Parent-child patterns in objectively measured movement behaviours were the highlight of this study. A total of 381 families (337 mothers, 256 fathers, 190 daughters, and 191 sons) from 36 randomly selected schools and kindergartens provided valid accelerometer data. Sedentary behaviour and physical activity (PA) were assessed using ActiGraph accelerometers. Spearman\'s rho was used to evaluate parent-child associations, while logistic regression analysis (the backward LR method) was used to recognize factors related to children\'s achievement of PA recommendations. Results indicated that girls engaged more in light PA, while boys showed higher levels of moderate and vigorous PA. Mothers spent less time sitting and more time in light PA compared to fathers, resulting in higher total PA levels. Father-son pairs showed a stronger association in total PA than mother-son pairs. Children aged 6-10 years and those with mothers who engaged in more vigorous PA were more likely to meet PA recommendations compared to younger children and those with less active mothers.

There is a positive association between heightened activity levels and improved working memory performance (WM) in individuals with Attention-Deficit/Hyperactivity Disorder (ADHD). Recent research suggests that stimulant medications may have a simultaneous positive impact on WM and motor skills. Yet, it is unclear the specific connection between movement, WM, and stimulant use. We examined how visuospatial (VS) and phonological (PH) WM performance varied with children\'s stimulant medication usage and naturally occurring activity level. In a repeated measures design, children with ADHD (n = 43; 7-12 years old) completed WM tasks while wearing actigraphy watches to monitor activity level on and off stimulant medication. Significant large sized main effects were observed for medication condition on PH (p < .05, ηp2 = .14) and VS (p < .001, ηp2 = .30) WM. Activity level also had significant medium sized main effects on PH (p < .01, ηp2 = .09) and VS (p < .005, ηp2 = .10) WM. There was a significant medium sized interaction for VS WM (p < .005, ηp2 = .11), indicating that the effect of medication on performance was greatest in the highest activity level category. The findings suggest that a combination of stimulant medication and an \"optimal\" level of movement may be most effective for improving VS WM.

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7-22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = -0.15; 95% LOA [-0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M.

BACKGROUND: Daily physical activity patterns differ by Alzheimer\'s disease (AD) status and might signal cognitive risk. It is critical to understand whether patterns are disrupted early in the AD pathological process. Yet, whether established AD risk markers (β-amyloid [Aβ] or apolipoprotein E-ε4 [APOE-ε4]) are associated with differences in objectively measured activity patterns among cognitively unimpaired older adults is unclear.
METHODS: Wrist accelerometry, brain Aβ (+/-), and APOE-ε4 genotype were collected in 106 (Aβ) and 472 (APOE-ε4) participants (mean age 76 [standard deviation{SD}: 8.5) or 75 [SD: 9.2] years, 60% or 58% women) in the Baltimore Longitudinal Study of Aging. Adjusted linear and function-on-scalar regression models examined whether Aβ or APOE-ε4 status was cross-sectionally associated with activity patterns (amount, variability, or fragmentation) overall and by time of day, respectively. Differences in activity patterns by combinations of Aβ and APOE-ε4 status were descriptively examined (n = 105).
RESULTS: There were no differences in any activity pattern by Aβ or APOE-ε4 status overall. Aβ+ was associated with lower total amount and lower within-day variability of physical activity overnight and early evening, and APOE-ε4 carriers had higher total amount of activity in the evening and lower within-day variability of activity in the morning. Diurnal curves of activity were blunted among those with Aβ+ regardless of APOE-ε4 status, but only when including older adults with mild cognitive impairment/dementia.
CONCLUSIONS: Aβ+ in cognitively unimpaired older adults might manifest as lower amount and variability of daily physical activity, particularly during overnight/evening hours. Future research is needed to examine changes in activity patterns in larger samples and by other AD biomarkers.

Sleep and light education (SLE) combined with relaxation is a potential method of addressing sleep and affective problems in older people. 47 participants took part in a four-week sleep education program. SLE was conducted once a week for 60-90 minutes. Participants were instructed on sleep and light hygiene, sleep processes, and practiced relaxation techniques. Participants were wearing actigraphs for 6 weeks, completed daily sleep diaries, and wore blue light-blocking glasses 120 minutes before bedtime. Measures included scores of the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS), Insomnia Severity Index (ISS), Beck Depression Inventory-II (BDI-II), State-Trait Anxiety Inventory (STAI) and actigraphy measurements of sleep latency, sleep efficiency, and sleep fragmentation. Sleep quality increased after SLE based on the subjective assessment and in the objective measurement with actigraphy. PSQI scores were statistically reduced indicating better sleep. Scores after the intervention significantly decreased in ESS and ISS. Sleep latency significantly decreased, whereas sleep efficiency and fragmentation index (%), did not improve. Mood significantly improved after SLE, with lower scores on the BDI-II and STAI. SLE combined with relaxation proved to be an effective method to reduce sleep problems and the incidence of depressive and anxiety symptoms.

