The analysis drew on data from 120 generally healthy, middle-aged adults enrolled in the Alpha-ketoglutarate supplementation and BiologicaL agE in middle-aged adults trial, known as ABLE. The study was a double-blind, randomized, placebo-controlled trial registered as NCT05706389, designed to test whether six months of daily oral supplementation with one gram of alpha-ketoglutarate, a naturally occurring metabolite implicated in cellular metabolism and previously studied in aging research, would influence biological age measures in people in midlife. Participants, who had a mean age of 50.6 years with a standard deviation of 6.0 years and were evenly split between women and men, were assigned to either the supplement arm, with 59 participants, or the placebo arm, with 61 participants, and followed for six months with an additional three-month follow-up period.

At each of four study visits, participants were instructed to wear an ActiGraph wGT3X-BT accelerometer for seven consecutive days. These devices are small, lightweight triaxial accelerometers widely used in physical activity research, capable of recording movement patterns around the clock and generating objective estimates of activity intensity, sleep timing, and circadian rhythm. In the context of a gerotherapeutic trial, such continuous data streams serve as digital phenotypes: objective, high-resolution markers of physical behavior that may change as a person’s biological aging trajectory shifts. The research team, led by Jessica K. Lu and colleagues including Brian K. Kennedy and Andrea B. Maier, set out to determine whether this level of monitoring was genuinely workable for a population of adults in their late forties and fifties, a group often overlooked in aging trials that tend to focus on either younger volunteers or frail older adults.

The adherence results were striking. The median accelerometer wear time was 24 hours per day, meaning that the typical participant kept the device on essentially continuously throughout each seven-day monitoring window, removing it only briefly if at all. More than 80 percent of participants achieved at least four valid days of wear per visit, a threshold commonly used in the field to define an adequate monitoring window for physical activity analysis. Supplement use adherence exceeded 90 percent across the six-month intervention, indicating that participants managed both the passive monitoring and the active intervention components of the trial simultaneously without meaningful compromise on either front. Device-related operational concerns, such as malfunctioning units, charging difficulties, or discomfort, were minimal across all four visits, suggesting that the ActiGraph platform and the study’s implementation protocols were well matched to the needs and habits of middle-aged participants.

Equally important was what the researchers learned about who stayed in the study. Overall dropout across the trial was 17 percent, a figure that compares favorably with many longitudinal intervention studies but still represents a meaningful loss of statistical power and resources. To understand what predicted attrition, the team turned to survival analysis, specifically Cox proportional hazards models with study visit as the time scale. This approach allowed the investigators to account for when during the trial each participant dropped out, rather than simply tallying who finished and who did not, thereby using the full longitudinal structure of the data. The models tested whether the amount of time participants wore the accelerometer, or their overall adherence to the device, was associated with their risk of leaving the study before completion.

The results of those models carried a provocative implication: every additional minute of accelerometer wear time was associated with a lower hazard of dropout, with a hazard ratio of 0.998 and a 95 percent confidence interval of 0.997 to 0.999. While a hazard ratio per minute may sound small, the effect compounds across the thousands of minutes in a seven-day wear window, and the confidence interval excludes the null value of 1.0, indicating a statistically reliable association. In practical terms, participants who kept their devices on longer were meaningfully less likely to abandon the trial. The authors suggest that wear time may function as a behavioral marker of engagement, a real-time signal that trial coordinators could monitor to identify participants at elevated risk of dropping out and intervene before they disengage entirely.

Sex emerged as the other significant predictor. Being male was associated with a substantially lower risk of dropout, with a hazard ratio of 0.208 and a 95 percent confidence interval of 0.065 to 0.663. In other words, men in this cohort were roughly one-fifth as likely to leave the study as women, a large and statistically robust difference. The finding raises questions about whether middle-aged women face different burdens or motivations in aging intervention trials, whether the supplement or monitoring protocol interacted differently with women’s daily routines, or whether other unmeasured factors drove the disparity. The authors note that understanding and addressing this gap will be important for designing future trials that retain diverse participants and produce generalizable results.

