Developing Data Sharing Models for Health Research with Real-World Data: A Scoping Review of Patient and Public Preferences

Factors influencing smartwatch use and comfort with health data sharing: a sequential mixed-method study protocol

INTRODUCTION

Smartwatches have become ubiquitous for tracking health metrics. These data sets hold substantial potential for enhancing healthcare and public health initiatives; it may be used to track chronic health conditions, detect previously undiagnosed health conditions and better understand public health trends. By first understanding the factors influencing one's continuous use of the device, it will be advantageous to assess factors that may influence a person's willingness to share their individual data sets. This study seeks to comprehensively understand the factors influencing the continued use of these devices and people's willingness to share the health data they generate.

METHODS AND ANALYSIS

A two-section online survey of smartwatch users over the age of 18 will be conducted (n ≥200). The first section, based on the expectation-confirmation model, will assess factors influencing continued use of smartwatches while the second section will assess willingness to share the health data generated from these devices. Survey data will be analysed descriptively and based on structural equation modelling.Subsequently, six focus groups will be conducted to further understand the issues raised in the survey. Each focus group (n=6) will consist of three smartwatch users: a general practitioner, a public health specialist and an IT specialist. Young smartwatch users (aged 18-44) will be included in three of the focus groups and middle-aged smartwatch users (aged 45-64) will be included in the other three groups. This is to enhance comparison of opinions based on age groups. Data from the focus groups will be analysed using the microinterlocutor approach and an executive summary.After the focus group, participants will complete a brief survey to indicate any changes in their opinions resulting from the discussion.

ETHICS AND DISSEMINATION

The results of this study will be disseminated through publication in a peer-reviewed journal, and all associated data will be deposited in a relevant, publicly accessible data repository to ensure transparency and facilitate future research endeavours.This study was approved by the Social Research Ethic Committee (SREC), University College Cork-SREC/SOM/21062023/2.

Implementation Science and Practice-Oriented Research: Convergence and Complementarity

Implementation science is the scientific study of methods to promote the uptake of research findings and other evidence-based practices in routine care, with the goal of improving the quality and effectiveness of health services (Bauer et al., 2015). In addition to this common goal, practice-oriented psychotherapy research (and researchers) and implementation science (and scientists) share a common focus on the people and the places where treatment happens. Thus, there exists strong potential for combining these two approaches. In this article, we provide a primer on implementation science for psychotherapy researchers and highlight important areas and examples of convergence and complementarity between implementation science and practice-oriented psychotherapy research. Specifically, we (a) define and describe the core features of implementation science; (b) discuss similarities and areas of complementarity between implementation science and practice-oriented psychotherapy research; (c) discuss a case example that exemplifies the integration of implementation science and practice-oriented research; and (d) propose directions for future research and collaborations that leverage both implementation science and practice-oriented research.

Lansoprazole Ameliorates Isoniazid-Induced Liver Injury

Isoniazid is a first-line drug in antitubercular therapy. Isoniazid is one of the most commonly used drugs that can cause liver injury or acute liver failure, leading to death or emergency liver transplantation. Therapeutic approaches for the prevention of isoniazid-induced liver injury are yet to be established. In this study, we identified the gene expression signature for isoniazid-induced liver injury using a public transcriptome dataset, focusing on the differences in susceptibility to isoniazid in various mouse strains. We predicted that lansoprazole is a potentially protective drug against isoniazid-induced liver injury using connectivity mapping and an adverse event reporting system. We confirmed the protective effects of lansoprazole against isoniazid-induced liver injury using zebrafish and patients' electronic health records. These results suggest that lansoprazole can ameliorate isoniazid-induced liver injury. The integrative approach used in this study may be applied to identify novel functions of clinical drugs, leading to drug repositioning.

From intention to action: The factors affecting health data sharing intention and action

INTRODUCTION

Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action.

OBJECTIVE

The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action.

METHODS

A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions.

RESULTS

Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72).

CONCLUSION

Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.