Background: The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. Objective: We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. Methods: We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. Results: During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged 70 years, and in non–shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. Conclusions: Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.

Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR.

The effectiveness of weight loss treatment displays dramatic inter-individual variabilities, even with well-controlled energy intake/expenditure. This study aimed to determine the association between daily rhythms of cardiac autonomic control and weight loss efficiency and to explore the potential relevance to weight loss resistance in humans carrying the genetic variant C at CLOCK 3111T/C. A total of 39 overweight/obese Caucasian women (20 CLOCK 3111C carriers and 19 non-carriers) completed a behaviour–dietary obesity treatment of ~20 weeks, during which body weight was assessed weekly. Ambulatory electrocardiographic data were continuously collected for up to 3.5 days and used to quantify the daily rhythm of fractal cardiac dynamics (FCD), a non-linear measure of autonomic function. FCD showed a 24 h rhythm (p < 0.001). Independent of energy intake and physical activity level, faster weight loss was observed in individuals with the phase (peak) of the rhythm between ~2–8 p.m. and with a larger amplitude. Interestingly, the phase effect was significant only in C carriers (p = 0.008), while the amplitude effect was only significant in TT carriers (p < 0.0001). The daily rhythm of FCD and CLOCK 3111T/C genotype is linked to weight loss response interactively, suggesting complex interactions between the genetics of the circadian clock, the daily rhythm of autonomic control, and energy balance control.

Objective: This longitudinal study aims to identify the key factors influencing the weight loss trajectory during a 24‐week intervention and their relevance at different stages of the treatment. Methods: We studied 1252 participants (age 18–65 years) of a cognitive behavioral program to lose weight. Body weight was measured weekly, and a range of variables at baseline and throughout the treatment period were assessed. Linear regression analyses were conducted to understand the factors involved in the weight loss trajectory across three treatment periods (0–6, 7–12, 13–24 weeks). Results: The rate of weight loss progressively decreased from the 1st to the 3rd period (770–198 g/week). During the 1st period, the recent history of the individual, including baseline metabolic status, dietary habits, and motivation, was the highest‐ranked determinant. In the 2nd period, the sole predictor was the long‐term personal history of obesity, that is, the duration of the individual's lifetime spent with overweight/obesity. In the 3rd period, emotional eating behaviors and related barriers emerged as the two highest‐ranked determinants of reduced rate of weight loss. Conclusions: Findings suggest targeted interventions that address the specific challenges of each period of weight loss interventions. As treatment progresses, strategies focused on emotional‐related behaviors may be crucial for sustaining weight loss. Trial Registration: ClinicalTrials.gov identifier NCT02829619

Background: Depression is a major health issue in adolescence and young adulthood, emphasising the need for early risk identification. Patients with major depressive disorder (MDD) often show disturbed daily rest-activity patterns, but such changes are often confounded by medication intake, comorbidities and disease duration. Objective: In this exploratory analysis, we tested whether there are specific changes in daily rest activity (from wrist-worn actigraphy) in women at the onset of MDD without medication, as compared with age-matched controls. Methods: Participants from the MDD group (age 19–32, 24.73±5.13 (mean±SD), N=15) and control group (age 20–31, 24.89±3.82, N=9) completed ~7 day ambulatory actigraphy recordings, followed by a stringently controlled circadian laboratory protocol to assess endogenous circadian melatonin levels. We analysed the daily rhythm of mean activity levels and non-linear fractal dynamics in eight 3-hour time bin across the 24 hours, correlating these measures with depressive symptom severity and endogenous melatonin levels. Findings: Using approaches from non-linear fractal dynamics, we showed that, compared with healthy controls, women at MDD onset had a higher fractal activity correlation (FAC) during the last hours of sleep, indicating more ‘wake-like’ patterns (FAC within 0–3 hour before wake: 0.92±0.64 (SD) in MDD vs 0.77±0.18 in controls, p=0.02). The alteration was independent of mean activity level and wake duration but appeared to be associated with depressive symptom severity (p=0.08). Moreover, there was a trend association for altered FAC with endogenous melatonin levels in the MDD group (for onefold increase in melatonin level in the last 3 hours before wake, the FAC increased by 0.33±0.17 (SE), p=0.08). Conclusions: Pre-wake FAC is elevated in unmedicated women at MDD onset and may serve as a potential biomarker associated with symptom severity and circadian physiology. Clinical implications: These findings provide proof-of-concept evidence that unique fractal motor activity patterns may support early detection of MDD.