Beyond the statistical associations, the study offers a practical playbook for trial designers considering wearable-based digital phenotyping. The near-universal 24-hour wear pattern suggests that middle-aged adults are willing to treat research accelerometers much like consumer fitness trackers, integrating them into daily life with little prompting. The high rate of achieving at least four valid days per visit indicates that a seven-day monitoring window provides a comfortable buffer, allowing participants to accumulate sufficient data even with occasional removals for bathing or charging. The minimal rate of device-related operational concerns implies that established research-grade hardware, paired with clear instructions and structured visit schedules, can avoid the technical pitfalls that sometimes plague wearable deployments, such as data loss, initialization errors, or participant confusion about when to wear the device.

The implications extend to the growing field of gerotherapeutics, where interventions aim not to treat a single disease but to slow, halt, or reverse the biological processes of aging themselves. Trials in this space increasingly rely on multidimensional readouts, from epigenetic clocks and blood biomarkers to continuous sensor data, to capture the subtle, systemic changes that aging interventions are designed to produce. Accelerometer-derived phenotypes, including total activity volume, activity fragmentation, and sleep-wake rhythms, are attractive because they are objective, repeatable, and sensitive to functional change. The ABLE analysis demonstrates that such data can be collected at high fidelity in a placebo-controlled supplement trial without overburdening participants or inflating dropout, removing a key practical objection to including wearables in future gerotherapeutic study designs.

The authors are careful to frame the work as exploratory, drawn from a single trial with a relatively homogeneous cohort of generally healthy middle-aged adults, so the findings may not generalize to older, frailer, or more clinically diverse populations, where device tolerance and dropout dynamics could differ substantially. Still, the central lessons are actionable: monitor wear time as an early-warning indicator of disengagement, attend to sex differences in retention, and design monitoring protocols around the demonstrated willingness of middle-aged adults to wear accelerometers continuously. As interest in alpha-ketoglutarate and other candidate gerotherapies accelerates, the operational insights from ABLE arrive at an opportune moment, offering trialists a data-driven foundation for building studies that keep participants engaged, keep sensors on wrists, and keep the digital phenotype data flowing from the first visit to the last.

Subject of Research: Feasibility and adherence of accelerometer-based digital phenotyping in a gerotherapeutic supplementation trial in middle-aged adults

Article Title: Optimizing accelerometer implementation in a gerotherapeutic trial: Feasibility, adherence, and operational insights of ABLE

Article References: Lu, J. K., Wang, W., Chew, Y. E., Guan, L., Kennedy, B. K., & Maier, A. B. (2026). Optimizing accelerometer implementation in a gerotherapeutic trial: Feasibility, adherence, and operational insights of ABLE. PLOS Digital Health, 5(9), e0001691. https://doi.org/10.1371/journal.pdig.0001691

Image Credits: AI Generated

DOI: 10.1371/journal.pdig.0001691

Keywords: accelerometers, digital phenotyping, gerotherapeutics, alpha-ketoglutarate, clinical trial adherence, study dropout, middle-aged adults, wearable technology, ActiGraph, Cox proportional hazards, aging intervention, PLOS Digital Health

The study, published in the Journal of Activity, Sedentary and Sleep Behaviors, set out to answer a deceptively simple question: do people with diabetes who struggle to sleep actually move less? Previous research had already established that insomnia symptoms are remarkably common in this population, with roughly 39 percent of adults with type 2 diabetes reporting them. It was also known that poor sleep is linked to weight gain, disrupted glucose regulation, and impaired daytime functioning. But no one had yet measured, with objective devices, whether insomnia translates into measurably lower physical activity in people with diabetes. That gap mattered, because regular moderate-to-vigorous physical activity is a cornerstone of diabetes care, helping to control blood sugar, prevent complications, and reduce the risk of premature death.

