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Characterizing Tuberculosis Medication Side Effects Using Video Directly Observed Therapy Monitoring in Uganda: Opportunities for Digital Pharmacovigilance

This study demonstrates that Video Directly Observed Therapy (VDOT) in Uganda effectively enables longitudinal monitoring of tuberculosis medication side effects, revealing distinct patient burden subgroups and associated risk factors to support the integration of digital pharmacovigilance into routine TB care.

Original authors: Juliet N. Sekandi, Kelvin Bwambale, Alif Indiralarasati, Doreen K. Kalembe, Kisha Patel, Damalie Nakkonde, Sarah Zalwango

Published 2026-08-06

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Juliet N. Sekandi, Kelvin Bwambale, Alif Indiralarasati, Doreen K. Kalembe, Kisha Patel, Damalie Nakkonde, Sarah Zalwango

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to solve a massive, invisible puzzle that is hurting millions of people around the world. This puzzle is Tuberculosis (TB), a sneaky germ that attacks the lungs. To beat it, doctors have to give patients a strict schedule of powerful medicine for many months. Think of this medicine like a heavy, complex backpack you have to carry every single day. While the backpack saves your life, it can also be heavy, hot, and sometimes make you feel sick, dizzy, or weak. These uncomfortable feelings are called "side effects."

For a long time, doctors have had a hard time knowing exactly how heavy that backpack feels for each person. Usually, they only ask, "Do you have a headache?" once a month when you visit the clinic. If you forget to mention it, or if the pain comes and goes between visits, the doctor might miss the whole story. But now, technology is changing the game. There is a new method called Video Directly Observed Therapy (VDOT). Imagine a patient taking a quick selfie video every day to prove they took their pills. This isn't just a security camera; it's a two-way street where patients can also whisper, "Hey, my legs hurt today," or "My urine looks weird." This study asks a big question: Can we use these daily video check-ins not just to make sure people take their medicine, but also to listen to their stories about how the medicine makes them feel, and use that to help them stay healthy?

The Digital Detective and the Heavy Backpack

In a bustling city in Uganda called Kampala, a team of researchers decided to put this idea to the test. They looked at data from 68 adults who were fighting drug-susceptible TB. Instead of just waiting for patients to show up at a clinic, these patients used a special app to send in videos of themselves taking their medicine every day. But here is the twist: the researchers didn't just count the videos. They treated the app like a digital diary, asking the patients to report every little ache, pain, or weird feeling they had along the way.

To make sense of all these complaints, the researchers invented a special "Burden Score." Think of this score like a video game health bar. If you get a mild headache, your health bar drops a tiny bit. If you get a severe stomach ache or feel incredibly weak, your health bar drops a lot. By adding up all the drops over time, they could see who was carrying the heaviest "side effect backpack."

What They Found: The Two Groups

When they looked at the data, they found that side effects were very common. In fact, almost everyone felt something. The most frequent complaints were:

Joint or body pain: 84% of people felt this.
Body weakness: 79% felt this.
Red or discolored urine: 68% noticed this.

But the real magic happened when they used a computer trick called "Latent Class Analysis." Imagine sorting a bag of mixed marbles into two piles based on how heavy they feel. The researchers found that the patients naturally fell into two distinct groups:

The High Burden Group (66.2% of people): These folks felt a lot of symptoms, often repeatedly. They were more likely to have body weakness, joint pain, red urine, fever, and dizziness.
The Low Burden Group (33.8% of people): These patients felt much better, with very few symptoms reported.

Who Carried the Heaviest Backpack?

The study also tried to figure out who was more likely to end up in the "High Burden" group. It turns out, certain factors made the backpack feel heavier:

Age: People older than 30 felt the burden more than younger folks.
HIV Status: People living with HIV reported higher side effect burdens.
Smoking: Smokers felt the weight more than non-smokers.
Knowledge: Interestingly, people who knew less about TB felt the burden more than those who knew more.

One surprising finding was about gender. Even though the group was split evenly between men and women, men were much more likely to be in the "High Burden" group (60% of men vs. 40% of women). However, the researchers noted that because the group was small, this finding needs more investigation before we can say it's a hard rule.

The Good News: Taking the Medicine Anyway

Here is the most important part of the story: Even though the "High Burden" group felt terrible, they didn't stop taking their medicine. Their adherence (how well they followed the rules) was just as good as the "Low Burden" group. This suggests that even when the backpack feels heavy, people are willing to carry it if they are supported.

Why This Matters

This paper suggests that using video apps like VDOT is a powerful tool. It's not just a camera to catch people skipping doses; it's a listening device. By letting patients report their symptoms every day, doctors can see the full picture of how the medicine affects them, rather than just guessing between monthly visits. The study shows that this digital approach can spot who is struggling the most and might need extra help, all without changing the medicine itself.

While the study was small and focused on one city, it opens a door. It suggests that in the future, we might be able to use these digital tools to make TB treatment safer and more comfortable for everyone, turning a lonely, heavy journey into a supported one where no one has to suffer in silence.

