The potential application of artificial intelligence for diagnosis and management of glaucoma in adults

BACKGROUND: Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma. SOURCES OF DATA: This literature review is based on articles published in peer-reviewed journals. AREAS OF AGREEMENT: There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior. AREAS OF CONTROVERSY: Concerns that the increased reliance on AI may lead to deskilling of clinicians. GROWING POINTS: AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable. AREAS TIMELY FOR DEVELOPING RESEARCH: There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results

Do children\u27s food preferences align with dietary recommendations?

Objectives: To examine how Australian children\u27s reported everyday food preferences reflect dietary recommendations, and the impact of sociodemographic factors on these associations.Design:&nbsp; Cross-sectional survey.Setting/subjects: Three hundred and seventy-one parents of children aged 2&ndash;5 years, recruited from three socio-economic groups in two Australian cities, completed a survey on their child\u27s liking for 176 foods and drinks on a 5-point Likert scale in addition to demographic descriptors. Preferences were compared with the recommendations of the Dietary Guidelines for Children and Adolescents in Australia and the Australian Guide to Healthy Eating.Results:&nbsp; Foods in the Extra Foods (non-nutritious foods) and Cereals groups of the Australian Guide to Healthy Eating were highly liked (mean: 4.02 and 4.01, respectively), whilst foods in the Vegetables group were liked least (mean: 3.01). A large percentage of foods in the Cereals and Extra Foods groups were liked (64% and 56%, respectively) in contrast to the other food groups, especially Vegetables (7%). Children liked foods that were higher in sugar (r = 0.29, P &lt; 0.0001) and more energy-dense (r = 0.34, P &lt; 0.0001) but not those higher in saturated fat (r = 0.16, P = 0.03), total fat (r = 0.12, P = 0.12) or sodium (r = 0.10, P = 0.18). Sociodemographic variables (e.g. socio-economic status, parental education, children\u27s age and sex) explained little of the variation in children\u27s food preferences.Conclusions:&nbsp; Australian pre-school children\u27s food preferences align with dietary guidelines in some respects, but not others. Interventions are needed to shift children\u27s preferences away from non-nutritious foods that are high in energy density and sugar, and towards vegetables and fruits.<br /

Infant Feeding Websites and Apps: A Systematic Assessment of Quality and Content

Background: Internet websites and smartphone apps have become a popular resource to guide parents in their children&rsquo;s feeding and nutrition. Given the diverse range of websites and apps on infant feeding, the quality of information in these resources should be assessed to identify whether consumers have access to credible and reliable information.Objective: This systematic analysis provides perspectives on the information available about infant feeding on websites and smartphone apps.Methods: A systematic analysis was conducted to assess the quality, comprehensibility, suitability, and readability of websites and apps on infant feeding using a developed tool. Google and Bing were used to search for websites from Australia, while the App Store for iOS and Google Play for Android were used to search for apps. Specified key words including baby feeding, breast feeding, formula feeding and introducing solids were used to assess websites and apps addressing feeding advice. Criteria for assessing the accuracy of the content were developed using the Australian Infant Feeding Guidelines.Results: A total of 600 websites and 2884 apps were screened, and 44 websites and 46 apps met the selection criteria and were analyzed. Most of the websites (26/44) and apps (43/46) were noncommercial, some websites (10/44) and 1 app were commercial and there were 8 government websites; 2 apps had university endorsement. The majority of the websites and apps were rated poor quality. There were two websites that had 100% coverage of information compared to those rated as fair or poor that had low coverage. Two-thirds of the websites (65%) and almost half of the apps (47%) had a readability level above the 8th grade level.Conclusions: The findings of this unique analysis highlight the potential for website and app developers to merge user requirements with evidence-based content to ensure that information on infant feeding is of high quality. There are currently no apps available to consumers that address a variety of infant feeding topics. To keep up with the rapid turnover of the evolving technology, health professionals need to consider developing an app that will provide consumers with a credible and reliable source of information about infant feeding, using quality assessment tools and evidence-based content

A qualitative study of the infant feeding beliefs and behaviours of mothers with low educational attainment

