Grant Schofield

Issue addressed: High consumption of ultra-processed foods (UPFs) is linked to poor health outcomes, yet consumers often struggle to recognise and interpret food processing. Digital tools using artificial intelligence (AI) can support nutritional literacy and UPF awareness. This study evaluated a HISS (Human Interference Scoring System)–based mobile application designed to classify foods by processing level and support dietary self-reflection. Methods: A three-day quantitative usability study was conducted in New Zealand. Thirty-one participants (13 adolescents aged 12–18 years, eight tertiary students aged 19–25 years, and nine Māori and Pacific health coaches) logged all meals, snacks and beverages using the HISS app. AI classification accuracy was assessed against expert ratings of food images. App engagement was measured using in-app metrics, and usability and perceived impact were assessed via surveys. Results: The AI system achieved 93% accuracy for HISS category classification. App engagement varied across features, with most time spent on meal logging and AI interaction screens. Adolescents and health coaches reported high usability and usefulness, while tertiary students expressed more mixed intentions regarding ongoing use. Conclusions: The app demonstrated high classification accuracy and was generally well received, particularly among users with lower baseline nutrition literacy. Findings support the feasibility of using AI-enabled image recognition to support awareness of UPF intake. So what?: Evidence based AI food classification tools such as HISS show promise for scalable UPF reduction. With further development and evaluation, such tools may support nutrition education and behaviour change in community and clinical settings.

High consumption of ultra-processed foods (UPFs) contributes to the growing burden of non-communicable disease, yet many consumers struggle to recognise and interpret levels of processing. Digital tools using artificial intelligence (AI) offer potential to support nutrition literacy and UPF awareness. This study explored user perceptions, usability and cultural relevance of a Human Interference Scoring System (HISS)-based mobile application designed to classify foods and support reflection on food quality and dietary choices. A qualitative study was conducted in New Zealand, where participants used the HISS app for three days followed by semi-structured interviews. Thirty-one participants were recruited via social media and word of mouth, including adolescents (n=13), tertiary students (n=9), and Māori and Pacific health coaches (n=9). Transcripts were analysed using inductive thematic analysis. Three evaluative categories were identified: positive user experiences (intuitive interface, perceived AI accuracy, enhanced nutrition literacy, visual feedback, inclusivity of cultural foods); challenges (technical issues, database gaps, limited depth for advanced users); and suggested improvements (expanded food database, enhanced logging, culturally tailored education, optional advanced features). Participants reported increased awareness of UPF intake and reflection on food choices. The HISS app was perceived as usable, acceptable and relevant across diverse user groups, particularly for those with lower nutrition literacy. Addressing technical limitations and expanding functionality may enhance engagement and applicability. AI-enabled, culturally responsive food classification tools such as HISS show promise as scalable health promotion approaches to support UPF awareness and dietary reflection in community and clinical settings.

Background/Objectives: The global epidemic of type 2 diabetes (T2D) is a critical public health issue, particularly in New Zealand, where prevalence rates are high, especially among Māori and Pacific people. Recent research indicates that dietary interventions, particularly carbohydrate reduction, can lead to the remission or reversal of T2D. However, little is known about how such approaches perform when implemented in routine New Zealand primary care, particularly within high-risk and underserved populations. This study aimed to evaluate changes in HbA1c, diabetes status, and cardiometabolic outcomes among adults with prediabetes and T2D engaged in such a model of care. Methods: This study reports findings from a retrospective, observational, real-world, multi-site clinical audit (service evaluation) of a holistic model of care implemented in three primary care practices in New Zealand. The model of care is characterised by a three-pronged approach: whole food, carbohydrate reduction; a health-coach, behaviour-change-based delivery approach; and community- or peer-based initiatives. Audit data from 106 patients with prediabetes (PD) and T2D were analysed (median follow-up 19 months; IQR 6–32) to assess changes in glycosylated haemoglobin (HbA1c) levels, diabetes status, and cardiometabolic outcomes. Results: We observed an overall reduction in HbA1c (median change −3 mmol/mol (IQR: −7 to 3), p = 0.004), with 32% of patients with T2D at baseline achieving reversal and 44% of those with PD attaining normoglycaemia at final follow-up. Weight loss was associated with greater HbA1c reduction (0.56 mmol/mol decrease per kg lost) and additional improvements seen in lowered alanine aminotransferase (ALT). HDL cholesterol showed a small decline (r = 0.31), and triglycerides and blood pressure showed no significant change, indicating that these measures remained broadly stable over the evaluation period. Conclusions: Given the retrospective and uncontrolled audit design, findings should be interpreted with appropriate caution. However, the consistent improvements observed across multiple practices suggest that carbohydrate-reduction strategies within holistic models of care can meaningfully improve diabetes outcomes in real-world primary care settings. Future research should evaluate longer-term sustainability, implementation fidelity, and the applicability of this model at scale, particularly for Māori and Pacific communities.

