R. C. Patel Institute of Pharmacy, Shirpur-425450
Obesity is a complex global health concern that is associated with increased rates of illness, death, and financial hardship. Traditional monitoring techniques, such as BMI and routine lab testing, usually fail to record the physiological and behavioral information required for real-time early intervention. The management of obesity has seen a revolution thanks to wearable health technology (WHTs), which offer continuous monitoring of metabolic indicators, sleep, stress, physical activity, and caloric expenditure. This review explores how WHTs can be integrated with telemedicine, smart textiles, and artificial intelligence to deliver personalized, predictive care. Clinical studies and systematic reviews have demonstrated that wearable-based therapies enhance behavioral adherence, weight loss, and metabolic outcomes. However, there are still problems with cost, privacy, data accuracy, and user compliance. These problems need to be fixed if wearables are to be extensively utilized in therapeutic contexts. The report concludes by outlining possible future directions, including implantable sensors, AI-enhanced analytics, and ethical data governance, to optimize the treatment of obesity and prevent chronic diseases.
Obesity is becoming more and more common in all socioeconomic groups and regions, endangering both the economy and public health worldwide. In OECD countries today, over 50% of people are overweight, and roughly one in four people are fat. Childhood obesity, which was once rare, is now common and rapidly increasing, putting future generations at risk for greater rates of morbidity and decreased economic output 1. Obesity has a major impact on a number of non-communicable diseases (NCDs), such as diabetes, heart disease, certain types of cancer, and respiratory disorders. Over 70% of all NCDs, which account for 74% of annual deaths globally, are known to be linked to obesity2. Given that obesity is expected to become the primary preventable cause of noncommunicable diseases by 2035, there has never been a more urgent need to address this issue. Obesity reduces life expectancy by up to three years and reduces GDP by roughly 3.3% in OECD nations. Obese people are more likely to miss work, retire early, and perform poorly in school, all of which contribute to inequality and hinder the growth of human capital. It is estimated that the annual cost of obesity-related disorders to OECD countries is about USD 425 billion, or about 8.4% of their health budgets1. It is possible to prevent obesity from a public health perspective. Sedentary lifestyles, poor diets, and increased calorie availability are all major contributors. The epidemic is currently spreading rapidly in low- and middle-income countries because they lack the healthcare infrastructure needed to address related chronic problems3. Investing in preventative measures such as food labeling, advertising regulations, and lifestyle changes is cost-effective. Up to six dollars can be saved for every dollar spent on long-term healthcare and productivity improvements1. In order to reduce the global burden of disease and advance sustainable development, tackling obesity is not only a health imperative but also a prudent financial decision.
Need for Effective and Continuous Monitoring of Obesity
Obesity is becoming a major global health concern due to its high prevalence and the complex and multifaceted nature of its causes and effects. Effective and continuous surveillance is required to control this epidemic. Recent statistics show that in 2019 alone, excess body weight (EBW) caused 160.3 million disability-adjusted life years (DALYs) and 5 million deaths globally. Particularly in South and Southeast Asia, this number is still growing4. These alarming findings call for routine surveillance to identify populations at risk and modify public health interventions accordingly. Obesity necessitates comprehensive and continuous monitoring programs due to its multifactorial causes, which include genetic, metabolic, behavioral, social, and environmental factors. Basic measures such as body mass index (BMI) often fail to capture the underlying pathophysiology or predict associated non-communicable diseases (NCDs), including cardiovascular disorders, type 2 diabetes, and certain types of cancer2. Wearable technology and other cutting-edge, ongoing monitoring tools can therefore assist in tracking metabolic parameters and lifestyle patterns over time, enabling early treatments. Additionally, inclusive and data-driven public health solutions are required. According to WHO's European Regional Obesity Report 20225, one monitoring system that should be integrated is the Childhood Obesity Surveillance Initiative (COSI), which facilitates cross-national comparisons of obesity trends and evaluates the effectiveness of implemented policies. All things considered, preventing obesity requires not only treatment but also proactive monitoring systems that combine real-time data, surveillance technologies, and multi-sectoral policy action. Without these strategies, it will be challenging for health systems worldwide to manage the growing incidence of obesity-related disorders.
Emergence of Wearable Technology in Healthcare
Wearable health technology has come a long way from basic fitness trackers to sophisticated medical-grade devices that can continuously monitor health in real time. While wearables were initially designed to track basic metrics like heart rate and steps, they now measure vital indications like oxygen saturation, glucose levels, and even ECGs 6,7. These technologies enable proactive and preventive management by detecting early signs of health decline and exacerbations of chronic diseases 6,8. They have proven especially effective in the treatment of conditions like diabetes, heart disease, and respiratory conditions by offering continuous feedback and enabling timely actions9. Thanks to integration with artificial intelligence (AI) and machine learning, wearables are now predictive tools that analyze large amounts of health data and offer personalized recommendations and danger alerts10,11. Additionally, implanted wearables and smart textiles represent the next frontier for seamless and less intrusive health tracking12.
