The research team, led by Janaka Godevithana and Champa Jayalakshmie Wijesinghe of the University of Ruhuna, together with Millawage Supun Dilara Wijesinghe of Sri Lanka’s Health Promotion Bureau, set out to address a growing public health problem. Unhealthy diets are a key risk factor for non-communicable diseases such as diabetes, cardiovascular disease and certain cancers, and office workers, with their long sedentary hours and easy access to convenience foods, are particularly exposed. The resulting burden is both medical and economic, straining health systems and reducing productivity across working populations in low- and middle-income countries.

At the heart of the intervention lies a well-known behavioural framework: the transtheoretical model, and specifically its Stage of Change construct, sometimes called the stages of readiness. Rather than treating all participants as a uniform group, the programme classified each worker according to whether they were precontemplating change, contemplating it, preparing for it, actively changing, or maintaining new habits. The content and intensity of the advice each participant received were then tailored to that stage, a principle known as stage matching. The idea is intuitive: someone who has never considered eating more vegetables needs a different conversation from someone already trying to do so.

The trial’s design gave the question unusual rigour for a workplace nutrition study. Rather than randomising individuals, which risks contamination when colleagues share meals and conversation, the researchers randomised whole clusters, meaning entire offices or administrative units, to either the intervention or a control condition. Control clusters received no intervention during the study period. Dietary intake was measured objectively at baseline and again after three months using a 24-hour dietary recall method, supplemented by a picture guide to help participants estimate portion sizes and by computer software to convert reported foods into nutrient and food-group data. Readiness to change was assessed with a validated staging algorithm.

After three months, the results were unambiguous. Both groups showed some positive movement in their stage of change, which the authors note may reflect natural progression or the effect of simply being assessed, but the change was far stronger in the intervention group, where it reached a p-value below 0.001 compared with a more modest p-value of 0.04 among controls. More importantly, the intervention group recorded significant improvements in cereal, fruit and vegetable intake alongside a reduction in unhealthy food consumption, all at the same stringent significance threshold.

The statistical strength of the effect is what makes the trial remarkable. In regression analyses, receiving the intervention was a significant predictor of progressing in stage of change, with an odds ratio of 12.2, and an even stronger predictor of achieving a measurably healthier dietary intake, with an odds ratio of 25.3. In plain terms, participants in the intervention arm had roughly twelve times the odds of advancing in their readiness to change and about twenty-five times the odds of actually improving their diet compared with controls. For a relatively brief, low-cost workplace programme, those are unusually large effects.

The analysis also revealed who benefited most. Being a clerical worker predicted a positive change in stage of change, with an odds ratio of 1.7 at the threshold of significance, suggesting that the programme resonated particularly with staff in routine administrative roles. Meanwhile, participants who were already in the action or maintenance stages at baseline were significantly more likely to achieve improvements in dietary intake, with an odds ratio of 3.5. This pattern fits the logic of the underlying theory: people who have already begun changing have the momentum to convert readiness into measurable behaviour, while those at earlier stages may need longer or more intensive support before their diets shift.

Why should a stage-matched approach outperform generic healthy-eating campaigns? Behavioural science offers several explanations. Generic messages often fail because they mismatch the recipient’s psychological state: a person in precontemplation, who sees no problem with their diet, is likely to dismiss advice framed for someone already trying to change. Stage-matched interventions meet people where they are, using consciousness-raising for the unmotivated, decisional balance exercises for the ambivalent, and concrete planning and relapse-prevention strategies for those already acting. The Sri Lankan programme also grounded its dietary targets in the Food Based Dietary Guidelines for Sri Lankans, translating international nutritional science into culturally familiar foods and portions, which likely strengthened its acceptability in local workplaces.

The trial was conducted with careful attention to research governance. It was registered in the Sri Lanka Clinical Trial Registry under registration number SLCTR/2020/025, ethical approval came from the Ethics Review Committee of the Faculty of Medicine at the University of Ruhuna, and administrative approval was obtained from the District Secretariat and health authorities in Galle. All participating office workers gave informed written consent, and the study was carried out in accordance with the Declaration of Helsinki. Workers identified with health problems during the study were referred to the nearest healthcare facility. The authors declare no competing interests, and the study was self-funded by the corresponding author.

The findings arrive at a moment when employers and governments worldwide are searching for scalable ways to curb non-communicable diseases, which now dominate the global burden of disease. Workplace interventions are attractive because they reach adults where they already gather, require little additional infrastructure, and can be delivered repeatedly. This trial suggests that the missing ingredient in many past workplace programmes may not be money or technology but psychological tailoring: asking each employee a simple question about their readiness to change and building the message around the answer. The authors caution that their results come from a single district and a three-month follow-up, and they recommend further studies assessing the intervention’s effectiveness in a more pragmatic manner across different settings. Longer follow-up would also show whether early gains persist once the programme ends. Even so, the size of the effects, the rigour of the cluster-randomised design and the simplicity of the underlying approach make this one of the most encouraging workplace nutrition results in recent years, and a template that health ministries far beyond Sri Lanka may soon want to test in their own offices.

