviors (β = 0.117, p = 0.043). (4) Conclusion: Medication self-efficacy, perceived susceptibility, and cue to action structures are predictors of the MSMs’ HIV-preventive behavior in western China. These results will provide theoretical plans for promoting PrEP adherence in MSMs.

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Section: Discussionsupporting

confidence: 94%

Section: Methodsmentioning

confidence: 99%

The Influencing Factors of HIV-Preventive Behavior Based on Health Belief Model among HIV-Negative MSMs in Western China: A Structural Equation Modeling Analysis

Liu

Lai

Shi

et al. 2022

IJERPH

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“…PE and SI do not affect the BI, but the role of these two factors could not be ignored in related studies [ 19 , 41 , 42 ], we think the reasons could be as follows: (1) Although HERS can improve the examination efficiency, the improvement is mainly reflected by the data in HIS (comparing the average waiting time before and after using the HERS), and patient’s perception is not obvious; (2) Patients don’t come to the hospital very often. Although HERS has been implemented for six months, some patients are still new to the system; (3) Doctors and patients are still not proficient in using HERS, so they think learning to use HERS will add their troubles, and there are few cases of doctors recommending to patients and patients recommending to patients.…”

Section: Discussionmentioning

confidence: 99%

Driving the implementation of hospital examination reservation system through hospital management

Wang,

Ma,

Mao

et al. 2024

BMC Health Serv Res

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Background Hospital Examination Reservation System (HERS) was designed for reducing appointment examination waiting time and enhancing patients’ medical satisfaction in China, but implementing HERS would encounter many difficulties. This study would investigate the factors that influence patients’ utilization of HERS through UTAUT2, and provide valuable insights for hospital managements to drive the effective implementation of HERS. It is helpful for improving patients’ medical satisfaction. Methods We conducted a survey through the Sojump platform, targeting patients were who have already used HERS. We collected questionnaire information related to factors behavior intention, performance expectancy, and effort expectancy. Subsequently, we employed a structural equation model to analyze the factors influencing patients’ utilization of HERS. Results A total of 394 valid questionnaires were collected. Habit was the main direct positive factor influencing the behavioral intention of HERS (β = 0.593; 95%CI: 0.072, 1.944; P = 0.002), followed by patient innovation (β = 0.269; 95%CI: 0.002, 0.443; P < 0.001), effort expectancy (β = 0.239; 95%CI: -0.022, 0.478; P = 0.048). Patient innovation and facilitating conditions also have an indirect effect on behavioral intention. Perceived privacy exposure has a significantly negative effect on behavioral intention (β=-0.138; 95%CI: -0.225, -0.047; P < 0.001). The above variables explained 56.7% of the variation in behavioral intention. Conclusions When HERS is implemented in hospitals, managements should arrange volunteers to guide patients to bring up the habit and solve the using difficulties, and managements could invite patients with high innovation to recommend HERS to others, what’s more, it is a valid way to retain the old form of appointment to pass the transition period to the new system. HERS utilization and patients’ medical satisfaction will be enhanced through the guidance of hospital management means.

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“…These findings align with a metaregression of studies that evaluated effective clinical decision support using the UTAUT model and suggested that effort expectancy, facilitating conditions, and performance expectancy had a significant impact on clinician behavior [ 42 ]. Numerous studies evaluated medication-related CDS using the UTAUT, including studies examining a comprehensive CDS suite of tools called Sentri7/Quantifi [ 43 ], use of a decision aid for psychotic disorders [ 44 ], medication management in oncology settings [ 45 ], and a tool to help remind HIV positive men to use antiviral therapies when undertaking sexual activities [ 46 ]. However, to our knowledge, none have applied the UTAUT model to DDI-specific SDM.…”

Section: Discussionmentioning

confidence: 99%

Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction

et al. 2022

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Background Warnings about drug-drug interactions (DDIs) between warfarin and nonsteroidal anti-inflammatory drugs (NSAIDs) within electronic health records indicate potential harm but fail to account for contextual factors and preferences. We developed a tool called DDInteract to enhance and support shared decision-making (SDM) between patients and physicians when both warfarin and NSAIDs are used concurrently. DDInteract was designed to be integrated into electronic health records using interoperability standards. Objective The purpose of this study was to conduct a formative evaluation of a DDInteract that incorporates patient and product contextual factors to estimate the risk of bleeding. Methods A randomized formative evaluation was conducted to compare DDInteract to usual care (UC) using physician-patient dyads. Using case vignettes, physicians and patients on warfarin participated in simulated virtual clinical encounters where they discussed the use of taking ibuprofen and warfarin concurrently and determined an appropriate therapeutic plan based on the patient’s individualized risk. Dyads were randomized to either DDInteract or UC. Participants completed a postsession interview and survey of the SDM process. This included the 9-item Shared Decision-Making Questionnaire (SDM-Q-9), tool usability and workload National Aeronautics and Space Administration (NASA) Task Load Index, Unified Theory of Acceptance and Use of Technology (UTAUT), Perceived Behavioral Control (PBC) scale, System Usability Scale (SUS), and Decision Conflict Scale (DCS). They also were interviewed after the session to obtain perceptions on DDInteract and UC resources for DDIs. Results Twelve dyad encounters were performed using virtual software. Most (n=11, 91.7%) patients were over 50 years of age, and 9 (75%) had been taking warfarin for more than 2 years (75%). Regarding scores on the SDM-Q-9, participants rated DDInteract higher than UC for questions pertaining to helping patients clarify the decision (P=.03), involving patients in the decision (P=.01), displaying treatment options (P<.001), identifying advantages and disadvantages (P=.01), and facilitating patient understanding (P=.01) and discussion of preferences (P=.01). Five of the 8 UTAUT constructs showed differences between the 2 groups, favoring DDInteract (P<.05). Usability ratings from the SUS were significantly higher (P<.05) for physicians using DDInteract compared to those in the UC group but showed no differences from the patient’s perspective. No differences in patient responses were observed between groups using the DCS. During the session debrief, physicians indicated little concern for the additional time or workload entailed by DDInteract use. Both clinicians and patients indicated that the tool was beneficial in simulated encounters to understand and mitigate the risk of harm from this DDI. Conclusions Overall, DDInteract may improve encounters where there is a risk of bleeding due to a potential drug-drug interaction involving anticoagulants. Participants rated DDInteract as logical and useful for enhancing SDM. They reported that they would be willing to use the tool for an interaction involving warfarin and NSAIDs.

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Factors Influencing the Intention of MSM to Use the PrEP Intelligent Reminder System

Cited by 4 publications

References 41 publications

The Influencing Factors of HIV-Preventive Behavior Based on Health Belief Model among HIV-Negative MSMs in Western China: A Structural Equation Modeling Analysis

The Influencing Factors of HIV-Preventive Behavior Based on Health Belief Model among HIV-Negative MSMs in Western China: A Structural Equation Modeling Analysis

Driving the implementation of hospital examination reservation system through hospital management

Shared Decision-Making for Drug-Drug Interactions: Formative Evaluation of an Anticoagulant Drug Interaction

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