Measuring peripheral oxygen saturation ([Formula: see text]) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of [Formula: see text] values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict [Formula: see text] 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 [Formula: see text] model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal [Formula: see text] 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 [Formula: see text] range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] 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 [Formula: see text], it could assist practitioners in interpreting [Formula: see text] values for diagnosis, prognosis, and oxygen administration at higher altitudes. NEW & NOTEWORTHY Our model describes the altitude-dependent decrease of [Formula: see text] in healthy pediatric residents based on physiological equations and can be adapted based on measureable clinical parameters. The proposed altitude-specific abnormal [Formula: see text] 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.

BackgroundMobile 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.ObjectiveThis 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.MethodsWe 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.ResultsPulse 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.ConclusionsWe 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.

21Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. 22However, since SpO2 is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations 23 living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 24 values for pediatric permanent residents living between 0 and 4000 m a.s.l. Based on a sensitivity analysis of 25 oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of 26 permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold 27 using patient parameters from literature. We compared the obtained model and threshold against a previously 28 proposed threshold derived statistically from data and two empirical datasets independently recorded from 29 Peruvian children living at altitudes up to 4100 m a.s.l. Our model followed the trends of empirical data, with the 30 empirical data having a narrower healthy SpO2 range below 2000 m a.s.l., but the medians did never differ more 31 than 2.29% across all altitudes. Our threshold estimated abnormal SpO2 in only 17 out of 5981 (0.3%) healthy 32 recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength 33 of our parametrised model is that it is rooted in physiology-derived equations and enables customisation. 34 Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, 35 prognosis, and oxygen administration at higher altitudes. 36 New & Noteworthy 37 Our model describes the altitude-dependent decrease of SpO2 in healthy pediatric residents based on physiological 38 equations and can be adapted based on measureable clinical parameters. The proposed altitude-specific abnormal 39 SpO2 threshold might be more appropriate than rigid guidelines for administering oxygen that currently are only 40 available for sea level patients. We see this as a starting point to discuss and adapt oxygen administration 41 guidelines. 42 65 abnormal SpO2 threshold. The physiology-backed altitude-adaptive model describes SpO2 values of healthy 66 children living permanently at altitudes up to 4000 m a.s.l. With this model, we aim to provide a better 67 understanding of healthy SpO2 values at altitudes above 2000 m a.s.l. for healthy children. The altitude-adaptive 68 abnormal SpO2 threshold is obtained by setting the model parameters to abnormal values found in hypoxemic 69 patients. We evaluate these results with a novel dataset obtained from healthy children living in the rural Andes 70 of Peru. 71 2011. 426 22.

BACKGROUND 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. OBJECTIVE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.