Subject of Research: A stage of change–based dietary intervention for sedentary office workers evaluated in a cluster-randomised trial in Southern Sri Lanka

Article Title: Transforming diet in the workplace: effectiveness of a stage of change–based dietary intervention for sedentary office workers in Southern Sri Lanka – a cluster randomised trial

Article References: Godevithana, J., Wijesinghe, C. J., & Wijesinghe, M. S. D. (2026). Transforming diet in the workplace: effectiveness of a stage of change–based dietary intervention for sedentary office workers in Southern Sri Lanka – a cluster randomised trial. BMC Public Health. https://doi.org/10.1186/s12889-026-29843-3

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29843-3

Keywords: dietary intervention, healthy eating, sedentary office workers, stage of change theory, cluster-randomised controlled trial, non-communicable diseases, workplace health, nutrition, Sri Lanka, behaviour change, public health, BMC Public Health

The research team focused on three sectors that contribute substantially to greenhouse gas emissions and where individual behaviour change can have the greatest impact: energy use in the home, transport choices, and food consumption. These are also the sectors in which international policy learning and transfer are most common, as governments routinely borrow instruments that have succeeded elsewhere, from carbon taxes to appliance efficiency standards. The problem, the authors argue, is that such transfers often rest on an implicit assumption that populations behave alike, or that a handful of socio-demographic characteristics such as age, income and education can stand in for actual behavioural patterns. When those assumptions fail, policies underperform and public money is wasted.

To test these assumptions rigorously, the researchers employed Multilevel Latent Class Analysis, a statistical technique that identifies unobserved groups of people who share similar behavioural profiles without the analyst predefining what those groups should look like. The multilevel structure of the method is crucial: it simultaneously classifies individuals and accounts for the fact that those individuals are nested within regions and countries, allowing the team to detect both the behavioural segments themselves and the way those segments are distributed geographically. Applied to the survey data, the technique revealed distinct population segments in each of the three sectors, ranging from highly sustainable to markedly unsustainable patterns of behaviour.

The headline finding is sobering. Less sustainable behavioural patterns were prevalent across all three sectors in every country examined, meaning that in energy, transport and food alike, the largest or most common groups of people exhibited relatively unsustainable habits. Yet the picture was not uniformly bleak, and it was certainly not uniform. The energy sector in particular displayed notable heterogeneity, with a wider variety of behavioural profiles than the other two sectors. This suggests that household energy behaviour is shaped by a more diverse set of circumstances, practices and constraints than, say, dietary choices, and that energy policy may therefore require finer targeting than policies aimed at transport or food.

Perhaps the most consequential result for policy design is what the study found about socio-demographics. Conventional segmentation approaches, which sort people by income bracket, education level or age cohort, assume these characteristics reliably predict environmental behaviour. The analysis showed that they do not, at least not consistently. Socio-demographic characteristics failed to predict segment membership reliably across countries and sectors, undermining a foundation on which much targeted policy communication rests. A wealthy, highly educated urbanite in one country may fall into a profligate energy-use segment, while a demographically similar person elsewhere may be among the most sustainable. If policymakers cannot infer behaviour from demographics, they need direct behavioural evidence, which is precisely what latent class methods provide.

The study also uncovered a striking pattern of behavioural consistency within individuals. People who engaged in sustainable behaviours in one sector were more likely to do so in the others, and, symmetrically, unsustainable behaviours were positively associated across sectors as well. In other words, environmental behaviour tends to cluster within people rather than existing in isolated silos. This has an important implication: interventions that successfully shift behaviour in one domain may create momentum in others, and coordinated cross-sector interventions are likely to be more effective than sector-by-sector campaigns. A household that adopts efficient heating may be more receptive to messages about low-carbon travel or reduced meat consumption, and policies that treat these domains together could exploit that correlation.

Geography turned out to matter enormously. When the researchers mapped the regional distribution of population segments, they found that regions typically grouped geographically by country, underscoring the influence of national context such as policy regimes, infrastructure, culture and climate on environmental behaviour. People in the same country tend to share a behavioural profile mix, which is exactly what makes national policy learning plausible. But the more surprising discovery lay beneath that national clustering: significant within-country variation. Some regions showed substantially different proportions of people in sustainable segments than their neighbours, despite operating under identical national policies. A region can be a behavioural outlier within its own country, hosting far more or far fewer sustainably behaving residents than the national policy environment alone would predict.

This within-country variation is where the study makes its most practical contribution. Traditional policy transfer is often ad hoc: a government observes that a scheme works in another country and imports it, hoping for similar results. Yang and colleagues propose instead a systematic framework for identifying evidence-based transfer opportunities. The framework answers three questions in sequence: who needs interventions, in the form of which population segments are prevalent and unsustainable in a given region; where successful patterns emerge, by locating regions with unusually high concentrations of sustainable segments; and which contexts may enable sustainable behaviours, by comparing the conditions of successful regions with those where unsustainable segments dominate. In effect, the method turns the map of behavioural segments into a targeting tool, directing policymakers to the places where a given intervention is most likely to find receptive conditions.

