Mobile health: an emerging technology with implications for global internal medicine

O’Shea, Catherine; McGavigan, Andrew D.; Clark, Robyn; Chew, Derek P.; Ganesan, Anand N.

doi:10.1111/imj.13440

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…Currently, many people of different medical professions are using different mobile health applications and these have a positive effect on their activities and performance [15,17]. mobile devices can provide easy and timely access to information and these tools has replaced traditional models [18,19]. These tools can reduce medical errors, particularly human errors [12].…”

Section: Introductionmentioning

confidence: 99%

Need Assessment and Development of a Mobile-based Medication Dosage Calculation Application for ICU nurses

Abbasi¹,

Khajouei²,

Nejad³

et al. 2020

Preprint

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Background Medication dosage calculation errors are among the most common errors of nurses in intensive care unit (ICU). Information technologies, particularly mobile devices, can advance drug dosage calculation processes. The objective of this study was to develop a medication dosage calculation application for nurses in intensive care units. Methods This study was performed in the ICUs of teaching hospitals in Kerman University of Medical Sciences in 2018. First, a need assessment survey of nurses was conducted to determine the required features of the application. As well two specialists were interviewed to determine the medications used in the ICUs. Second, a medication dosage calculation application was developed using formative usability testing. Results Overall, 80% of the participants (n=120) answered the need assessment questionnaire. Of 29 features determined in the needs assessment, 19 were selected to develop the application. Moreover, 25 medications were selected by specialists and participants for the development of the application and its prototype. The usability test of the prototype found 15 problems. After fixing these problems 2 problems were identified, which were fixed in the final version. Conclusion According to the participants, development of the medication dosage calculation application increases the accuracy of drug dosage and reduces the errors. The user-identified features were developed in-app. User-centered usability testing in this study improved development based on needs of users. The method used in this study can be used by developers of health applications to develop applications which are consistent with user needs.

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Section: Introductionmentioning

confidence: 99%

Need Assessment and Development of a Mobile-based Medication Dosage Calculation Application for ICU nurses

Abbasi¹,

Khajouei²,

Nejad³

et al. 2020

Preprint

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“…Since the varying prevalence of leading risk factors for IHD has a direct or indirect impact on risks of morbidity and mortality, 5 efficient responses of international and national health systems and cost‐effective interventions should be strengthened among low‐to‐middle‐income countries with restricted access to health care. Furthermore, as the emerging field of mobile health, known as “mHealth,” provides a potential opportunity to deliver public health services to IHD patients, 41 individual‐targeted interventions based on mobile phone technology may be implemented considering that mHealth can effectively monitor and identify risk factors on time. The differences observed in this study call for income‐classified region‐ and country‐specific initiatives to implement interventions that will reduce preventable IHD burden.…”

Section: Discussionmentioning

confidence: 99%

Risk‐Attributable Burden of Ischemic Heart Disease in 137 Low‐ and Middle‐Income Countries From 2000 to 2019

Wang

Sun

Jiang

et al. 2021

JAHA

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Background Ischemic heart disease (IHD) imposes the greatest disease burden globally, especially in low‐ and middle‐income countries (LMICs). We aim to examine the population‐attributable fraction and risk‐attributable death and disability‐adjusted life years (DALYs) for IHD in 137 low‐ and middle‐income countries. Methods and Results Using comparative risk assessment framework from the 2019 Global Burden of Disease study, the population‐attributable fraction and IHD burden (death and DALYs) attributable to risk factors in low‐income countries, lower‐middle‐income countries (LMCs), and upper‐middle‐income countries were assessed from 2000 to 2019. In 2019, the population‐attributable fraction (%) of IHD deaths in relation to all modifiable risk factors combined was highest in lower‐middle‐income countries (94.2; 95% uncertainty interval, 91.9–96.2), followed by upper‐middle‐income countries (93.5; 90.4–95.8) and low‐income countries (92.5; 90.0–94.7). There was a >13‐fold difference between Peru and Uzbekistan in age‐standardized rates (per 100 000) of attributable death (44.3 versus 660.4) and DALYs (786.7 versus 10506.1). Dietary risks accounted for the largest proportion of IHD’s behavioral burden in low‐ and middle‐income countries, primarily attributable to diets low in whole grains. High systolic blood pressure and high low‐density lipoprotein cholesterol remained the 2 leading causes of DALYs, with the former topping the list in 116 countries, while the latter led in 21 of the 137 countries. Compared with 2000 to 2010, the increases in risk‐attributable deaths and DALYs among upper‐middle income countries were slower from 2010 to 2019, while the trends in low‐income countries and lower‐middle income countries were opposite. Conclusions IHD’s attributable burden remains high in low‐ and middle‐income countries. Considerable heterogeneity was observed among different income‐classified regions and countries.

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“…In the last few years, the growing use of health tracking mobile apps offers a new potential data source for predicting pregnancy in broader populations. Health tracking apps have been recognized as delivering a “data bounty” [19] for healthcare [32, 49]. Within women’s health specifically, health tracking apps are used by millions of women worldwide [7, 15, 37], and have already been used to study sexually transmitted infections [2], menstrual cycle fluctuations [35, 47], and menstrual cycle lengths [21].…”

Section: Introductionmentioning

confidence: 99%

Predicting pregnancy using large-scale data from a women's health tracking mobile application

Liu

Shi

et al. 2019

The World Wide Web Conference

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Predicting pregnancy has been a fundamental problem in women’s health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women’s health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models – a logistic regression model, and 3 LSTM models – to predict a woman’s probability of becoming pregnant using data from a women’s health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women’s health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.

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Mobile health: an emerging technology with implications for global internal medicine

Cited by 18 publications

References 21 publications

Need Assessment and Development of a Mobile-based Medication Dosage Calculation Application for ICU nurses

Need Assessment and Development of a Mobile-based Medication Dosage Calculation Application for ICU nurses

Risk‐Attributable Burden of Ischemic Heart Disease in 137 Low‐ and Middle‐Income Countries From 2000 to 2019

Predicting pregnancy using large-scale data from a women's health tracking mobile application

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