Despite these advancements, wearable technology continues to face challenges with privacy, data integrity, and user compliance, particularly when worn for extended periods of time13,14. Increases in sensor accuracy, battery life, and secure data transfer are necessary for their wider application13,15. As healthcare shifts to individualized, data-driven models, wearable technology offers a promising means of enhancing long-term results, reducing hospital stays, and boosting patient involvement6,9,10. Their incorporation into telemedicine and electronic health systems underscores their importance in the future of global healthcare delivery6,11.
UNDERSTANDING OF OBESITY:
Definition and Epidemiology:
The World Health Organization (WHO) defines obesity as abnormal or excessive accumulation of fat that poses a risk to health, and a body mass index (BMI) of ≥30 kg/m² is considered obese 16. It is a complex, multifaceted disease that is influenced by genetic, environmental, and behavioral factors. Globally, the prevalence of obesity has nearly tripled since 1975, and as of 2022, over 1 billion people—650 million adults, 340 million adolescents, and 39 million children—are obese16. According to a global systematic study, the combined prevalence of overweight and obesity rose from 29.8% to 38.0% in women and from 28.8% to 36.9% in men between 1980 and 201317. Obesity rates are highest in the Middle East, North America, and Pacific Island nations, but they are also rising in sub-Saharan Africa and some parts of Asia due to urbanization and dietary changes 17,18. Children and teenagers are becoming more obese. Obesity rates among children aged 5 to 19 were 0.7% in 1975, but by 2016, they had increased to 6.8% for girls and 8.0% for boys globally3. The main causes of this change are increased consumption of calorie-dense processed foods, decreased physical activity, and obesogenic environments3.
TABLE 1: CLASSIFICATION OF OBESITY AS PER WHO 16
|
Sr. No. |
Category |
BMI (kg/m2) |
|
1 |
Underweight |
<18.5 |
|
2 |
Normal weight |
18.5-24.9 |
|
3 |
Overweight |
25.0-29.9 |
|
4 |
Obese class I (Mild) |
30.0-34.9 |
|
5 |
Obese class II (Moderate) |
35.0-39.9 |
|
6 |
Obese class III (severe) |
>40.0 |
Health implications of obesity:
Obesity significantly affects the cardiovascular system by increasing blood volume and cardiac output. This results in left ventricular dilatation and hypertrophy and raises the risk of heart failure (obesity cardiomyopathy). The risk of coronary artery disease (CAD) is increased by concomitant conditions like type 2 diabetes, hypertension, dyslipidemia, and inflammation. Obese people often require weight control and ACE inhibitors or ARBs to treat their hypertension, which is caused by insulin resistance, salt retention, and neurohormonal activation. Anatomical changes and metabolic abnormalities in the heart are often the cause of arrhythmias, especially atrial fibrillation (AF). Preoperative ECGs are essential for risk assessment19.
The metabolic syndrome is a collection of conditions linked to obesity and chronic inflammation that increases the risk of type 2 diabetes and cardiovascular disease. In addition to any two of the following, abdominal obesity is included: high blood pressure (≥130/85 mmHg), low HDL cholesterol (≤40 mg/dl in men, ≤50 mg/dl in women), high triglycerides (≥150 mg/dl), and high fasting glucose (≥100 mg/dl) 19.
Obesity attracts immune cells, especially macrophages, which results in persistent low-grade inflammation due to the death of larger fat cells caused by an excess of calories. By disrupting insulin signaling, these enter a pro-inflammatory state (M1) and release cytokines like TNF-α and IL-6, which raise insulin resistance. Adipokine imbalance (high leptin, low adiponectin) further impairs metabolism. This inflammation aggravates conditions associated with fat, such as type 2 diabetes and cardiovascular disease19.
Obesity exacerbates NAFLD in a number of ways, including increased insulin resistance, lipolysis, and chronic inflammation. Excess adipose tissue causes the liver to accumulate pro-inflammatory cytokines and free fatty acids, which lead to steatosis and hasten the onset of fibrosis and non-alcoholic steatohepatitis (NASH). Since obesity-induced hormonal and metabolic abnormalities play a significant role in the pathophysiology of non-alcoholic fatty liver disease20, controlling weight is a crucial treatment objective.
