Digital Health

Journal Information
ISSN / EISSN: 20552076 / 20552076
Published by: SAGE Publications
Total articles ≅ 683

Latest articles in this journal

Inmaculada Carmen Lara-Palomo, Eduardo Antequera-Soler, Guillermo A Matarán-Peñarrocha, Manuel Fernández-Sánchez, , Adelaida María Castro-Sánchez, María Encarnación Aguilar-Ferrándiz
Published: 28 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074482

Abstract:
Objetive: We conducted a randomized double blind clinical trial, to compare the effectiveness of McKenzie exercises and electroanalgesia via an e-Health program versus a home rehabilitation program on functionality, pain, fear of movement and quality of life in patients with non-specific chronic low back pain. Methods: Seventy-four participants with non-specific chronic low back pain were randomized to either the e- Health program group (n = 39) or the home rehabilitation program group (n = 35). The interventions consisted of the e-Health program group performing McKenzie exercises and received transcutaneous electrical nerve stimulation, while the home rehabilitation group attended an information session to explain the exercises, which they then performed at home with printed instructions. Both groups performed 3 weekly sessions for 8 weeks. The following were analyzed main measures: pain, disability, fear of movement, quality of life, trunk muscle endurance and trunk anteflexion motion were assessed at baseline and at 2 months. Results: Independent samples Student’s t-tests showed that although the patients who followed the e-Health program showed significantly greater improvement than those who followed the home disability rehabilitation program in terms of intensity of pain, lumbar flexion mobility ( P < 0.001), and the following dimensions of quality of life ( P < 0.005), both groups improved significantly in the immediate post-treatment follow up compared with baseline scores. Conclusions: Patients with chronic low back pain who followed an unsupervised home intervention supported by an individualized video exercise program showed greater post-treatment improvement than those who followed the same program with printed instructions.
, A-M Kaihlanen, A Kouvonen, , S Taipale, K Gluschkoff
Published: 28 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074485

Abstract:
Objective: Online health and social care services are getting widespread which increases the risk that less advantaged groups may not be able to access these services resulting in digital exclusion. We examined the combined effects of age and digital competence on the use of online health and social care services. Methods: We used a large representative population-based sample of 4495 respondents from Finland. Paper-based self-assessment questionnaire with an online response option was mailed to participants. The associations were analyzed using survey weighted logistic regression, exploring potential non-linear effects of age and controlling for potential sex differences. Results: Higher age, starting from around the age of 60 was associated with a lower likelihood of using online services for receiving test results, renewing prescriptions and scheduling appointments. Good digital competence was able to hinder the age-related decline in online services use, but only up to around the age of 80. Conclusions: Our results suggest that older adults are at risk of digital exclusion, and not even good digital competence alleviates this risk among the oldest. We suggest that health and social care providers should consider older users’ needs and abilities more thoroughly and offer easy to use online services. More digital support and training possibilities should be provided for older people. It is equally important that face-to-face and telephone services will be continued to be provided for those older people who are not able to use online services even when supported.
, Emre Sümer, Seda Kibaroğlu
Published: 27 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221075147

Abstract:
Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.
Ashley Cha Yin Lim, Pragadesh Natarajan, R Dineth Fonseka, Monish Maharaj, Ralph J Mobbs
Published: 27 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074128

Abstract:
Background: The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods: Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results: A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions: Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.
, Tim Walker, Karyn Morrissey, on behalf of the Smartline project team
Published: 24 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074124