To capture what participants actually did, rather than what they said they did, the researchers relied on a sophisticated measurement setup. Each participant wore two small tri-axial accelerometers for seven days, one strapped to the right thigh and another to the lower back. The devices sampled movement at 50 hertz, and a machine learning classifier built on eXtreme Gradient Boosting was trained to recognize specific activities in free-living conditions: walking at slow, moderate, or brisk speeds, running, cycling, sitting, standing, and lying down. From these data, the team calculated total physical activity, which includes all walking speeds, and moderate-to-vigorous physical activity, defined as brisk walking above 4 kilometers per hour, running, and cycling. This approach sidesteps the well-known biases of self-reported exercise questionnaires, which tend to overestimate how active people really are.

Insomnia was assessed through four questions covering difficulty falling asleep, repeated nighttime awakenings, waking too early, and impaired daytime functioning, each rated over the previous three months. Participants who reported at least one sleep symptom three or more nights per week were classified as having insomnia symptoms, while those who also reported daytime impairment at that frequency met the criteria for insomnia disorder, mirroring current diagnostic classification systems. Of the 1,354 participants analyzed, 36 percent had insomnia symptoms and 3 percent met the stricter threshold for insomnia disorder. The median age was 67 years, and the sample reflected the realities of living with diabetes: many participants with sleep problems were also more likely to be obese, to suffer from bodily pain, and to report moderate to severe physical impairment.

The headline result concerns older women. Among women aged 65 and above, those with insomnia symptoms accumulated 73 minutes less total physical activity per week than their peers without sleep complaints, a difference of roughly ten minutes a day. Their moderate-to-vigorous activity was reduced by 33 minutes per week, equivalent to only four to five minutes a day, but the gap in overall movement was substantial. Just 28 percent of older women with insomnia symptoms met the World Health Organization’s recommendation of at least 150 minutes of moderate activity per week, compared with 26 percent of those without symptoms in the same age band, and 43 percent among younger women. Men, by contrast, showed no meaningful difference in activity levels whether or not they reported insomnia symptoms, and neither did women under 65.

The picture sharpened dramatically for the small group with full-blown insomnia disorder. Regardless of age or sex, these participants moved far less than everyone else. Women with insomnia disorder logged 192 minutes less total physical activity per week than those sleeping well, while men logged 276 minutes less, a deficit of nearly an hour a day. Moderate-to-vigorous activity was lower by 37 minutes per week in women and 67 minutes per week in men. Because total physical activity includes slow walking while moderate-to-vigorous activity does not, the pattern suggests that people with insomnia disorder rely almost entirely on gentle movement, with the energetic activity that benefits glucose control largely absent from their weeks.

Why would sleepless nights hit older women hardest? The researchers point to a convergence of biological and social factors. Compared with men, older women tend to take longer to fall asleep, sleep for shorter periods, and report more daytime sleepiness. The postmenopausal stage of life brings its own burden of symptoms, including insomnia, fatigue, joint pain, and depression, any of which can make physical activity feel out of reach. There is also a broader context of inequity: women with type 2 diabetes appear to benefit less than men from lifestyle interventions and drug treatments, and are often undertreated. The authors caution that their cross-sectional design cannot determine whether insomnia causes inactivity, whether inactivity worsens sleep, or whether both stem from shared causes such as pain, obesity, or depression.

The clinical implications may be larger than the raw numbers suggest. Recent research has shown that even brief bouts of one to five minutes of vigorous daily activity, so-called vigorous intermittent lifestyle physical activity, can confer measurable health benefits, meaning that a few extra minutes of brisk movement each day could matter for glucose control, physical function, mood, and quality of life. Conversely, an experimental study in the general population found that a four-week behavioral treatment for insomnia improved sleep significantly but did not change device-measured physical activity, a sobering result that highlights how much remains unknown. Whether treating insomnia in people with diabetes would actually get them moving more is a question only prospective trials can answer, and the reverse possibility, that boosting activity improves sleep, is equally plausible.