The technical machinery behind this framework deserves emphasis, because it is what elevates the study beyond a descriptive survey. Multilevel Latent Class Analysis avoids the arbitrariness of hand-drawn segments by letting the data define the behavioural classes, while its hierarchical structure prevents the ecological fallacy of treating national averages as if they described individuals. By estimating segment prevalence separately for each of the 61 regions, the method produces a granular behavioural geography that neither national surveys nor purely local studies can deliver. The cross-national design then allows genuine comparison: a region can be judged not only against its own country but against the full range of OECD contexts in the dataset, revealing whether its behavioural mix is typical, exceptional, or transferable.

The implications reach well beyond the nine countries studied. As governments race to meet climate targets, behaviour change in energy, transport and food is widely recognised as essential to achieving mitigation at the scale and speed required, yet it remains one of the least systematically measured dimensions of climate policy. This study demonstrates that the raw material for smarter policy already exists in large international surveys, and that sophisticated but well-established statistical methods can convert that data into actionable maps. The message for the international policy community is twofold: stop assuming that populations are homogeneous or that demographics predict behaviour, and start using evidence about who behaves unsustainably and where enabling contexts exist. If policy transfer is to deliver on its promise, it must begin not with a successful scheme in search of a home, but with a rigorous account of the people and places it is meant to serve.

Subject of Research: Population segmentation and regional patterns of environmental behaviour across nine OECD countries

Article Title: From segmentation to strategy: Identifying population segments and regional patterns of environmental behaviour

Article References: Yang, G., Rodger, A., Çoker, E. N., & Shipworth, D. (2026). From segmentation to strategy: Identifying population segments and regional patterns of environmental behaviour. PLOS Climate, 5(8), e0001039. https://doi.org/10.1371/journal.pclm.0001039

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0001039

Keywords: environmental behaviour, climate policy, policy transfer, latent class analysis, energy use, transport, food consumption, regional variation, OECD countries, behaviour change, sustainability, PLOS Climate

The study, led by Suzanne M. Debeij of Leiden University Medical Center and colleagues, is part of a larger multicentre cohort project called Better@Home, which followed 110 patients across eight Dutch geriatric rehabilitation centres. For the sensor sub-study, the researchers recruited 26 patients from a single rehabilitation centre in an urban area, along with nine care professionals, most of them physical therapists. The participants were typical of the geriatric rehabilitation population: the median age was 77 years, roughly six in ten were women, and most lived alone. Their reasons for rehabilitation ranged from elective orthopaedic surgery and trauma to respiratory disease, and they spent a median of about 25 days as inpatients followed by roughly 46 days of home-based rehabilitation.

The technology at the heart of the study was the Hipper sensor monitoring system, a hip-worn device containing a three-dimensional accelerometer that samples movement 12.5 times per second and produces an activity output every minute. That output is expressed as a Physical Activity Measure, or PAM score, which reflects the amount of body movement recorded and can be converted into metabolic equivalents for analysis. Data travel wirelessly from the sensor to a Raspberry Pi-based transmitter and then to web dashboards accessible to both patients and care professionals. Crucially, the system was not just a measurement tool: therapists were expected to use the data to coach patients about their movement behaviour, often during regular therapy sessions. Three versions of the sensor were used during the study period, including an updated prototype that provided minute-by-minute rather than 15-minute output and required redesigned dashboards.

The researchers assessed usability and feasibility using a mixed-methods design. A focus group with the care professionals was structured around the User Experience Honeycomb model and analysed with the qualitative Framework method, while patients completed interviewer-administered surveys at admission and after discharge. Quantitative adherence came from the sensors themselves, with wear days counted as a measure of how consistently the system was actually used. This triangulation allowed the team to compare what professionals and patients said about the technology with what the devices recorded in practice.

The adherence numbers were striking. Twenty-three patients wore the sensor during inpatient rehabilitation for a median of 11 days, corresponding to 100 percent of the days the sensor was offered. During home-based rehabilitation, 19 patients used the device, wearing it on 96.6 percent of the days it was available. For a population characterised by frailty, multimorbidity, and often some degree of cognitive impairment, those adherence rates are remarkable and suggest that, at a basic level, the system worked as intended. The researchers concluded that feasibility — the extent to which the digital health system functions as intended — was sufficient, though they caution that high adherence may partly reflect compliance with clinician instructions rather than genuine engagement with the technology.

What the sensors recorded, however, was sobering. During inpatient rehabilitation, patients spent a median of 93 percent of their waking day — about 666 minutes, or more than 11 hours — in sedentary behaviour. At home, the proportion dipped slightly to 90.5 percent, but because total wear time increased, the absolute sedentary time actually rose to a median of about 704 minutes per day. Time spent in moderate-to-vigorous physical activity was essentially zero in both settings, at 0.0 percent of waking time, while light physical activity accounted for only about 7 percent in hospital and roughly 10 percent at home. The pattern of sedentary bouts did shift between settings: patients had more frequent but shorter sedentary episodes at home, and time spent in uninterrupted bouts longer than an hour decreased by nearly 48 minutes.

