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Specifically, the team began by enrolling 25 patients in the app. After six months, 56 percent of the participants were still using the app. What stood out to researchers was that the patients living a considerable distance from the clinic were more likely to utilize the app.
“Our study suggests that patients possess the desire and motivation to stay engaged with providers and to remain in care,” says Jackie Hodges, MD, a UVA Health infectious disease fellow involved with the study, in the press release. “Ultimately, though, low barrier models of care need to be expanded to support that desire and more effectively meet patients where they are. An app like HOPE can facilitate that type of care, including coordination of needed social support services, like transportation.”
Although more testing is needed for the app, the trial information indicates that the service will be a successful modality of care, according to the researchers.
“We are excited to see that patients are using the app to stay engaged with recovery services,” said Rebecca Dillingham, MD, an infectious disease expert at UVA who helped develop the app, in the press release. “We partnered with patients in the design of the app, and this helped to create a welcoming, low-barrier portal to facilitate staying in care.”
In recent years, and especially during the COVID-19 pandemic, smartphone apps have played a key role in caring for patients with chronic conditions remotely.
A March 2021 study published in CJASN described a mHealth app known as eKidneyCare that supported treatment for chronic kidney disease. eKidneyCare helped boost medication adherence while maintaining patient safety.
Another app developed in July 2021 by researchers at Michigan State University picked up various cues such as patient speech and vocabulary to predict the likelihood of Alzheimer’s disease. According to Jiayu Zhou, PhD, an associate professor in MSU's College of Engineering, the app is a highly affordable and practical service that can provide predictions at a similar level of accuracy as MRIs.
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