The Activity Calculator was developed by two occupational therapists from the Netherlands, Greke Hulstein–van Gennep and Karin ten Hove–Moerdijk, who recognized from their clinical experience that therapists needed a standardized, structured method to help patients with chronic fatigue participate in daily occupations in line with fatigue management recommendations. The method has since spread remarkably widely: certification courses have been organized for occupational therapists in the Netherlands since 2008 and in Norway since 2015, and more than 25 percent of all occupational therapists in the Netherlands and 8 percent of those in Norway have completed the training. Manuals are available in Dutch, Norwegian and English. That level of uptake, the researchers note, suggests the method fills a genuine clinical need. Yet until now, the tool has been criticized for lacking a solid theoretical foundation—a serious gap, since program theory describing how an intervention is expected to work, and under what conditions, is considered necessary before an intervention can be meaningfully evaluated.

The new paper, published open access in the Scandinavian Journal of Occupational Therapy by Irma Pinxsterhuis and Fanny Alexandra Jakobsen of Oslo Metropolitan University and Kirsti Nordstokkå of Sørlandet Hospital, addresses that gap by elaborating three core concepts from occupational therapy and occupational science: occupational adaptation, patterns of daily occupation, and occupational balance. The authors systematically discuss how each concept maps onto the phases of the Activity Calculator intervention, from evaluation and goal-setting through the intervention itself to re-evaluation. Their conclusion is that the three concepts appear to serve as a suitable theoretical foundation, providing a conceptual framework that may explain how the method helps people organize and adjust their everyday occupations to promote health, participation and well-being.

The Activity Calculator itself is a person-centred, time-contingent method. Patients begin by categorizing their usual activities as light, medium, strenuous or relaxing, then complete time registration lists for three to five representative days. Points are assigned to each activity according to its category and the time spent on it, and the total yields an activity level—the number of points the patient can manage in one day, whether it is a good day or a bad day. Patients then plan their activities against this budget using an app, activity cards, a pre-set day or week schedule, a diary, or an individually designed tool. This makes the approach time-contingent, in contrast to symptom-contingent approaches that assume patients have an accurate view of their capabilities and respond reliably to warning signs. Over time, activity levels can be increased gradually to avoid relapses or worsening of fatigue, and the calculator can eventually be retired once patients have internalized the planning habit.

The first theoretical pillar, occupational adaptation, is historically and philosophically rooted in occupational therapy but has been defined ambiguously in the literature. First described by Schkade and Schultz in 1992 as a normative process most pronounced in periods of transition, the concept has been reconceptualized by later researchers into clinically applicable language. In contemporary usage, occupational adaptation describes both a transactional process between the person, their occupations and the occupational environment, and an outcome of occupational participation. When a person encounters an occupational challenge—an obstacle or demand encountered while performing an occupation—it is the desire for occupational engagement that drives the adaptive response, linked to internal processes such as perception of choice, conscious awareness, self-reflection, problem-solving, acceptance and emotional regulation. Adaptation is a lifelong, cumulative, non-linear process, and people can learn from earlier experiences to support future adaptation.

Two measures of adaptation as an outcome are particularly relevant to the Activity Calculator. Relative mastery refers to a person’s subjective experience of competence in an occupation, assessed through effective participation, efficiency in using available personal and environmental resources such as time, energy and social supports, and satisfaction with occupational performance. Adaptive capacity, meanwhile, describes whether an individual has sufficient tools in their toolbox to respond to occupational challenges—including the ability to creatively develop new strategies when old ones fail. Some studies have shown positive associations between occupational adaptation and experiences of health and well-being, which matters because the two aims of the Activity Calculator explicitly target adaptation: helping patients find a balance between what they want to do and what they are able to do, and helping them obtain congruence between occupational participation and their values and personal meaning.

The second pillar, patterns of daily occupation, is often described as doing in time. Researchers have explored these patterns by categorizing occupations using taxonomies such as work, play, rest and sleep, or, in a scheme from Erlandsson and Eklund, distinguishing main occupations that dominate time and awareness, hidden occupations performed routinely, and unexpected occupations that interrupt the others. Other work has examined the complexity of 24-hour cycles, including duration, frequency, parallel occupations performed simultaneously, and the social coordination of occupations with other people. Diaries are a standard tool for documenting and reflecting on these patterns, and the Activity Calculator’s time registration lists function in exactly this way. Review of the lists reveals the prevalence of different occupation types, temporal aspects such as duration and frequency, perceived time pressure, orchestration of activities with others, and changes over time. Patients may also be asked to label registered activities as main, hidden or unexpected occupations, and perceptions can be complemented by significant others—for instance when patients are minors or have cognitive impairments.

