Selection biases in technology-based intervention research: patients’ technology use relates to both demographic and health-related inequities

Abstract

Objective Researchers conduct studies with selection biases, which may limit generalizability and outcomes of intervention research. In this methodological reflection, we examined demographic and health characteristics of implantable cardioverter defibrillator patients who were excluded from an informatics intervention due to lack of access to a computer and/or the internet. Materials and Methods Using information gathered from surveys and electronic health records, we compared the intervention group to excluded patients on demographic factors, computer skills, patient activation, and medical history. Results Excluded patients were older, less educated, less engaged and activated in their health, and had worse health (ie, more medical comorbidities) than nonexcluded patients. Discussion Although excluded from the intervention based solely on lack of access to a computer and/or internet, excluded patients may have needed the intervention more because they were sicker with more comorbidities. Conclusion Researchers must be mindful of enrollment biases and demographic and health-related inequities that may exist during recruitment for technology-based interventions.

Medication adherence for atrial fibrillation patients: triangulating measures from a smart pill bottle, e-prescribing software, and patient communication through the electronic health record

“…This may have led to a selection bias whereby those who were most ill (and perhaps least adherent to medications) may not have been included in the study. 37 These results need to be interpreted with these limitations in mind.…”

Patient characteristics associated with objective measures of digital health tool use in the United States: A literature review

“…[56,57] Our findings suggest that studies that prioritize inclusion of adequate sample sizes of diverse populations and of those with lived experiences with the health conditions of interest [58] might be better positioned to provide greater generalizability about uptake of patient-facing digital health tools in real-world dissemination. [59] Furthermore, despite the known high digital literacy, health literacy, numeracy, and language demands of many digital health tools, there were few studies examining use by these characteristics. [60][61][62][63] It is imperative that these characteristics be included in evaluation studies of digital health tools in order to inform the real-world usefulness and likely uptake of such tools.…”

Untold Stories in User-Centered Design of Mobile Health: Practical Challenges and Strategies Learned From the Design and Evaluation of an App for Older Adults With Heart Failure

“…Others have described the additional costs of recruiting end users for UCD projects [ 20 , 57 ]. It is even more difficult to recruit a representative sample in a technology project when technology ownership and proficiency are among the eligibility criteria [ 62 ] or the notion of technology leads individuals to decline participation [ 63 ]. Age- and illness-related physical and cognitive limitations may also exclude some individuals from mHealth studies [ 6 , 64 ].…”