Feasibility and Effectiveness of a Motion Tracking-Based Online Fitness Program for Office Workers

Predicting and comparing the long-term impact of lifestyle interventions on individuals with eating disorders in active population: a machine learning evaluation

Objective

This study aims to evaluate and predict the long-term effectiveness of five lifestyle interventions for individuals with eating disorders using machine learning techniques.

Methods

This study, conducted at Dr. Irandoust's Health Center at Qazvin from August 2021 to August 2023, aimed to evaluate the effects of five lifestyle interventions on individuals with eating disorders, initially diagnosed using The Eating Disorder Diagnostic Scale (EDDS). The interventions were: (1) Counseling, exercise, and dietary regime, (2) Aerobic exercises with dietary regime, (3) Walking and dietary regime, (4) Exercise with a flexible diet, and (5) Exercises through online programs and applications. Out of 955 enrolled participants, 706 completed the study, which measured Body Fat Percentage (BFP), Waist-Hip Ratio (WHR), Fasting Blood Sugar (FBS), Low-Density Lipoprotein (LDL) Cholesterol, Total Cholesterol (CHO), Weight, and Triglycerides (TG) at baseline, during, and at the end of the intervention. Random Forest and Gradient Boosting Regressors, following feature engineering, were used to analyze the data, focusing on the interventions' long-term effectiveness on health outcomes related to eating disorders.

Results

Feature engineering with Random Forest and Gradient Boosting Regressors, respectively, reached an accuracy of 85 and 89%, then 89 and 90% after dataset balancing. The interventions were ranked based on predicted effectiveness: counseling with exercise and dietary regime, aerobic exercises with dietary regime, walking with dietary regime, exercise with a flexible diet, and exercises through online programs.

Conclusion

The results show that Machine Learning (ML) models effectively predicted the long-term effectiveness of lifestyle interventions. The current study suggests a significant potential for tailored health strategies. This emphasizes the most effective interventions for individuals with eating disorders. According to the results, it can also be suggested to expand demographics and geographic locations of participants, longer study duration, exploring advanced machine learning techniques, and including psychological and social adherence factors. Ultimately, these results can guide healthcare providers and policymakers in creating targeted lifestyle intervention strategies, emphasizing personalized health plans, and leveraging machine learning for predictive healthcare solutions.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review

BACKGROUND

Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.

OBJECTIVE

This study aims to review and describe the current evidence on the HFs in AI-driven digital solutions for increasing PA.

METHODS

We conducted a scoping review by searching for publications containing terms related to PA, HFs, and AI in the titles and abstracts across 3 databases-PubMed, Embase, and IEEE Xplore-and Google Scholar. Studies were included if they were primary studies describing an AI-based solution aimed at increasing PA, and results from testing the solution were reported. Studies that did not meet these criteria were excluded. Additionally, we searched the references in the included articles for relevant research. The following data were extracted from included studies and incorporated into a qualitative synthesis: bibliographic information, study characteristics, population, intervention, comparison, outcomes, and AI-related information. The certainty of the evidence in the included studies was evaluated using GRADE (Grading of Recommendations Assessment, Development, and Evaluation).

RESULTS

A total of 15 studies published between 2015 and 2023 involving 899 participants aged approximately between 19 and 84 years, 60.7% (546/899) of whom were female participants, were included in this review. The interventions lasted between 2 and 26 weeks in the included studies. Recommender systems were the most commonly used AI technology in digital solutions for PA (10/15 studies), followed by conversational agents (4/15 studies). User acceptability and satisfaction were the HFs most frequently evaluated (5/15 studies each), followed by usability (4/15 studies). Regarding automated data collection for personalization and recommendation, most systems involved fitness trackers (5/15 studies). The certainty of the evidence analysis indicates moderate certainty of the effectiveness of AI-driven digital technologies in increasing PA (eg, number of steps, distance walked, or time spent on PA). Furthermore, AI-driven technology, particularly recommender systems, seems to positively influence changes in PA behavior, although with very low certainty evidence.

CONCLUSIONS

Current research highlights the potential of AI-driven technologies to enhance PA, though the evidence remains limited. Longer-term studies are necessary to assess the sustained impact of AI-driven technologies on behavior change and habit formation. While AI-driven digital solutions for PA hold significant promise, further exploration into optimizing AI's impact on PA and effectively integrating AI and HFs is crucial for broader benefits. Thus, the implications for innovation management involve conducting long-term studies, prioritizing diversity, ensuring research quality, focusing on user experience, and understanding the evolving role of AI in PA promotion.

Comparison of the Effects of Two Different Exercise Programs on Lower Limb Functions, Posture, and Physical Activity in Office Workers Working at Home and in Office Alternately: A Randomized Controlled Trial

OBJECTIVE

The aim of this study was to compare the effects of online functional exercises and posture exercises on lower limb functions, posture, and physical activity in office workers working at home and in office alternately during the COVID-19 pandemic.

DESIGN

Forty individuals were included in the study and were randomized into two groups: group I (functional exercise group, 20 participants) and group II (posture exercise group, 20 participants). The exercises programs were performed online for 2 days/in a week/8 wk. Lower limb functions, posture, and physical activity were evaluated before and after the exercise program.