These findings matter because prolonged sedentary behaviour is not a benign habit. It is associated with increased risk of non-communicable diseases and all-cause mortality, even in older adults who meet recommended physical activity guidelines, and it may delay functional recovery, worsen deconditioning, and increase dependency in daily activities. Both total sedentary time and long, uninterrupted sedentary bouts contribute to the harm. For frail older patients, simply breaking up sitting with short, low-effort movements may be a more realistic starting point than pushing them toward higher-intensity exercise — which is precisely the kind of behavioural nudge that monitoring-and-feedback systems are designed to deliver.

Yet the qualitative data revealed why the system struggled to fulfil that promise. Care professionals valued the sensor as a conversation starter that opened meaningful, motivational discussions and offered insight into when and how intensely patients moved — one therapist described being able to see whether a patient practised all morning and then did nothing in the afternoon. But they were troubled by the opacity of the PAM score, which patients could not interpret, and by perceived measurement inaccuracies that eroded trust in the data. One physiotherapist recounted a patient who was offended when the sensor failed to record her climbing the stairs, even though she had been working hard. The denser data streams from the updated sensors made the dashboard graphs more complex and harder to interpret, and practical frictions — the risk of losing the device, logistical hurdles, and the many steps required to enrol and transfer patients between settings — further impeded integration into clinical workflows.

Patients echoed many of these concerns. Thirteen of the fifteen who answered the sensor-related survey questions said the system offered them no added value, and only seven said they fully or partially trusted the sensor. Some were unsure why they were wearing it at all, with answers ranging from “for research” to “I don’t know, I’m in a wheelchair.” Nearly half reported that their care professionals did not provide adequate support, and several wished they could review the results together with a therapist. Still, the picture was not uniformly negative: eight patients found the system easy to use, five said they would recommend it, and one noted that it created a sense of external accountability for staying active.

When asked what a better intervention would look like, the professionals were clear: it should be easy to use, have a clearly defined objective, incorporate technology that provides direct visual or tactile feedback, and show patterns, intensity, type, and timing of activity over time. They also stressed preconditions that go beyond the device itself — educating patients and informal caregivers about the risks of sedentary behaviour, conducting a pre-implementation analysis of needs and available technology, involving the entire multidisciplinary team, and cultivating a rehabilitation climate that invites and makes it safe to be physically active, with staff acting as role models. The study’s authors conclude that sensor-based feedback in geriatric rehabilitation can indeed be feasible and valuable, but only if measurement accuracy improves, the technology is tailored to individual patient goals, its added value becomes evident to users, and support is provided to both patients and professionals. In other words, the hardware may be ready; the harder engineering challenge is human — designing eHealth that fits the routines, abilities, and motivations of the older adults and clinicians it is meant to serve.

Subject of Research: Usability and feasibility of wearable sensor-based feedback on sedentary behaviour in geriatric rehabilitation

Article Title: Usability and feasibility of a wearable sensor-based monitoring system providing feedback on sedentary behaviour during inpatient and home-based geriatric rehabilitation

Article References: Debeij, S. M., Haaksma, M. L., de Waal, M. W. M., van Haastregt, J. C. M., Pol, M. C., Kasteleyn, M. J., & van Dam van Isselt, E. F. (2026). Usability and feasibility of a wearable sensor-based monitoring system providing feedback on sedentary behaviour during inpatient and home-based geriatric rehabilitation. European Geriatric Medicine. https://doi.org/10.1007/s41999-026-01616-6

Image Credits: AI Generated

DOI: 10.1007/s41999-026-01616-6

Keywords: wearable sensors, sedentary behaviour, geriatric rehabilitation, eHealth, physical activity, older adults, feasibility, usability, behaviour change, digital health, home-based rehabilitation, patient adherence

The research team, led by Sarah O’Donovan and Lisa Ryan of Atlantic Technological University in Galway, Ireland, together with collaborators in Sweden, France, Romania, and Ukraine, undertook a structured curriculum review as part of the Erasmus+ co-funded Nutrition Education for HealthCare Professionals consortium. Their method was deliberately grounded in the documents that actually govern what students learn. The researchers first identified publicly available curricula and programme module descriptions for undergraduate programmes in each target discipline, then mapped nutrition-related learning outcomes and assessments, and finally analysed how well the content covered key thematic areas such as patient-centred care, digital health, behaviour change, and public health. Data were synthesised using descriptive analysis to surface recurrent patterns and gaps across countries and disciplines.

The scale of the review lends it weight. The team analysed 41 medicine programmes, 44 nursing programmes, 37 pharmacy programmes, and 38 physiotherapy programmes across the five countries, dissecting nearly five thousand modules in total. Because the analysis relied on official curricula, module descriptors, and handbooks rather than surveys or interviews, it captures what institutions formally commit to teaching, not merely what educators believe they cover. That distinction matters: a learning outcome that never appears in a module descriptor is unlikely to be reliably taught, timetabled, or examined, and graduates can qualify without ever being formally assessed on it.

The headline finding is stark. Across all 4,964 modules, nutrition was most commonly embedded implicitly within biomedical or clinical subjects rather than delivered as standalone content. In practice, this means a student might encounter a passing reference to malnutrition inside a gastroenterology lecture or a mention of dietary considerations in a pharmacology module, without any guarantee that the underlying nutritional science, assessment skills, or counselling techniques are systematically built upon. Implicit embedding makes nutrition education fragile: it depends on individual lecturers choosing to include it, and it disappears from the record when those individuals move on.

