Habitual Physical Activity of People with or at Risk of Diabetes-Related Foot Complications.

Regular physical activity is an important component of diabetes management. However, there are limited data on the habitual physical activity of people with or at risk of diabetes-related foot complications. The aim of this study was to describe the habitual physical activity of people with or at risk of diabetes-related foot complications in regional Australia. Twenty-three participants with diabetes from regional Australia were recruited with twenty-two participants included in subsequent analyses: no history of ulcer (N = 11) and history of ulcer (N = 11). Each participant wore a triaxial accelerometer (GT3X+; ActiGraph LLC, Pensacola, FL, USA) on their non-dominant wrist for 14 days. There were no significant differences between groups according to both participant characteristics and physical activity outcomes. Median minutes per day of moderate-to-vigorous physical activity (MVPA) were 9.7 (IQR: 1.6-15.7) while participants recorded an average of 280 ± 78 min of low-intensity physical activity and 689 ± 114 min of sedentary behaviour. The sample accumulated on average 30 min of slow walking and 2 min of fast walking per day, respectively. Overall, participants spent very little time performing MVPA and were largely sedentary. It is important that strategies are put in place for people with or at risk of diabetes-related foot complications in order that they increase their physical activity significantly in accordance with established guidelines.

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.

BACKGROUND: The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic. OBJECTIVE: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework. METHODS: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. RESULTS: The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles. CONCLUSIONS: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.

Deep Learning to Predict Energy Expenditure and Activity Intensity in Free Living Conditions using Wrist-specific Accelerometry.

Wrist-worn accelerometers are more comfortable and yield greater compliance than hip-worn devices, making them attractive for free-living activity assessments. However, intricate wrist movements may require more complex predictive models than those applied to hip-worn devices. This study developed a novel deep learning method that predicts energy expenditure and physical activity intensity of adults using wrist-specific accelerometry. Triaxial accelerometers were worn by 119 participants on their wrist and hip for two weeks during waking hours. A deep learning model was developed from week 1 data of 60 participants and tested using week 2 data for: (i) the remaining 59 participants (Group UT), and (ii) participants used for training (Group TR). Estimates of physical activity were compared to a reference hip-specific method. Moderate-to-vigorous physical activity predicted by the wrist-model was not different to the reference method for participants in Group UT (5.9±3.1vs. 6.3±3.3 hour/week) and Group TR (6.9±3.7 vs. 7.2±4.2 hour/week). At 60-s epoch level, energy expenditure predicted by the wrist-model on Group UT was strongly correlated with the reference method (r=0.86, 95%CI: 0.84-0.87) and closely predicted activity intensity (83.7%, 95%CI: 80.9-86.5%). The deep learning method has application for wrist-worn accelerometry in free-living adults.

Wrist-specific accelerometry methods for estimating free-living physical activity.

OBJECTIVES: To compare accelerometry-derived estimates of physical activity from 9 wrist-specific predictive models and a reference hip-specific method. DESIGN: Prospective cohort repeated measures study. METHODS: 110 participants wore an accelerometer at wrist and hip locations for 1 week of free-living. Accelerometer data from three axes were used to calculate physical activity estimates using existing wrist-specific models (3 linear and 6 artificial neural network models) and a reference hip-specific method. Estimates of physical activity were compared to reference values at both epoch (≤60-s) and weekly levels. RESULTS: 9044h were analysed. Physical activity ranged from 7 to 96min per day of moderate-to-vigorous physical activity (MVPA). Method of analysis influenced determination of sedentary behaviour (<1.5 METs), light physical activity (1.5 to <3 METs) and MVPA (>3 METs) (p<0.001, respectively). All wrist-specific models produced total weekly MVPA values that were different to the reference method. At the epoch level, Hildebrand et al. (2014) produced the strongest correlation (r=0.69, 95%CI: 0.67-0.71) with tightest ratio limits of agreement (95%CI: 0.53-1.30) for MVPA, and highest agreement to predict MVPA (94.1%, 95%CI: 94.0-94.1%) with sensitivity of 63.1% (95%CI: 62.6-63.7%) and specificity of 96.0% (95%CI: 95.9-96.0%). CONCLUSIONS: Caution is required when comparing results from studies that use inconsistent analysis methods. Although a wrist-specific linear model produced results that were most similar to the hip-specific reference method when estimating total weekly MVPA, modest absolute and relative agreement at the epoch level suggest that additional analysis methods are required to improve estimates of physical activity derived from wrist-worn accelerometers.