Abstract:
Objective: The aim of this study was to explore the feasibility and acceptability of digital technology for improving health and wellbeing in social housing residents living in a deprived area in Cornwall, England. Methods: Qualitative scoping study with focus groups and telephone interviews (23 participants in total). Focus groups and interviews were audio-recorded, transcribed verbatim and analysed thematically. Results: Levels of use and experience with digital technology were diverse in this group, ranging from ‘willing and unable’ to ‘expert’ on a self-perceived scale. Overall, participants had positive perceptions of technology and were keen to try new technologies. Five categories of factors influencing technology use were identified: functional, physical / health, psychological and attitudinal, technology-associated barriers, and privacy, safety and security. Preferred types of digital technology were wearable activity monitors (e.g. Fitbit®), virtual assistants (e.g. Amazon Alexa) and social messaging (e.g. WhatsApp). There was a strong consensus that technology should be easy to use and should have a clear purpose. There was a need to improve awareness, knowledge and confidence in technology use and participants desired further training and support. Conclusions: There is a need and desire to use digital technology to improve health, wellbeing and social connectedness in social housing residents in Cornwall. The findings will be used to inform a digital training and support programme for the participants of the Smartline project. This study also serves as a template for future research that seeks to scope the feasibility and acceptability of different digital interventions in similar populations.
Gaurav Laroia, Benjamin D Horne, Sean Esplin, Vasant K Ramaswamy
Published: 21 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074126

Abstract:
The single biggest factor driving health outcomes is patient behavior. The CHR Model (County Health Rankings Model) weights socioeconomic factors, lifestyle behaviors, and physical environment factors collectively at 80% in driving impact on health outcomes, to the 20% weight for access to and quality of clinical care. Commercial determinants of health affect everyone today and unhealthy choices worsen pre-existing economic, social, and racial inequities. Yet there is a disproportionate focus on therapeutic intervention to the exclusion of shaping patient behaviors to improve healthcare. If the recent pandemic taught us a critically important lesson, it is the imperative to look beyond clinical care. According to the Centers for Disease Control and Prevention (CDC), long-standing systemic health and social inequities put various groups of people at higher risk of getting sick and dying from COVID-19, including many racial and ethnic minority groups. The virus was simply more efficient in detecting such vulnerabilities than the guardians of these physiologies. These insights from the pandemic come at the heel of a confluence of three major accelerants that may radically reshape our approaches to hot-spotting vulnerabilities and managing them before they manifest in a derangement or disease. They are the recent strides in behavioral economics and behavior science; advances in remote monitoring and personal health technologies; and developments in artificial intelligence and data sciences. These accelerants allow us to imagine a previously impossible vision—we can now build and maintain a unified health algorithm for every individual that can dynamically track the two interdependent streams of risk, clinical and behavioral.
Stefanie Scholz, Laura Teetz
Published: 20 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221074127

Abstract:
Objective: Today there are several health and medical apps (mHealth) in app stores. Germany is the world's first country that introduced apps paid by the regular health insurance service. Even though breast cancer is the most common cancer in women, mHealth for breast cancer has been largely unexplored. Methods: A total of 33 apps from two major mobile application marketplaces (Google Play Store/Android; App Store/iOS) have been selected for analysis. Results: The app analysis shows that there are currently only 10 mHealth apps in German, which are specifically dedicated to breast cancer patients. The features of these apps fall into two categories: improvement of health literacy and indirect intervention. These apps can be used for all phases of the patient journey starting with the diagnosis. Conclusions: mHealth apps have the potential to support the adherence of breast cancer patients. In order to exploit this future potential, the app quality, as well as the information about the available apps, must be urgently improved. Currently, it is very difficult both for laypersons and for doctors/other therapists to identify high-quality apps. Guidance from independent or governmental institutions would be helpful to further the digitalization in health care.
Lisha Jiang, Qingxin Ma, Shanzun Wei, Guowei Che
Published: 20 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076211070454

Abstract:
With the approval of the vaccine in mainland China, concerns over its safety and efficacy emerged. Since the Chinese vaccine has been promoted by the Chinese government for months and got emergency approval from the World Health Organization. The Chinese vaccination program is yet to be identified from the perspective of local populations. The COVID-19 vaccine-related keywords for the period from January 2019 to April 2021 were examined and queried from the Baidu search index. The searching popularity, searching trend, demographic distributions and users’ demand were analyzed. The first vaccine enquiry emerged on 25th January 2020, and 17 vaccination keywords were retrieved and with a total BSI value of 13,708,853. The average monthly searching trend growth is 21.05% ( p < 0.05) and was led by people aged 20–29 (39.22%) years old. Over 54.93% of the demand term search were pandemic relevant, and the summed vaccine demand ratio was 44.79%. With the rising search population in COVID-19 vaccination, education programs and materials should be designed for teens and people above the 40 s. Also, vaccine-related birth safety should be alerted and further investigated.
Jiun-Ruey Hu, Chia-Hung Yo, Hsin-Ying Lee, Chin-Hua Su, Ming-Yang Su, Amy Huaishiuan Huang, Ye Liu, Wan-Ting Hsu, Matthew Lee, Yee-Chun Chen, et al.
Published: 20 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076211072400