The country comparison revealed striking variation in how visible nutrition is within official curricula. Romania demonstrated the highest proportion of explicit nutrition modules at 14 percent, followed by Sweden at 7 percent, Ireland at 4 percent, Ukraine at 3 percent, and France at just 2 percent. Even the best-performing country in the sample left the overwhelming majority of modules without explicit nutrition content, and the sevenfold spread between Romania and France suggests that national regulatory frameworks, accreditation standards, and professional bodies play a decisive role in determining whether nutrition is treated as a core competency or an optional afterthought. For a single European labour market in which health professionals increasingly move across borders, such divergence raises questions about whether graduates in different countries arrive with comparable baseline capabilities in dietary care.

The disciplinary patterns were equally revealing. Medicine and pharmacy programmes focused primarily on biomedical and disease-related nutrition, emphasising metabolism, nutrient biochemistry, and the role of diet in disease processes. Nursing programmes more frequently incorporated applied, patient-centred content, reflecting the profession’s hands-on role in feeding, monitoring, and supporting patients. Physiotherapy programmes addressed nutrition mainly in rehabilitation and chronic disease contexts, where diet intersects with recovery, exercise, and long-term condition management. Each discipline, in other words, has developed a partial lens on nutrition that mirrors its clinical role, but none appears to provide comprehensive coverage spanning prevention, assessment, intervention, and behaviour change.

That disciplinary siloing has practical consequences at the bedside. Modern nutrition care is inherently interdisciplinary: a malnourished older patient may need a physician to diagnose the underlying cause, a nurse to implement and monitor a feeding plan, a pharmacist to check for drug-nutrient interactions, and a physiotherapist to ensure that nutritional rehabilitation translates into restored muscle function and mobility. If each professional is trained only in their narrow slice of the problem, and if the training is implicit rather than explicit, the seams between professions become points where nutritional risk is missed. The review’s authors argue that the findings highlight the need for more visible and consistent integration of contemporary, practice-oriented nutrition competencies across healthcare curricula.

The gaps extend beyond basic nutritional science into the domains that increasingly define modern diet-related care. The study specifically probed coverage of patient-centred care, digital health, behaviour change, and public health, thematic areas that reflect how nutrition is actually practised in the 2020s. Helping a patient with type 2 diabetes change long-standing eating habits requires counselling and behaviour-change skills, not just biochemical knowledge. Telehealth consultations and nutrition apps are now routine parts of care pathways. And with obesity and diet-related chronic disease dominating the public health burden across Europe, frontline clinicians are expected to engage with prevention at a population level. A curriculum that teaches nutrient metabolism but omits behaviour change leaves graduates technically informed yet practically unequipped for the conversations that determine whether patients actually change what they eat.

Why has nutrition education lagged so far behind its clinical importance? The review’s structure offers clues. Curricula in health professional programmes are notoriously overcrowded, and nutrition competes for hours against disciplines with strong examination traditions and dedicated departments. Because nutrition is frequently dispersed across other subjects, it lacks the institutional ownership, dedicated faculty, and assessment infrastructure that consolidate a topic into the curriculum. The multi-country design of this study also suggests that where national bodies explicitly require nutrition content, as appears to be the case to a greater degree in Romania, programmes respond; where requirements are vague or absent, nutrition quietly evaporates. The authors position their findings as a framework for targeted curriculum enhancement and future collaborative curriculum development across institutions, implying that the remedy lies in deliberate, coordinated design rather than hoping individual educators fill the void.

The implications reach well beyond the five countries studied. Europe faces a rising tide of diet-related disease, ageing populations with complex nutritional needs, and growing recognition that malnutrition, both undernutrition and overnutrition, worsens surgical outcomes, prolongs hospital stays, and drives healthcare costs. Health professional education is the upstream intervention point: every physician, nurse, pharmacist, and physiotherapist trained today will practise for decades, and the nutritional competencies they carry into that practice are being set now, in the module descriptors and handbooks this study dissected. The picture that emerges from nearly five thousand modules is one of fragmented foundations, in the study’s own words, with nutrition present in fragments but absent as a coherent, visible, assessable competency. For educators, regulators, and professional bodies across Europe, the message is that if nutrition is everyone’s business in principle, it is currently no one’s curriculum in practice, and closing that gap will require the kind of systematic, cross-border collaboration this review itself exemplifies.