The study has caveats worth keeping in mind. Only 37 participants met the criteria for insomnia disorder, so those estimates carry wide uncertainty, and the true prevalence is likely underestimated because the daytime impairment criterion was strict. Volunteers who agree to wear accelerometers for a week tend to be healthier than the general diabetic population, which may inflate measured activity levels. The researchers could not distinguish between types of diabetes, though type 2 accounts for 85 to 90 percent of cases in high-income countries, making the findings most relevant to that group. A single week of measurement may also not capture habitual behavior. Still, the overall adherence rate of about 40 percent to WHO activity guidelines matches similar studies in Denmark, lending credibility to the sample.

What emerges is a clear signal for clinicians and researchers alike: sleep deserves a place in the conversation about diabetes management, particularly for older women. With more than 60 million people in Europe living with diabetes, and only a minority meeting activity recommendations, identifying modifiable barriers is a public health priority. The Norwegian team suggests that future work should examine what distinguishes people with insomnia symptoms who still manage to stay active, and whether addressing insomnia, especially insomnia disorder, as part of routine diabetes care can lift activity levels and, with them, long-term health outcomes. For now, the message is that a restless night in an older woman with diabetes may be more than an inconvenience; it may be a quiet warning sign that her body is moving less than it needs to stay well.

Subject of Research: The association between insomnia symptoms and device-measured physical activity in adults with diabetes

Article Title: Assessing the level of device-measured physical activity according to insomnia symptoms in 1,354 individuals with diabetes: the HUNT Study, Norway

Article References: Mortensen, S. R., Mork, P. J., Skou, S. T., Kongsvold, A., Åsvold, B. O., Nilsen, T. I. L., & Skarpsno, E. S. (2024). Assessing the level of device-measured physical activity according to insomnia symptoms in 1,354 individuals with diabetes: the HUNT Study, Norway. Journal of Activity, Sedentary and Sleep Behaviors, 3(1), Article 27. https://doi.org/10.1186/s44167-024-00066-4

Image Credits: AI Generated

DOI: 10.1186/s44167-024-00066-4

Keywords: diabetes, insomnia, physical activity, HUNT Study, accelerometers, sleep, type 2 diabetes, moderate-to-vigorous physical activity, older women, WHO guidelines, Norway, cross-sectional study

The team, led by Marian Marchiori of the University of Southern Denmark, conducted an umbrella review, a systematic search of reviews, to catalogue the ways accelerometer data are condensed into meaningful numbers. Searching Scopus and Web of Science in February 2025, they screened 324 reviews and ultimately included 93, published between 2012 and 2024. Those reviews collectively covered 5,667 original studies, of which 4,669 relied on accelerometers. From this enormous body of work, the researchers identified 134 distinct output metrics used to describe human movement behaviour, a striking illustration of how fragmented measurement practice has become in a field that ostensibly studies the same handful of behaviours.

The most familiar metrics dominated the landscape. Step counts appeared in 67 of the 93 reviews, making it the single most cited measure, followed closely by time spent in moderate-to-vigorous physical activity, or MVPA, which appeared in 65 reviews. Time in light physical activity, raw accelerometer counts, total sedentary time and total physical activity time rounded out the top tier. But beyond these household names, the long tail of metrics reveals genuine complexity: measures of bout duration, fragmentation, circadian patterns, intensity gradients and machine-learned activity types all jostle for space in the literature, each capturing a different facet of how people move through their days.

To bring order to this sprawl, the researchers performed an adapted thematic analysis, grouping the 134 metrics into five conceptual themes: relative measures, frequency, absolute time, point in time, and summary, pattern or derived measures. These categories map neatly onto the questions researchers actually ask of their data. Frequency metrics answer the question of how often a behaviour occurs, such as how many times a day someone stands up from sitting. Absolute time metrics answer how long a behaviour lasts. Relative measures capture what proportion of the day is spent in each behaviour, while point-in-time metrics pinpoint when behaviours begin and end. Summary and derived measures, such as raw acceleration aggregates like ENMO or MIMS units, describe the overall quantity and intensity of movement.

The visualisation side of the story proved even more revealing. When the researchers attempted a systematic search for studies on visualising accelerometer metrics, they came up essentially empty-handed; the literature simply does not treat visualisation as a topic worthy of systematic investigation in its own right. Falling back on non-systematic web searches and consultations with the generative AI tool Microsoft Copilot, they identified 28 different types of visualisations applicable to the metrics they had catalogued. Yet in practice, researchers overwhelmingly rely on just three: bar charts, line graphs and pie charts, typically chosen without any explicit rationale for why one format suits a particular metric or message better than another.

