A Wearable Device and AI-Supported Diet and Exercise Intervention

Introduction The prevalence of obesity has grown globally, and leading to increases in obesity-related conditions and the medical costs of obesity. Obesity is a complex endocrine and metabolic disorder with features of adipose tissue inflammation, remodeling hypertrophy and hyperplasia of adipocytes. Furthermore, obesity induces an imbalance in the regulation of adipokines, which are a group of cytokines that are produced and secreted in adipose tissue. Many studies have investigated the role of different adipokines, and galectin-1 (Gal-1) has emerged as a novel adipokine. Galectins are carbohydrate-binding proteins with an affinity for β-galactosides. Gal-1 is expressed by various tissues and exhibits a wide range of biological activities. Evidence has indicated that galectin-1 is a regulator of immune responses, inflammation, allergy, homeostasis and development.Gal-1 mainly performs an anti-inflammatory role. However, only in recent years have studies shown that the pro-inflammatory properties of Gal-1 in certain diseases, indicating the dual effects of Gal-1.The expression of Gal-1 is highest in subcutaneous adipose tissue. Gal-1 has been proposed as a novel adipokine that circulates during adipocyte differentiation and adipose tissue homeostasis in the literature.In human studies, circulating Gal-1 is positively associated with body mass index (BMI),body fat and waist circumference.

The promotion of physical activity/exercise training and weight loss is advocated to reduce cardiovascular risk in overweight and obese patients. Exercise training programs consistent with the minimum physical activity guidelines are associated with cardiovascular health benefits.With the development of technology, wearable device plus artificial intelligence APPs can be an effective and low-cost tool for patients by helping facilitate their interactions with health care providers. In a systemic review focused on the characteristics of fitness APPs and wearables to assist users overcome obesity and discovered that the wearables emphasize reminders, tracking / monitoring/ feedback and goals / rewards. The use of wearable devices for improving health care processes and outcomes is promising for health promotion among patients with chronic diseases such as obesity and diabetes.

The level of circulating Gal-1 after diet- and exercise-induced body composition changes has previously never been examined. Therefore, we implemented a 6-month diet and exercise program which applied wearable device plus artificial intelligence APPs to investigate the association of changes in circulating Gal-with weight loss, body composition and metabolic parameters among individuals with obesity.

Materials and Methods Participants This study adopted a personalized 6-month personalized diet and exercise program to promote weight loss. Subjects were recruited in outpatient clinics at the Department of Family Medicine, National Taiwan University Hospital. Overweight and obese adults were eligible for this study. The criteria for overweight and obesity in Taiwan are as follows: overweight is defined as 24 ≤ BMI (kg/m2)≤ 27; mild obesity is defined as 27 ≤ BMI (kg/m2) ≤ 30; moderate obesity is defined as 30 ≤ BMI (kg/m2) ≤ 35; and severe obesity is defined as 35 ≤ BMI (kg/m2). The exclusion criteria were as follows: a body weight change greater than 3% in the previous three months; a history of bariatric procedures; untreated psychiatric disorders; or currently using medications known to affect body weight. This study was approved by the Ethics Committee of National Taiwan University Hospital (202011101RINB), and written informed consent was obtained from all participants.

Study design A total of 171 subjects were screened at the outpatient clinics. Twenty-nine of the 171 participants were excluded from the study after screening because their BMI did not meet the criteria. The remaining 142 subjects participated in body composition and biochemical analysis at baseline. Ninety-eight of these subjects were willing to enter the 6-month weight loss program for diet and exercise intervention, while the other 44 subjects preferred to stay in the cohort as the control group. After enrollment, the subjects were divided into two groups based on their preferences: one group participated in a standard diet and exercise program, while the other group received the same program along with a wearable watch and artificial intelligence APPs. The wearable watch could be used to track and monitor various metrics, including steps, heart rates and calorie expenditure. Outpatient clinic visits were scheduled in week 0, week 12, and week 24. Face-to-face diet and exercise education were conducted at each visit. Participants also completed phone consultations in week 2, week 4, and week 8 by the case managers. The case managers provided diet and exercise consultation and coaching for 10 minutes per consultation. Medication for weight loss was not allowed during the training program.

Data collection Participants' weight, height, systolic blood pressure (BP), and diastolic BP were measured by a standard electronic scale of stadiometer and sphygmomanometer. Body weight was assessed to the nearest 0.1 kg with the participant clothed in lightweight clothing. Waist circumference was measured by the same trained operator. Bioelectrical impedance analysis was performed for skeletal muscle, fat-free mass, body fat mass and body fat percentage. Regarding blood analysis, venous blood samples were collected from the participants following a minimum eight-hour fasting period. Serum glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride levels were assessed by an automatic spectrophotometric assay (HITACHI 7250, Japan). Fasting insulin levels were measured by a microparticle enzyme immunoassay using an AxSYM system (Abbott Laboratories, Dainabot Co, Tokyo, Japan). The homeostatic model assessment of insulin resistance (HOMA-IR) was applied as an indirect measure of the degree of insulin resistance (HOMA-IR=fasting insulin x fasting plasma glucose/22.5, with glucose in mmol/L and insulin in mU/L).Serum galectin-1 (cat no: DGAL10, RD Systems, MN, USA) levels were measured by an enzyme-linked immunosorbent assay (ELISA) kit that employed the quantitative sandwich enzyme immunoassay technique. All body composition, blood pressure, and biochemical variables were measured on day 1 (baseline) and at the end of the intervention (week 24).

Diet and exercise intervention The subjects were divided into two groups based on their preferences. Each participant received a standard diet and exercise program. For those who opted for additional wearable devices, a wearable watch and artificial intelligence apps were administered. Each participant was suggested to aim for a daily target of 7,500 steps. This recommendation aligns with the guideline of achieving approximately 150 minutes of moderate-intensity aerobic physical activity per week, as recommended by the World Health Organization.The standard dietary education encompasses instruction on the calculation of total caloric intake and emphasizes the principles of the Mediterranean diet. Tailored dietary concepts are developed based on individual patient habits to give practical advice. Each participant who chose an extra wearable device downloaded the APP to connect with the health management platform. User interface of the health management platform collected data on the number of steps walked, distance, and heart rate spontaneously. Participants recorded a dietary diary every day with a smartphone APP. All collected information was uploaded to the health management platform, too. The well-trained case managers offered a platform to coach the participants to set-up a better self-managed lifestyle modification through strategies of emphasize reminders, tracking / monitoring/ feedback and goals / rewards.