An omni-channel, outcomes-focused approach to scale digital health interventions in resource-limited populations: a case study.

Populations in resource-limited communities have low health awareness, low financial literacy levels, and inadequate access to primary healthcare, leading to low adoption of preventive health behaviours, low healthcare-seeking behaviours, and poor health outcomes. Healthcare providers have limited reach and insights, limiting their ability to design relevant products for resource limited settings. Our primary preventive health intervention, called the Saathealth family health interventions, is a scaled digital offering that aims to improve knowledge levels on various health topics, nudge positive behaviour changes, and drive improved health outcomes. This case study presents our learnings and best practices in scaling these digital health interventions in resource-limited settings and maximising their impact. We scaled the Saathealth interventions to cumulatively reach >10 million users across India using a multi-pronged approach: (1) ensuring localization and cultural relevance of the health content delivered through user research; (2) disseminating content using omni-channel approaches, which involved using diverse content types and multiple digital platforms; (3) using iterative product features such as gamification and artificial intelligence-based (AI-based) predictive models; (4) using real-time analytics to adapt the user's digital experience by using interactive content to drive them towards products and services and (5) experiments with sustainability models to yield some early successes. The Saathealth family health mobile app had >25,000 downloads and the intervention reached >873,000 users in India every month through the mobile app, Facebook, and Instagram combined, from the time period of February 2022 to January 2023. We repeatedly observed videos and quizzes to be the most popular content types across all digital channels being used. Our AI-based predictive models helped improve user retention and content consumption, contributing to the sustainability of the mobile apps. In addition to reaching a high number of users across India, our scaling strategies contributed to deepened engagement and improved health-seeking behaviour. We hope these strategies help guide the sustainable and impactful scaling of mobile health interventions in other resource-limited settings.

Systemic Solutions for Addressing Non-Communicable Diseases in Low- and Middle-Income Countries.

Non-communicable diseases (NCDs) have been on the rise in low- and middle-income countries (LMICs) over the last few decades and represent a significant healthcare concern. Over 85% of "premature" deaths worldwide due to NCDs occur in the LMICs. NCDs are an economic burden on these countries, increasing their healthcare expenditure. However, targeting NCDs in LMICs is challenging due to evolving health systems and an emphasis on acute illness. The major issues include limitations with universal health coverage, regulations, funding, distribution and availability of the healthcare workforce, and availability of health data. Experts from across the health sector in LMICs formed a Think Tank to understand and examine the issues, and to offer potential opportunities that may address the rising burden of NCDs in these countries. This review presents the evidence and posits pragmatic solutions to combat NCDs.

Effects of an mHealth voice message service (mMitra) on maternal health knowledge and practices of low-income women in India: findings from a pseudo-randomized controlled trial.

BACKGROUND: Mobile Health (mHealth) is becoming an important tool to improve health outcomes in maternal, newborn and child health (MNCH). Studies of mHealth interventions, have demonstrated their effectiveness in improving uptake of recommended maternal services such as antenatal visits. However, evidence of impact on maternal health outcomes is still limited. METHODS: A pseudo-randomized controlled trial (single blind) was conducted to assess the impact of a voice-message based maternal intervention on maternal health knowledge, attitudes, practices and outcomes over time: Pregnancy (baseline/Time 1); Post-partum (Time 2) and when the infant turned one year old (Time 3). Women assigned to the mMitra intervention arm received gestational age- and stage-based educational voice messages via mobile phone in Hindi and Marathi, while those assigned to the control group did not. Both groups received standard care. RESULTS: Two thousand sixteen women were enrolled. Interviews were conducted with 1516 women in the intervention group and 500 women in the control group at baseline and post-partum. The intervention group performed significantly better than controls on four maternal health practice indicators: receiving the tetanus toxoid injection (OR: 1.6, 95% Confidence Interval (CI): 1.05-2.4, p = 0.028), consulting a doctor if spotting or bleeding (OR: 1.72, 95%CI: 1.07-2.75, p = 0.025), saving money for delivery expenses (OR: 1.79, 95%CI: 1.38-2.33, p = 0.0001), and delivering in hospital (OR: 2.5, 95%CI: 1.49-4.35, p = 0.001). The control group performed significantly better than the intervention group on two practice indicators: resting regularly during pregnancy (OR: 0.7, 95%CI: 0.54-0.88, p = 0.002) and having at-home deliveries attended by a skilled birth attendant (OR: 0.46, 95%CI: 0.23-0.91, p = 0.027). Both groups' knowledge improved from Time 1 to Time 2. Only one knowledge indicator, on seeking medical care during pregnancy, was statistically increased in the intervention group compared to controls. Anemia status at or near the time of delivery was unable to be assessed due to missing data from maternal health cards. CONCLUSIONS: This study provides evidence that in low-resource settings, mobile voice messages providing tailored and timed information about pregnancy can positively impact maternal health care practices proven to improve maternal health outcomes. Additional research is needed to assess whether voice messaging can motivate behavior change better than text messaging, particularly in low literacy settings. TRIAL REGISTRATION: The mMitra impact evaluation is registered with ISRCTN under Registration # 88968111, assigned on 6 September 2018 (See https://www.isrctn.com/ISRCTN88968111).

AI for Improving Children's Health: A Community Case Study.