This gap matters more than it might seem. Data visualisation is not a cosmetic afterthought; it shapes how findings are understood, and the optimal chart for one audience may fail another entirely. A policy maker glancing at a report, a clinician interpreting a patient’s activity profile and a researcher scrutinising statistical patterns bring different expectations and different levels of statistical literacy. Terms like MVPA, second nature to exercise scientists, mean little to the general public. The authors point out that presenting only point estimates without any indication of variability can actively mislead audiences without statistical training, and that the same data rendered in different visual forms can elicit competing interpretations.

To address the problem, the team built a conceptual framework grounded in the sender-receiver model of communication, borrowed from the philosophy of language. In this framing, the researcher is the sender, the target audience is the receiver, and the visualisation is the coding strategy that transforms raw data into a transmissible message. The data themselves constitute the message, while journals and conference presentations serve as the channel. Noise, in this model, is anything that obscures the intended meaning, from cluttered graphics to jargon-heavy labels. The framework walks researchers through a sequence of decision points: identify whether the underlying data are unclassified, producing intensity or quantity metrics, or classified, producing behaviour types and intensity levels; determine which research question the metric answers, whether how much, how long, how often, when, or what proportion; and only then select a visualisation suited to that category and context.

The framework is deliberately non-prescriptive. Rather than dictating that a given metric must always be drawn a certain way, it organises options and illustrates pathways. One worked example contrasts a pie chart showing daily proportions of movement behaviours with a stacked bar plot, which preserves information about day-to-day variation and temporal patterns while still allowing comparison between averages and individual days. Another contrasts a simple bar plot counting activity bouts with an event plot, which additionally reveals the duration and timing of each individual bout. In both cases, the less conventional option can convey substantially more insight, depending on what the researcher actually wants to communicate.

The study’s authors are candid about its limitations. No review protocol was pre-registered, the search captured reviews published only up to 2024, and the exploratory visualisation searches, by necessity, were not systematic. The framework also lacks empirical validation: nobody yet knows how different audiences actually perceive the visualisations it recommends. The team calls for future work involving co-design with end users, comprehension testing and iterative refinement, ideally extending the framework to offer audience-specific guidance. They also note that the field would benefit enormously from a common taxonomy of movement behaviour metrics and from harmonised accelerometer data processing, initiatives already underway in consortia such as ProPASS, which would make visualisations more comparable across studies.

Nevertheless, the significance of the work lies in reframing visualisation as a communication problem rather than a technical one. With the World Health Organization reporting that insufficient physical activity has been rising globally since 2000, with stark disparities by sex, age and geography, the stakes of clear communication are high. Physical activity guidelines can only change behaviour if their messages land, and charts are often the first and most persuasive point of contact between research and the public. By cataloguing 134 metrics, mapping 28 visualisation types onto them, and embedding the whole exercise in a theory of communication, the LABDA team has given movement scientists something the field has never had: a systematic starting point for deciding not just what to measure, but how to show it.

Subject of Research: Visualisation of accelerometer-based 24/7 human movement behaviour metrics

Article Title: Visualising accelerometer-based 24/7 human movement behaviour data: an umbrella review and framework development from the LABDA project

Article References: Visualising accelerometer-based 24/7 human movement behaviour data: an umbrella review and framework development from the LABDA project. (n.d.). https://doi.org/10.1186/s44167-025-00088-6

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00088-6

Keywords: accelerometers, physical activity, sedentary behaviour, sleep, data visualisation, wearables, 24/7 movement behaviours, MVPA, step counts, umbrella review, LABDA project, science communication

The research forms part of the SUNRISE International Study of Movement Behaviours in the Early Years, a global effort to understand how young children around the world accumulate movement and rest. In the Canadian arm, published in the Journal of Activity, Sedentary and Sleep Behaviors, researchers analysed data from 654 preschoolers with an average age of about forty-seven months, roughly fifty-three percent of whom were boys. Families and childcare centres from across Canada participated, and the study received funding from the Canadian Institutes of Health Research.

