Plantar pressure measurement in diabetic foot disease: A scoping review.

AIMS/INTRODUCTION: Patients with a healed diabetic foot ulcer (DFU) have a 40% risk of ulcer recurrence within a year. New and effective measures to prevent DFU recurrence are essential. We aimed to highlight emerging trends and future research opportunities in the use of plantar pressure measurement to prevent DFU recurrence. MATERIALS AND METHODS: Our scoping review protocol was drafted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis - Scoping Review protocol. Peer-reviewed, English-language papers were included that addressed both plantar pressure measurement and diabetic foot disease, either as primary studies that have advanced the field or as review papers that provide summaries and/or opinion on the field as a whole, as well as specific papers that provide guidelines for future research and advancement in the field. RESULTS: A total of 24 eligible publications were identified in a literature search using PubMed. A further 36 eligible studies were included after searching the references sections of these publications, leaving a total of 60 publications included in this scoping review. CONCLUSIONS: Plantar pressure measurement can and will play a major role in the prevention of DFU. There is already a strong, albeit limited, evidence base in place to prove its benefit in reducing DFU recurrence. More research is required in larger populations, using remote monitoring in real-world settings, and with improved technology.

Angiographic cross-filling between inferior petrosal sinuses and alteration of adrenocorticotropic hormone sampling results for tumor localization in Cushing disease.

OBJECTIVE: Inferior petrosal sinus (IPS) sampling (IPSS) is a diagnostic procedure used to guide diagnostic localization of imaging-negative adrenocorticotropic hormone (ACTH)-secreting pituitary microadenomas. However, the efficacy of IPSS has been suboptimal at accurately lateralizing the adenoma, reducing surgical cure rates and leading to unintended pituitary dysfunction due to the added exploration. One rationale for the occasional imprecision is the existence of additional petrosal sinus collateral channels that connect the IPS bilaterally, which may lead to false localization results during sampling. The aim of this study was to explore a potential connection between normal anatomical variation in the angioarchitecture of the IPSs and the ACTH results obtained in subsequent IPSS tests. METHODS: A retrospective review was performed on all cases between 1998 and 2013 involving patients at a single institution who underwent IPSS for radiographically equivocal pituitary microadenomas. Cases were reviewed for tumor laterality noted on either operative or pathology reports, as well as the presence of angiographic evidence of cross-filling between the sinuses. In addition, ACTH levels from the right and left IPSs were documented at baseline and at 2, 5, and 10 minutes after corticotropin-releasing hormone (CRH) administration. A ratio of the change in ACTH levels measured at the time of maximal response (10 minutes) versus the levels measured at the initial response (2 minutes) was computed for each patient and compared between patients by their angiographic cross-filling status. RESULTS: There were 41 patients with a histopathologically confirmed right- or left-sided ACTH-secreting pituitary microadenoma who underwent preoperative IPSS. Among these patients, 28 (68%) showed angiographic evidence of cross-filling between the IPSs, and 13 showed no cross-filling. On average, ACTH levels increased by a factor of 3.91 ± 0.77 in the contralateral IPS in patients with angiographic cross-filling, compared with a factor increase of only 1.80 ± 0.27 in patients without cross-filling (p = 0.014). In comparison, ACTH levels increased by a factor of 2.01 ± 0.57 in the ipsilateral IPS in patients with cross-filling, and by 8.78 ± 7.30 in those without cross-filling (p = 0.373). CONCLUSIONS: The presence of angiographic cross-filling, suggestive of a greater degree of vascular channel networking between the right and left IPS, is a significant factor influencing the measured rates of change of ACTH in IPSS and may impact the specificity of this test to accurately determine microadenoma laterality in the preoperative setting.

Early Metformin in Gestational Diabetes: A Randomized Clinical Trial.