Obesity is linked to at least 13 cancer types, including colorectal, endometrial, kidney, pancreatic, liver, and postmenopausal breast cancers. Because excess adipose tissue leads to insulin resistance, chronic inflammation, and elevated levels of estrogen and insulin-like growth factors, it can hasten the growth and progression of tumors. Obesity also alters immune surveillance and adipokine secretion, which further increases the risk of cancer and adverse outcomes21.
Obesity is associated with a higher risk of mood and anxiety disorders, including depression, panic disorder, and social anxiety disorder. For every unit increase in BMI, this risk rises by 3–5%. Contributing factors include increased cortisol, negative effects of mental medications, and hormonal imbalances caused by sleep issues. Mental health is also impacted by social stigma and weight-based discrimination. Because these psychological problems can make it difficult for patients to follow their treatment plans, many bariatric centers require mental health exams before surgery19.
Obesity negatively impacts reproductive health in both men and women. It is closely associated with irregular menstruation, anovulation, infertility, and polycystic ovarian syndrome (PCOS) in women, primarily because of insulin resistance and impaired hypothalamic-pituitary-ovarian axis function. Excess adipose tissue increases the production of peripheral estrogen, which can interfere with ovulatory cycles. Male obesity is linked to erectile dysfunction, low testosterone levels, and poor sperm quality due to hormonal imbalances and elevated scrotal temperature. Numerous reproductive disorders are made worse by systemic inflammation and altered adipokine levels, which affect gonadal function and gamete quality. It has been shown that addressing obesity with lifestyle modifications or bariatric surgery improves reproductive outcomes for both sexes22.
ETIOLOGY AND RISK FACTOR OF OBESITY:
Genetic predisposition plays a major role in obesity, particularly when caloric intake surpasses expenditure. Energy metabolism, appetite regulation, and fat storage are all impacted by gene polymorphisms affecting FTO, MC4R, and LEP. Twin, adoption, and family studies have reported heritability estimates for obesity ranging from 40% to 70%. While monogenic variants are rare, most cases of obesity are polygenic, involving multiple genes interacting with environmental triggers23.
2. Environmental and Lifestyle Factors
The contemporary environment promotes high calorie intake and sedentary behavior. Urbanization, reduced physical activity, excessive consumption of processed foods, and increased screen time have all contributed to a consistently positive energy balance. In the "obesogenic environment," which promotes weight growth, especially in settings with limited resources, most people struggle to maintain their weight24.
3. Psychosocial and Behavioral Factors
Emotional eating, binge eating, and eating in response to stress or depression are behavioral factors that contribute to excessive calorie intake and obesity. Long-term stress alters cortisol levels, which influences fat storage, especially in the abdomen. Social isolation and low self-esteem are also linked to unhealthy eating patterns and a sedentary lifestyle25.
4. Endocrine and Metabolic Disorders
Endocrine conditions like hypothyroidism, Cushing's syndrome, and PCOS that interfere with hormone balance and metabolism are the cause of weight gain. These disorders may increase fat accumulation, alter insulin sensitivity, and affect appetite regulation, making weight control difficult even with lifestyle changes26.
5. Medications and Iatrogenic Causes
Weight gain can be caused by a variety of medications, including insulin, corticosteroids, antidepressants (like mirtazapine), antipsychotics (like olanzapine), and others that alter metabolism, increase appetite, or cause fluid retention. It takes awareness and careful monitoring to manage weight fluctuations brought on by medication27.
6. Sleep Deprivation and Circadian Disruption
Obesity is associated with short sleep duration and poor sleep quality due to dysregulation of the hunger hormones ghrelin and leptin. Lack of sleep increases insulin resistance and decreases energy expenditure, and these hormonal changes increase appetite and the propensity to consume high-calorie foods28.
7. Socioeconomic Status (SES)
Particularly in developed countries, obesity rates are strongly associated with lower socioeconomic status. For people with lower incomes and educational attainment, access to safe exercise facilities, wholesome food, and healthcare is often limited. Sedentary lifestyles and inexpensive, high-energy meals may be more popular among those with less money29.
Fig. Risk factors of obesity
CONVENTIONAL METHODS FOR OBESITY MONITORING:
Anthropometric measures:
Planning actions and assessing health risks depend on an accurate assessment of obesity. A number of traditional methods are employed to evaluate fat distribution and body composition. Among the most popular techniques are:
1. Body Mass Index (BMI)
BMI is a simple and reasonably priced method of classifying individuals based on their weight and height. It is calculated by dividing the square of an individual's weight in kilograms (kg/m2) by their height in meters. According to the World Health Organization, a BMI of 25.0–29.9 kg/m² is considered overweight, and a BMI of ≥30 kg/m² is considered obese. Although BMI is widely used in epidemiological research and clinical practice, its use in assessing metabolic risk in particular populations, such as athletes and the elderly, is limited because it cannot distinguish between muscle and fat mass or display the distribution of fat30.