What sets this study apart methodologically is its use of compositional data analysis, a statistical framework designed for data that are inherently constrained to sum to a constant. Traditional analyses often examine each behaviour in isolation, asking, for instance, whether more sleep predicts better memory while ignoring the fact that extra sleep must necessarily displace some other behaviour. Compositional approaches treat the full twenty-four-hour day as an interdependent system, expressing each behaviour’s share relative to the others through isometric log ratio transformations before testing associations with outcomes.

The measurement of behaviour was equally rigorous. Each child wore a waist-mounted ActiGraph wGT3X+ accelerometer for seven consecutive days, providing an objective record of how much time was spent asleep, sedentary, or moving at light and moderate-to-vigorous intensities. Cognitive abilities were assessed with validated Early Years Toolbox tasks delivered on iPads, which measure inhibition, the capacity to suppress impulses and automatic responses, and working memory, the mental workspace that holds and manipulates information over short periods. Both skills are considered foundational for school readiness and later academic achievement.

In unadjusted statistical models, the overall composition of movement behaviours was significantly associated with both inhibition and working memory, although the amount of variance explained was small, with R-squared values of 0.03 for inhibition and 0.02 for working memory. However, once the researchers accounted for covariates such as demographic characteristics, those associations were no longer statistically significant. This pattern suggests that while movement behaviours may relate to cognition, the magnitude of any effect in preschool years is likely small and partly entangled with other family and child factors.

The compositional substitution analyses offered a more granular view. These models estimate what would happen to a cognitive outcome if one behaviour were swapped for another while the rest of the day stayed constant. One notable finding emerged: replacing light physical activity with sedentary time was associated with better inhibition. In other words, in this dataset, shifting a young child from gentle movement into sedentary pursuits appeared linked to slightly stronger impulse control, a counterintuitive result the authors flag as a point for future investigation rather than a prescription.

Perhaps the most eye-catching results came from the so-called Goldilocks analysis, a technique that searches for the daily allocation of the four behaviours associated with the best cognitive outcomes. For inhibition, the optimal day included about 118 minutes of moderate-to-vigorous physical activity, 726 minutes of sleep, 258 minutes of light activity, and 337 minutes of sedentary time. For working memory, the optimum shifted to roughly 164 minutes of moderate-to-vigorous activity, 664 minutes of sleep, 323 minutes of light activity, and 288 minutes of sedentary time.

Those numbers carry a striking implication when set against current guidance. The sleep values associated with optimal cognition fell comfortably within existing twenty-four-hour movement guidelines for preschoolers, reinforcing sleep as the one behaviour where current recommendations and this study’s optimal profile align. But the optimal amounts of moderate-to-vigorous physical activity exceeded current recommendations by one to two hours, suggesting, on the surface, that preschoolers might benefit cognitively from roughly double the recommended dose of energetic play. The authors themselves urge caution, noting that more work is needed before such targets could be promoted with confidence.

Another strength of the study was its attention to demographic subgroups. Previous compositional studies have largely ignored whether associations differ between three- and four-year-olds, between boys and girls, or between children in urban and rural settings. When the Canadian team ran their models separately for these groups, no significant reallocation effects emerged in any subgroup, indicating that the overall pattern of weak, composition-level associations holds broadly rather than being driven by a particular slice of the preschool population.

The authors are careful about what these findings can and cannot support. Movement behaviours, they conclude, may play a role in preschool cognitive development, but the effect is likely small, and the adjusted models remind us that many other influences shape how a four-year-old’s mind matures. Still, the study provides a useful benchmark for researchers worldwide, offering concrete estimates of optimal daily activity and sleep patterns against which future work can be compared. For parents, the practical takeaway is reassuring rather than radical: protect good sleep, encourage plenty of active play, and keep sedentary time modest, while remembering that a child’s growing brain depends on far more than the clock-time ledger of a single day.