Importance: Gestational diabetes is a common complication of pregnancy and the optimal management is uncertain. Objective: To test whether early initiation of metformin reduces insulin initiation or improves fasting hyperglycemia at gestation weeks 32 or 38. Design, Setting, and Participants: Double-blind, placebo-controlled trial conducted in 2 centers in Ireland (one tertiary hospital and one smaller regional hospital). Participants were enrolled from June 2017 through September 2022 and followed up until 12 weeks' postpartum. Participants comprised 510 individuals (535 pregnancies) diagnosed with gestational diabetes based on World Health Organization 2013 criteria. Interventions: Randomized 1:1 to either placebo or metformin (maximum dose, 2500 mg) in addition to usual care. Main Outcomes And Measures: The primary outcome was a composite of insulin initiation or a fasting glucose level of 5.1 mmol/L or greater at gestation weeks 32 or 38. Results: Among 510 participants (mean age, 34.3 years), 535 pregnancies were randomized. The primary composite outcome was not significantly different between groups and occurred in 150 pregnancies (56.8%) in the metformin group and 167 pregnancies (63.7%) in the placebo group (between-group difference, -6.9% [95% CI, -15.1% to 1.4%]; relative risk, 0.89 [95% CI, 0.78-1.02]; P = .13). Of 6 prespecified secondary maternal outcomes, 3 favored the metformin group, including time to insulin initiation, self-reported capillary glycemic control, and gestational weight gain. Secondary neonatal outcomes differed by group, with smaller neonates (lower mean birth weights, a lower proportion weighing >4 kg, a lower proportion in the >90% percentile, and smaller crown-heel length) in the metformin group without differences in neonatal intensive care needs, respiratory distress requiring respiratory support, jaundice requiring phototherapy, major congenital anomalies, neonatal hypoglycemia, or proportion with 5-minute Apgar scores less than 7. Conclusion and relevance: Early treatment with metformin was not superior to placebo for the composite primary outcome. Prespecified secondary outcome data support further investigation of metformin in larger clinical trials. Trial Registration: ClinicalTrials.gov Identifier: NCT02980276; EudraCT: 2016-001644-19.

Clare Island Digital Health Project - using technology to enable health for all.

INTRODUCTION: Island communities have a different experience of health services compared with urban dwellers. Islanders face challenges of accessing equitable health services with varied availability of local services and the added difficulty of sea and weather conditions on top of the physical distance to reach specialised services. In a 2017 review of primary care island services in Ireland, it was proposed that telemedicine solutions could potentially improve delivery of health services. However, these solutions must meet the unique needs of the island population. AIMS AND METHODS: This collaborative project brings together healthcare professionals, academic researchers, technology partners, business partners and the Clare Island community, to improve the health of the island population via novel technological interventions. The Clare Island project aims to identify specific healthcare needs of the island through community engagement, develop innovative solutions, and assess the impact of the interventions via a mixed-methods approach. RESULTS: Preliminary engagement with the Clare Island community via facilitated round table discussions has identified Islanders' overarching enthusiasm for digital solutions and added benefit of provision of 'health at home' for Island communities, particularly, the potential to better support the older person at home via technology. Key challenges including basic infrastructure, useability and sustainability of digital health initiatives were common themes identified. We also will discuss in detail the needs-led process of innovation of telemedicine solutions deployed on Clare Island. Finally, we will present the anticipated impact of the project and the challenges and opportunities of telehealth for island health services. DISCUSSION: Technology has the potential to narrow the gap of inequity of health services for island communities. This project is an example of how through cross-disciplinary collaboration, and needs-led, specifically 'island-led', innovation of digital health solutions, the unique challenges for island communities can be addressed.

Statistical techniques used in analysing simultaneous continuous glucose monitoring and ambulatory electrocardiography in patients with diabetes: A systematic review.