Abstract:
Objective: Sepsis is the leading cause of in-hospital mortality in the United States (US). Quality improvement initiatives for improving sepsis care depend on accurate estimates of sepsis mortality. While hospital 30-day risk-standardized mortality rates have been published for patients hospitalized with acute myocardial infarction, heart failure, and pneumonia, risk-standardized mortality rates for sepsis have not been well characterized. We aimed to construct a sepsis risk-standardized mortality rate map for the United States, to illustrate disparities in sepsis care across the country. Methods: This cross-sectional study included adults from the US Nationwide Inpatient Sample who were hospitalized with sepsis between 1 January 2010 and 30 December 2011. Hospital-level risk-standardized mortality rates were calculated using hierarchical logistic modelling, and were risk-adjusted with predicted mortality derived from (1) the Sepsis Risk Prediction Score, a logistic regression model, and (2) gradient-boosted decision trees, a supervised machine learning (ML) algorithm. Results: Among 1,739,033 adults hospitalized with sepsis, 50% were female, and the median age was 71 years (interquartile range: 58–81). The national median risk-standardized mortality rate for sepsis was 18.4% (interquartile range: 17.0, 21.0) by the boosted tree model, which had better discrimination than the Sepsis Risk Prediction Score model (C-statistic 0.87 and 0.78, respectively). The highest risk-standardized mortality rates were found in Wyoming, North Dakota, and Mississippi, while the lowest were found in Arizona, Colorado, and Michigan. Conclusions: Wide variation exists in sepsis risk-standardized mortality rates across states, representing opportunities for improvement in sepsis care. This represents the first map of state-level variation of risk-standardized mortality rates in sepsis.
, Adam Hopper, Jack Brophy, Elle Pearson, Risheka Suthantirakumar, Maariyah Vankad, Natalie Igharo, Sud Baitule, Cain Ct Clark, , et al.
Published: 7 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076211059350

Abstract:
Background: COVID-19 placed significant challenges on healthcare systems. People with diabetes are at high risk of severe COVID-19 with poor outcomes. We describe the first reported use of inpatient digital flash glucose monitoring devices in a UK NHS hospital to support management of people with diabetes hospitalized for COVID-19. Methods: Inpatients at University Hospitals Coventry & Warwickshire (UHCW) NHS Trust with COVID-19 and diabetes were considered for digitally enabled flash glucose monitoring during their hospitalization. Glucose monitoring data were analysed, and potential associations were explored between relevant parameters, including time in hypoglycaemia, hyperglycaemia, and in range, glycated haemoglobin (HbA1c), average glucose, body mass index (BMI), and length of stay. Results: During this pilot, digital flash glucose monitoring devices were offered to 25 inpatients, of whom 20 (type 2/type 1: 19/1; mean age: 70.6 years; mean HbA1c: 68.2 mmol/mol; mean BMI: 28.2 kg/m2) accepted and used these (80% uptake). In total, over 2788 h of flash glucose monitoring were recorded for these inpatients with COVID-19 and diabetes. Length of stay was not associated with any of the studied variables (all p-values >0.05). Percentage of time in hyperglycaemia exhibited significant associations with both percentage of time in hypoglycaemia and percentage of time in range, as well as with HbA1c (all p-values <0.05). The average glucose was significantly associated with percentage of time in hypoglycaemia, percentage of time in range, and HbA1c (all p-values <0.05). Discussion: We report the first pilot inpatient use of digital flash glucose monitors in an NHS hospital to support care of inpatients with diabetes and COVID-19. Overall, there are strong arguments for the inpatient use of these devices in the COVID-19 setting, and the findings of this pilot demonstrate feasibility of this digitally enabled approach and support wider use for inpatients with diabetes and COVID-19.
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