Subject of Research: Associations between 24-hour movement behaviours and cognitive development in preschoolers

Article Title: Compositional data analysis of the associations among 24-hour movement behaviours and cognitive development of preschoolers across Canada: a SUNRISE study

Article References: Buchanan, S., Boyd, M., Bremer, E., Bruijns, B. A., Caldwell, H. A. T., Carson, V., Faulkner, G., Harandian, K., Hawthorne, M., Kuzik, N., Martins, R., Pagani, L., Potter, M., Tucker, P., & Tremblay, M. S. (2026). Compositional data analysis of the associations among 24-hour movement behaviours and cognitive development of preschoolers across Canada: a SUNRISE study. Journal of Activity, Sedentary and Sleep Behaviors. https://doi.org/10.1186/s44167-026-00115-0

Image Credits: AI Generated

DOI: 10.1186/s44167-026-00115-0

Keywords: preschoolers, cognitive development, working memory, inhibition, physical activity, sedentary behaviour, sleep, compositional data analysis, accelerometers, SUNRISE study, movement behaviours, 24-hour guidelines

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

The study, led by Umar Abubakar Hassan and colleagues including Adewale Luqman Oyeyemi, Kylie Wilson, Pamela Hodges Kulinna, and Allison Poulos, focused on 72 children aged 8 to 12 years in grades 3 through 6. These children attended fee-based after-school programs at 14 elementary schools in a public school district in Maricopa County, Arizona. The programs offered a mix of recreation, games, academic support, and arts and crafts, running roughly three hours each weekday from about 3:00 p.m. to 6:00 p.m. None of the programs was specifically designed to promote physical activity, which makes the activity findings all the more notable. To capture what children actually did throughout the day and night, each participant wore a wrist-mounted ActiGraph GT3X+ or GT9X Link accelerometer for ten consecutive days in October 2022, removing the device only for water-based activities.

The technical demands of measuring both movement and sleep in free-living children are considerable, and the researchers followed rigorous protocols to ensure their estimates were trustworthy. Accelerometers were initialized at 50 Hz and data were processed in 60-second epochs using ActiLife software. Periods of non-wear were identified using the Choi algorithm, which flags sustained inactivity of at least 90 consecutive minutes, allowing up to two minutes of sporadic counts. To qualify for analysis, children needed at least four valid days, three weekdays and one weekend day, with a minimum of 10 hours of wear time each day. Physical activity intensity was classified using cut points developed by Chandler and colleagues, which were specifically validated for wrist-worn accelerometers in children aged 8 to 12. Sleep scoring relied on the Sadeh algorithm, which classifies each minute as sleep or wake based on activity counts within an 11-minute moving window, with sleep onset and offset determined by visual inspection of activity graphs.

Out of 382 children invited to participate, 153 provided consent, and 72 met the stringent wear-time criteria for the final analysis. The results on the activity side were remarkable. Nearly all participants, 99 percent, met the daily guideline of at least 60 minutes of moderate-to-vigorous physical activity averaged across all days, including weekends, where 93 percent still met the target. Children accumulated an average of roughly two hours of moderate-to-vigorous activity per day, drawing on time before school, during physical education and recess, and after dismissal. During the after-school programs themselves, children attended for an average of 179 minutes and accrued about 35 minutes of moderate-to-vigorous activity. That figure represents more than half of the 60-minute daily requirement, confirming that these programs make a meaningful, quantifiable contribution to children’s movement even when they are not explicitly designed for exercise.

Perhaps the most surprising discovery came from an exploratory reanalysis. When the researchers recalculated daily activity after stripping out the 35 minutes accumulated during after-school programming, they found that 93 percent of children still met the 60-minute guideline. This suggests the children in this sample were highly active outside of structured programming as well, likely through active commuting, recess, physical education, and free play. The authors caution that this unusually active population may partly reflect the study’s baseline sample, which was drawn from a larger intervention trial, and the fact that the fee-based nature of the programs may have favored children from higher-income households with more resources for weekend activities and sports. Nevertheless, the finding underscores that after-school hours are a valuable lever for supporting daily activity, particularly on weekdays when children’s routines are most structured.