Background/Objectives: Classifying foods by processing has gained traction, with NOVA the most widely used framework. NOVA helped shift focus away from nutrients toward food processing, but has been critiqued for rigidity, inconsistent classifications, and weak links to health outcomes. This paper introduces the CHIPS (Combining Health, Intuition, Processing and Science) framework (previously known as the Human Interference Scoring System (HISS)), which retains NOVA’s food-based perspective while addressing these limitations by integrating processing level with health evidence. Methods: CHIPS was developed through critique synthesis, epidemiological evidence, and expert input from nutrition professionals. Foods were classified using a three-layered approach: (1) baseline placement by processing level, (2) adjustment based on evidence of health benefit or harm, and (3) an intuition check to ensure pragmatic classification. Key divergences from NOVA were recorded. Results: CHIPS places foods with demonstrated benefits in lower categories and those with consistent evidence of harm in higher ones, while also resolving common inconsistencies and better aligning classifications with real-world understanding. Conclusions: A CHIPS builds on NOVA’s strengths while addressing its limitations by combining processing level, health evidence, and a pragmatic or intuitive lens. This approach resolves inconsistencies in existing systems and better reflects how foods contribute to health in real-world contexts. The framework has been successfully integrated into an AI enabled tool, demonstrating feasibility for reliable food classification and potential for further validation in diverse populations.

Purpose: Obsessive-Compulsive Disorder (OCD) is a debilitating condition characterized by the presence of intrusive thoughts, images or urges, and/or compulsions. Despite well-established treatment pathways of psychological therapy and psychopharmacology, most individuals with OCD are failing to experience meaningful benefit. Nutritional psychiatry, an emerging field of research, has not yet explored whether a dietary intervention can support the management of OCD symptoms. This review aims to examine the plausibility of using metabolically informed dietary change in the treatment of OCD through exploring key biological factors implicated in the development of OCD, and the impact of diet on these factors. Recent Findings: There is currently no prospective literature that has investigated what those with OCD eat, or whether a dietary intervention can improve their symptoms. Nutritional psychiatry research has centred on anxiety and mood disorders, as well as neurodevelopmental disorders leaving OCD underrepresented. Summary: Disruption to metabolic homeostasis is implicated in the pathogenesis of OCD, with these metabolic factors able to be ameliorated through dietary changes. It is suggested that a whole foods ketogenic diet may be effective in the management of OCD symptoms.

Introduction: Wearable accelerometers are a valuable tool for monitoring sleep, sedentary behaviour, and physical activity patterns within 24h time-use in free-living environments. While wrist-worn accelerometers are favoured for monitoring sleep, they do not accurately distinguish between sitting and lying positions (Narayanan et al., 2020). This study aims to determine whether back or thigh-mounted accelerometers yield sleep metrics comparable to wrist-worn devices using an open-source algorithm originally validated for the wrist. Methods: Data from 20 healthy sleepers were collected using Axivity AX3 accelerometers. Participants wore accelerometers on their right thigh, low-back, and wrist for one night of sleep in their own bed. Sleep metrics were calculated using the van Hees algorithm through the GGIR package in R. The primary outcomes were: Total Sleep Time (TST), Wake After Sleep Onset (WASO), Awakenings (AWK), Sleep Efficiency (SE), Sleep Interval (SI) and Sleep Onset Timestamp (SOT). Within-subject ANOVA with Tukey’s post hoc, Pearson correlation coefficients, Bland-Altman plots, and Cohen’s d were used to assess the comparability of sleep metrics between the body placements. Results: Data analysis included all 20 participants. Mid-thigh accelerometers demonstrated a strong linear relationship with wrist accelerometers across all metrics ( r = 0.86-0.98). Bland-Altman plots demonstrated a narrow 95% confidence interval suggesting that wrist and mid-thigh metrics are in good agreement, except for AWK which is slightly underestimated by the mid-thigh device. Conversely, low-back accelerometers demonstrated moderate linear relationship with the wrist ( r = 0.63-0.98) and the Bland-Altman results showed wide limits of agreement with significant overestimations of TST, SE, SI and underestimations of WASO, AWK, SOT. Cohen’s d demonstrated small differences between mid-thigh and wrist devices, except for AWK (d= 0.42). Low-back values for WASO, SE, and AWK showed moderate differences. Conclusions: This analysis demonstrates that the mid-thigh accelerometer yields comparable sleep metrics to wrist-worn devices when processed with the van Hees algorithm.