RESULT

Significant improvement was obtained in lower limb functions (muscular endurance, balance, and functional capacity), posture, and physical activity in group I, while significant improvement was obtained in balance and functional capacity in group II after treatment. The change in scores of lower limb functions, posture, and physical activity after treatment was statistically superior in group I compared with group II ( P < 0.05).

CONCLUSIONS

It was found that both online exercise programs provided effective results in office workers working at home and in office alternately due to the COVID-19 pandemic, but the functional exercise program had superior effects on lower limb functions, posture, and physical activity compared with the posture exercise program.

Effects of short-term upper extremity exercise training in office workers during COVID-19 restrictions: A randomized controlled trial

BACKGROUND

During the COVID-19 pandemic, physical inactivity and inactivity-related health problems have deepened in many individuals, including office workers. It is not yet known whether there are exercise programs through telerehabilitation that will provide rapid relief in a short time in office workers who apply part or full-time teleworking system.

OBJECTIVE

To comparatively investigate influences of short-term upper extremity exercise trainings (UEET) on pain, musculoskeletal discomforts (MSD), physical activity (PA), mood, and quality of life (QOL) in office workers during COVID-19 restrictions.

METHODS

Thirty office workers were divided into exercise (EG) (UEET and walking advice) and control (CG) (walking advice) groups. The UEET was applied for at least 20-40 minutes/day, 5-7 days/week for a one week between February 2022 and June 2022. Office workers' pain, MSD, PA level, mood and QOL were measured.

RESULTS

Baseline characteristics of groups (EG: 37.8±7.04 years, CG: 41.6±7.97 years) were similar (p > 0.05). Following UEET, scores of office workers in EG on total step count, vigorous PA, moderate-intensity PA, walking, total PA, physical functioning, and body pain subscales of QOL significantly increased compared to scores of office workers in CG, while scores on neck, back and hip discomforts and anxiety and depression significantly decreased (p < 0.05).

CONCLUSIONS

One-week UEET and walking advice can improve office workers' daily step counts, MSD, PA levels, mood, and QOL. Office workers who have a busy work schedule may do these UEET and walking exercises in break times to relieve perception of discomfort.

Effect of Video Camera Angle on the Detection of Compensatory Movements during Motion Observation

When exercise instructions are provided over the Internet, such as in online personal training, an instructor checks the user's form by watching their motion video recorded using a single camera device. However, fixed shooting angles may affect the detection of incorrect forms, including compensatory movements. This study aimed to verify whether differences in the shooting direction could influence compensatory movement detection by conducting motion observation using training motion videos shot from two angles. Videos of four training movements, including compensatory movements, were simultaneously captured from the front and side. Ten university students studying physical therapy watched the videos from each angle to detect compensatory movements. This study revealed significant differences between the plane of motion in which the compensatory action occurred and the direction of shooting for the false responses in the compensatory action detection for the three movements (p < 0.05). The results indicated that the shooting direction and the plane of motion in which the compensatory action occurred affected the detection of compensatory movements, which was attributable to differences in information on the amount of joint change depending on the direction of joint motion observation and to a lack of binocular visual information necessary for depth motion detection.

An Artificial Intelligence Exercise Coaching Mobile App: Development and Randomized Controlled Trial to Verify Its Effectiveness in Posture Correction

BACKGROUND

Insufficient physical activity due to social distancing and suppressed outdoor activities increases vulnerability to diseases like cardiovascular diseases, sarcopenia, and severe COVID-19. While bodyweight exercises, such as squats, effectively boost physical activity, incorrect postures risk abnormal muscle activation joint strain, leading to ineffective sessions or even injuries. Avoiding incorrect postures is challenging for novices without expert guidance. Existing solutions for remote coaching and computer-assisted posture correction often prove costly or inefficient.

OBJECTIVE

This study aimed to use deep neural networks to develop a personal workout assistant that offers feedback on squat postures using only mobile devices-smartphones and tablets. Deep learning mimicked experts' visual assessments of proper exercise postures. The effectiveness of the mobile app was evaluated by comparing it with exercise videos, a popular at-home workout choice.

METHODS

Twenty participants were recruited without squat exercise experience and divided into an experimental group (EXP) with 10 individuals aged 21.90 (SD 2.18) years and a mean BMI of 20.75 (SD 2.11) and a control group (CTL) with 10 individuals aged 22.60 (SD 1.95) years and a mean BMI of 18.72 (SD 1.23) using randomized controlled trials. A data set with over 20,000 squat videos annotated by experts was created and a deep learning model was trained using pose estimation and video classification to analyze the workout postures. Subsequently, a mobile workout assistant app, Home Alone Exercise, was developed, and a 2-week interventional study, in which the EXP used the app while the CTL only followed workout videos, showed how the app helps people improve squat exercise.