OBJECTIVE: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations.
METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords \"ActiGraph,\"\"Apple Watch,\"\"Empatica,\"\"Fitbit,\"\"Garmin,\" and \"wearable devices\" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables.
RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations.
CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Quantifying pain in patients admitted to intensive care units (ICUs) is challenging due to the increased prevalence of communication barriers in this patient population. Previous research has posited a positive correlation between pain and physical activity in critically ill patients. In this study, we advance this hypothesis by building machine learning classifiers to examine the ability of accelerometer data collected from daily wearables to predict self-reported pain levels experienced by patients in the ICU. We trained multiple Machine Learning (ML) models, including Logistic Regression, CatBoost, and XG-Boost, on statistical features extracted from the accelerometer data combined with previous pain measurements and patient demographics. Following previous studies that showed a change in pain sensitivity in ICU patients at night, we performed the task of pain classification separately for daytime and nighttime pain reports. In the pain versus no-pain classification setting, logistic regression gave the best classifier in daytime (AUC: 0.72, F1-score: 0.72), and CatBoost gave the best classifier at nighttime (AUC: 0.82, F1-score: 0.82). Performance of logistic regression dropped to 0.61 AUC, 0.62 F1-score (mild vs. moderate pain, nighttime), and CatBoost\'s performance was similarly affected with 0.61 AUC, 0.60 F1-score (moderate vs. severe pain, daytime). The inclusion of analgesic information benefited the classification between moderate and severe pain. SHAP analysis was conducted to find the most significant features in each setting. It assigned the highest importance to accelerometer-related features on all evaluated settings but also showed the contribution of the other features such as age and medications in specific contexts. In conclusion, accelerometer data combined with patient demographics and previous pain measurements can be used to screen painful from painless episodes in the ICU and can be combined with analgesic information to provide moderate classification between painful episodes of different severities.

BACKGROUND: Regular physical activity and limited sedentary time are recommended for adult childhood cancer survivors. The Swedish National Board of Health and Welfare designed a questionnaire to assess levels of physical activity (BHW-Q), including two questions: one on vigorous physical activity (BHW-Q VPA) and one on moderate physical activity (BHW-Q MPA). Furthermore, a single-item question was developed to measure sedentary time (SED-GIH-Q). These questions are recommended for clinical practice and have been found valid for the general population but have so far not been tested in adult childhood cancer survivors. The aim of the study was to assess test-retest reliability, agreement and criterion-related validity of the BHW-Q and the SED-GIH-Q in adult childhood cancer survivors.
METHODS: A non-experimental methodological study. In total 60 participants (50% women), median age 28 (min-max 18-54) years were included at the Long-Term Follow-Up Clinic at Sahlgrenska University Hospital. Participants were instructed to wear an accelerometer for seven days, and to answer the BHW-Q and the SED-GIH-Q before and after the seven days. Test-retest reliability and criterion-related validity comparing the BHW-Q and SED GIH-Q with accelerometer data were calculated with weighted Kappa (k) (agreement) and by using Spearman´s rho (r) (correlation).
RESULTS: Test-retest reliability regarding the SED-GIH-Q showed a high agreement (k = 0.88) and very strong correlation (r = 0.93), while the BHW-Q showed a moderate agreement and moderately strong correlation, BHW-Q VPA (k = 0.50, r = 0.64), BHW-Q MPA (k = 0.47, r = 0.58). Both the agreement and the correlation of the criterion-related validity were interpreted as fair for the BHW-Q VPA (k = 0.29, r = 0.45), while the agreement for BHW-Q MPA was interpreted as low (k = 0.07), but the correlation as fair (r = 0.37). The agreement of the SED-GIH-Q (k = 0.13) was interpreted as low and the correlation as poor (r = 0.26).
CONCLUSIONS: These simple questions assessing physical activity and sedentary time can be used as screening tools in clinical practice to identify adult childhood cancer survivors in need of support to increase physical activity level. Further development is needed on the design of a sufficiently valid question measuring sedentary time.
BACKGROUND: This research project was registered in the Swedish National Database of Research and Development; identifier 275251, November 25, 2020. https://www.researchweb.org/is/vgr/project/275251 .

A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.

OBJECTIVE: Commercially available wearable activity monitors can promote physical activity behaviour. Clinical trials typically quantify physical activity with research grade activity monitors prior to testing interventions utilising commercially available wearable activity monitors aimed at increasing step count. Therefore, it is important to test the agreement of these two types of activity monitors.
OBJECTIVE: Observational.
METHODS: Thirty adults (20-65 years, n = 19 females) were provided a Fitbit Charge 4©. To determine reliability using an intraclass correlation coefficient, two, one-minute bouts of treadmill walking were performed at a self-selected pace. Subsequently, participants wore both an ActiGraph wGT3X-BT and the Fitbit for seven days. To determine agreement, statistical equivalence and the mean absolute percentage error were calculated and represented graphically with a Bland-Altman plot. Ordinary least products regression was performed to identify fixed or proportional bias.
RESULTS: The Fitbit showed \'good\' step count reliability on the treadmill (intraclass correlation coefficient = 0.75, 95 % CI = 0.53-0.87, p < 0.001). In free-living however, it overestimated step count when compared to the ActiGraph wGT3X-BT (mean absolute percentage error = 26.02 % ± 14.63). Measurements did not fall within the ± 10 % equivalence region and proportional bias was apparent (slope 95 % CI = 1.09-1.35).
CONCLUSIONS: The Fitbit Charge 4© is reliable when measuring step count on a treadmill. However, there is an overestimation of daily steps in free-living environments which may falsely indicate compliance with physical activity recommendations.