2. Waist-to-Hip Ratio (WHR)
One important factor in determining the distribution of abdominal fat is WHR, which is calculated by dividing waist circumference by hip circumference. A WHR > 0.90 in men and 0.85 in women indicates central or visceral obesity, which is more strongly linked to metabolic syndrome and cardiovascular diseases than BMI. WHR is especially useful in identifying individuals who are at risk for non-communicable diseases, even those with normal BMI. However, it can be influenced by posture and breathing and requires precise identification of anatomical landmarks31.
3. Skinfold Thickness Measurement
Skinfold calipers measure subcutaneous fat by measuring the thickness of skinfolds at specific locations (e.g., the abdomen, subscapular, and triceps). These measurements are used in equations that calculate the percentage of body fat. Because skinfold examination is portable and reasonably priced, it is useful in field settings. However, the experience and proper methodology of the examiner determine accuracy. It is less reliable in fat people because they have problems holding large skinfolds32.
4. Bioelectrical Impedance Analysis (BIA)
BIA is a non-invasive method of estimating body composition by measuring the resistance of body tissues to a small electrical current. Because lean tissue contains more water than fat, it conducts electricity more effectively. BIA estimates total body water, fat mass, and fat-free mass. Despite its speed and accessibility, its accuracy may be affected by factors such as food intake, physical activity prior to evaluation, and level of hydration33.
5. Dual-Energy X-ray Absorptiometry (DEXA)
The most accurate method for figuring out body composition is DEXA. It uses low-dose X-rays to accurately distinguish between bone mass, lean mass, and fat mass. DEXA provides detailed regional fat distribution information, including visceral versus subcutaneous fat. Despite its high accuracy and reproducibility, its limited availability in routine clinical practice and high cost hinder its widespread use34.
Laboratory and clinical assessment methods:
Obesity is a complex metabolic disease that affects many physiological systems. Laboratory and clinical assessments, in addition to anthropometric measurements, provide vital information on metabolic health, disease risk, and the underlying pathophysiology of obesity. Important laboratory techniques that help doctors evaluate the systemic effects of excessive obesity include lipid profiles, markers of glucose metabolism, and hormone assays.
1. Lipid Profile
The lipid profile is a crucial method for determining cardiovascular risk in obese individuals. This test is frequently used to measure triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). Obese people often have dyslipidemia, a major risk factor for coronary heart disease and atherosclerosis. Reduced HDL-C and elevated triglycerides and LDL-C are characteristics of this condition. The accumulation of visceral fat promotes lipolysis, which increases the amount of free fatty acids in the liver. This impairs the elimination of lipids and raises the production of hepatic triglycerides35.
2. Glucose Metabolism Assessment
It is crucial to keep an eye on glucose levels in obese people because of the strong link between obesity and insulin resistance. The oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), and fasting blood glucose (FBG) are commonly used to assess glycemic status. By reducing cells' capacity to absorb glucose and resulting in compensatory hyperinsulinemia, insulin resistance—which is commonly seen in abdominal obesity—increases the risk of type 2 diabetes mellitus (T2DM). The OGTT is particularly sensitive in detecting prediabetes, even though HbA1c shows average glucose levels over the preceding three months36.
3. Hormonal Assays
Hormonal profiling is necessary to comprehend metabolic dysfunction and related endocrine disorders in obesity. Important hormones include cortisol, thyroid hormones (TSH, T3, and T4), ghrelin, insulin, leptin, and adiponectin. Obesity is frequently associated with hyperleptinemia due to leptin resistance, low adiponectin (an insulin-sensitizing adipokine), and altered ghrelin levels that affect appetite control. Thyroid issues, particularly hypothyroidism, are more common in obese people and can exacerbate weight gain. Additionally, elevated cortisol levels caused by Cushing's syndrome or chronic stress can promote central fat deposition37,38.
4. Inflammatory and Liver Markers
Biomarkers like C-reactive protein (CRP) and interleukin-6 (IL-6) are often elevated due to the inflammatory nature of obesity. These inflammatory markers are associated with visceral obesity and insulin resistance. Liver function tests such as ALT and AST are also crucial because non-alcoholic fatty liver disease (NAFLD) is common in obesity and can cause cirrhosis and steatohepatitis39.