RESULTS

The EXP significantly improved their squat postures evaluated by the app after 2 weeks (Pre: 0.20 vs Mid: 4.20 vs Post: 8.00, P=.001), whereas the CTL (without the app) showed no significant change in squat posture (Pre: 0.70 vs Mid: 1.30 vs Post: 3.80, P=.13). Significant differences were observed in the left (Pre: 75.06 vs Mid: 76.24 vs Post: 63.13, P=.02) and right (Pre: 71.99 vs Mid: 76.68 vs Post: 62.82, P=.03) knee joint angles in the EXP before and after exercise, with no significant effect found for the CTL in the left (Pre: 73.27 vs Mid: 74.05 vs Post: 70.70, P=.68) and right (Pre: 70.82 vs Mid: 74.02 vs Post: 70.23, P=.61) knee joint angles.

CONCLUSIONS

EXP participants trained with the app experienced faster improvement and learned more nuanced details of the squat exercise. The proposed mobile app, offering cost-effective self-discovery feedback, effectively taught users about squat exercises without expensive in-person trainer sessions.

TRIAL REGISTRATION

Clinical Research Information Service KCT0008178 (retrospectively registered); https://cris.nih.go.kr/cris/search/detailSearch.do/24006.

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network

Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yoga mats, and horizontal bars to complete fitness exercises and can effectively avoid contact with people, so it is deeply loved by people. People who work out at home use social media to obtain fitness knowledge, but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people's fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a method based on deep transfer learning to establish a fitness database. After that, a deep neural network was trained to detect the type and completeness of fitness movements. We used Yolov4 and Mediapipe to instantly detect fitness movements and stored the 1D fitness signal of movement to build a database. Finally, MLP was used to classify the 1D signal waveform of fitness. In the performance of the classification of fitness movement types, the mAP was 99.71%, accuracy was 98.56%, precision was 97.9%, recall was 98.56%, and the F1-score was 98.23%, which is quite a high performance. In the performance of fitness movement completeness classification, accuracy was 92.84%, precision was 92.85, recall was 92.84%, and the F1-score was 92.83%. The average FPS in detection was 17.5. Experimental results show that our method achieves higher accuracy compared to other methods.

Exergaming to improve physical, psychological and cognitive health among home office workers: A COVID-19 pandemic commentary

BACKGROUND

The COVID-19 pandemic has resulted in increased sedentary behaviour and poorer health among office workers. Exergaming is a technology-driven mode of exercise that can improve health while physically distancing.

OBJECTIVE

The purpose of this commentary was to explain the benefits of exergaming on physical function, psychological health, and cognition among office workers.

RESULTS

Exergaming improves these health outcomes, reduces pain, and decreases the risk for chronic disease. It is easily accessible on smart devices and can be performed both indoors and outdoors.

CONCLUSIONS

Twenty-one minutes of exergaming per day can improve health outcomes and reduce the risk of pain and disease. Employers and policy-makers should consider promoting exergaming among office workers.

Design of the Exercise Load Data Monitoring System for Exercise Training Based on the Neural Network

In order to monitor the sports load data of athletes in sports training, this paper studies the methods and systems of sports load monitoring and fatigue warning based on neural network technology. In this paper, the neural network parallel optimization algorithm based on big data is used to accurately estimate the motion load and intensity according to the determined motion mode and acceleration data, so as to realize the real-time monitoring of the exercise training. The results show that the value of η is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, 176 samples were extracted from the monitoring data collected by the "National Tennis Team Information Platform," 160 of which were selected as training samples and the other 16 as test samples. Ant colony size M = 20. The minimum value W _min of the weight interval is -2, and the maximum value W _max is 2. The maximum number of iterations is set to 200. σ = 1; that is, only one optimal solution is retained. The domain is divided into 60 parts evenly; that is, r = 60. Generally, η can be taken as any number [28] between [10-3, 10], but the value is usually small to ensure that the weight correction can truly follow the direction of the gradient descent. In this paper, the value is 0.003. In the early warning stage of exercise fatigue, reasonable measurement units of exercise fatigue time were divided according to the characteristics of different exercise items. It is proved that the Bayesian classification algorithm can effectively avoid the sports injury caused by overtraining by warning the fatigue and preventing the sports injury caused by overtraining.

Effect of online low-intensity exercise training on fitness and cardiovascular parameters

Online exercise is undoubtedly useful and important; however, chronic adaptations to online exercise, particularly strength gain, muscle hypertrophy, and cardiovascular parameters, remain unclear. We investigated the effect of online exercise training using Zoom on fitness parameters compared with the same exercises supervised directly. In the present study, 34 subjects (age: 42.9±14.4 years) were included. Twenty-three subjects performed eight weeks of body mass-based exercise training online using Zoom, and eleven subjects performed the same exercise supervised directly as the control group. The subjects performed low-load resistance exercises twice a week for 8 weeks for a total of 16 sessions. The sessions included 9 exercises: leg raises, squats, rear raises, shoulder presses, rowing, dips, lunges, Romanian dead lifts, and push-ups. Chair-stand, push-up, and sit-and-reach tests were performed on all subjects. Overall, the home exercise program effectively increased strength and muscle mass and decreased blood pressure and arterial stiffness, but there were no differences between the groups. Changes in chair-stand and sit-and-reach test results were higher in the control group than in the online group. Our results show that there is a similar training response to body mass-based training in both groups, even with virtual experiences using Zoom.