Leveraging Providers' Preferences to Customize Instructional Content in Information and Communications Technology-Based Training Interventions: Retrospective Analysis of a Mobile Phone-Based Intervention in India

A Scoping Review on Accentuating the Pragmatism in the Implication of Mobile Health (mHealth) Technology for Tuberculosis Management in India

Background: India continues to share a colossal count of the global tuberculosis load, with a perturbing 19% spring in the reported cases in 2021. With the National Tuberculosis Elimination Program (NTEP) consolidated to bring this epidemic to an end by 2025, the rapidly growing mobile health technologies can be utilized to offer promising results. Even though the implementation of this novel strategy is escalating around the globe, its triumph is still sub optimal in India. Objectives: This scoping review intends to explore the available mobile health (mHealth) technologies and analyse the effectiveness of the same for tuberculosis management in India. Methods: An elaborate search in electronic databases, such as PubMed and Google scholar, using the key terms and focussing from the year 2015, provided very broad results focussing on mHealth interventions and their utilisation in TB management in India. Further selection of the inclusive publications was carried out based upon the eligibility requirements as formulated for this review, pertaining to the objective of this study. Results: The collaborate search yielded a total of 858 scientific research papers. After the filtering of the obtained results, a total of 45 articles were selected to be analysed for this review. Published manuscripts, articles in peer review and abstracts from reliable databases were included to obtain vast range of information. Conclusion: The extensive literature search showed a preponderance of mHealth intervention studies focusing on TB treatment and drug monitoring. There exists a paucity of mHealth applications targeted to educate the public and intercept this infectious disease. The scientific articles reviewed and analysed in this scoping review strongly recommend the demployment of mHealth applications to achieve the target of eradicating TB by 2025 in India.

Pandemics and technology engagement: New evidence from m-Health intervention during COVID-19 in India

Information provision for social welfare via cheap technological media is now a widely available tool used by policymakers. Often, however, an ample supply of information does not translate into high consumption of information due to various frictions in demand, possibly stemming from the pecuniary and non-pecuniary cost of engagement, along with institutional factors. We test this hypothesis in the Indian context using a unique data set comprising 2 million call records of enrolled users of ARMMAN, a Mumbai-based nongovernmental organization that sends timely informational calls to mobile phones of less-privileged pregnant women. The strict lockdown induced by COVID-19 in India was an unexpected shock on engagement with m-Health technology, in terms of both reductions in market wages and increased time availability at home. Using a difference-in-differences design on unique calls tracked at the user-time level with fine-grained time-stamps on calls, we find that during the lockdown period, the call durations increased by 1.53 percentage points. However, technology engagement behavior exhibited demographic heterogeneity increasing relatively after the lockdown for women who had to borrow the phones vis-à-vis phone owners, for those enrolled in direct outreach programs vis-à-vis self-registered women, and for those who belonged to the low-income group vis-à-vis high-income group. These findings are robust with coarsened exact matching and with a placebo test for a 2017-2018 sample. Our results have policy implications around demand-side frictions for technology engagement in developing economies and maternal health.

Exploring and Characterizing Patient Multibehavior Engagement Trails and Patient Behavior Preference Patterns in Pathway-Based mHealth Hypertension Self-Management: Analysis of Use Data

Background

Hypertension is a long-term medical condition. Mobile health (mHealth) services can help out-of-hospital patients to self-manage. However, not all management is effective, possibly because the behavior mechanism and behavior preferences of patients with various characteristics in hypertension management were unclear.

Objective

The purpose of this study was to (1) explore patient multibehavior engagement trails in the pathway-based hypertension self-management, (2) discover patient behavior preference patterns, and (3) identify the characteristics of patients with different behavior preferences.

Methods

This study included 863 hypertensive patients who generated 295,855 use records in the mHealth app from December 28, 2016, to July 2, 2020. Markov chain was used to infer the patient multibehavior engagement trails, which contained the type, quantity, time spent, sequence, and transition probability value (TP value) of patient behavior. K-means algorithm was used to group patients by the normalized behavior preference features: the number of behavioral states that a patient performed in each trail. The pages in the app represented the behavior states. Chi-square tests, Z-test, analyses of variance, and Bonferroni multiple comparisons were conducted to characterize the patient behavior preference patterns.

Results

Markov chain analysis revealed 3 types of behavior transition (1-way transition, cycle transition, and self-transition) and 4 trails of patient multibehavior engagement. In perform task trail (PT-T), patients preferred to start self-management from the states of task blood pressure (BP), task drug, and task weight (TP value 0.29, 0.18, and 0.20, respectively), and spent more time on the task food state (35.87 s). Some patients entered the states of task BP and task drug (TP value 0.20, 0.25) from the reminder item state. In the result-oriented trail (RO-T), patients spent more energy on the ranking state (19.66 s) compared to the health report state (13.25 s). In the knowledge learning trail (KL-T), there was a high probability of cycle transition (TP value 0.47, 0.31) between the states of knowledge list and knowledge content. In the support acquisition trail (SA-T), there was a high probability of self-transition in the questionnaire (TP value 0.29) state. Cluster analysis discovered 3 patient behavior preference patterns: PT-T cluster, PT-T and KL-T cluster, and PT-T and SA-T cluster. There were statistically significant associations between the behavior preference pattern and gender, education level, and BP.

Conclusions

This study identified the dynamic, longitudinal, and multidimensional characteristics of patient behavior. Patients preferred to focus on BP, medications, and weight conditions and paid attention to BP and medications using reminders. The diet management and questionnaires were complicated and difficult to implement and record. Competitive methods such as ranking were more likely to attract patients to pay attention to their own self-management states. Female patients with lower education level and poorly controlled BP were more likely to be highly involved in hypertension health education.