Subject of Research: Nutrition education in undergraduate medicine, nursing, pharmacy, and physiotherapy curricula across five European countries

Article Title: Fragmented foundations: a comparative review of nutrition education in medicine, nursing, pharmacy, and physiotherapy programmes across five European countries

Article References: O’Donovan, S., McDonagh, M., Willmer, M., Engstrom, M., Fortrat, J.-O., Le Ray, A.-M., Dejeu, I., Saveanu, T., & Ryan, L. (2026). Fragmented foundations: a comparative review of nutrition education in medicine, nursing, pharmacy, and physiotherapy programmes across five European countries. BMC Medical Education. https://doi.org/10.1186/s12909-026-10536-9

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10536-9

Keywords: nutrition education, medical education, curriculum review, nursing, pharmacy, physiotherapy, Europe, healthcare professionals, Erasmus+, public health, behaviour change, patient-centred care

The population under scrutiny is far from arbitrary. First-year undergraduates face a perfect storm of academic pressure, irregular schedules, and newfound independence, and their movement behaviours, meaning sleep, physical activity, and screen time, are tightly interwoven. Insufficient physical activity contributes to obesity and cardiovascular disease over the long term, while poor sleep has been linked to impaired cognition, weaker academic performance, and elevated risks of mental health problems. Excessive screen time, meanwhile, displaces physical activity, disrupts sleep through blue-light exposure and psychological stimulation, and has been associated with poorer mental health in young people. Because these behaviours influence one another throughout the day, the researchers argue that any serious intervention must capture them simultaneously and in real time, rather than relying on retrospective questionnaires that are vulnerable to recall bias.

The technological rationale is compelling. In Singapore, smartphone penetration among people aged 15 to 24 reached 100 percent by 2021, and wearable device use in the general population is estimated at 35 to 55 percent. Modern iPhones and Apple Watches continuously record step counts, stair climbs, heart rate, and time in bed, all retrievable through Apple’s HealthKit framework, which has independently demonstrated validity for measuring gait parameters in adults. The MOVE@NUS team built a dedicated study app on the Cogniss no-code platform, integrating HealthKit data streams with two distinct research tools: ecological momentary assessments, or EMAs, which collect self-reported data in real time, and ecological momentary interventions, or EMIs, which deliver tailored health prompts at the moment they are most likely to matter. Every EMA and EMI is text-based and takes less than two minutes to complete, a deliberate design choice to minimise participant burden.

The study’s architecture is its most distinctive feature. Rather than testing one sprawling multi-component programme, the pilot embeds three sequential randomised controlled trials within a single continuous monitoring cohort. RCT-1 targets sleep, RCT-2 targets physical activity, and RCT-3 targets screen time, each running for two weeks and separated by at least two weeks of passive observation. For every trial, participants are re-randomised in a 1:1:1 ratio into two intervention arms and a control group, with allocation concealed from both the delivery system and the participants themselves. Because each participant serves as their own comparator across phases, and because prior allocation status is modelled statistically as a covariate, the design allows the team to isolate the effect of each intervention while balancing carryover effects across the study population.

The interventions themselves are grounded in behavioural science and tailored to the rhythms of student life. In the sleep trial, one group receives weekly personalised feedback on sleep duration aligned with the recommended seven to nine hours per night for young adults, while a second group selects weekly from evidence-based sleep hygiene strategies, such as avoiding screens before bed or skipping coffee after 3 p.m., with daily reminders tied to their chosen tactics. In the physical activity trial, one arm receives daily nudges to climb stairs instead of taking lifts, with feedback drawn from HealthKit data on flights climbed, while the other is prompted to choose short bursts of vigorous activity, like brisk walks between classes or squats while waiting. The screen time trial contrasts context-aware break reminders, which query current phone usage before suggesting stretches or walks, against a goal-setting arm in which participants set daily reduction targets supported by iOS screen time data.

Feasibility, not definitive effectiveness, is the primary endpoint, and the team measures it from every angle. Recruitment metrics include eligibility, enrolment, and non-response rates. Retention and engagement are tracked through questionnaire completion, EMA burst response rates, and the number of days of valid HealthKit data, with a day counted as valid only if the watch records at least ten hours of wear time. User experience is evaluated through a mixed-methods approach combining short surveys after each trial phase with optional semi-structured interviews, structured by a micro-macro framework that draws on the Technology Acceptance Model, the COM-B model of behaviour, and social and digital determinants of health. This dual lens captures both immediate app-level experiences and the broader contextual factors that shape whether students actually engage with the technology.

Quality assurance receives unusually rigorous treatment for a pilot. The app underwent internal alpha testing to catch functional errors, followed by beta testing with a small independent group of five users who assessed usability, navigation, and system reliability before launch. Intervention delivery fidelity is verified on two axes: notifications must arrive within ten minutes of their programmed schedule, and dynamically personalised content is tested to ensure it parses and displays correctly according to predefined triggers. The team is candid about one technical constraint: because some personalised messages depend on participants manually checking and entering their own Apple Health data, the system functions as an adaptive intervention rather than a fully automated just-in-time adaptive intervention. The main MOVE@NUS cohort is expected to close this gap by integrating real-time HealthKit data directly into the personalisation algorithms.

The analytical plan reflects the complexity of the design. Preliminary effectiveness will be explored separately for each embedded trial using linear mixed-effects models, which handle repeated measures by incorporating random effects for individual participants. Sleep duration is proxied by HealthKit time-in-bed records restricted to nights with valid nocturnal data, physical activity is captured through step counts and flights climbed, and screen time comes from weekly uploads of iOS Settings screenshots. Missingness patterns will be examined across groups, with sensitivity analyses planned if attrition appears linked to intervention allocation, since digital health studies often produce data that are missing not at random. Generalised estimating equations may be deployed for categorical outcomes, and all analyses will be conducted in R with two-sided tests at a five percent significance level.