OBJECTIVES: There has been a steady increase in the number of studies of the complex relationship between glucose and electrical cardiac activity which use simultaneous continuous glucose monitors (CGM) and continuous electrocardiogram (ECG). However, data collected on the same individual tend to be similar (yielding correlated or dependent data) and require analyses that take into account that correlation. Many opt for simplified techniques such as calculating one measure from the data collected and analyse one observation per subject. These simplified methods may yield inconsistent and biased results in some instances. In this systematic review, we aim to examine the adequacy of the statistical analyses performed in such studies and make recommendations for future studies. RESEARCH QUESTIONS: What are the objectives of studies collecting simultaneous CGM and ECG data? Do methods used in analysing CGM and continuous ECG data fully optimise the data collected? DESIGN: Systematic review. DATA SOURCES: PubMed and Web of Science. METHODS: A comprehensive search of the PubMed and Web of Science databases to June 2022 was performed. Studies utilising CGM and continuous ECG simultaneously in people with diabetes were included. We extracted information about study objectives, technologies used to collect data and statistical analysis methods used for analysis. Reporting was done following PRISMA guidelines. RESULTS: Out of 118 publications screened, a total of 31 studies met the inclusion criteria. There was a diverse array of study objectives, with only two studies exploring the same exposure-outcome relationship, allowing only qualitative analysis. Only seven studies (23%) incorporated methods which fully utilised the study data using methods that yield the correct power and minimize type I error rate. The rest (77%) used analyses that summarise the data first before analysis and/or totally ignored data dependency. Of those who applied more advanced methods, one study performed both simple and correct analyses and found that ignoring data structure resulted in no association whilst controlling for repeated measures yielded a significant relationship. CONCLUSION: Most studies underutilised statistical methods suitable for analysis of dynamic continuous data, potentially attenuating their statistical power and overall conclusions. We recommend that aggregated data be used only as exploratory analysis, while primary analysis should use methods applied to the raw data such as mixed models or functional data analyses. These methods are widely available in many free, open source software applications.

Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns.

In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.

Effects of Alternate Insulin Pump Settings in Patients With Type 1 Diabetes During Ramadan: A Randomized Pilot Study.

BACKGROUND: Various studies have evaluated the safety and efficacy of using insulin pumps during Ramadan; some of them demonstrated favorable outcomes in reducing hypoglycemia and hyperglycemia. However, there is no consensus on the recommendations for basal insulin adjustments and the utilization of technical features of insulin pumps to improve glycemic control. OBJECTIVES: We aimed to investigate the effects of different insulin pump settings on time in range in patients with type 1 diabetes during Ramadan. METHODS: In this randomized pilot study, 30 patients classified to have low to moderate risk for fasting were assigned to either a control group to receive basal insulin adjustments only or an intervention group to use the temporary basal rate and extended bolus features in addition to the basal insulin modifications. The percentage of time spent at different glucose ranges was measured by continuous glucose monitoring. RESULTS: The percentage of time spent within target (70-180 mg/dL) increased significantly in the intervention group from 63.0 ± 10.7 to 76 ± 16.2% (mean difference, 27% points; P < .001). The percentage of time spent in hyperglycemia level 1 (>180 mg/dL) and level 2 (>250 mg/dL) met the criterion of significance, indicating that the intervention group spent less time in hyperglycemia. However, there was no significant difference in the percentage of time spent in hypoglycemia ranges. CONCLUSIONS: Incorporating technological approaches of pump therapy with clinical practice guidelines could improve glycemic control during Ramadan.

Changes in the Leptin to Adiponectin Ratio Are Proportional to Weight Loss After Meal Replacement in Adults With Severe Obesity.

Hypocaloric diets are known to induce changes in adipokine secretion, but the influence of a low energy liquid diet (LELD) on the leptin: adiponectin ratio (LAR), a measure of insulin resistance and cardiovascular risk, has not previously been investigated in patients with severe obesity. We conducted a prospective, single-center cohort study of adults with severe obesity (defined as body mass index (BMI) ≥40 kgm-2, or ≥35 kgm-2 with co-morbidities) who completed a 24-week milk-based LELD. We measured leptin, adiponectin and LAR at the start and on completion of the programme. Of 120 patients who started, 52 (43.3 %) completed the programme. Their mean age was 50.3 ± 11.2 (range 18-74) years, 29 (55.8 %) were female and 20 (38.5 %) had type 2 diabetes mellitus (T2DM). Weight decreased from 148.2 ± 39.6 to 125.4 ± 34.8 kg and BMI decreased from 52.4 ± 11.1 to 44.3 ± 9.8 kgm-2, respectively (all p < 0.001). In patients with T2DM, HbA1c decreased from 60.0 ± 17.4 to 47.5 ± 15.5 mmol/mol (p < 0.001). Leptin decreased (from 87.2 [48.6, 132.7] to 39.1 [21.0, 76.4] ng/ml) and adiponectin increased (from 5.6 [4.5, 7.5] to 7.1 [5.5, 8.5] µg/ml), with a reduction in LAR from 15 [8.4, 22.4] to 5.7 [3.0, 9.1] ng/µg (all p < 0.001), indicating decreased insulin resistance. The percentage weight lost was associated with the percentage reduction in LAR (ß = 2.9 [1.7, 4.1], p < 0.001) and this association was stronger in patients with T2DM. Patients with severe obesity who completed a milk-based LELD had a substantial reduction in LAR, consistent with decreased insulin resistance and cardiovascular risk, proportional to weight loss.

