The authors begin from an uncomfortable observation: decades of behavior change research, from the theory of planned behavior to self-determination theory and socioecological models, explain only a modest proportion of the differences in physical behavior between and within individuals. People are notoriously inconsistent—one week they jog daily, the next they barely leave the sofa. The researchers attribute this weak predictive power to three structural shortcomings in existing models. First, most frameworks focus either on the short-term determinants that drive behavior or on the long-term health consequences that flow from it, rarely both. Second, they overlook the fact that consequences can loop back and become determinants: fitness gained through months of exercise also shapes how active you feel like being tomorrow. Third, and most fundamentally, they ignore the arithmetic of the day itself.

That arithmetic is unforgiving. Sleep, sedentary behavior, and physical activity are locked together within the fixed 1,440 minutes available, so every additional minute spent in one behavior must come at the expense of another. A person who adds thirty minutes of morning running necessarily removes thirty minutes from sleep, sitting, or some other activity. Traditional single-behavior studies, which examine physical activity in isolation, cannot capture these trade-offs. The new model builds on earlier 24-hour frameworks—including Pedišić’s Activity Balance Model, the Framework for Viable Integrative Research in Time-Use Epidemiology, and Rosenberger and colleagues’ 24-hour Activity Cycle—but goes further by explicitly embedding the full daily composition within a dual-process psychological architecture and a health-outcome architecture, connected by eight distinct pathways labeled A through H.

The left side of the model, adapted from the neurocognitive affect-related model, describes how the brain’s moment-to-moment machinery shapes what we do with our time. Its central constructs are affective states and executive functions. Following the work of Duncan and Barrett, the authors treat affect as a neurophysiological state defined by two dimensions: valence, the pleasantness or unpleasantness of a feeling, and energetic arousal, the level of activation. Executive functions, as defined by Diamond, encompass inhibition control, working memory, and cognitive flexibility—the mental capacities that let us plan, resist temptation, and stay focused on long-term goals despite distractions. The model proposes that optimal daily compositions of sleep, sitting, and activity can enhance these executive functions, which in turn foster more positive affective responses, which then facilitate healthier future behavior compositions.

Crucially, these pathways are reciprocal. Pathway A suggests that executive-function-based cognition shapes the emotional responses induced by exercise or prolonged sitting; pathway B connects those emotional responses to the future balance of daily behaviors, since affective reactions during a workout predict whether a person returns to exercise. Pathways C and D close the loop: cognitive preparation such as time management and goal setting influences the day’s activity composition, while the composition itself—regular exercise, adequate sleep—feeds back to improve cognitive function. The authors also acknowledge bidirectionality in the reverse direction, noting that physical behavior can predict momentary affect and that momentary affect can predict executive function performance, making the entire left side of the model a dynamic, self-modifying system rather than a one-way causal chain.

The right side of the model, adapted from the health model of Bouchard, Blair, and Haskell, traces the physiological consequences. Here the claim is that an optimal daily composition improves health-related fitness markers such as cardiorespiratory fitness (pathway E), which then improves the state of physical and mental health, from well-being to mortality risk (pathway F). The evidence base for these links is substantial. A systematic review of fifty-six isotemporal substitution studies concluded that reallocating time between sleep, sedentary behavior, light-to-moderate activity, and moderate-to-vigorous activity is associated with numerous health outcomes. A meta-analysis further showed that short sleep is significantly linked to mortality, diabetes, cardiovascular disease, coronary heart disease, and obesity. These pathways, too, can run backward: fitness markers predict individual behaviors, health status predicts fitness, and illness or injury—captured by pathway H—can abruptly reshape the whole day, as anyone with a broken leg who suddenly spends far more time sleeping and sitting can attest.

What distinguishes this framework from its predecessors is its insistence on temporal resolution. The authors divide the eight pathways into short-term dynamics operating within days, medium-term associations across weeks and months, long-term effects over years, and mixed pathways spanning days to years. Affective states fluctuate hour to hour and even minute to minute, so pathways A, B, and C demand high-granularity, real-world data. The state-of-the-art tool here is ambulatory assessment: smartphone-based electronic diaries capturing real-time self-reports, paired with wearable sensors measuring the full 24-hour behavior composition objectively and repeatedly. This approach preserves ecological validity and avoids the retrospective biases that plague questionnaire studies. By contrast, the fitness pathway E requires weeks or months of observation, ideally through randomized interventions with repeated and follow-up measurements, while the health pathways F and G unfold over years and call for long-term cohort studies—one 18-year community study found that habitual physical activity related positively to both fitness and health status decades later.