The sleep results told a very different story. On average, children slept just over seven hours per night, far below the 9-to-12-hour range recommended by the American Academy of Sleep Medicine and the National Sleep Foundation for children aged 6 to 12. Not one child met the sleep duration guideline on any day type, weekdays, weekends, or overall. The arithmetic behind this failure is revealing: children averaged only about 8 hours and 33 minutes of total time in bed, which is insufficient even before accounting for sleep latency and nighttime awakenings. In other words, the problem begins with bedtime itself. Children were simply not spending enough time in bed to achieve nine hours of sleep, a shortfall the authors attribute in part to factors such as evening screen time, inconsistent bedtime routines, homework, video games, and environmental disruptions like noise or bedroom sharing, which are more common in lower-income neighborhoods.

Sleep efficiency, defined as the percentage of time in bed actually spent asleep, painted a moderately better but still concerning picture. The National Sleep Foundation recommends that school-aged children achieve at least 85 percent sleep efficiency, yet only about 40 percent of participants reached that threshold across all days. The average sleep efficiency was 83 percent. Interestingly, girls showed higher physical activity levels, longer sleep duration, and better sleep efficiency than boys across all day types, a pattern that ran counter to the researchers’ initial hypotheses and to some prior literature. Fisher’s exact tests found no statistically significant differences in guideline adherence by age, gender, or school-level income, though the authors note that with 99 percent meeting the activity guideline and zero percent meeting the sleep duration guideline, there was little variability left to detect.

The study does have limitations that warrant careful interpretation. Wrist-worn accelerometers can overestimate moderate-to-vigorous activity in children because upper-limb movements inflate activity counts, while visual sleep scoring may underestimate true sleep duration by misclassifying quiet wakefulness, and the sleep data did not capture daytime naps. The strict wear-time criteria excluded roughly 40 percent of consenting participants, the sample size was modest, the observational analysis was nested within a larger intervention trial and was not powered for these specific outcomes, and the study did not account for clustering by school. School-level income, measured through Title I status, served only as a proxy for individual socioeconomic status. These constraints limit generalizability, but the authors argue the device-based measurement of both activity and sleep in this population represents a genuine strength and provides critical preliminary data for future research.

The broader implications of the findings extend well beyond Arizona. Chronic insufficient sleep in childhood is linked to disrupted attention and memory, emotional and behavioral difficulties, and elevated risks of obesity and other physical health problems, undermining academic success and well-being. Prior evidence consistently shows that meeting physical activity guidelines improves the odds of good sleep quality, and indeed, among the small number of children in this study who failed to meet the activity guideline on weekends, 80 percent had poor sleep quality. The authors suggest that after-school programs could evolve beyond their focus on physical activity to actively promote healthy sleep, for example by helping children finish homework earlier, reducing evening screen exposure, and encouraging consistent wind-down routines. They also call for research comparing sleep on days when children do and do not attend after-school programs. For now, the takeaway is clear: structured after-school hours are a proven engine for keeping children active, but ensuring children get enough sleep remains a challenge that families, schools, and communities must tackle together.

Subject of Research: Physical activity and sleep patterns among elementary school children attending after-school programs in Arizona

Article Title: Physical activity and sleep patterns in children attending after-school programs in Arizona: a cross-sectional study

Article References: Hassan, U. A., Oyeyemi, A. L., Wilson, K., Kulinna, P. H., & Poulos, A. (2026). Physical activity and sleep patterns in children attending after-school programs in Arizona: a cross-sectional study. Journal of Activity, Sedentary and Sleep Behaviors, 5(1), Article 7. https://doi.org/10.1186/s44167-025-00093-9

Image Credits: AI Generated

DOI: 10.1186/s44167-025-00093-9

Keywords: physical activity, sleep, after-school programs, children, moderate-to-vigorous physical activity, sleep efficiency, accelerometers, MVPA guidelines, sleep duration, elementary school, child health, Arizona