Public opinion of the Irish "COVID Tracker" digital contact tracing App: A national survey.

Objective: This study aims to gather public opinion on the Irish "COVID Tracker" digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App. Methods: A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App. Results: A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure. Conclusions: DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.

An analysis of patient self-reported COVID-19 symptoms during the first wave of the pandemic in Ireland.

BACKGROUND: Since the outbreak of COVID-19 in December 2019, there have been more than 115 million cases worldwide (1). Symptoms of COVID-19 vary widely and the spectrum of clinical presentation has yet to be fully characterised (2). Many countries have detailed their early experience with COVID-19, with a focus on the clinical characteristics of the disease. However, to our knowledge, there has been no such study detailing symptoms in the Irish population. AIM: Our aim is to describe COVID-19 symptoms in the Irish population at the beginning of the COVID-19 pandemic and compare symptoms between those reporting positive and negative test results. METHOD: A Web page MyCovidSymptoms.ie was created by researchers at the National University of Ireland, Galway, in April 2020 to investigate COVID-19 symptoms in Ireland. The Web page invited participants to self-report RT-PCR test outcome data (positive, negative, untested), temperature and a range of symptoms (cough, shortness of breath, fatigue, loss of taste, loss of smell). RESULTS: One hundred and twenty-three Irish participants who had a RT-PCR test for COVID-19 logged their symptoms. Eighty-four patients reported that they tested positive for COVID-19, and 39 patients reported a negative COVID-19 test. In our cohort of respondents with a positive COVID-19 test, 49/84 (58%) respondents reported a cough. Of the 39 respondents with a negative COVID-19 test, 17 (44%) reported having a cough. The distribution of temperature was similar in both those with and without COVID-19. Levels of self-reported fatigue were high in both groups with 65/84 (77%) of COVID-19-positive patients reporting fatigue and 30/39 (77%) of those who were COVID-19-negative reporting fatigue. New symptoms emerging at the time of data collection included loss of taste and smell. We demonstrated a higher proportion of loss of smell (p = 0.02) and taste (p = 0.01) in those reporting a positive result, compared to those reporting a negative result. CONCLUSION: These data represents an early picture of the clinical characteristics of COVID-19 in an Irish population. It also highlights the potential use of self-reported data globally as a powerful tool in helping with the pandemic.

Bites by the noble false widow spider Steatoda nobilis can induce Latrodectus-like symptoms and vector-borne bacterial infections with implications for public health: a case series.

CONTEXT: In recent years, the Noble false widow spider Steatoda nobilis (Thorell, 1875) has expanded its range globally and may represent a potential threat to native ecosystems and public health. Increasing numbers in synanthropic habitats have led to more human encounters and envenomations. Steatoda nobilis bites were previously classed as medically significant with similarities to bites from true black widows of the genus Latrodectus but deemed milder in onset, with symptoms generally ranging from mild to moderate. CASE DETAILS: In this manuscript we present 16 new cases of S. nobilis envenomations bringing the total number of confirmed cases reported in the literature to 24. We report new symptoms and provide discussion on the contributing factors to pathology following bites by S. nobilis. DISCUSSION: We report a range of pathologies including necrosis, Latrodectus-like envenomation symptoms that include debilitating pain, tremors, fatigue, nausea, hypotension, and vectored bacterial infections including cellulitis and dermatitis. Symptoms ranged from mild to severe, requiring hospitalisation in some cases.

Sentiment analysis of user feedback on the HSE's Covid-19 contact tracing app.