The researchers are equally transparent about the study’s limitations. Requiring participants to own an iPhone and a recent Apple Watch introduces selection bias that may limit generalisability across socioeconomic groups, and the impossibility of blinding participants to whether they receive daily nudges creates a risk of performance bias and the Hawthorne effect, the well-documented tendency for people to change behaviour when they know they are being observed. Objective HealthKit metrics are less susceptible to this distortion than self-reports, but subjective findings will be interpreted with caution. No formal data monitoring committee or interim stopping rules are in place, reflecting the low-risk, non-invasive nature of the interventions, though the team reviews operational issues every two weeks.

What makes MOVE@NUS more than a campus experiment is its scalability thesis. Smartphones can reach vast populations in naturalistic settings at low marginal cost, and students who learn healthier routines during the transition to independent living will soon carry those habits into the workforce. By demonstrating whether continuous monitoring can be combined with rapid-cycle, embedded trials of personalised nudges, the pilot aims to establish the methodological foundation for a full-scale digital intervention cohort. If it succeeds, the payoff could extend well beyond Singapore’s universities, offering a template for real-time, behaviour-aware health promotion that meets people where they already are: on their phones.

Subject of Research: A pilot digital intervention study using smartwatch monitoring and embedded randomised trials to promote healthy movement behaviours in university students

Article Title: MOVE@NUS digital intervention cohort: protocol of a pilot study to promote healthy movement in university students

Article References: Chen, M., Movia, M., Chua, X. H., Tan, S. Y. X., Zheng, S., Jin, K., Topothai, T., Padmapriya, N., Müller-Riemenschneider, F., & Edney, S. (2026). MOVE@NUS digital intervention cohort: protocol of a pilot study to promote healthy movement in university students. Journal of Activity, Sedentary and Sleep Behaviors, 5(1), Article 5. https://doi.org/10.1186/s44167-026-00097-z

Image Credits: AI Generated

DOI: 10.1186/s44167-026-00097-z

Keywords: digital health, mHealth, ecological momentary intervention, wearable technology, physical activity, sleep, screen time, university students, randomised controlled trial, behaviour change, Apple HealthKit, Singapore

The stakes are enormous. The second Lancet Commission on Adolescent Health and Wellbeing has estimated that by 2030 more than one billion adolescents aged 10 to 24 — roughly half of all adolescents globally — will live in countries where preventable health problems threaten their wellbeing. During early and middle adolescence, six intertwined risk behaviours tend to emerge: poor nutrition, inadequate physical activity, poor sleep, excessive recreational screen time, alcohol use, and smoking or vaping. Researchers call these the “Big 6,” and they rarely appear in isolation. They cluster together, reinforce one another, and track stubbornly into adulthood, laying the foundations for chronic disease decades later. Families experiencing socio-economic disadvantage are hit hardest, facing structural and systemic barriers that make every one of these behaviours more likely. In Australia, adolescents in the most disadvantaged areas are more likely to drink at risky levels and to smoke, and less likely to meet fruit consumption guidelines, than their more advantaged peers.

Parents, the research team argues, are the most underused lever in prevention. Caregivers shape adolescent health through role modelling, rule-setting, monitoring and communication, and prior trials of parent-based programs have shown real promise, particularly when paired with youth-focused components. Yet engaging parents is notoriously difficult. Time scarcity and competing demands consistently emerge as barriers, and these are magnified for parents juggling financial stress, household pressure and limited social support. Digital interventions promise a way around these obstacles — flexible, remote, and free of travel and childcare costs — but a nagging question has remained: how much human support do parents actually need? Guided programs tend to boost engagement and adherence, while unguided ones are cheaper and scale effortlessly. Nobody knew where the sweet spot lay, especially for disadvantaged families, and a systematic review by the same group found only one parent intervention in the entire literature designed specifically for them.

Enter the Multiphase Optimisation Strategy, or MOST, a framework borrowed from engineering that treats an intervention not as a monolithic package but as an assembly of independently testable parts. Rather than running a traditional randomised controlled trial that asks “does the whole program work?”, MOST asks “which parts work, which parts are dead weight, and which combinations deliver the most benefit for the least burden?” The framework unfolds in three phases — preparation, optimisation and evaluation — and the new study represents the optimisation phase, the first time MOST has ever been applied to a digital intervention helping parents improve multiple adolescent health behaviours. In the preparation phase, the team built a conceptual model grounded in Social Cognitive Theory and Family Systems Theory, then co-designed every component with parents and adolescents from disadvantaged communities through workshops, school focus groups and a standing parent–adolescent advisory group.

The trial itself was a feat of experimental design. Between April and October 2025, the researchers recruited 303 parents of 11-to-15-year-olds living in the 40 per cent most disadvantaged areas of New South Wales, Australia, identified using the Australian Bureau of Statistics’ Index of Relative Socio-economic Disadvantage. Every parent received a core package of six online learning modules — one each for screen time, smoking and vaping, alcohol, sleep, healthy eating and physical activity — each roughly 20 minutes long, built around podcast-style videos with health professionals, interactive strategy summaries and goal-setting activities. Parents were then randomised across 16 experimental conditions in a 2×2×2×2 factorial design, with four additional components switched on or off: automated text messages, tailored feedback on their parenting, a stress management module, and weekly telehealth coaching calls from trained non-specialist health coaches. After data quality checks removed fraudulent responses, 292 parents formed the final baseline sample, with 79 per cent retained at the three-month follow-up.