The third pillar, occupational balance, is commonly defined as an individual’s perception of having the right amount of occupations and the right variation between them. A review by Eklund and colleagues identified three indicators: perception of the right variation and harmony between occupations, having the ability and resources for occupational engagement, and engagement in line with personal meaning and values. Jonsson and Persson proposed a tentative model in which balance is perceived through a relational mix of Exacting occupations that exceed actual skills, Flowing occupations with a reasonable match between skills and challenges, and Calming low-challenge experiences such as relaxation. A scoping review by Wagman and Håkansson added that occupational balance must be considered interpersonally, since it affects and is affected by partners, family members, employers and colleagues. The research base is striking: occupational balance is positively related to health, well-being, quality of life, life satisfaction and work ability, while imbalance has been linked to fatigue, stress and depression—one study finding that people with fatigue limited its consequences by applying adaptive strategies.

The authors’ discussion walks through how these concepts anchor each phase of the intervention. In the evaluation and goal-setting phase, occupational adaptation as a transactional process helps therapists identify occupational roles, role demands, occupational challenges and previously used responses, while assessments of relative mastery and adaptive capacity provide a picture of perceived adaptation. The Transtheoretical Model of Change, embedded in the Activity Calculator manuals, ensures the method is introduced only when patients have the intention and willingness to change, since decision-making about behaviour change depends on weighing the pros and cons of current behaviour. In the intervention phase, categorizing activities provides insight into the complexity of each patient’s daily patterns; registration reveals discrepancies between current occupational responses and perceived energy for mental or physical activity; and planning against the calculated activity level gives patients a framework for optimizing the relationship between time, energy and occupation. Patterns of daily occupation form the more objective, partly observable aspect of the framework, while occupational balance adds the subjective, emotional and personal dimension—and because the two constructs are interrelated, changes in one affect the other. Therapists support the process with education in energy conservation, spreading activities over the day and week, prioritizing and delegating, balancing engagement with rest, and modifying environments or adding assistive devices to make activities less strenuous.

At the end of the intervention, therapists assess the temptations and personal challenges patients face in maintaining adaptive occupational responses. Some patients continue to use the calculator indefinitely to maintain or improve their adaptation; others learn to engage without calculating points, responding to warning signs instead—taking a break, or switching to an activity that uses different muscles. At times of high risk, such as personal crises, social pressure, holidays or moving house, the calculator can be reintroduced, since these triggers may reactivate dysadaptive responses. Regular re-evaluation of patterns, balance, relative mastery and adaptive capacity detects when the activity level needs adjusting or new occupational responses need developing. The authors acknowledge that further research is needed to explore the relationship between using the Activity Calculator and improvements in health and well-being—but with this theoretical foundation in place, those studies can finally be designed on solid conceptual ground.

Subject of Research: A theoretical foundation for the Activity Calculator, an occupational therapy method for managing chronic fatigue through activity planning

Article Title: Theoretical foundation of the Activity Calculator – a method for fatigue management

Article References: Pinxsterhuis, I., Nordstokkå, K., & Jakobsen, F. A. (2025). Theoretical foundation of the Activity Calculator – a method for fatigue management. Scandinavian Journal of Occupational Therapy, 32(1), Article 2608471. https://doi.org/10.1080/11038128.2025.2608471

Image Credits: AI Generated

DOI: 10.1080/11038128.2025.2608471

Keywords: chronic fatigue, Activity Calculator, occupational therapy, occupational adaptation, occupational balance, patterns of daily occupation, energy conservation, pacing, long Covid, occupational science, fatigue management, rehabilitation

Executive functions have become one of the most intensively studied targets in developmental science, and for good reason. These capacities, which allow children to resist impulses, hold information in mind, and shift flexibly between rules or perspectives, develop rapidly during the preschool years and robustly predict later cognitive, academic, and socioemotional outcomes. Longitudinal work has linked early executive function skills to achievement in mathematics and literacy, to emotion regulation, and even to social acceptance among peers. Because these abilities are so consequential, researchers have grown increasingly concerned about the possibility that a dominant feature of modern childhood, screen media, might interfere with their development during this sensitive window.

The scale of young children’s screen exposure makes the question urgent. Surveys such as the Common Sense Media census indicate that media use among children from birth to age eight has risen steadily, and studies tracking the COVID-19 pandemic documented sharp increases in screen time as families locked down and moved learning and entertainment indoors. The World Health Organization has issued guidelines recommending strict limits on sedentary screen time for children under five, and a large body of observational research has associated heavy screen use with lower developmental screening scores, attentional difficulties, and weaker self-regulation. Yet observational studies can only show correlations, leaving open the possibility that family circumstances, parenting practices, or child temperament drive both media habits and cognitive outcomes.

