The research, led by Mona Eklund of Lund University together with colleagues at the Swedish University of Agricultural Sciences, Skåne University Hospital, and Universidad San Jorge in Spain, was a secondary analysis of the Nature Stroke Study (NASTRU), a randomized controlled trial conducted in 2013 and 2014 that tested whether nature-based rehabilitation could ease post-stroke fatigue. From the trial’s 101 recruits, 95 participants with sub-acute or chronic stroke formed the psychometric sample. All were community-dwelling adults aged 50 to 80, independent in personal activities of daily living, cognitively intact, and troubled by post-stroke fatigue. Nine in ten had experienced an ischemic stroke, and for most it was their first. Disability levels were generally mild to moderate, and the group’s average score on the Montreal Cognitive Assessment was 26 out of 30, indicating preserved thinking abilities.

The OVal-pd is grounded in the Value and Meaning in Occupations (ValMO) model, which proposes that a sense of meaning in life emerges when people repeatedly experience three distinct kinds of reward from what they do. Concrete value refers to tangible payoffs, such as the satisfaction of a cleaned house or a salary earned. Socio-symbolic value arises from activities that reinforce identity and social bonds, like cooking and sharing a traditional meal with others. Self-rewarding value captures the sheer pleasure of joyful, creative pursuits—a beloved hobby, for instance. The questionnaire asks respondents how often, over the past month, they felt they had done things for each of these reasons, using a four-point scale from ‘very rarely or never’ to ‘very often.’ Originally developed for people with mental illness, the instrument has since been validated in several languages, with versions ranging from 18 to 26 items depending on the population.

Because stroke patients differ from the psychiatric populations in which the Swedish version was first tested, the team evaluated both the original 26-item form and the streamlined 18-item Swedish version. Participants also completed a battery of comparison instruments: the Mental Fatigue Scale, which probes general fatigue, stress sensitivity, sleep problems, concentration difficulties, and sensory sensitivity; the EuroQoL-5D quality-of-life questionnaire covering mobility, self-care, usual activities, pain, and mood; and the Hospital Anxiety and Depression Scale, a 14-item self-rating of emotional distress. Statistical analyses followed internationally recognized COSMIN guidelines, with floor and ceiling effects, internal consistency, construct validity, responsiveness, long-term stability, and measurement error each assessed against predefined criteria.

The results were largely reassuring. Neither version of the questionnaire showed floor or ceiling effects—almost no one scored at the very bottom or top of the scale, meaning the instrument can distinguish among patients across the full range of experience. Internal consistency, measured by Cronbach’s alpha, was acceptable for the total scores of both versions, at 0.88 for the 26-item form and 0.86 for the 18-item form, with subscale values also clearing the 0.70 threshold for the longer version. The two versions correlated with each other at an extraordinarily high level, above 0.97, indicating they capture essentially the same construct. Domain subscores correlated strongly with their respective totals, while correlations with the quality-of-life index were weak to negligible—exactly the pattern expected if the questionnaire is measuring something distinct from general health status.

Known-groups validity provided one of the study’s most intriguing findings. Participants whose Hospital Anxiety and Depression Scale scores suggested possible depression scored significantly lower on the OVal-pd than those without depression, with effect sizes exceeding 0.74 for total scores—a moderate to large difference. Yet the questionnaire failed to distinguish between groups defined by anxiety symptoms. The authors suggest this asymmetry makes sense: prior research indicates that post-stroke depression is more strongly tied to activity-related factors than anxiety is, so an activity-centered measure would be expected to track depression more closely. The finding underscores that occupational value and mood are related but not identical constructs, and it hints that the questionnaire may be particularly sensitive to the motivational and meaning-related dimensions of post-stroke depression.

Not every property came out favorably. Responsiveness—the instrument’s ability to detect clinically meaningful change over time—proved poor. Using an anchor-based method with the Mental Fatigue Scale as the reference, the researchers constructed receiver operating characteristic curves for participants who had improved minimally or substantially over eight months. The area under the curve hovered near 0.55 for both versions, statistically indistinguishable from chance, and no minimal clinically important difference could be established. The authors caution that only 13 of 95 participants showed large improvements in mental fatigue during follow-up, so the sparse spread of real change may have hampered the analysis rather than the instrument itself. Still, the practical message is clear: the OVal-pd should not be used to quantify individual or group-level change in stroke populations.