The Indian health care system lacks the infrastructure to meet the health care demands of the country. Physician and nurse availability is 30 and 50% below WHO recommendations, respectively, and has led to a steep imbalance between the demand for health care and the infrastructure available to support it. Among other concerns, India still struggles with challenges like undernutrition, with 38% of children under the age of five being underweight. Despite these challenges, technological advancements, mobile phone ubiquity and rising patient awareness offers a huge opportunity for artificial intelligence to enable efficient healthcare delivery, by improved targeting of constrained resources. The Saathealth mobile app provides low-middle income parents of young children nflwith interactive children's health, nutrition and development content in the form of an entertaining video series, a gamified quiz journey and targeted notifications. The app iteratively evolves the user journey based on dynamic data and predictive algorithms, empowering a shift from reactive to proactive care. Saathealth users have registered over 500,000 sessions and over 200 million seconds on-app engagement over a year, comparing favorably with engagement on other digital health interventions in underserved communities. We have used valuable app analytics data and insights from our 45,000 users to build scalable, predictive models that were validated for specific use cases. Using the Random Forest model with heterogeneous data allowed us to predict user churn with a 93% accuracy. Predicting user lifetimes on the mobile app for preliminary insights gave us an RMSE of 25.09 days and an R2 value of 0.91, reflecting closely correlated predictions. These predictive algorithms allow us to incentivize users with optimized offers and omni-channel nudges, to increase engagement with content as well as other targeted online and offline behaviors. The algorithms also optimize the effectiveness of our intervention by augmenting personalized experiences and directing limited health resources toward populations that are most resistant to digital first interventions. These and similar AI powered algorithms will allow us to lengthen and deepen the lifetime relationship with our health consumers, making more of them effective, proactive participants in improving children's health, nutrition and early cognitive development.

The Impact of an mHealth Voice Message Service (mMitra) on Infant Care Knowledge, and Practices Among Low-Income Women in India: Findings from a Pseudo-Randomized Controlled Trial.

Objectives mHealth interventions for MNCH have been shown to improve uptake of antenatal and neonatal services in low- and middle-income countries (LMICs). However, little systematic analysis is available about their impact on infant health outcomes, such as reducing low birth weight or malnutrition among children under the age of five. The objective of this study is to determine if an age- and stage-based mobile phone voice messaging initiative for women, during pregnancy and up to 1 year after delivery, can reduce low birth weight and child malnutrition and improve women's infant care knowledge and practices. Methods We conducted a pseudo-randomized controlled trial among pregnant women from urban slums and low-income areas in Mumbai, India. Pregnant women, 18 years and older, speaking Hindi or Marathi were enrolled and assigned to receive mMitra messages (intervention group N = 1516) or not (Control group N = 500). Women in the intervention group received mMitra voice messages two times per week throughout their pregnancy and until their infant turned 1 year of age. Infant's birth weight, anthropometric data at 1 year of age, and status of immunization were obtained from Maternal Child Health (MCH) cards to assess impact on primary infant health outcomes. Women's infant health care practices and knowledge were assessed through interviews administered immediately after women enrolled in the study (Time 1), after they delivered their babies (Time 2), and after their babies turned 1 year old (Time 3). 15 infant care practices self-reported by women (Time 3) and knowledge on ten infant care topics (Time 2) were also compared between intervention and control arms. Results We observed a trend for increased odds of a baby being born at or above the ideal birth weight of 2.5 kg in the intervention group compared to controls (odds ratio (OR) 1.334, 95% confidence interval (CI) 0.983-1.839, p = 0.064). The intervention group performed significantly better on two infant care practice indicators: giving the infant supplementary feeding at 6 months of age (OR 1.4, 95% CI 1.08-1.82, p = 0.009) and fully immunizing the infant as prescribed under the Government of India's child immunization program (OR 1.531, 95% CI 1.141-2.055, p = 0.005). Women in the intervention group had increased odds of knowing that the baby should be given solid food by 6 months (OR 1.89, 95% CI 1.371-2.605, p < 0.01), that the baby needs to be given vaccines (OR 1.567, 95% CI 1.047-2.345, p = 0.028), and that the ideal birth weight is > 2.5 kg (OR 2.279, 95% CI 1.617-3.213, p < 0.01). Conclusions for Practice This study provides robust evidence that tailored mobile voice messages can significantly improve infant care practices and maternal knowledge that can positively impact infant child health. Furthermore, this is the first prospective study of a voice-based mHealth intervention to demonstrate a positive impact on infant birth weight, a health outcome of public health importance in many LMICs.

The Elusive Path Toward Measuring Health Outcomes: Lessons Learned From a Pseudo-Randomized Controlled Trial of a Large-Scale Mobile Health Initiative.

Mobile health (mHealth) offers new opportunities to improve access to health services and health information. It also presents new challenges in evaluating its impact, particularly in linking the use of a technology intervention that aims to improve health behaviors with the health outcomes that are impacted by changed behaviors. The availability of data from a multitude of sources (paper-based and electronic) provides the conditions to facilitate making stronger connections between self-reported data and clinical outcomes. This commentary shares lessons and important considerations based on the experience of applying new research frameworks and incorporating maternal and child health records data into a pseudo-randomized controlled trial to evaluate the impact of mMitra, a stage-based voice messaging program to improve maternal, newborn, and child health outcomes in urban slums in India.