© 2016 Russell et al. Background: Infancy is an important period for the promotion of healthy eating, diet and weight. However little is known about how best to engage caregivers of infants in healthy eating programs. This is particularly true for caregivers, infants and children from socioeconomically disadvantaged backgrounds who experience greater rates of overweight and obesity yet are more challenging to reach in health programs. Behaviour change interventions targeting parent-infant feeding interactions are more likely to be effective if assumptions about what needs to change for the target behaviours to occur are identified. As such we explored the precursors of key obesity promoting infant feeding practices in mothers with low educational attainment. Methods: One-on-one semi-structured telephone interviews were developed around the Capability Opportunity Motivation Behaviour (COM-B) framework and applied to parental feeding practices associated with infant excess or healthy weight gain. The target behaviours and their competing alternatives were (a) initiating breastfeeding/formula feeding, (b) prolonging breastfeeding/replacing breast milk with formula, (c) best practice formula preparation/sub-optimal formula preparation, (d) delaying the introduction of solid foods until around six months of age/introducing solids earlier than four months of age, and (e) introducing healthy first foods/introducing unhealthy first foods, and (f) feeding to appetite/use of non-nutritive (i.e., feeding for reasons other than hunger) feeding. The participants' education level was used as the indicator of socioeconomic disadvantage. Two researchers independently undertook thematic analysis. Results: Participants were 29 mothers of infants aged 2-11 months. The COM-B elements of Social and Environmental Opportunity, Psychological Capability, and Reflective Motivation were the key elements identified as determinants of a mother's likelihood to adopt the healthy target behaviours although the relative importance of each of the COM-B factors varied with each of the target feeding behaviours. Conclusions: Interventions targeting healthy infant feeding practices should be tailored to the unique factors that may influence mothers' various feeding practices, taking into account motivational and social influences

A mixed methods study to explore the effects of program design elements and participant characteristics on parents' engagement with an mHealth program to promote healthy infant feeding: The growing healthy program

Copyright © 2019 Taki, Russell, Lymer, Laws, Campbell, Appleton, Ong and Denney-Wilson. Purpose: Mobile health (mHealth) interventions have great potential to promote health. To increase consumer engagement in mHealth interventions it is necessary to address factors that influence the target demographic. The Growing healthy (GH) program is the first obesity prevention program delivered via a smartphone app and website offering evidence-based information on infant feeding from birth until 9 months of age. This sub-study aimed to explore how the design features, quality of the app and participant characteristics influenced parents' engagement with the GH app. Methods: A sequential mixed methods design was used. The GH app participants (225/301) were considered for this sub-study. Participant app engagement was measured through a purpose-built Engagement Index (EI) using app metrics. Participants were categorized as low, moderately or highly engaged based on their EI score upon completing the 9 months program and were then invited to participate in semi-structured telephone interviews. Participants who used the app program, given an EI score and expressed interest to participate in these interviews were eligible. The interviews explored factors that influenced app engagement including delivery features and quality. Thematic analysis networks was used for analysis. Results: 108/225 expressed interest and 18 interviews were conducted from low (n = 3), moderately (n = 7), or highly (n = 8) engaged participants based on purposeful sampling. Participants defined as highly engaged were likely to be a first-time parent, felt the app content to be trustworthy and the app design facilitated easy navigation and regularly opened the push notifications. Participants defined as having low or moderate engagement were likely to have experience from previous children, felt they had sufficient knowledge on infant feeding and the app did not provide further information, or experienced technological issues including app dysfunction due to system upgrades. Conclusions/Implications: This study demonstrated a novel approach to comprehensively analyse engagement in an mHealth intervention through quantitative (Engagement Index) and qualitative (interviews) methods. It provides an insight on maximizing data collected from these programs for measuring effectiveness and to understand users of various engagement levels interaction with program features. Measuring this can determine efficacy and refine programs to meet user requirements

Assessing User Engagement of an mHealth Intervention: Development and Implementation of the Growing Healthy App Engagement Index.

