Indigenous Women and Their Nutrition During Pregnancy (the Mums and Bubs Deadly Diets Project): Protocol for a Co-designed mHealth Resource Development Study.

BACKGROUND: Nutrition in pregnancy is pivotal to optimizing infant growth and maternal well-being. The factors affecting Indigenous people's food and nutrition intake are complex with a history of colonization impacting the disproportionate effect of social determinants to this day. Literature regarding the dietary intake or dietary priorities of Indigenous women in Australia is scarce, with supportive, culturally appropriate resources developed for and with this group rare. Research suggests mobile health (mHealth) tools are effective in supporting health knowledge of Indigenous people and positive health behavior changes when designed and developed with the expertise of Indigenous communities. OBJECTIVE: This study seeks to build the body of knowledge related to nutrition needs and priorities for Indigenous women in Australia during pregnancy. Further, this project team and its participants will co-design an mHealth digital tool to support these nutrition needs. METHODS: The Mums and Bubs Deadly Diets study recruits Indigenous women and health care professionals who support Indigenous women during pregnancy into 2 phases. Phase 1 (predesign) uses a mixed methods convergent design using a biographical questionnaire and social or focus groups to inform phase 2 (generative). Phase 2 will use a participatory action research process during co-design workshops to iteratively develop the digital tool; the exact actions within a workshop will evolve according to the participant group decisions. RESULTS: To date, this project has undertaken phase 1 focus groups at all Queensland sites, with New South Wales and Western Australia to begin in early to mid-2023. We have recruited 12 participants from Galangoor Duwalami, 18 participants from Carbal in Toowoomba, and 18 participants from Carbal in Warwick. We are expecting similar numbers of recruits in Western Australia and New South Wales. Participants have been both community members and health care professionals. CONCLUSIONS: This study is an iterative and adaptive research program that endeavors to develop real-world, impactful resources to support the nutrition needs and priorities of pregnant Indigenous women in Australia. This comprehensive project requires a combination of methods and methodologies to ensure Indigenous voices are heard at each stage and in all aspects of research output. The development of an mHealth resource for this cohort will provide a necessary bridge where there is often a gap in access to nutrition resources for women in pregnancy in Indigenous communities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45983.

How Can We Support Healthy Eating in Young Adults with Low Diet Quality? A Survey of Users of the 'No Money No Time' Healthy Eating Website.

Nutrition interventions to support young adults are needed due to low diet quality. The aims were to explore the (1) circumstances and (2) barriers regarding dietary habits of the young adult users of the No Money No Time (NMNT) healthy eating website with the lowest diet quality scores. An online cross-sectional survey was conducted from August-September 2022 with a sample of NMNT users aged 18-35 years with low diet quality (defined as Healthy Eating Quiz score 0-38/73). The survey included demographics (e.g., gender), circumstances (6-item US Food Security Survey, Cooking and Food Skills Confidence Measures), and challenges and resources used in relation to healthy eating (open-responses). Theoretical thematic analysis was used to analyse open-response questions and derive main themes. The study sample (n = 108; 71.3% female, median age 28; 28.7% food insecure) had a mean (standard deviation) Cooking Skills score 70.2 (17.5)/98, and median (interquartile range) Food Skills score 96.0 (83.5-107.5)/133. The main challenges regarding healthy eating were (1) time and (2) cost, and the main resources to support healthy eating were (1) online resources (e.g., websites, Google) and (2) recipes. Findings identify possible targets for future interventions to support healthy eating in this vulnerable group (e.g., supporting cooking and food skills).

Diet Quality of Adolescents and Adults Who Completed the Australian Healthy Eating Quiz: An Analysis of Data over Six Years (2016-2022).

Diet quality is influenced by demographics and can change over time. This study aimed to (1) compare diet quality among adolescents/adults who completed the online Healthy Eating Quiz (HEQ) by demographic characteristics, and (2) to evaluate change in score over time for repeat completers. HEQ data collected between July 2016 and May 2022 were analysed, including demographics (age, gender, vegetarian status, socio-economic status, number of people main meals are shared with, country), and diet quality calculated using the Australian Recommended Food Score (ARFS) (range 0−73) for respondents aged ≥ 16 years. Differences in ARFS by demographic characteristics and change in score over time, adjusted for age, gender and vegetarian status, were tested by linear regression. The participants (n = 176,075) were predominantly female (70.4%), Australian (62.8%), and aged 18−24 years (27.7%), with 4.0% (n = 7087) repeat completers. Mean ± SD ARFS was 33.9 ± 9.4/73. Results indicate that ARFS was significantly lower among males and significantly higher with increasing age group, higher socio-economic status, in vegetarians, those who shared main meals with others, and those living in Australia (p-values < 0.001). Mean change in ARFS over time (2.3 ± 6.9) was significantly higher for those with lower baseline scores (p < 0.001). Publicly available, brief dietary assessment tools have the potential to improve diet quality at the population level.