Fig. Conventional methods for obesity monitoring
Limitations of traditional methods:
Despite their widespread use, traditional methods of evaluating obesity have several shortcomings that render them unsuitable for continuous monitoring and long-term treatment. One of the primary challenges is the episodic nature of data collection. Conventional diagnostic procedures, like blood tests, waist circumferences, or BMI calculations, are typically carried out during scheduled clinic visits and only offer information at a particular point in time. These recurrent evaluations often fail to detect daily or weekly changes in weight, physical activity, and metabolic markers, delaying the detection of adverse trends or health decline. This lack of real-time data leads to suboptimal outcomes in the treatment of obesity and also hinders the ability to quickly adjust therapeutic approaches40. Additionally, the majority of these methods rely on patient compliance, which is often absent in chronic conditions such as obesity. Self-reported dietary records and physical activity questionnaires are vulnerable to recall bias and underreporting, while methods such as skinfold thickness or bioimpedance analysis require patient cooperation and multiple visits, which many people cannot sustain on a regular basis41. Moreover, these traditional techniques are often operator-dependent, leading to inconsistent measurements and reduced repeatability across environments and practitioners. Cost is yet another significant barrier associated with traditional clinical and laboratory-based evaluations. While some basic tests like BMI and fasting glucose are inexpensive, many patients may not be able to afford more accurate diagnostic methods like Dual-Energy X-ray Absorptiometry (DEXA), hormone assays, or serial lipid profiles, particularly in low-resource or rural settings. The high expense of these diagnostic tests restricts their use to certain individuals in areas with high obesity prevalence, which results in underdiagnosis and postponed intervention42. Furthermore, many of these techniques are only applicable in hospital environments and are not suitable for continuous, remote, or preventive monitoring. These cumulative limitations show that more readily available, real-time, and user-friendly technologies are needed for effective obesity surveillance.
INTRODUCTION TO WEARABLE HEALTH TECHNOLOGY
Definition and types:
Wearable health technologies (WHTs) are a rapidly emerging class of electronic devices designed to be worn on the body and used to continuously or intermittently monitor physiological and behavioral data. These technologies are at the forefront of preventive and personalized healthcare, enabling remote health monitoring, feedback, and real-time data collection. WHTs are increasingly being used in clinical research, chronic illness management, fitness tracking, and early diagnosis because they provide a discrete, non-invasive method of recording health data over time and space40.
Fitness trackers are among the most well-liked and extensively available types of wearable technology. They are typically used by customers to monitor their heart rates, sleep habits, and physical activity (steps, distance), primarily for the maintenance of their health and fitness. Devices like the Fitbit and Xiaomi Mi Band fall under this category.
Smartwatches, Clinical settings are increasingly utilizing devices such as the Apple Watch and Samsung Galaxy Watch, which combine fitness tracker features with additional features like blood oxygen monitoring, ECG monitoring, and even fall detection43.
Biosensors represent a more advanced category of wearable technology. These include patches, chest straps, or implanted devices that continuously monitor vital signs such as body temperature, electrocardiograms (ECGs), glucose levels, and electrodermal activity. Unlike traditional fitness trackers, biosensors are commonly used for real-time, high-fidelity physiological monitoring in hospital and ambulatory care settings44. Electronic textiles, sometimes referred to as smart textiles or e-textiles, integrate sensors and conductive materials directly into garments. Because these textiles can recognize biomechanical and physiological cues like posture, breathing, and muscle activation, they hold promise for use in the management of chronic illnesses and rehabilitation45.
When incorporated into health systems, wearable technology has the potential to completely change traditional episodic healthcare models and offer continuous, customized treatment. These devices can be used to treat lifestyle-related diseases like diabetes, obesity, and heart disease, as well as to promote patient involvement and self-monitoring.
Key technology used Wearable Health Devices:
Wearable health technology tracks and logs physiological and movement-related data in real time using a variety of sensors. Devices like fitness trackers, smartwatches, and medical wearables rely on these sensors as their technological core. Among the most popular sensor technologies are temperature sensors, gyroscopes, photoplethysmography (PPG), electrocardiography (ECG), and accelerometers. For the collection of health-related data, each is necessary.
1. Accelerometers
Accelerometers are motion sensors that measure linear acceleration in one or more directions. They are primarily used in wearable technology to track physical activity, sleep patterns, and posture. Accelerometers can also be used to gather indirect data on energy use. Because they are low-power and lightweight, they are ideal for wearable applications. For example, three-axis accelerometers can distinguish between different activities, such as walking, running, and sitting46. However, problems like signal noise and difficulty detecting low-intensity movement persist.
2. Gyroscopes
Gyroscopes and accelerometers collaborate to measure angular velocity in motion tracking. Accelerometers track linear movement, while gyroscopes detect rotation and orientation. This is particularly useful for spotting abnormalities linked to falls, balance, and posture. Together, gyroscopes and accelerometers increase the accuracy of wearable motion classification, especially for applications such as rehabilitation monitoring and gait analysis47.