A scoping review of the use of e-learning and e-consultation for healthcare workers in low- and middle-income countries and their potential complementarity

OBJECTIVE

Although the provision of e-learning (EL) training for healthcare workers (HCWs) and provider-to-HCW e-consultation (EC) is considered useful for health outcomes, research on their joint use is limited. This scoping review aimed to create an overview of what is currently known in the literature about the use and implementation of EC and EL by HCWs in LMICs and to answer the question of whether there is evidence of complementarity.

MATERIALS AND METHODS

Scientific databases were searched and peer-reviewed papers were reviewed systematically according to predefined inclusion/exclusion criteria. Data were extracted including the study focus (EC/EL), year of publication, geographical location, target population, target disease(s) under study, type(s) of study outcomes, and article type.

RESULTS

A total of 3051 articles were retrieved and screened for eligibility, of which 96 were kept for analysis. Of these, only 3 addressed both EL and EC; 54 studies addressed EL; and 39 addressed EC. Most studies looked at gain in knowledge/skills usability, efficiency, competence, and satisfaction of HCW, or barriers/challenges to implementation. Descriptive studies focused on the application of EL or EC for targeting specific health conditions. Factors contributing to the success of EC or EL networks were institutional anchoring, multiple partnership, and capacity building of local experts.

CONCLUSIONS

Our review found an important gap in the literature in relation to the complementary role of EL and EC for HCWs in LMICs evidenced by outcome measures. There is an important role for national and international academic institutions, learned medical societies, and networks to support regional experts in providing EL and EC for added value that will help the clinical performance of HCWs and improve health outcomes.

Patterns for Patient Engagement with the Hypertension Management and Effects of Electronic Health Care Provider Follow-up on These Patterns: Cluster Analysis

Background

Hypertension is a long-term medical condition. Electronic and mobile health care services can help patients to self-manage this condition. However, not all management is effective, possibly due to different levels of patient engagement (PE) with health care services. Health care provider follow-up is an intervention to promote PE and blood pressure (BP) control.

Objective

This study aimed to discover and characterize patterns of PE with a hypertension self-management app, investigate the effects of health care provider follow-up on PE, and identify the follow-up effects on BP in each PE pattern.

Methods

PE was represented as the number of days that a patient recorded self-measured BP per week. The study period was the first 4 weeks for a patient to engage in the hypertension management service. K-means algorithm was used to group patients by PE. There was compliance follow-up, regular follow-up, and abnormal follow-up in management. The follow-up effect was calculated by the change in PE (CPE) and the change in systolic blood pressure (CSBP, SBP) before and after each follow-up. Chi-square tests and z scores were used to ascertain the distribution of gender, age, education level, SBP, and the number of follow-ups in each cluster. The follow-up effect was identified by analysis of variances. Once a significant effect was detected, Bonferroni multiple comparisons were further conducted to identify the difference between 2 clusters.

Results

Patients were grouped into 4 clusters according to PE: (1) PE started low and dropped even lower (PELL), (2) PE started high and remained high (PEHH), (3) PE started high and dropped to low (PEHL), and (4) PE started low and rose to high (PELH). Significantly more patients over 60 years old were found in the PEHH cluster (P≤.05). Abnormal follow-up was significantly less frequent (P≤.05) in the PELL cluster. Compliance follow-up and regular follow-up can improve PE. In the clusters of PEHH and PELH, the improvement in PE in the first 3 weeks and the decrease in SBP in all 4 weeks were significant after follow-up. The SBP of the clusters of PELL and PELH decreased more (–6.1 mmHg and –8.4 mmHg) after follow-up in the first week.

Conclusions

Four distinct PE patterns were identified for patients engaging in the hypertension self-management app. Patients aged over 60 years had higher PE in terms of recording self-measured BP using the app. Once SBP reduced, patients with low PE tended to stop using the app, and a continued decline in PE occurred simultaneously with the increase in SBP. The duration and depth of the effect of health care provider follow-up were more significant in patients with high or increased engagement after follow-up.

Field sales force model to increase adoption of a novel tuberculosis diagnostic test among private providers: evidence from India

BACKGROUND

Impact of novel high-quality tuberculosis (TB) tests such as Xpert MTB/RIF has been limited due to low uptake among private providers in high-burden countries including India. Our objective was to assess the impact of a demand generation intervention comprising field sales force on the uptake of high-quality TB tests by providers and its financial sustainability for private labs in the long run.

METHODS

We implemented a demand generation intervention across five Indian cities between October 2014 and June 2016 and compared the change in the quantity of Xpert cartridges ordered by labs in these cities from before (February 2013-September 2014) to after intervention (October 2014-December 2015) to corresponding change in labs in comparable non-intervention cities. We embedded this difference-in-differences estimate within a financial model to calculate the internal rate of return (IRR) if the labs were to invest in an Xpert machine with or without the demand generation intervention.

RESULTS

The intervention resulted in an estimated 60 additional Xpert cartridges ordered per lab-month in the intervention group, which yielded an estimated increase of 11 500 tests over the post-intervention period, at an additional cost of US$13.3-US$17.63 per test. Further, we found that investing in this intervention would increase the IRR from 4.8% to 5.5% for hospital labs but yield a negative IRR for standalone labs.

CONCLUSIONS

Field sales force model can generate additional demand for Xpert at private labs, but additional strategies may be needed to ensure its financial sustainability.