Long-term stability told a different story. Among the 43 participants who were clinically stable between the 8-month and 14-month follow-ups, test-retest reliability was good for the total scores of both versions, with intraclass correlation coefficients of 0.75 and 0.76, and ranged from moderate to excellent for the subscale scores. The team also calculated the standard error of measurement and the smallest detectable change at 90 and 95 percent confidence levels, giving clinicians and researchers concrete thresholds for judging whether observed score differences reflect genuine change or mere measurement noise. Because short-term reliability over one to two weeks could not be assessed within the trial design, the authors note that future studies should fill that gap in a Swedish stroke population.

The clinical implications are nuanced. Rather than serving as an outcome tracker, the researchers argue, the OVal-pd’s strength lies in structured assessment and dialogue. At initial evaluation, it can give occupational therapists a rapid overview of how a patient perceives everyday occupations, spotlight areas where activities have lost their value, clarify what the patient wants to prioritize, and anchor person-centered goal setting. In research, its robust cross-sectional properties make it suitable for characterizing groups, comparing subgroups, and probing links with well-being—connections already demonstrated in other populations, where occupational value has been tied to life satisfaction after acquired brain injury, to self-mastery and self-esteem in severe mental illness, and to perceived meaningfulness in healthy adults.

The authors are careful about generalizability. Their sample was a selected group: largely independent, cognitively preserved, and motivated enough to volunteer for nature-based rehabilitation, so the findings may not extend to people with more severe strokes or different occupational interests. Convergent validity, moreover, was tested only internally, by correlating subscores with the total score, leaving open the need to compare the OVal-pd against an independent measure of a similar construct. Even so, the bottom line is pragmatic and positive. Both versions performed comparably, and since the 18-item form saves time without sacrificing quality, the researchers recommend it for clinical and research use with stroke patients—while urging further trials, particularly of known-groups and convergent validity, and qualitative studies of how the instrument works as a framework for assessment and goal setting in real therapy rooms.

Subject of Research: Psychometric validation of the OVal-pd questionnaire for assessing occupational value in stroke patients

Article Title: Psychometric properties of the occupational value with pre-defined items in stroke patients

Article References: Eklund, M., Pálsdóttir, A. M., Pessah-Rasmussen, H., & Bellosta-López, P. (2025). Psychometric properties of the occupational value with pre-defined items in stroke patients. Scandinavian Journal of Occupational Therapy, 32(1), Article 2597214. https://doi.org/10.1080/11038128.2025.2597214

Image Credits: AI Generated

DOI: 10.1080/11038128.2025.2597214

Keywords: stroke, occupational therapy, psychometrics, OVal-pd, questionnaire validation, rehabilitation, occupational value, mental fatigue, depression, reliability, validity, quality of life

The new method, described in the journal Medical & Biological Engineering & Computing by Wanchao Yao, Tianshu Gu, and Fuwang Wang, combines three technologies into a single pipeline called MVMD-FCASNN: multivariate variational mode decomposition, a frequency and channel attention spiking neural network, and a carefully designed signal-processing front end. The system reads multichannel electroencephalogram, or EEG, signals collected noninvasively from the scalp and classifies whether the operator is mentally fatigued in real time. In benchmark tests, the model achieved a classification accuracy of 98.81 percent and, critically, maintained strong performance even when the EEG recordings were deliberately corrupted with noise at a signal-to-noise ratio of minus six decibels, a level of interference far harsher than most laboratory experiments ever attempt.

The problem the researchers set out to solve is fundamentally one of signal integrity. EEG is famously vulnerable to contamination. On a construction site, the electrical systems of large machinery, radio transmissions, vibration transmitted through the operator’s body, and the operator’s own muscle movements and eye blinks all inject artifacts into the faint electrical potentials measured at the scalp. Classical decompositions of EEG signals, such as empirical mode decomposition, have long been used to tease apart the mixed frequency content of brain recordings, but they are notoriously sensitive to noise. When noise corrupts the decomposition process itself, the resulting components lose their physical meaning, and downstream classifiers inherit the error. Previous work, including studies on driver fatigue detection, has struggled with exactly this weakness, which is why many published fatigue-detection systems perform well in quiet laboratories but falter in the field.

