Personalization and localization as key expectations of digital health intervention in women pre- to post-pregnancy

Investigating socioeconomic disparities of Kangaroo mother care on preterm infant health outcomes

Several studies have been conducted to examine the complicated relationships between various factors that influence Kangaroo mother care (KMC) for preterm infants. However, the extended socio-economic model has not been seen in any of the previous studies that looked into the factors related to KMC and how it affects the health outcomes of babies born before in our study population. This study examines the various dimensions of KMC implementation and its influence on the health outcomes of premature infants. The current cross-sectional study was carried out in South Punjab, Pakistan, covering both private and public KMC units in obstetrics and gynecology departments. The study included a sample size of 719 patients and was conducted during a period covering 21 September 2022 to 14 October 2023. Multinomial logistic regression analysis is employed to ascertain the factors by using SPSS-26 (SPSS Inc., Chicago, IL). The use of folic acid (OR: 1.44; 95% CI: 0.87-3.11) and factor anemia (OR: 8.82; 95% CI: 1.69-14.59) no significantly correlated with better health outcomes, while environmental toxin exposure had a negative impact (OR: 0.90). The findings underscore the need for comprehensive interventions and policies to bridge socioeconomic gaps, ensuring all preterm infants benefit from KMC.

Digital versus non-digital health interventions to improve iron supplementation in pregnant women: a systematic review and meta-analysis

Objective

To investigate the effects of digital health interventions for improving adherence to oral iron supplementation in pregnant women.

Literature search

Five databases were searched from their inception to October 2023 with no date restrictions.

Study selection

Randomized controlled trials (RCTs) that assessed the effects of digital health interventions on adherence to oral iron supplementation (e.g., tablets and capsules) compared to non-digital health interventions for pregnant women were eligible.

Data synthesis

We calculated standardized mean differences (SMDs) and mean differences (MDs) with 95% confidence intervals (CIs) for continuous variables using the inverse variance method. We calculated odds ratios (OR) with 95%CI for categorical variables using the Mantel-Haenszel model. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. The risk of bias of the included RCTs was assessed using the Cochrane risk of bias tool 2.0.

Results

Ten trials with 1,633 participants were included. Based on 7 trials, digital health interventions can improve objective adherence rate comparing with non-digital health interventions (1,289 participants, OR = 4.07 [2.19, 7.57], p < 0.001, I2 = 69%) in pregnant women. Digital health interventions can improve subjective adherence behavior comparing with non-digital health interventions (3 trials, 434 participants, SMD = 0.82 [0.62, 1.01], p < 0.001, I2 = 0%) in pregnant women. Based on 3 trials, digital health interventions can improve tablets consumption comparing with non-digital health interventions (333 participants, SMD = 1.00 [0.57, 1.42], p < 0.001, I2 = 66%) in pregnant women. Digital health interventions can improve hemoglobin level comparing with non-digital health interventions (7 trials, 1,216 participants, MD = 0.59 [0.31, 0.88], p < 0.001, I2 = 93%) in pregnant women.

Conclusion

Digital health interventions were effective at improving adherence to oral iron supplementation and hemoglobin levels in pregnant women.

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.