Data integrity based methodology and checklist for identifying implementation risks of physiological sensing in mHealth projects

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.; This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks.; We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved.; Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified.; We developed a data integrity-based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects

Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2000 m a.s.l

Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since SpO2 is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical datasets independently recorded from Peruvian children living at altitudes up to 4100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2000 m a.s.l., but the medians did never differ more than 2.29% across all altitudes. Our threshold estimated abnormal SpO2 in only 17 out of 5981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrised model is that it is rooted in physiology-derived equations and enables customisation. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.ISSN:8750-7587ISSN:1522-1601ISSN:0161-7567ISSN:1522-1601ISSN:0021-898

Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l

Measuring peripheral oxygen saturation (; S; p; O; 2; ) with pulse oximeters at the point of care is widely established. However, since; S; p; O; 2; is dependent on ambient atmospheric pressure, the distribution of; S; p; O; 2; values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict; S; p; O; 2; values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive; S; p; O; 2; model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal; S; p; O; 2; threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy; S; p; O; 2; range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal; S; p; O; 2; in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference; S; p; O; 2; , it could assist practitioners in interpreting; S; p; O; 2; values for diagnosis, prognosis, and oxygen administration at higher altitudes.; NEW & NOTEWORTHY; Our model describes the altitude-dependent decrease of; S; p; O; 2; in healthy pediatric residents based on physiological equations and can be adapted based on measureable clinical parameters. The proposed altitude-specific abnormal; S; p; O; 2; threshold might be more appropriate than rigid guidelines for administering oxygen that currently are only available for patients at sea level. We see this as a starting point to discuss and adapt oxygen administration guidelines

A factorial cluster-randomised controlled trial combining home-environmental and early child development interventions to improve child health and development: rationale, trial design and baseline findings

BACKGROUND Exposure to unhealthy environments and inadequate child stimulation are main risk factors that affect children's health and wellbeing in low- and middle-income countries. Interventions that simultaneously address several risk factors at the household level have great potential to reduce these negative effects. We present the design and baseline findings of a cluster-randomised controlled trial to evaluate the impact of an integrated home-environmental intervention package and an early child development programme to improve diarrhoea, acute respiratory infections and childhood developmental outcomes in children under 36 months of age living in resource-limited rural Andean Peru. METHODS We collected baseline data on children's developmental performance, health status and demography as well as microbial contamination in drinking water. In a sub-sample of households, we measured indoor kitchen 24-h air concentration levels of carbon monoxide (CO) and fine particulate matter (PM2.5) and CO for personal exposure. RESULTS We recruited and randomised 317 children from 40 community-clusters to four study arms. At baseline, all arms had similar health and demographic characteristics, and the developmental status of children was comparable between arms. The analysis revealed that more than 25% of mothers completed primary education, a large proportion of children were stunted and diarrhoea prevalence was above 18%. Fifty-two percent of drinking water samples tested positive for thermo-tolerant coliforms and the occurrence of E.coli was evenly distributed between arms. The mean levels of kitchen PM2.5 and CO concentrations were 213 μg/m3 and 4.8 ppm, respectively. CONCLUSIONS The trial arms are balanced with respect to most baseline characteristics, such as household air and water pollution, and child development. These results ensure the possible estimation of the trial effectiveness. This trial will yield valuable information for assessing synergic, rational and cost-effective benefits of the combination of home-based interventions. TRIAL REGISTRY ISRCTN-26548981.ISSN:1471-228