The team’s answer to this challenge is multivariate variational mode decomposition, or MVMD. Unlike methods that process each EEG channel independently, MVMD jointly decomposes all channels at once, searching for a set of intrinsic mode functions, or IMFs, that are frequency-aligned across every recording electrode. In practical terms, this means that the alpha-band oscillation detected over the frontal cortex and the alpha-band oscillation detected over the central electrodes are extracted as matching components of the same decomposition, preserving the spatial relationships between brain regions that conventional approaches tend to scramble. Because variational mode decomposition formulates the problem as an optimization with explicit bandwidth constraints, it is also far more robust against noise corruption than empirical mode decomposition, a property that earlier studies in motor-imagery brain-computer interfaces had already hinted at. Applying it to fatigue detection in a genuinely noisy setting is one of the paper’s key contributions.

Once the EEG signals have been decomposed, the question becomes which of the resulting components actually carry the signature of fatigue. Not all frequency bands are equally informative, and the answer can shift from person to person and from moment to moment. To handle this, the researchers designed an IMF attention mechanism that adaptively evaluates the contribution of each extracted frequency component, automatically weighting the bands that best discriminate fatigue from alertness. Neuroscientifically, this makes good sense: mental fatigue is typically accompanied by changes in relative spectral power, such as increases in theta and alpha activity over frontal regions and alterations in beta activity associated with sustained attention. By letting the network learn which IMFs matter, the system avoids the brittleness of hand-picked frequency features.

Alongside frequency attention, the architecture incorporates a channel attention module, inspired in part by squeeze-and-excitation networks developed in computer vision, that emphasizes critical brain regions. The authors report that the model places its strongest weight on frontal and central areas of the scalp, precisely where neurophysiological research locates much of the neural activity associated with cognitive fatigue, vigilance decrements, and the executive control of sustained attention. This spatial selectivity strengthens the model’s representation of the brain’s fatigue signature while suppressing electrodes that contribute mostly noise. The combination means the network effectively learns both where to look on the scalp and which rhythms to listen for, two questions that traditional pipelines answer with fixed, hand-engineered rules.

The final stage of processing is perhaps the most futuristic element: a spiking neural network, or SNN. Unlike conventional artificial neural networks that transmit continuous-valued activations, spiking networks communicate through discrete spikes, mirroring the way biological neurons actually fire. This choice serves two purposes. First, spiking neurons are inherently event-driven, meaning they respond to changes in their input rather than to every sample, which makes them energy efficient and well suited to portable, battery-powered monitoring hardware that an operator could realistically wear on a job site. Second, spiking dynamics preserve the temporal structure of the EEG signal, retaining information about how brain activity evolves over milliseconds, information that frame-based networks tend to discard. The researchers point to earlier work on low-power driving fatigue monitoring with spiking networks as a foundation for this design philosophy.

The experimental results are the reason this study is attracting attention. On mental fatigue classification tasks, MVMD-FCASNN reached 98.81 percent accuracy, a figure the authors describe as significantly outperforming traditional methods. More striking is the model’s resilience: at a noise level of minus six decibels, where the interference is actually stronger than the underlying EEG signal, the system continued to classify fatigue reliably, while conventional comparison methods degraded sharply. For a technology intended to run continuously in environments where electrical noise from cranes, welding equipment, and generators is unavoidable, this anti-interference capability may matter more than headline accuracy. The study also reports informed consent from all participants and ethics approval consistent with the Helsinki declaration, and the authors declare no conflicts of interest.

The broader context explains why this line of research is urgent. Tower crane accidents remain a persistent source of fatalities and injuries in the construction industry, and studies in Hong Kong, Indonesia, and elsewhere have tied a substantial share of unsafe hoisting behavior to operator fatigue and excessive mental workload. Earlier detection systems have relied on eye-tracking glasses, facial landmark analysis, smart cushions, heart rate variability, and other physiological proxies, each with tradeoffs in comfort, invasiveness, and robustness. EEG offers a direct window into cognitive state, but until now its practical deployment in high-noise industrial settings has been hampered by exactly the interference problem this new architecture is built to withstand. Related work by overlapping research groups, including flexible dry electrodes designed for real driving environments, suggests a growing ecosystem of wearable brain-monitoring hardware maturing alongside the algorithms.