3. Photoplethysmography (PPG)
PPG is an optical method for detecting changes in blood volume within the microvascular bed of the tissue. It is commonly used in wrist-worn wearables to monitor heart rate, oxygen saturation (SpO?), and pulse rate variability. The amount of light that bounces back from the skin is measured by PPG sensors and varies depending on blood flow. The light is usually infrared, red, or green. Even though PPG is widely used, motion artifacts, ambient light, and skin tone can cause it to lose accuracy in high-motion scenarios48.
4. Electrocardiography (ECG)
The identification of electrical impulses generated by the heart is made possible by wearable ECG technology. Advanced smartwatches now frequently have single-lead ECG sensors that can capture waveforms in order to identify arrhythmias, especially atrial fibrillation. Long-term, ambulatory cardiac monitoring is now possible thanks to miniature ECG modules, which were previously only utilized in clinical settings. This greatly increases the diagnostic potential outside of hospitals49.
5. Temperature Sensors
Temperature sensors measure the surface temperature of the skin to identify changes in the body's circadian rhythm, disease (like fever), or metabolism. Continuous body temperature monitoring wearables are growing in popularity, especially in the wake of the COVID-19 pandemic and the heightened interest in infection detection. Skin temperature sensors are often combined with other physiological data for more comprehensive health assessments44.
Behavioral data captured by wearables relevant to obesity:
1. Physical Activity
Wearables use accelerometers and gyroscopes to measure steps, movement intensity, and activity duration. This helps quantify physical inactivity, which is a major contributor to obesity. Continuous monitoring of physical activity helps identify sedentary tendencies and supports behavioral therapy46.
2. Calorie Expenditure
Energy expenditure is estimated using motion data, heart rate, and personal biometrics (weight, age, and sex). Even though it is indirect, this information helps track calorie balance, which is crucial for managing obesity and weight-loss programs. Wearable technology can be used to estimate total daily energy expenditure (TDEE) and activity-based energy expenditure (AEE) 50.
3. Sleep Patterns
Accelerometers and heart rate variability (HRV) are used to monitor the duration and quality of sleep. Inadequate sleep is associated with obesity, hormonal imbalances (ghrelin, leptin), and increased appetite. Sleep duration, sleep phases (REM, deep, and light), and disruptions can all be tracked by wearable technology51.
4. Heart Rate (HR) and Heart Rate Variability (HRV)
Accelerometers and heart rate variability (HRV) are used to monitor the duration and quality of sleep. Inadequate sleep is associated with obesity, hormonal imbalances (ghrelin, leptin), and increased appetite. Sleep length, sleep stages (REM, deep, and light), and disturbances can all be tracked by wearable technology52.
5. Stress Monitoring
Wearables measure stress by analyzing HRV, skin conductance (electrodermal activity), and sometimes cortisol levels (in lab-grade wearables). Emotional eating and metabolic changes brought on by ongoing stress result in obesity. Continuous stress monitoring supports behavioral and psychological treatments53.
6. Sedentary Behaviour
Idle periods are recorded by the lack of movement signals. Long-term inactivity is independently associated with obesity, even when physical activity goals are met. By motivating users to move after prolonged periods of inactivity, wearable technology reduces the risk of obesity54.
7. Skin Temperature and Electrodermal Activity
Some advanced wearables measure skin temperature and sweating (EDA), which are linked to stress, circadian cycles, and metabolic rate. These are novel markers for obesity-related metabolic disorders44
Fig. Wearable health technology
ROLE OF WEARABLE IN OBESITY MONITORING:
Wearable technology has transformed the monitoring and treatment of obesity because it makes it possible to continuously and in real-time collect health-related data. Their inclusion in obesity management and prevention plans encourages personalized medical care, informed medical judgment, and behavioral modification.
1. Tracking Physical and Sedentary Behaviour
One of the primary applications of wearables in obesity monitoring is the tracking of sedentary behavior and physical activity. Devices such as Fitbit, Garmin, and ActiGraph use accelerometers and gyroscopes to track step counts, movement patterns, and the amount of time spent in varying intensities of physical activity. This makes it simpler to spot those who spend a lot of time sitting down or are physically inactive, two significant risk factors for obesity. The data collected allows physicians and users to objectively quantify movement, providing a more realistic picture than self-reports40. Furthermore, studies have shown that wearable technology can effectively encourage users to increase their daily activity levels and reduce idle time44.