BACKGROUND: Childhood obesity is an ongoing problem in developed countries that needs targeted prevention in the youngest age groups. Children in socioeconomically disadvantaged families are most at risk. Mobile health (mHealth) interventions offer a potential route to target these families because of its relatively low cost and high reach. The Growing healthy program was developed to provide evidence-based information on infant feeding from birth to 9 months via app or website. Understanding user engagement with these media is vital to developing successful interventions. Engagement is a complex, multifactorial concept that needs to move beyond simple metrics. OBJECTIVE: The aim of our study was to describe the development of an engagement index (EI) to monitor participant interaction with the Growing healthy app. The index included a number of subindices and cut-points to categorize engagement. METHODS: The Growing program was a feasibility study in which 300 mother-infant dyads were provided with an app which included 3 push notifications that was sent each week. Growing healthy participants completed surveys at 3 time points: baseline (T1) (infant age ≤3 months), infant aged 6 months (T2), and infant aged 9 months (T3). In addition, app usage data were captured from the app. The EI was adapted from the Web Analytics Demystified visitor EI. Our EI included 5 subindices: (1) click depth, (2) loyalty, (3) interaction, (4) recency, and (5) feedback. The overall EI summarized the subindices from date of registration through to 39 weeks (9 months) from the infant's date of birth. Basic descriptive data analysis was performed on the metrics and components of the EI as well as the final EI score. Group comparisons used t tests, analysis of variance (ANOVA), Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests as appropriate. Consideration of independent variables associated with the EI score were modeled using linear regression models. RESULTS: The overall EI mean score was 30.0% (SD 11.5%) with a range of 1.8% - 57.6%. The cut-points used for high engagement were scores greater than 37.1% and for poor engagement were scores less than 21.1%. Significant explanatory variables of the EI score included: parity (P=.005), system type including "app only" users or "both" app and email users (P<.001), recruitment method (P=.02), and baby age at recruitment (P=.005). CONCLUSIONS: The EI provided a comprehensive understanding of participant behavior with the app over the 9-month period of the Growing healthy program. The use of the EI in this study demonstrates that rich and useful data can be collected and used to inform assessments of the strengths and weaknesses of the app and in turn inform future interventions

Early respiratory viral infections in infants with cystic fibrosis

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Background Viral infections contribute to morbidity in cystic fibrosis (CF), but the impact of respiratory viruses on the development of airway disease is poorly understood. Methods Infants with CF identified by newborn screening were enrolled prior to 4 months of age to participate in a prospective observational study at 4 centers. Clinical data were collected at clinic visits and weekly phone calls. Multiplex PCR assays were performed on nasopharyngeal swabs to detect respiratory viruses during routine visits and when symptomatic. Participants underwent bronchoscopy with bronchoalveolar lavage (BAL) and a subset underwent pulmonary function testing. We present findings through 8.5 months of life. Results Seventy infants were enrolled, mean age 3.1 ± 0.8 months. Rhinovirus was the most prevalent virus (66%), followed by parainfluenza (19%), and coronavirus (16%). Participants had a median of 1.5 viral positive swabs (range 0–10). Past viral infection was associated with elevated neutrophil concentrations and bacterial isolates in BAL fluid, including recovery of classic CF bacterial pathogens. When antibiotics were prescribed for respiratory-related indications, viruses were identified in 52% of those instances. Conclusions Early viral infections were associated with greater neutrophilic inflammation and bacterial pathogens. Early viral infections appear to contribute to initiation of lower airway inflammation in infants with CF. Antibiotics were commonly prescribed in the setting of a viral infection. Future investigations examining longitudinal relationships between viral infections, airway microbiome, and antibiotic use will allow us to elucidate the interplay between these factors in young children with CF

Impact of the growing healthy mhealth program on maternal feeding practices, infant food preferences, and satiety responsiveness: Quasi-experimental study

© Catherine Georgina Russell, Elizabeth Denney-Wilson, Rachel A Laws, Gavin Abbott, Miaobing Zheng, Sharyn J Lymer, Sarah Taki, Eloise-Kate V Litterbach, Kok-Leong Ong, Karen J Campbell. Background: Infancy is an important life stage for obesity prevention efforts. Parents’ infant feeding practices influence the development of infants’ food preferences and eating behaviors and subsequently diet and weight. Mobile health (mHealth) may provide a feasible medium through which to deliver programs to promote healthy infant feeding as it allows low cost and easy access to tailored content. Objective: The objective of this study was to describe the effects of an mHealth intervention on parental feeding practices, infant food preferences, and infant satiety responsiveness. Methods: A quasi-experimental study was conducted with an mHealth intervention group (Growing Healthy) and a nonrandomized comparison group (“Baby's First Food"). The intervention group received access to a free app with age-appropriate push notifications, a website, and an online forum that provided them with evidence-based advice on infant feeding for healthy growth from birth until 9 months of age. Behavior change techniques were selected using the Behaviour Change Wheel framework. Participants in both groups completed three Web-based surveys, first when their infants were less than 3 months old (baseline, T1), then at 6 months (time 2, T2), and 9 months of age (time 3, T3). Surveys included questions on infant feeding practices and beliefs (Infant Feeding Questionnaire, IFQ), satiety responsiveness (Baby Eating Behaviour Questionnaire), and infant’s food exposure and liking. Multivariate linear regression models, estimated using maximum likelihood with bootstrapped standard errors, were fitted to compare continuous outcomes between the intervention groups, with adjustment for relevant covariates. Multivariate logistic regression adjusting for the same covariates was performed for categorical outcomes. Results: A total of 645 parents (Growing Healthy: n=301, Baby's First Food: n=344) met the eligibility criteria and were included in the study, reducing to a sample size of 546 (Growing Healthy: n=234, Baby's First Food: n=312) at T2 and a sample size of 518 (Growing Healthy: n=225, Baby's First Food: n=293) at T3. There were approximately equal numbers of boy and girl infants, and infants were aged less than 3 months at baseline (Growing Healthy: mean 7.0, SD 3.7 weeks; Baby's First Food: mean 7.9, SD 3.8 weeks), with Growing Healthy infants being slightly younger than Baby's First Food infants (P=.001). All but one (IFQ subscale “concerns about infant overeating or becoming overweight” at T2) of the measured outcomes did not differ between Growing Healthy and Baby's First Food. Conclusions: Although mHealth can be effective in promoting some health behaviors and offers many advantages in health promotion, the results of this study suggest that design and delivery characteristics needed to maximize the impact of mHealth interventions on infant feeding are uncertain. The sensitivity of available measurement tools and differences in baseline characteristics of participants may have also affected the results