The researchers, funded by the National Natural Science Foundation of China and supported by Northeast Electric Power University and the Jilin City Science and Technology Bureau, argue that their approach offers a reliable and efficient solution for real-time mental fatigue monitoring in high-noise construction scenarios. If such systems can be integrated into crane cabins as a routine safeguard, an operator whose brainwaves betray dangerous drowsiness could be alerted, or their work schedule could be flagged for review, before a lapse in attention translates into a dropped load. The work also points beyond construction: the same pipeline of joint multichannel decomposition, dual attention mechanisms, and spiking computation could plausibly be adapted to truck drivers, pilots, air traffic controllers, and any profession where sustained vigilance is a matter of life and death. For now, the result stands as a striking demonstration that brain-inspired computing can withstand the messiness of the real world, not just the laboratory, and that the human brain’s own warning signs of exhaustion can be read clearly enough to act on them in time.

Subject of Research: EEG-based detection of mental fatigue in tower crane operators using multivariate variational mode decomposition and a frequency and channel attention spiking neural network in real construction environments

Article Title: Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments

Article References: Yao, W., Gu, T., & Wang, F. (2026). Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments. Medical & Biological Engineering & Computing. https://doi.org/10.1007/s11517-026-03669-y

Image Credits: AI Generated

DOI: 10.1007/s11517-026-03669-y

Keywords: mental fatigue, crane operators, EEG, spiking neural network, multivariate variational mode decomposition, attention mechanism, construction safety, brain-computer interface, signal processing, neural decoding, occupational health, machine learning

The study, led by Jessica Vollertsen of Linköping University together with Mathilda Björk, Anna-Karin Norlin, and Elin Ekbladh, recruited 48 working-age individuals in Sweden who had experienced a stroke within the previous five years and who reported fatigue. All had been gainfully employed at the time of their stroke. The participants, 37 of whom were women, were interviewed using the Swedish version of the Worker Role Interview, a semi-structured assessment grounded in the Model of Human Occupation that rates sixteen psychosocial and environmental factors on a four-point scale indicating whether each item interferes with or supports return to work. A questionnaire adding demographic data, the Mental Fatigue Scale, and the Hospital Anxiety and Depression Scale completed the picture. Data were analysed with a qualitative abductive content analysis complemented by descriptive statistics, and the study was approved by the Swedish Ethical Review Authority.

The technical profile of the sample underscores why post-stroke fatigue deserves attention as an obstacle to employment. On the Mental Fatigue Scale, 89 percent of participants reported mental fatigue, most at moderate to severe levels, even though the majority did not show clinically significant depression or anxiety. At interview, 96 percent were employed, but only 15 percent were working full-time; 58 percent worked reduced part-time schedules of 25 to 75 percent, and 23 percent remained on full-time sick leave. The Worker Role Interview ratings revealed a telling pattern: the items most often rated as supportive of returning to work were enjoying work, appraising work expectations, and commitment to work, while perceptions of the work environment, expectations of job success, and pursuing interests were most often rated as interfering. In other words, motivation was rarely the problem. Capacity and context were.

The qualitative analysis produced two overarching categories. The first, re-evaluating work ability while experiencing fatigue, captures how participants confronted an altered, fluctuating capacity that often manifested through memory difficulties, impaired concentration and multitasking, and heightened stress sensitivity. This altered capacity maps onto disruptions in what occupational scientists call personal causation, the appraisal of one’s abilities and the confidence to use them effectively. Participants described learning to recognise early warning signs of fatigue, because ignoring them could trigger headaches, migraines, or exhaustion lasting from a single day to a full week. Crucially, because the impairment was largely invisible, participants struggled not to express their needs but to have them understood and regarded as legitimate by supervisors, co-workers, and external contacts such as customers. Concrete needs, like sensitivity to noise or light, were easier for employers to accommodate than task-related needs, which were frequently dismissed.

The social architecture of the workplace emerged as decisive. Supervisors who actively sought to understand the nature of fatigue were described as crucial, yet frequent leadership changes repeatedly forced participants to re-explain their condition, eroding continuity and trust. One participant recounted how a supportive supervisor had arranged adapted work tasks, only for the next supervisor to remove that possibility. Close working relationships with colleagues were especially valuable because familiar co-workers could notice subtle signs of fatigue and informally adjust workloads. Gradual return-to-work programs and work training helped participants understand their changed abilities, but several experienced an abrupt withdrawal of vocational rehabilitation support the moment they reached full-time hours. Some also reported external pressure from employers or the Social Insurance Agency to increase working hours prematurely, leading to relapses and renewed sick leave, an experience one participant described as being forced to crash three or four times to prove the limitations were real.