2. Monitoring Sleep and Energy Expenditure
Because obesity causes hormonal dysregulation (e.g., ghrelin, leptin) and decreased glucose metabolism, it is linked to short sleep duration and poor quality. Wearables measure body movement, heart rate variability, and peripheral skin temperature to track sleep duration and cycles51. Energy expenditure is also estimated by combining information from accelerometers, heart rate monitors, and personal traits like weight, age, and sex. Although less precise than indirect calorimetry, wearable-based estimates of total energy expenditure (TEE) and activity-related energy expenditure (AEE) are sufficient for lifestyle monitoring and interventions in adults who live freely50.
3. Biofeedback and Behavioural Modification
One way wearables help with behavior change is through real-time biofeedback, which has been shown to boost motivation and adherence. Quick behavioral changes may result from continuous data on the user's physiological state, such as heart rate, calories burned, or idle time. Another advantage of biofeedback mechanisms is that they increase self-awareness of one's habits, which is an important component of behavior modification theories like the Self-Determination Theory and the Transtheoretical Model55.
4. Personalized Alerts and Goal Setting
Wearable technology enables customizable features such as personalized goals, daily reminders, and alerts to encourage movement when prolonged periods of inactivity are detected. These goal-setting characteristics have been associated with increased physical activity and improved weight control in several populations, including those who are overweight or obese56. Real-time notifications serve as digital coaches that enhance user engagement and promote sustained behavioral adherence.
5. Integration with Apps and Healthcare Systems
Modern wearables synchronize data with smartphone apps and cloud-based systems. These apps provide visual analytics, trend tracking, and personalized health recommendations. More importantly, integration with electronic health records (EHRs) and digital health systems enables remote patient progress monitoring. This facilitates data-driven therapeutic decision-making and personalized care planning57. Some healthcare facilities have begun incorporating wearable data into telemedicine procedures to enhance continuity of care, especially in obesity and lifestyle management programs.
EVIDENCES FROM LITERATURE
Systematic review and meta-analysis case studies and trials
Because obesity is a serious worldwide health issue, there is increasing interest in evidence-based weight loss techniques. Comprehensive behavioral therapies are successful in producing moderate but clinically significant weight loss, according to systematic reviews and meta-analyses. According to a 2021 meta-analysis by Palacios et al., behavioral weight loss interventions, such as dietary changes, more exercise, and behavioral therapy, resulted in an average weight loss of 3.63 kg at 12 months when compared to usual care; the reductions were larger when goal-setting and self-monitoring were included in the interventions58. Wearable technology and mobile health apps have shown promise in enhancing behavioral adherence and long-term engagement. A 2021 Cochrane comprehensive review by Tang et al. found that wearable activity tracker-based therapies led to modest weight loss and increased physical activity across short- to medium-term follow-ups59. Another meta-analysis by Gal et al. found that digital health interventions, particularly those that included feedback and reminders, were effective in helping obese and overweight individuals change their behavior and lose weight60. Even slight weight loss has a big clinical impact. A thorough analysis by LeBlanc et al. (2018) found that when overweight or obese people lose 5–10% of their body weight, there are clear therapeutic benefits because this is associated with improved glycaemic control, decreased blood pressure, and decreased cholesterol61. Additionally, behavioral interventions in primary care were associated with improved metabolic health metrics, including insulin sensitivity and C-reactive protein levels62. Furthermore, Johnston et al. (2014) evaluated various diets in a network meta-analysis and found that calorie restriction and adherence were the main factors influencing weight loss, regardless of the macronutrient composition63.The significance of environmental and cognitive factors in weight management was highlighted by the fact that behavioral support increased adherence and produced long-lasting results across all diet types.
Limitation and challenges reported, adherence, accuracy, privacy and cost
Although behavioral therapies and wearable technology may aid in the management of obesity, there are still a number of obstacles to overcome. One of the primary problems is adherence. The long-term effectiveness of wearable fitness trackers in weight management is limited because, as per a thorough study by Brickwood et al. (2019), user engagement tends to drastically decline after three to six months64. Inconsistent use reduces the capacity to generate accurate longitudinal data for behavior monitoring and feedback. Accuracy is another disadvantage, especially when it comes to energy expenditure and calorie counting. Evenson et al. (2015) found that step counts and heart rate accuracy in commercial wearables often vary significantly depending on the device type and user characteristics such as body mass index and gait style65. This could undermine trust in the precision of health indicators and technology. The lack of adoption of digital health solutions is increasingly attributed to privacy concerns. According to Lupton (2014), many users are hesitant to share sensitive health information, particularly when app privacy policies are unclear or when data may be used for commercial purposes without explicit consent66. Lastly, one major obstacle to fair access is cost. Health inequities are exacerbated by the fact that many high-quality wearable technology and app subscriptions are still out of reach for lower-income groups. The necessity of scalable, affordable therapies that don't only rely on pricey commercial technologies was underlined by Patel et al. (2015) 67.