Experimental studies offer a sharper tool. By randomly assigning children to watch different types of content and then immediately testing their executive function performance, researchers can isolate the causal effect of the content itself. Earlier influential work, including a widely cited 2011 study by Angeline Lillard and Jennifer Peterson published in Pediatrics, found that children who watched a fast-paced fantastical cartoon performed worse on subsequent executive function tasks than children who watched slower-paced educational programming or drew pictures. That finding, and replications and extensions that followed, suggested that the architecture of a video, its pacing, its reliance on fantasy, its interactivity, might be capable of temporarily degrading young children’s self-control.

What remained unclear, Cai and Xiang argue, was how these different content characteristics relate to distinct executive function domains. Previous reviews tended to focus on screen time duration, on single components of executive function, or on one content feature at a time. To fill that gap, the researchers followed the PRISMA 2020 reporting guidelines, searching the PsycINFO, Scopus, and Web of Science databases for peer-reviewed experimental studies published between 2000 and 2025. Their search identified 13 articles reporting 19 independent experimental studies, a small but conceptually focused evidence base that allowed them to compare how educational intent, interactivity, pacing, and fantastical elements each relate to immediate executive function performance in early childhood.

The pattern that emerged across this heterogeneous literature was consistent for the riskier content features. Studies examining highly fantastical or fast-paced material generally reported poorer performance on some immediate executive function measures, with inhibitory control emerging as the domain most frequently affected. Inhibitory control, the capacity to suppress a dominant or automatic response, is precisely the skill taxed by tasks in which children must wait, resist touching an attractive object, or follow a rule that conflicts with their first instinct. Some of the reviewed studies went beyond behavioral measures, using electroencephalography to record N2 and P3 event-related potential components during go/no-go tasks, providing neural correlates of how video editing pace may alter children’s inhibitory processing shortly after exposure.

Theoretical frameworks help explain why such effects might occur. According to the limited capacity model of mediated message processing, human attentional and working memory resources are finite, and rapidly changing, overloaded media environments can consume processing capacity in ways that leave fewer resources available for subsequent self-regulation. Fantastical content, meanwhile, may violate young children’s expectations about how the world works, requiring additional cognitive effort to parse and potentially disrupting the scripts children rely on to guide behavior. A complementary account suggests that arousal and priming mechanisms are at work: exciting, unpredictable content may leave children in a heightened state that interferes with the calm, effortful control that executive function tasks demand.

Not all content effects pointed in the same direction, however. Two studies of interactive content reported more favorable outcomes on selected measures, particularly working memory. Interactivity, in which children respond to, manipulate, or make choices within the medium, may keep children actively engaged rather than passively absorbing stimulation, and could recruit rather than deplete the very memory systems that executive function depends on. The evidence for educational content was more limited and less encouraging than many parents might hope. Educational labeling alone was not consistently associated with better immediate executive function outcomes when pacing and fantastical elements were not controlled. In other words, an app or program marketed as educational does not automatically confer cognitive benefits if its underlying structure remains fast, frenetic, or heavily fantastical.

The authors are careful to emphasize the limits of the available evidence. The 19 studies included in the review employed varied samples, stimuli, exposure durations, and outcome measures, making precise meta-analytic synthesis difficult, and the overall evidence base remains small. The effects documented are immediate and short-term, measured minutes after exposure, and the review does not establish that any single viewing session causes lasting harm to children’s developing executive systems. The authors call for future research using larger and more diverse samples and ecologically valid designs that better reflect how children actually use media in their homes, across devices and over extended periods.

Even with those caveats, the review carries a practical message for parents, educators, and app designers at a moment when screens are woven into early childhood across the globe. The findings suggest that caregivers worried about screens should look beyond the clock and scrutinize the content itself, favoring slower-paced, realistic, and genuinely interactive material over rapid-fire fantastical entertainment, and treating educational labels as insufficient guarantees. For policymakers updating screen time guidance and for developers designing children’s media, the work provides an evidence-based rationale for considering pacing and fantasy content when evaluating what young children consume. As Cai and Xiang conclude, the characteristics of screen content appear to be genuinely relevant to children’s immediate executive function performance, and understanding those characteristics may prove more productive than counting minutes alone.

Subject of Research: The effects of screen content features on executive functions in early childhood

Article Title: Screen Exposure and Executive Functions in Early Childhood: A Systematic Review of Experimental Studies

Article References: Cai, X., & Xiang, J. (2026). Screen Exposure and Executive Functions in Early Childhood: A Systematic Review of Experimental Studies. International Journal of Early Childhood. https://doi.org/10.1007/s13158-026-00547-4

Image Credits: AI Generated

DOI: 10.1007/s13158-026-00547-4

Keywords: screen exposure, digital media, executive functions, early childhood, inhibitory control, working memory, cognitive flexibility, fantastical content, pacing, interactive media, systematic review, preschoolers