A Mixed-Method Study Exploring Experiences and Perceptions of Nutritionists Regarding Use of an Image-Based Dietary Assessment System in Tanzania.

Due to global advances in technology, image-based food record methods have emerged as an alternative to traditional assessment methods. The use of image-based food records in low and lower-middle income countries such as Tanzania is limited, with countries still using traditional methods. The current study aimed to determine the feasibility of using a new voice and image-based dietary assessment system (VISIDA) in Dar es Salaam, Tanzania. This mixed-method study recruited 18 nutritionists as participants who collected image-based records of food and drinks they consumed using the VISIDA smartphone app. Participants viewed an online demonstration of the VISIDA web platform and the analysis process for intake data collected using the VISIDA app. Then, participants completed an online survey and were interviewed about the VISIDA app and web platform for food and nutrient intake analysis. The method was reported as being acceptable and was found to be easy to use, although technical challenges were experienced by some participants. Most participants indicated a willingness to use the VISIDA app again for one week or longer and were interested in using the VISIDA system in their current role. Participants acknowledged that the VISIDA web platform would simplify some aspects of their current job. Image-based food records could potentially be used in Tanzania to improve the assessment of dietary intake by nutritionists in urban areas. Participants recommended adding sound-on notifications, using the VISIDA app in both Apple and Android phones, enabling installation from the app store, and improving the quality of the fiducial markers.

Using Co-design in Mobile Health System Development: A Qualitative Study With Experts in Co-design and Mobile Health System Development.

BACKGROUND: The proliferation of mobile devices has enabled new ways of delivering health services through mobile health systems. Researchers and practitioners emphasize that the design of such systems is a complex endeavor with various pitfalls, including limited stakeholder involvement in design processes and the lack of integration into existing system landscapes. Co-design is an approach used to address these pitfalls. By recognizing users as experts of their own experience, co-design directly involves users in the design process and provides them an active role in knowledge development, idea generation, and concept development. OBJECTIVE: Despite the existence of a rich body of literature on co-design methodologies, limited research exists to guide the co-design of mobile health (mHealth) systems. This study aims to contextualize an existing co-design framework for mHealth applications and construct guidelines to address common challenges of co-designing mHealth systems. METHODS: Tapping into the knowledge and experience of experts in co-design and mHealth systems development, we conducted an exploratory qualitative study consisting of 16 semistructured interviews. Thereby, a constructivist ontological position was adopted while acknowledging the socially constructed nature of reality in mHealth system development. Purposive sampling across web-based platforms (eg, Google Scholar and ResearchGate) and publications by authors with co-design experience in mHealth were used to recruit co-design method experts (n=8) and mHealth system developers (n=8). Data were analyzed using thematic analysis along with our objectives of contextualizing the co-design framework and constructing guidelines for applying co-design to mHealth systems development. RESULTS: The contextualized framework captures important considerations of the mHealth context, including dedicated prototyping and implementation phases, and an emphasis on immersion in real-world contexts. In addition, 7 guidelines were constructed that directly pertain to mHealth: understanding stakeholder vulnerabilities and diversity, health behavior change, co-design facilitators, immersion in the mHealth ecosystem, postdesign advocates, health-specific evaluation criteria, and usage data and contextual research to understand impact. CONCLUSIONS: System designers encounter unique challenges when engaging in mHealth systems development. The contextualized co-design framework and constructed guidelines have the potential to serve as a shared frame of reference to guide the co-design of mHealth systems and facilitate interdisciplinary collaboration at the nexus of information technology and health research.

Single-Stage Intake Gesture Detection Using CTC Loss and Extended Prefix Beam Search.

Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. Both inertial sensor data of wrist movements and video data depicting the upper body have been used for this purpose. The most advanced approaches to date use a two-stage approach, in which (i) frame-level intake probabilities are learned from the sensor data using a deep neural network, and then (ii) sparse intake events are detected by finding the maxima of the frame-level probabilities. In this study, we propose a single-stage approach which directly decodes the probabilities learned from sensor data into sparse intake detections. This is achieved by weakly supervised training using Connectionist Temporal Classification (CTC) loss, and decoding using a novel extended prefix beam search decoding algorithm. Benefits of this approach include (i) end-to-end training for detections, (ii) simplified timing requirements for intake gesture labels, and (iii) improved detection performance compared to existing approaches. Across two separate datasets, we achieve relative F1 score improvements between 1.9% and 6.2% over the two-stage approach for intake detection and eating/drinking detection tasks, for both video and inertial sensors.