The model also arrives at a moment when the statistical machinery for 24-hour research has matured. Compositional data analysis treats sleep, sedentary time, and activity as parts of a finite whole, analyzing them relative to one another rather than as independent variables, and the compositional isotemporal substitution method estimates what happens to a health outcome when a fixed block of time is shifted from one behavior to another. The empirical payoff is already visible. In an analysis of six cross-sectional studies covering 15,253 participants, moderate-to-vigorous physical activity showed the strongest and most time-efficient protective associations with cardiometabolic outcomes, while sedentary behavior was the only behavior with clearly adverse associations regardless of duration. The Maastricht Study, with 2,388 participants, found that less sitting and more standing, physical activity, and sleep were associated with better cardiometabolic health and glycaemic control. Device-based measurement via wearables now makes it possible to capture every facet of the daily composition with time-stamped precision, and the model’s components can be refined as needed—splitting activity into standing, light, and vigorous categories, or sleep into REM and non-REM stages, or even distinguishing outdoor from indoor activity.

The long-term ambition is strikingly concrete: to identify the optimal balance of 24-hour behaviors that maximizes health benefits and promotes longevity and well-being. Tremblay and colleagues anticipate that the 24-hour approach will eventually underpin individualized, precision movement guidelines tailored to personal characteristics and circumstances—several countries and the World Health Organization already issue integrated 24-hour movement guidelines. The authors are careful to note that the relationships in their model are shaped by genetic predispositions, social and physical environments, and individual circumstances, which may moderate, mediate, or confound the pathways, so generalization demands caution. But by combining behavioral, affective, and health determinants and consequences in a single flexible framework, and by specifying exactly which timescale each hypothesis should be tested on, the 24-hour cognitive-affective physical behavior model gives researchers something previous theories did not: a shared map of the entire day, drawn to scale, with the clock built in.

Subject of Research: The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep

Article Title: The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep

Article References: Giurgiu, M., & Ebner-Priemer, U. W. (2025). The 24-hour cognitive-affective physical behavior model: a theoretical framework for studying determinants and health consequences of physical activity, sedentary behavior, and sleep. Journal of Activity, Sedentary and Sleep Behaviors, 4(1), Article 6. https://doi.org/10.1186/s44167-025-00077-9

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00077-9

Keywords: hour, cognitive-affective, physical, behavior, model, theoretical, framework, studying, determinants, health, consequences, activity

The study drew on two long-running Dutch cohorts. The ReLife study enrolled 368 patients with newly diagnosed, localised renal cell carcinoma between 2018 and 2021, while the EnCoRe study followed 504 patients with stages I to III colorectal cancer recruited from 2012 onwards. From these cohorts, 326 patients with renal cell carcinoma and 358 with colorectal cancer produced valid accelerometer data. Each participant wore a small, validated activity monitor — an activPAL in the ReLife study and a MOX accelerometer in EnCoRe — strapped continuously to the front of the thigh for seven consecutive days. Because these devices sit directly on the leg, they can distinguish sitting and reclining from standing with far greater accuracy than wrist-worn trackers or memory-based questionnaires, which research has shown tend to overestimate activity and underestimate sitting time.

The timing of the measurements is what makes the findings clinically striking. Patients with kidney cancer were assessed at a median of twelve weeks after surgery, and patients with colorectal cancer at roughly six to eight weeks after treatment — a window when recovery habits are still being formed and when clinicians have a genuine opportunity to intervene. Sedentary behaviour was defined as any waking activity expending 1.5 metabolic equivalents or less while sitting or reclining, while physical activity was captured across light-intensity and moderate-to-vigorous categories. On average, patients with kidney cancer spent 10.1 hours per day sedentary, of which 5.4 hours accumulated in prolonged bouts, alongside 42 minutes of moderate-to-vigorous activity. Patients with colorectal cancer fared worse still, averaging 10.7 sedentary hours per day and just 90 minutes of total physical activity.

Rather than treating sitting and moving as separate variables, the researchers used a statistical technique called latent profile analysis, which searches for hidden subgroups of people who share similar patterns across multiple behaviours simultaneously. This matters because the two behaviours compete for the same 24 hours: every additional hour of sitting necessarily displaces an hour that could have been spent standing, walking, or exercising. The analysis considered sedentary time, prolonged sedentary bouts, standing time, sedentary breaks, and various measures of physical activity, standardising each indicator before testing models containing one to seven profiles. Fit statistics including the Akaike and Bayesian Information Criteria pointed consistently to a three-profile solution in both cancer populations, with entropy values of 0.84 indicating good separation between the groups and average membership probabilities exceeding 90 percent.

The three profiles that emerged were labelled the most sedentary, the average, and the highly active. In the kidney cancer group, 28 percent of patients fell into the most sedentary profile, spending an average of 11.9 hours per day sitting or reclining — 7.6 of those hours in unbroken stretches — while still managing 72 minutes of daily physical activity. In the colorectal cancer group, 16 percent belonged to the most sedentary profile, logging a remarkable 13.1 sedentary hours per day, 9.1 of them in prolonged bouts, against only 44 minutes of total physical activity. At the other extreme, the highly active kidney cancer patients spent 8.1 hours sedentary but accumulated 61 minutes of moderate-to-vigorous activity and 5.2 hours of standing daily, while their colorectal counterparts managed 125 minutes of physical activity and 4.1 hours standing against 9.0 sedentary hours.