EMERGING TRENDS AND FUTURE PERSPECTIVE:
Artificial Intelligence and Predictive Analytics
Artificial intelligence (AI) and machine learning are increasingly being used to enhance obesity monitoring by enabling personalized and predictive treatment. By examining intricate datasets from wearables, electronic health records, and behavioral inputs, these algorithms predict trends in weight gain, physical inactivity, or nutritional deficiencies. Wang et al.'s review from 2021 demonstrated how AI-driven interventions improved behavioral adherence and weight reduction outcomes by personalizing feedback and support systems68. Similarly, Chawla et al. (2022) highlighted the application of deep learning to identify individuals at risk and develop personalized exercise and nutrition plans based on metabolic profiles69.
Smart Clothing and Implantable
Beyond traditional wearables, the future is symbolized by smart clothing and implantable biosensors. Smart clothes with textile-based sensors can monitor posture, heart rate, breathing rate, and activity patterns with minimal help from the wearer. Heikenfeld et al. (2018) emphasized the potential of biosensing textiles for continuous metabolic monitoring, including water and glucose levels, which can indirectly aid in weight management44. Implantable technology, while still in its early stages, offers accurate, continuous physiological monitoring without the need for additional devices. These devices raise questions regarding clinical suitability, cost, and invasiveness despite their potential.
Integration with Telehealth and Remote Monitoring
The combination of obesity monitoring technologies and telemedicine platforms allows for real-time intervention and opportunities for continuous care. Remote monitoring enables doctors to assess patients' progress, provide timely counseling, and adjust treatment plans without having to visit patients in person. A study by Martinez et al. (2022) found that integrating wearable data into teleconsultation platforms improved clinical outcomes and patient engagement, particularly in rural or underserved areas70. Furthermore, remote coaching via smartphone apps and virtual reality interfaces is gaining popularity as a scalable way to deliver cognitive-behavioral therapy and fitness advice.
Ethical and Data Security Considerations
The spread of ubiquitous monitoring systems also raises important ethical and data security concerns. The problems include algorithmic bias, unauthorized access to personal health data, and opaque AI-driven recommendations. The "datafication" of health may result in new forms of surveillance and self-discipline that are incompatible with patient autonomy, according to Lupton (2014) 66. Additionally, preserving trust necessitates safe data transmission and compliance with privacy laws like GDPR and HIPAA. End-to-end encryption, anonymization, and ethical AI frameworks should be given top priority by developers of health monitoring software, according to a policy paper published by the European Society of Cardiology (ESC) 71.
CONCLUSION
Obesity, which contributes significantly to the burden of non-communicable diseases like diabetes, heart disease, and various forms of cancer, is currently one of the largest global health and economic problems. Traditional obesity monitoring methods are limited by their episodic nature, reliance on self-reporting, and lack of real-time data, despite being useful for clinical evaluations. These disadvantages highlight the growing need for scalable, continuous, patient-centered monitoring methods. This review emphasizes the transformative potential of wearable health technology (WHTs) in the management of obesity. Modern wearables provide a multitude of physiological and behavioral data, including energy expenditure, heart rate variability, stress, physical activity, and sleep patterns, in addition to fitness tracking. These real-time insights enable tailored, data-driven interventions that promote sustained behavior change. According to meta-analyses and systematic reviews, incorporating wearable technology into lifestyle treatments enhances adherence, improves metabolic profiles, and results in noticeable weight loss. Additionally, wearable platforms that employ predictive analytics and artificial intelligence (AI) offer the ability to anticipate weight-related risks and offer personalized, preventive treatment. More comprehensive but less intrusive monitoring is becoming possible thanks to sensor fusion technologies, implantables, and smart textiles. Additionally, scalable chronic disease management, timely feedback, and remote supervision are made possible by telehealth systems that integrate wearable data, all of which are particularly beneficial in underserved areas. However, several challenges remain. Long-term use is still significantly hampered by issues with cost, privacy, device accuracy, and user adherence. Due to population-specific differences in sensor performance, concerns about data exploitation, and the decline in user involvement after early novelty, strict ethical standards and regulatory control are required.
REFERENCES
Rajeshwari Malode, Sayyed Shiban Ali, S. P. Zambad, From Fat Cell to Fit Tech: Review on Management and Monitoring of Obesity and Role of Wearable Device, Int. J. of Pharm. Sci., 2025, Vol 3, Issue 6, 5347-5365. https://doi.org/10.5281/zenodo.15757154
10.5281/zenodo.15757154
