Applying an extended theory of planned behaviour to predict breakfast consumption in adolescents

BACKGROUND/OBJECTIVES: Breakfast skipping increases during adolescence and is associated with lower levels of physical activity and weight gain. Theory-based interventions promoting breakfast consumption in adolescents report mixed findings, potentially because of limited research identifying which determinants to target. This study aimed to: (i) utilise the Theory of Planned Behaviour (TPB) to identify the relative contribution of attitudes (affective, cognitive and behavioural) to predict intention to eat breakfast and breakfast consumption in adolescents and (ii) determine whether demographic factors moderate the relationship between TPB variables, intention and behaviour. SUBJECTS/METHODS: Questionnaires were completed by 434 students (mean 14+/-0.9 years) measuring breakfast consumption (0-2, 3-6 or 7 days), physical activity levels and TPB measures. Data were analysed by breakfast frequency and demographics using hierarchical and multinomial regression analyses. RESULTS: Breakfast was consumed everyday by 57% of students, with boys more likely to eat a regular breakfast, report higher activity levels and report more positive attitudes towards breakfast than girls (P<0.001). The TPB predicted 58% of the variation in intentions. Overall, the model was predictive of breakfast behaviours (P<0.001), but the relative contribution of TPB constructs varied depending on breakfast frequency. Interactions between gender and intentions were significant when comparing 0-2- and 3-6-day breakfast eaters only highlighting a stronger intention-behaviour relationship for girls. CONCLUSIONS: Findings confirm that the TPB is a successful model for predicting breakfast intentions and behaviours in adolescents. The potential for a direct effect of attitudes on behaviours should be considered in the implementation and design of breakfast interventions

Programmable Radio Environments for Smart Spaces

Smart spaces, such as smart homes and smart offices, are common Internet of Things (IoT) scenarios for building automation with networked sensors. In this paper, we suggest a different notion of smart spaces, where the radio environment is programmable to achieve desirable link quality within the space. We envision deploying low-cost devices embedded in the walls of a building to passively reflect or actively transmit radio signals. This is a significant departure from typical approaches to optimizing endpoint radios and individual links to improve performance. In contrast to previous work combating or leveraging per-link multipath fading, we actively reconfigure the multipath propagation. We sketch design and implementation directions for such a programmable radio environment, highlighting the computational and operational challenges our architecture faces. Preliminary experiments demonstrate the efficacy of using passive elements to change the wireless channel, shifting frequency “nulls” by nineWi-Fi subcarriers, changing the 2×2 MIMO channel condition number by 1.5 dB, and attenuating or enhancing signal strength by up to 26 dB

Buy or Build: Challenges Developing Consumer Digital Health Interventions.

BACKGROUND: Digital health interventions offer opportunities to improve collaborative care between clinicians and patients. Designing and implementing digital health interventions requires decisions about buying or building each technology-related component, all of which can lead to unanticipated issues. OBJECTIVES: This study aimed to describe issues encountered from our buy or build decisions developing two digital health interventions over different timeframes, designed to use patient-generated health data to: (1) improve hypertension control and (2) measure and improve adherence to HIV-related medications. METHODS: CONDUIT-HID (CONtrolling Disease Using Information Technology-Hypertension In Diabetes) was developed during 2010 to 2015 to allow patients receiving care from a multispecialty group practice to easily upload home blood pressure readings into their electronic health record and trigger clinician action if mean blood pressure values indicated inadequate control. USE-MI (Unobtrusive SEnsing of Medication Intake) was developed from 2016 to 2022 to allow entry of patients\u27 HIV-related medication regimens, send reminders if patients had not taken their medications by the scheduled time(s), attempt to detect medication ingestion through machine learning analysis of smartwatch motion data, and present graphical adherence summaries to patients and clinicians. RESULTS: Both projects required multiple buy or build decisions across all system components, including data collection, transfer, analysis, and display. We used commercial, off-the-shelf technology where possible, but virtually all of these components still required substantial custom development. We found that, even though our projects spanned years, issues related to our buy or build decisions stemmed from several common themes, including mismatches between existing and new technologies, our use case being new or unanticipated, technology stability, technology longevity, and resource limitations. CONCLUSION: Those designing and implementing digital health interventions need to make numerous buy or build decisions as they create the technologies that underpin their intervention. These buy or build decisions, and the ensuing issues that will arise because of them, require careful planning, particularly if they represent an edge case use of existing commercial systems

Phyjama

A key challenge that has emerged in recent years is the ability to remotely monitor patients, particularly elderly individuals while they are at home. This is particularly important given demographic shifts; the WHO estimates that the global share of the population aged 65 years or over increased from 6 percent in 1990 to 9 percent in 2019, and is projected to increase to 16 percent in the next two decades [1]. While the increase in life expectancy is a tremendous achievement, it also presents a growing need to go beyond episodic measurement collected in clinical settings to continuous monitoring at home. Indeed, this issue has become particularly acute in recent times in light of the COVID-19 pandemic and the need to ensure timely treatment delivery while at the same time protecting elderly from exposure to the virus and alleviating burden on support staff.</jats:p