Technical Summary: Characterizing Tuberculosis Medication Side Effects Using Video Directly Observed Therapy Monitoring in Uganda

Problem Statement
Monitoring medication-related adverse events is a critical component of tuberculosis (TB) pharmacovigilance but remains difficult in resource-limited settings. Traditional monitoring often relies on cross-sectional assessments during scheduled clinic visits, leading to underreporting and delayed identification of adverse drug reactions (ADRs). Furthermore, existing literature frequently summarizes side effects as simple counts or binary indicators, failing to capture the cumulative burden, duration, and severity of symptoms over the course of treatment. This limitation obscures the true patient experience and hinders the development of targeted interventions for drug-resistant TB, which requires longer, more toxic regimens.

Methodology
This study conducted a secondary analysis of data from the "DOT Selfie" randomized controlled trial (RCT) involving 68 adults with drug-susceptible TB treated in Kampala, Uganda, between July 2020 and October 2021. Participants were assigned to a Video Directly Observed Therapy (VDOT) arm, where they submitted video recordings of medication ingestion via a secure, HIPAA-compliant cloud platform.

Data Collection: Trained research assistants collected baseline demographics and TB knowledge via structured interviews. Side effects were tracked longitudinally through the VDOT platform by a study nurse, capturing the occurrence and duration of 15 specific symptoms.
Severity-Weighted Burden Score: To address the limitations of simple event counts, the authors developed a "Severity-Weighted Side Effect Burden Score." This metric combined reporting frequency with severity weights assigned based on the Common Terminology Criteria for Adverse Events (CTCAE) v5.0:
- Grade 1 (Mild, weight 1): Fever, dizziness, headache, red urine, anorexia, increased appetite.
- Grade 2 (Moderate, weight 2): GI upset, joint/body pain, body weakness/malaise, chest pain, skin rash, puffy face.
- Grade 3 (Severe, weight 3): Jaundice, numbness, pedal edema.
- The score was calculated as the sum of (Severity Weight × Reporting Frequency) across all 15 side effects, standardized to a 30-day period.
Statistical Analysis: Descriptive statistics summarized participant characteristics. The Mann–Whitney U and Kruskal–Wallis tests compared median burden scores across covariates. Latent Class Analysis (LCA) was performed on 14 binary side effect indicators to identify distinct subgroups of symptom burden patterns.

Key Results

Participant Demographics: The median age was 30 years; 50% were female, and 31% were living with HIV.
Symptom Prevalence: The most frequently reported side effects were joint/body pain (84%), body weakness (79%), and red/discolored urine (68%). Notably, while skin rash had a lower prevalence (19%), it generated a high cumulative frequency of reports (131 reports among 13 patients), illustrating the value of longitudinal tracking over simple prevalence counts.
Burden Score Associations: Higher burden scores were significantly associated with:
- Age > 30 years ( $p=0.030$ )
- Living with HIV ( $p=0.023$ )
- Smoking status ( $p=0.023$ )
- Lower TB knowledge ( $p=0.033$ )
- A borderline association was observed with low digital literacy ( $p=0.057$ ).
- No significant differences were found regarding sex, education, marital status, employment, income, alcohol use, or prior TB treatment history.
Latent Class Analysis: The analysis identified two distinct subgroups:
- High Burden Class (66.2%): Characterized by high probabilities of body weakness (100%), joint pain (97.8%), red urine (97.8%), fever (95.6%), and dizziness (88.9%). This group also exclusively reported jaundice, numbness, headache, skin rash, and puffy face.
- Low Burden Class (33.8%): Showed low item-response probabilities across all side effects.
- Class Characteristics: The High Burden class had a significantly higher median burden score (3.82) compared to the Low Burden class (0.56, $p<0.001$ ). Males were disproportionately represented in the High Burden class (60% vs. 30% females, $p=0.039$ ). Medication adherence remained high in both classes and did not differ significantly ( $p=0.263$ ).

Significance and Claims
The paper posits that VDOT offers a viable mechanism for digital pharmacovigilance, moving beyond its traditional role as an adherence-support tool. By capturing longitudinal, real-time data on symptom frequency and persistence, VDOT reveals substantial heterogeneity in patient experiences that cross-sectional methods miss.

The authors claim that this approach allows for the identification of "high-burden" patient profiles and associated risk factors (such as HIV status and age) that might otherwise go unnoticed. The study suggests that integrating structured symptom reporting into digital TB care platforms could strengthen pharmacovigilance in resource-limited settings, enabling earlier identification of at-risk patients and more timely management of side effects. The authors conclude that while the study is limited by its small sample size and reliance on self-reported data without clinical verification, it demonstrates the potential of VDOT to support more patient-centered TB care and warrants further evaluation for integration into routine TB programs.

Technical Summary: Characterizing Tuberculosis Medication Side Effects Using Video Directly Observed Therapy Monitoring in Uganda

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