Effectiveness of Enhanced Video Directly Observed Treatment (DOT Selfie), a Mobile Health Intervention to Increase Treatment Adherence Monitoring and Support for Patients with Tuberculosis in Uganda: A Randomized Controlled Trial.

IF 5.4 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Juliet Nabbuye Sekandi, Esther Buregyeya, Sarah Zalwango, Damalie Nakkonde, Patrick Kaggwa, Trang Ho Thu Quach, David Asiimwe, Lynn Atuyambe, Kevin Dobbin
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引用次数: 0

Abstract

Background: Directly observed therapy (DOT) is the standard for monitoring adherence for tuberculosis (TB) treatment. However, the implementation of DOT is difficult for patients and providers due to a lack of financial and human resources. Mounting evidence suggests that emerging digital adherence technologies like video directly observed therapy (VDOT) can serve as an option.

Objective: The objective of the study was to evaluate the effectiveness of VDOT compared to usual care DOT.

Methods: Between July, 2020 and October 2021, we conducted a two-arm parallel group, open-label randomized trial with 1:1 assignment to receive the VDOT intervention (n=72) or usual care DOT (UCDOT) (n=72) for treatment adherence monitoring at public health clinics in Kampala, Uganda. Each group was stratified further to have equal numbers of males and females. Eligible patients were 18-65 years old with a confirmed diagnosis of TB and on daily treatment. The VDOT group received a smartphone with an app while the UCDOT group used the routine practice for monitoring treatment per the Uganda National TB program. We tested the hypothesis that VDOT was more effective for monitoring medication adherence compared to UCDOT. The primary outcome was adherence defined as having >=80% of the expected doses observed during the treatment period of 6 months. Intention-to-treat (ITT) analysis was done, and we performed multivariable logistic regression to estimate the effect of the intervention on adherence monitoring. We present adjusted relative risk ratios and the associated 95% confidence intervals. Secondary outcomes were treatment completion, loss to follow-up, death and reasons for missed videos in the intervention group.

Results: The intention-to-treat analysis included 142 participants. Two participants were excluded due to failure to continue their medication within the first week after enrollment. The median age was 34 years (IQR:26-45). The mean fraction of expected doses observed (FEDO) was significantly higher for the VDOT than the UCDOT group (90% mean FEDO vs 30% mean FEDO, p < 0.001). When using a FEDO cut-off of >=80% as optimal adherence, 63 (44%) patients achieved the set threshold with a significant difference between VDOT and UCDOT (78.9% vs. 9.9%, p=< 0.001). After adjusting for confounders, VDOT users were significantly more likely to have >=80% of their expected doses observed compared to UCDOT (Adjusted RR. 8.4, 95% CI 4.16-17.0). The commonest reasons for failure to submit videos of medication intake were an uncharged phone battery, forgetting to record videos during medication intake, and losing a smartphone.

Conclusions: The enhanced VDOT was more effective for increasing observation of adherence to treatment than UCDOT among patients with TB in Uganda. This evidence supports the promise of digital technologies for improving monitoring and support of treatment adherence in high TB burden settings where human resources are limited.

Clinicaltrial: ClinicalTrials.govNCT04134689, http://clinicaltrials.gov/ct2/show/NCT04134689.

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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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