BACKGROUND: Digital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns. AIM: This work manually analyzes user reviews of the Irish Heath Service Executive's (HSE) Contact Tracker app, to identify user concerns and to lay the foundations for subsequent, large-scale, automated analyses of reviews. While this might seem tightly scoped to the Irish context, the HSE app provides the basis for apps in many jurisdictions in the USA and Europe. METHODS: Manual analysis of (1287) user reviews from the Google/Apple playstores was performed, to identify the aspects of the app that users focused on, and the positive/negative sentiment expressed. RESULTS: The findings suggest a largely positive sentiment towards the app, and that users thought it handled data protection and transparency aspects well. But feedback suggests that users would appreciate more targeted feedback on the incidence of the virus, and facilities for more proactive engagement, like notifications that prompt users to submit their health status daily. Finally, the analysis suggests that the "android battery" issue and the backward-compatibility issue with iPhones seriously impacted retention/uptake of the app respectively. CONCLUSION: The HSE have responded to the public's desire for targeted feedback in newer versions, but should consider increasing the app's proactive engagement. The results suggest they should also raise the backward compatibility issue, regarding older iPhones, with Apple.

Artificial Intelligence for Detecting COVID-19 With the Aid of Human Cough, Breathing and Speech Signals: Scoping Review.

Goal: Official tests for COVID-19 are time consuming, costly, can produce high false negatives, use up vital chemicals and may violate social distancing laws. Therefore, a fast and reliable additional solution using recordings of cough, breathing and speech data for preliminary screening may help alleviate these issues. Objective: This scoping review explores how Artificial Intelligence (AI) technology aims to detect COVID-19 disease by using cough, breathing and speech recordings, as reported in the literature. Here, we describe and summarize attributes of the identified AI techniques and datasets used for their implementation. Methods: A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). Electronic databases (Google Scholar, Science Direct, and IEEE Xplore) were searched between 1st April 2020 and 15th August 2021. Terms were selected based on the target intervention (i.e., AI), the target disease (i.e., COVID-19) and acoustic correlates of the disease (i.e., speech, breathing and cough). A narrative approach was used to summarize the extracted data. Results: 24 studies and 8 Apps out of the 86 retrieved studies met the inclusion criteria. Half of the publications and Apps were from the USA. The most prominent AI architecture used was a convolutional neural network, followed by a recurrent neural network. AI models were mainly trained, tested and run-on websites and personal computers, rather than on phone apps. More than half of the included studies reported area-under-the-curve performance of greater than 0.90 on symptomatic and negative datasets while one study achieved 100% sensitivity in predicting asymptomatic COVID-19 from cough-, breathing- or speech-based acoustic features. Conclusions: The included studies show that AI has the potential to help detect COVID-19 using cough, breathing and speech samples. The proposed methods (with some time and appropriate clinical testing) could prove to be an effective method in detecting various diseases related to respiratory and neurophysiological changes in the human body.

Key requirements of a video-call system in a critical care department as discovered during the rapid development of a solution to address COVID-19 visitor restrictions.

The COVID-19 pandemic necessitated stringent visitor restrictions in critical care departments worldwide, creating challenges in keeping family members connected to patients and clinical staff. Previous studies have examined how hospitals addressed this challenge by repurposing existing tele-ICU systems or by using personal smartphones as a workaround and have analyzed clinical and family feedback. This case report addresses the experience of rapidly implementing a video-call system in the critical care department of a tertiary referral hospital that had no prior video-call system in place, detailing the key requirements in that setting. The 24 requirements were identified via interviews and surveys to both clinical and technical professionals. The top requirements identified were sound and video quality, usability for clinical staff, call control by staff, and patient privacy. From tailoring a video-call solution for this setting, we learned that video-endpoint selection is a key design decision. The initial proposal was to use wireless tablets, but the selection of a large wired video-endpoint allowed us to better address the requirements in the critical care setting. This was based on several characteristics of the large wired video-endpoint, including: high-fidelity video and sound, with directional noise-cancelling; large touch-screen setup for minimal-click navigation; wired as well as wireless connectivity.

Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques.

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists' decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign-malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature.

BACKGROUND: Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. OBJECTIVE: This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. METHODS: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. RESULTS: Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. CONCLUSIONS: In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.