Outside the workplace, the study documented how everyday life shaped work ability in a continuous feedback loop. Fatigue left no buffer for unexpected events, so a child missing the school bus or misplaced keys could drain enough mental energy to derail an entire workday. Participants engineered elaborate routines, planning clothing the night before, using calendars and reminders, and keeping their homes meticulously organised, because, as one put it, a cluttered mind requires order and tidiness around it. All participants emphasised scheduling rest and recovery into their days, whether through sleeping, lying in a dark room, pausing during walks with a carried yoga mat, or retreating discreetly to restrooms at work. Remote work arrangements, accelerated by the COVID-19 pandemic, were welcomed because eliminating commutes and using home rest areas conserved scarce energy. Yet the researchers highlight a central paradox: structure and routine are the very strategies needed to manage fatigue, but fatigue itself makes those routines difficult to establish and maintain.

The second category, redefining identity in the context of everyday life, is arguably the study’s most striking contribution. Many participants had built careers around helping others and defined themselves through high performance, making it emotionally wrenching to reconcile current limitations with previous expectations. Family members, witnessing the toll of fatigue firsthand, often questioned whether participants should work full-time at all, fearing another stroke. Energy limits strained parental roles, with one mother describing how work overload left her without the energy to cook proper meals for her children, and reshaped spousal dynamics, sometimes producing closeness through emotional support and sometimes provoking worry about how long a partner would tolerate the changes. Deliberate occupational choices, such as moving closer to work or downsizing housing, conferred a sense of control, whereas giving up cherished social and physical activities was experienced as loss and grief. One participant captured the arc from mourning to acceptance with the phrase: this is the new me, Eva 2.0.

From a clinical standpoint, the authors argue that sustainable return to work cannot be achieved through rehabilitation that treats work in isolation. Because challenges at work and at home proved to be integral to the same adaptive process rather than parallel problems, they call for person-centred rehabilitation that integrates work and everyday life, supports the reconstruction of occupational identity, and enables engagement in meaningful activities despite fatigue. The findings align with the Action Plan for Stroke in Europe 2018 to 2030 and Sweden’s integrated care reform, both of which emphasise person-centred, preventive, and long-term support. The researchers also caution that previous studies have focused overwhelmingly on the binary outcome of whether people return to work, while the sustainability of that return over time has been comparatively neglected, leaving clinicians without evidence-based guidance for the long tail of recovery.

The study has limitations worth noting. Recruitment relied solely on Facebook groups for stroke survivors, which may have excluded marginalised populations and limits generalisability, and interviews were conducted by telephone without audio recordings, restricting verbatim analysis, a constraint the authors offset by conducting 48 interviews to ensure sufficient information power. The COVID-19 pandemic also overlapped with early data collection, although recruitment continued post-pandemic. Still, the scale and depth of the qualitative dataset make this one of the richest accounts to date of how fatigue reshapes working life after stroke. The message for employers is concrete: flexible task and schedule adjustments, consistent supervisory relationships, systematic information about post-stroke fatigue, and continued individualised follow-up beyond the return to full-time hours are not accommodations of convenience but prerequisites for keeping skilled people in the workforce. For clinicians, the message is equally clear: supporting someone back to work after stroke means treating the return not as an event to be achieved, but as an ongoing negotiation between a changed self and a demanding everyday life, one in which identity, not just capacity, must be given time to heal.

Subject of Research: How post-stroke fatigue affects sustainable return to work among working-age stroke survivors

Article Title: Return to work with fatigue after stroke: A complex occupational adaptation process

Article References: Vollertsen, J., Björk, M., Norlin, A.-K., & Ekbladh, E. (2025). Return to work with fatigue after stroke: A complex occupational adaptation process. Scandinavian Journal of Occupational Therapy, 32(1), 1-13. https://doi.org/10.1080/11038128.2026.2613621

Image Credits: AI Generated

DOI: 10.1080/11038128.2026.2613621

Keywords: post-stroke fatigue, return to work, stroke rehabilitation, occupational therapy, occupational identity, Worker Role Interview, Model of Human Occupation, vocational rehabilitation, mental fatigue, workplace accommodation, everyday life, person-centred care