The primary outcome was change in parental encouragement of adolescent health habits, measured with the validated Health Habits sub-scale of the Parenting to Reduce Adolescent Depression Anxiety Scale, a 12-item measure scored from 0 to 12 with very high reliability in this sample. The statistical machinery was correspondingly sophisticated: pre-specified linear mixed models with effect coding estimated each component’s main effect over time, while every possible two-, three- and four-way interaction was tested for synergy or antagonism. Missing data were handled by maximum likelihood, and analyses ran on an intention-to-treat basis. Notably, the team used a more liberal significance threshold of p < 0.10, consistent with MOST guidance, to avoid prematurely discarding potentially useful components during this exploratory screening stage.

The results delivered surprises in both directions. Telehealth coaching significantly improved parental encouragement (Cohen’s d = 0.38), as did tailored feedback (d = 0.26), and there was no statistical difference between the two. Crucially, combining them produced no significant synergistic effect — a direct challenge to the assumption that more support is always better. Text messages, meanwhile, backfired spectacularly: the component significantly reduced parental encouragement over time (d = −0.41), despite being co-designed with parents and intended as gentle encouragement. The authors speculate that the semi-automated messages may have added pressure for time-poor parents, felt too generic, or simply drowned in the noise of daily notifications. Stress management training showed no effect at all, possibly because a single module delivered late in the sequence could not shift entrenched household stress within the trial’s timeframe.

The optimisation decision then hinged on burden, not just efficacy. Parents who received both effective components rated telehealth coaching as significantly more effortful than tailored feedback (mean 2.4 versus 2.1 on a five-point effort scale). Coaching also demands real-world resources — trained staff, scheduling, fidelity monitoring — that cap scalability, whereas automated feedback costs almost nothing once built. Weighing effectiveness against effort, the team selected the package combining the online modules with automated tailored feedback alone as the optimised intervention. Feedback surveys suggested parents embraced it: 97.7 per cent of respondents reported a good or very good experience, 90.6 per cent found the length about right, and 80 per cent found it easy to fit into their lives. Coaching fidelity, for the record, averaged 91.9 per cent, so the component’s screening-out reflected burden, not poor delivery.

The study is not without limitations. The sample was 87 per cent mothers and drawn from a single Australian state, and because eligibility was based on area-level rather than individual-level disadvantage, many participants held post-secondary qualifications and moderate incomes, meaning the findings may not extend to families facing the most severe deprivation. The primary outcome relied on parent self-report, leaving room for social desirability bias, and internet access was required to participate. Yet the strengths are considerable: strong retention, rigorous fraud screening, prospectively registered methods, and a design that finally puts disadvantaged families at the centre of prevention science rather than its margins.

The bigger story is methodological. By dismantling a multicomponent intervention and rebuilding it from empirically validated parts, the researchers demonstrated that intervention science can work like engineering — identifying what earns its place and what does not. The optimised Health4Life Parents & Teens program now moves into the evaluation phase, a cluster randomised controlled trial with economic analysis that will test whether pairing it with the existing school-based Health4Life program improves the Big 6 behaviours in parent–adolescent dyads. If it succeeds, the payoff extends far beyond Australia: the same optimisation logic could stretch scarce prevention dollars further in low- and middle-income settings, and future work may explore whether artificial intelligence can deliver coaching-like support without the human cost. For a field long guilty of bolting components together and hoping for the best, this trial offers a sharper, more honest blueprint — and a reminder that sometimes the most powerful thing a program can do is know what to leave out.

Subject of Research: Optimisation of a digital parent-based intervention for adolescent health risk behaviours using the MOST framework

Article Title: Optimising a digital parent-based intervention to improve adolescent health risk behaviours in families living in socio-economically disadvantaged communities: findings from the Health4Life Parents & Teens factorial randomised trial

Article References: Champion, K. E., Davidson, L., Sunderland, M., Hunter, E., Spring, B., Thornton, L., Haidinger, A., Finn, T., Osman, B., Chapman, C., Burrows, T., Slade, T., Partridge, S. R., Gardner, L. A., Parmenter, B. J., Baur, L. A., Teesson, M., Mihalopoulos, C., Johnson, G., … Newton, N. C. (2026). Optimising a digital parent-based intervention to improve adolescent health risk behaviours in families living in socio-economically disadvantaged communities: findings from the Health4Life Parents &amp; Teens factorial randomised trial. The Lancet Regional Health – Western Pacific, Article 101987. https://doi.org/10.1016/j.lanwpc.2026.101987

Image Credits: AI Generated

DOI: Not provided

Keywords: adolescent health, digital intervention, parenting, MOST framework, factorial randomised trial, health risk behaviours, socio-economic disadvantage, telehealth coaching, tailored feedback, prevention, Australia, behaviour change