The Human Interference Scoring System (HISS) is a novel food-based diet-quality-classification system based on the existing NOVA method. HISS involves food and fluid allocation into categories from digital imagery based on food processing levels, followed by meal plan analysis using food-servings quantification. The primary purpose of this work was to evaluate the reliability of HISS. Trained nutrition professionals analyzed digital photographs from five hypothetical 24 h food recalls and categorized foods into one of four HISS categories. A secondary purpose was to assess the nutrient composition of the food recalls and other selected foods from the HISS categories. Participants effectively categorized foods into HISS categories, with only minor discrepancies noted. High inter-rater reliability was observed in the outer HISS categories: unprocessed and ultra-processed foods. Ultra-processed items consistently displayed elevated energy, carbohydrates, and sugar compared to unprocessed foods, while unprocessed foods exhibited notably higher dietary fiber. This study introduces the HISS as a potentially useful tool for quantifying a food-quality-based system using digital-photography-based assessments. Its high inter-rater reliability and ability to capture relationships between food processing levels and nutrient composition make it a promising method for assessing dietary habits and food quality.

This study explores a novel healthcare model employed in the primary care setting integrating a carbohydrate‐reduction dietary approach and health coaching for managing prediabetes (PD) and Type 2 diabetes (T2D) in New Zealand. Using qualitative methods, we conducted focus groups with 46 patients and individual interviews with health coaches and general practitioners across two regions. Five major themes emerged from inductive thematic analysis: reduced carbohydrate lifestyles, health coaching, implementation, empowerment, and sustainability. Patients reported significant health improvements, including weight loss, reduced medication burden, and increased energy. Challenges included resistance from some medical professionals and negative public perceptions. Health coaching played a crucial role in patient care, providing individualised support and enhancing health literacy. The study found that this model both improved patient outcomes and also alleviated the burden on healthcare professionals by managing time‐intensive aspects of patient care. Barriers to the adoption of this model include scepticism about low‐carbohydrate diets and the need for more education and awareness among healthcare professionals. The findings suggest that this healthcare model has the potential to transform the management of PD and T2D in primary care, shifting patients from lifelong medication dependence to significant health improvements and potential disease remission or reversal.

Eating patterns characterised by low intakes of processed carbohydrates and higher intakes of fat- and Vitamin D-rich foods are associated with protection against dental caries. The aim of this formative study was to evaluate the extent to which the knowledge of children and adults of foods for oral health reflects dietary guideline advice, and the evidence base for foods associated with increased and decreased caries burdens. Using a novel card-sorting task, the participants categorised foods according to their knowledge of each food for oral health. There were no differences between children and adults in the categorisation of fresh, minimally processed foods. Fish, chicken, and red meat were categorised as healthy by significantly fewer children than adults. High-sugar foods were correctly characterised as unhealthy by nearly all participants. More children categorised breakfast cereals as healthy than adults. There were no statistically significant differences between children and adults for the categorisation of brown or wholegrain breads categorised as healthy. The alignment of the participants’ beliefs with dietary guideline recommendations suggests education through health promotion initiatives is successful in achieving knowledge acquisition in children and adults. However, recommendations to increase the intake of refined carbohydrates inadvertently advocate foods associated with increased caries burdens.