A mediation approach to understanding socio-economic inequalities in maternal health-seeking behaviours in Egypt.

BACKGROUND: The levels and origins of socio-economic inequalities in health-seeking behaviours in Egypt are poorly understood. This paper assesses the levels of health-seeking behaviours related to maternal care (antenatal care [ANC] and facility delivery) and their accumulation during pregnancy and childbirth. Secondly, it explores the mechanisms underlying the association between socio-economic position (SEP) and maternal health-seeking behaviours. Thirdly, it examines the effectiveness of targeting of free public ANC and delivery care. METHODS: Data from the 2008 Demographic and Health Survey were used to capture two latent constructs of SEP: individual socio-cultural capital and household-level economic capital. These variables were entered into an adjusted mediation model, predicting twelve dimensions of maternal health-seeking; including any ANC, private ANC, first ANC visit in first trimester, regular ANC (four or more visits during pregnancy), facility delivery, and private delivery. ANC and delivery care costs were examined separately by provider type (public or private). RESULTS: While 74.2% of women with a birth in the 5-year recall period obtained any ANC and 72.4% delivered in a facility, only 48.8% obtained the complete maternal care package (timely and regular facility-based ANC as well as facility delivery) for their most recent live birth. Both socio-cultural capital and economic capital were independently positively associated with receiving any ANC and delivering in a facility. The strongest direct effect of socio-cultural capital was seen in models predicting private provider use of both ANC and delivery. Despite substantial proportions of women using public providers reporting receipt of free care (ANC: 38%, delivery: 24%), this free-of-charge public care was not effectively targeted to women with lowest economic resources. CONCLUSIONS: Socio-cultural capital is the primary mechanism leading to inequalities in maternal health-seeking in Egypt. Future studies should therefore examine the objective and perceived quality of care from different types of providers. Improvements in the targeting of free public care could help reduce the existing SEP-based inequalities in maternal care coverage in the short term

Pulse Wave Velocity Measurement in the Carotid Artery Using an LED-LED Array Pulse Oximeter

Pulse wave velocity (PWV) is frequently used as an early indicator of risk of cardiovascular disease. Conventional methods of PWV measurement are invasive and measure the regional PWV, introducing errors from unknown measurement distance to masking local changes in compliance. This paper describes the development and testing of a non-invasive PWV sensor using photoplethysmograph signals. The sensor measures the pulse in the carotid artery with three sensor arrays spaced at 20 mm, 30 mm and 50 mm spacing. Each array of 20 LED-LED sensors are placed at 5 mm to get the largest amplitude pulse across the neck, and to allow for inaccurate sensor placement. LEDs are used as light emitters and the inherent capacitance of reverse biased LEDs measure the reflected light. The foot-foot and phase difference methods were used to calculate the PWV at each measurement distance. The foot-foot method was more reliable than the phase difference at all distances with a PWV of 5.26 m s−1 in a single-subject trial. The sample rate of 570 Hz was deemed too slow as one sample difference resulted in a PWV change of 1.5ms−1. The developed sensor measured the local PWV within the expected physiological range around 6 m s−1. All future measurements will be measured at 1 kHz and an increased LED output intensity