Going, going, gone: competitive decision-making in Dutch auctions.

In a Dutch auction, an item is offered for sale at a set maximum price. The price is then gradually lowered over a fixed interval of time until a bid is made, securing the item for the bidder at the current price. Bidders must trade-off between certainty and price: bid early to secure the item and you pay a premium; bid later at a lower price but risk losing to another bidder. These properties of Dutch auctions provide new opportunities to study competitive decision-making in a group setting. We developed a novel computerised Dutch auction platform and conducted a set of experiments manipulating volatility (fixed vs varied number of items for sale) and price reduction interval rate (step-rate). Triplets of participants ([Formula: see text]) competed with hypothetical funds against each other. We report null effects of step-rate and volatility on bidding behaviour. We developed a novel adaptation of prospect theory to account for group bidding behaviour by balancing certainty and subjective expected utility. We show the model is sensitive to variation in auction starting price and can predict the associated changes in group bid prices that were observed in our data.

Learning Deep Representations for Video-Based Intake Gesture Detection.

Automatic detection of individual intake gestures during eating occasions has the potential to improve dietary monitoring and support dietary recommendations. Existing studies typically make use of on-body solutions such as inertial and audio sensors, while video is used as ground truth. Intake gesture detection directly based on video has rarely been attempted. In this study, we address this gap and show that deep learning architectures can successfully be applied to the problem of video-based detection of intake gestures. For this purpose, we collect and label video data of eating occasions using 360-degree video of 102 participants. Applying state-of-the-art approaches from video action recognition, our results show that (1) the best model achieves an F1 score of 0.858, (2) appearance features contribute more than motion features, and (3) temporal context in form of multiple video frames is essential for top model performance.

An Evolutionary Game Model with Punishment and Protection to Promote Trust in the Sharing Economy.

In this paper, we present an evolutionary trust game, taking punishment and protection into consideration, to investigate the formation of trust in the so-called sharing economy from a population perspective. This sharing economy trust model comprises four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Punishment in the form of penalty for untrustworthy providers and protection in the form of insurance for consumers are mechanisms adopted to prevent untrustworthy behaviour. Through comprehensive simulation experiments, we evaluate dynamics of the population for different initial population setups and effects of having penalty and insurance in place. Our results show that each player type influences the 'existence' and 'survival' of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Additionally, we observe that imposing a heavier penalty or having insurance for all consumers (trustworthy and untrustworthy) can be counterproductive for promoting trustworthiness in the population and increasing the global net wealth. Our findings have important implications for understanding trust in the context of the sharing economy, and for clarifying the usefulness of protection policies within it.

Diagnostic Accuracy of Random ECG in Primary Care for Early, Asymptomatic Cardiac Autonomic Neuropathy.

AIMS: Cardiac autonomic reflex tests (CARTs) are time consuming and require patient cooperation for detecting cardiac autonomic neuropathy (CAN). Heart rate variability (HRV) analysis requires less patient cooperation and is quicker to complete. However the reliability of HRV results as a clinical tool, with respect to length of recording and accuracy of diagnosis is inconclusive. The current study investigated the reproducibility associated with varying length of recording for early CAN (eCAN) assessment. METHODS: Participants were 68 males, 72 females with average age of 55 for controls and 63 for early CAN. Inclusion criteria were that participants were medication free and presented with no comorbidities. ECGs of control and eCAN were recorded and heart rate changes analyzed with the fast Fourier transform (FFT) and Lomb-Scargle periodogram (LSP). Ten-second to 5-minute recordings were extracted from a 15-minute lead-II ECG and accuracy in assessment of eCAN determined. RESULTS: The eCAN group was older ( P < .001) and systolic blood pressure was higher ( P < .01). HDL-cholesterol was also higher in the eCAN group ( P < .05). HRV analysis showed that both FFT and LSP results were significantly different between eCAN and control down to a 10-second ECG length for low frequency (LSP: P = .013, FFT: P = .024) and high frequency (HF-LSP: P = .002, FFT: P = .002) power. eCAN assessment was optimal down to 90-second recordings with a sensitivity of 100% and specificity of 29.49%. CONCLUSION: HRV is suitable for clinical practice from ECG recordings of more than 90 seconds with high accuracy and repeatability within a session for each participant.