Using conjoint analysis to study health policy changes: An example from a cohort of persons who use drugs

Is This Safe? Examining Safety Assessments of Illicit Drug Purchasing on Social Media Using Conjoint Analysis

Background: Illicit substance sales facilitated by social media platforms are a growing public health issue given recent increases in overdose deaths, including an alarming rise in cases of fentanyl poisoning. However, little is known about how online users evaluate what features of social media posts convey safety, which can influence their intent to source illicit substances. Objectives: This study adapts conjoint analysis which assessed how attributes of social media posts (i.e., features) influence safety evaluations of mock posts selling illicit substances. 440 participants were recruited online for self-reporting use or purchase of controlled substances or prescription medicines recreationally. The following attributes were tested: drug packaging, drug offerings, profile photo of seller, payment info provided, and use of emojis. Results: Packaging was ranked the most important attribute (Average Importance =43.68, Offering=14.94, Profile=13.86, Payment=14.11, Emoji=13.41), with posts that displayed drugs in pill bottles assessed as the most safe. Attribute levels for advertising multiple drugs, having a blank profile photo, including payment information, and including emojis also ranked higher in perceived safety. Rankings were consistent across tested demographic factors (i.e., gender, age, and income). Survey results show that online pharmacies were most likely to be perceived as safe for purchasing drugs and medications. Additionally, those who were younger in age, had higher income, and identified as female were more likely to purchase from a greater number of platforms. Conclusions: These findings can assist in developing more precise content moderation for platforms seeking to address this ongoing threat to public safety.

Understanding Healthcare Providers’ Electronic Health Record (EHR) Interface Preferences via Conjoint Analysis

OBJECTIVE

The emergence of Electronic Health Records (EHRs) has been beneficial in processing administrative and clinical data for quality healthcare information. Despite being patient-centered, a number of these technologies have a fractional consideration of the human-computer interaction, which affects the healthcare professionals as end-users. This attempted to surface the healthcare providers' preferences of an ideal EHR system interface in the community setting.

MATERIALS AND METHOD

Using an orthogonal main effect design of conjoint analysis, a select group of healthcare providers (n = 300) were asked to sort choice cards, which contains five (5) attributes of EHR interface with specific level. Data were analyzed using Sawtooth v.18 and SPSS v.21.

RESULTS

High importance was given to color scheme and device platform. Further, the part-worth analysis revealed the preference for an EHR with the following attributes: (a) smartphone device, (b) triadic color, (c) minimalist design, (d) chunked layout and (e) icon-centered menu.

DISCUSSION

Visual interest and technology needs of the community healthcare providers shaped their preferences. These provide substantial perspectives on how to improve usability of EHR interface systems.

CONCLUSION

Findings underscored the expanded roles of the healthcare professionals in the successful development of EHR systems.

Designing Personalized Persuasive Game Elements for Older Adults in Health Apps

The use of gamification elements in health apps has been shown to promote healthy behaviors. However, one-size-fits-all gamification strategy does not have the best persuasive effect. Therefore, the aim of this paper was to determine how to select personalized game elements for older adults and use them in health education app design. Firstly, based on the DMC model, the Kano model was used to identify the demand attributes of game mechanisms, and three gamification mechanisms (winning, feedback and reward) preferred by the elderly were selected. Secondly, the corresponding gamification elements were selected by the focus group method, and nine virtual products were generated. Thirdly, the virtual products were rated and conjointly analyzed to obtain the relative importance of gamification mechanisms and the utility values of gamification elements, and a comparative analysis was conducted on four characteristics of older adults: age, gender, personal income, and education. The results obtained the best combination of gamification elements chosen by the elderly under different classifications. Finally, design guidelines on persuasive gamification elements were developed based on the characteristics of older adults, and a modified gamification model was proposed. The results of this study provide suggestions and guidelines for the design of persuasive gamification in health education apps, which will help improve the satisfaction of older adults with health apps.