One of the most provocative insights is that high sitting and high moving can coexist in the same person. Even patients in the most sedentary profiles engaged in some daily physical activity, meaning it is entirely possible to meet the standard recommendation of 150 minutes of weekly moderate-to-vigorous exercise while still spending the majority of waking life motionless. More than three quarters of the kidney cancer patients in the study met the physical activity guideline, yet average sedentary time in both groups exceeded the 9.1 hours per day reported for the general Dutch population. For cancer survivors, whose long-term health depends on both sides of the energy balance, this dissociation between exercise and sedentary time represents a blind spot in conventional lifestyle advice.

The correlates of profile membership add another layer of complexity. Using multinomial logistic regression that adjusted for all factors simultaneously, the researchers found that men with kidney cancer were more than twice as likely as women to belong to the most sedentary profile, with an odds ratio of 2.18. Men with colorectal cancer, conversely, were less likely to be in the highly active group. Higher educational attainment was associated with a lower likelihood of belonging to the highly active profile in both populations — a counterintuitive finding that contrasts with some previous studies and may reflect differences in occupational activity that the analysis could not fully capture. Higher body mass index was consistently linked to a lower chance of being highly active, with each additional kilogram per square metre reducing the odds by roughly 8 to 9 percent in both groups.

Two findings in particular challenge intuition. First, patients with kidney cancer who reported better physical functioning were less likely to sit all day, which fits the idea that a minimum functional capacity is needed to break up sitting time. Second, and more surprisingly, higher fatigue scores were associated with a lower likelihood of belonging to the most sedentary profile in both cancer groups. The authors caution that these effect sizes were small and may not be clinically meaningful, and they note that the relationship between fatigue and activity is bidirectional: fatigue is a well-known barrier to exercise, yet exercise is one of the most effective treatments for cancer-related fatigue. Former smokers with colorectal cancer were nearly twice as likely as never smokers to be highly active, a result the researchers describe as unexpected given that smoking has generally been linked to inactivity in prior work.

The study has honest limitations. Response rates were around 44 to 45 percent in both cohorts, and the researchers acknowledge that health-conscious individuals are more likely to volunteer for such studies — which helps explain why physical activity levels in the sample were relatively high compared with other cancer survivor populations. The cross-sectional design means no conclusions about cause and effect can be drawn, and because the analysis was exploratory without adjustment for multiple testing, some associations may be false positives that require external validation. Age, living situation, employment, comorbidities, alcohol consumption, tumour stage, and treatment details showed no statistically significant associations with profile membership.

Nevertheless, the implications for survivorship care are clear. Activity patterns after cancer treatment are heterogeneous, and the same three-profile structure appeared in two very different cancer populations even though the absolute hours of sitting and moving differed between them. Men, patients with higher education, and those with higher body mass index emerged as consistent candidates for targeted support, while the coexistence of high sitting with adequate exercise suggests that interventions should address sedentary behaviour directly rather than assuming that exercise prescriptions alone will solve the problem. The recent CHALLENGE trial, which showed that a structured three-year exercise programme significantly improved disease-free survival in patients with colon cancer, has already demonstrated how powerful movement can be after treatment. This new study adds a crucial nuance: to fully realise that potential, clinicians may need to look not just at how much their patients move, but at how much of the day they never leave the chair.

Subject of Research: Physical activity and sedentary behaviour profiles in patients shortly after treatment for localised renal cell carcinoma and colorectal cancer

Article Title: Activity profiles and their correlates in patients with localised renal and colorectal cancer shortly after treatment: a cross-sectional analysis

Article References: Siffels, A., Bours, M. J. L., van Roekel, E. H., Weijenberg, M. P., Sedelaar, J. P. M., Breukink, S. O., Keulen, E. T. P., Janssen-Heijnen, M., Frenken, K. G., Aben, K. K. H., Maurits, J. S. F., Kiemeney, L. A. L. M., Buffart, L. M., & Vrieling, A. (2026). Activity profiles and their correlates in patients with localised renal and colorectal cancer shortly after treatment: a cross-sectional analysis. Journal of Cancer Survivorship. https://doi.org/10.1007/s11764-026-02106-1

Image Credits: AI Generated

DOI: 10.1007/s11764-026-02106-1

Keywords: renal cell carcinoma, colorectal cancer, cancer survivorship, physical activity, sedentary behaviour, accelerometers, latent profile analysis, fatigue, body mass index, health-related quality of life, lifestyle interventions, Activity