Digital health sensing for personalized dermatology

The rapid evolution of technology, sensors and personal digital devices offers an opportunity to acquire health related data seamlessly, unobtrusively and in real time. In this opinion piece, we discuss the relevance and opportunities for using digital sensing in dermatology, taking eczema as an exemplar

Cross-sectional analysis of university student’s health using a digitised health survey

University student years are a particularly influential period, during which time students may adopt negative behaviours that set the precedent for health outcomes in later years. This study utilised a newly digitised health survey implemented during health screening at a university in Singapore to capture student health data. The aim of this study was to analyze the health status of this Asian university student population. A total of 535 students were included in the cohort, and a cross-sectional analysis of student health was completed. Areas of concern were highlighted in student’s body weight, visual acuity, and binge drinking. A large proportion of students were underweight (body mass index (BMI) 30). Although the overall prevalence of alcohol use was low in this study population, 9% of females and 8% of males who consumed alcohol had hazardous drinking habits. Around 16% of these students (male and female combined) typically drank 3–4 alcoholic drinks each occasion. The prevalence of mental health conditions reported was very low (<1%). This study evaluated the results from a digitised health survey implemented into student health screening to capture a comprehensive health history. The results reveal potential student health concerns and offer the opportunity to provide more targeted student health campaigns to address these

Mobile messaging with patients

Public Health and primary carePrevention, Population and Disease management (PrePoD

Digital phenotyping for assessment and prediction of mental health outcomes: A scoping review protocol

Introduction: Rapid advancements in technology and the ubiquity of personal mobile digital devices have brought forth innovative methods of acquiring healthcare data. Smartphones can capture vast amounts of data both passively through inbuilt sensors or connected devices and actively via user engagement. This scoping review aims to evaluate evidence to date on the use of passive digital sensing/phenotyping in assessment and prediction of mental health.Methods and analysis: The methodological framework proposed by Arksey and O’Malley will be used to conduct the review following the five-step process. A three-step search strategy will be used: 1. Initial limited search of online databases namely, MEDLINE for literature on digital phenotyping or sensing for key terms; 2. Comprehensive literature search using all identified keywords, across all relevant electronic databases: IEEE Xplore, MEDLINE, the Cochrane Database of Systematic Reviews, PubMed, the ACM Digital Library and Web of Science Core Collection (Science Citation Index Expanded and Social Sciences Citation Index), Scopus; and 3. Snowballing approach using the reference and citing lists of all identified key conceptual papers and primary studies. Data will be charted and sorted using a thematic analysis approach.Ethics and Dissemination: The findings from this systematic scoping review will be reported at scientific meetings and published in a peer-reviewed journal

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

Introduction Rapid advancements in technology and the ubiquity of personal mobile digital devices have brought forth innovative methods of acquiring healthcare data. Smartphones can capture vast amounts of data both passively through inbuilt sensors or connected devices and actively via user engagement. This scoping review aims to evaluate evidence to date on the use of passive digital sensing/phenotyping in assessment and prediction of mental health.Methods and analysis The methodological framework proposed by Arksey and 'Malley will be used to conduct the review following the five-step process. A three-step search strategy will be used: (1) Initial limited search of online databases namely, MEDLINE for literature on digital phenotyping or sensing for key terms; (2) Comprehensive literature search using all identified keywords, across all relevant electronic databases: IEEE Xplore, MEDLINE, the Cochrane Database of Systematic Reviews, PubMed, the ACM Digital Library and Web of Science Core Collection (Science Citation Index Expanded and Social Sciences Citation Index), Scopus and (3) Snowballing approach using the reference and citing lists of all identified key conceptual papers and primary studies. Data will be charted and sorted using a thematic analysis approach.Ethics and dissemination The findings from this systematic scoping review will be reported at scientific meetings and published in a peer-reviewed journal.Prevention, Population and Disease management (PrePoD)Public Health and primary car

The computer will see you now: Overcoming barriers to adoption of computer assisted history taking (CAHT) in primary care

Patient health information is increasingly collected through multiple modalities, including electronic health records, wearables, and connected devices. Computer-assisted history taking could provide an additional channel to collect highly relevant, comprehensive, and accurate patient information while reducing the burden on clinicians and face-to-face consultation time. Considering restrictions to consultation time and the associated negative health outcomes, patient-provided health data outside of consultation can prove invaluable in health care delivery. Over the years, research has highlighted the numerous benefits of computer-assisted history taking; however, the limitations have proved an obstacle to adoption. In this viewpoint, we review these limitations under 4 main categories (accessibility, affordability, accuracy, and acceptability) and discuss how advances in technology, computing power, and ubiquity of personal devices offer solutions to overcoming these

Running power: lab based vs. portable devices measurements and its relationship with aerobic power

In recent years, different companies have developed devices that estimate \u201crunning power\u201d. The main objective of this paper is to evaluate the effect of running speed on aerobic and running powers measured using force plates and by different devices. The second objective is to evaluate the relationship between aerobic power and running powers measured using force plates and by different devices. We enrolled 11 subjects in the study, they performed 5-min running trials at 2.22, 2.78, 3.33, 3.89 and 4.44 m/s respectively on a force-measuring treadmill while we collected metabolic data. We calculated running power as the dot product of ground reaction force and velocity of the centre of mass and compared it to the running power estimates of three devices: Skillrun (Technogym), Stryd Summit Powermeter (Stryd) and Garmin HRM-Run (Garmin). We found statistically significant linear correlations with running powers measured by all devices and running speed. Although absolute running power measurements were different among devices, an increase of 1 m/s in running speed translated to an increase of 0.944 W/kg in running power (p < 0.001). We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices (p < 0.001). For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions. Highlights We evaluated the effect of running speed on aerobic and running powers measured using force plates and by different devices. We also compared the relationship between aerobic power and running powers measured using force plates and by different devices. We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices. For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions

Improving the primary care consultation for diabetes and depression through digital medical interview assistant systems: Narrative review

10.2196/18109Journal of Medical Internet Research228e1810