Can Digital Technologies Be Useful for Weight Loss in Individuals with Overweight or Obesity? A Systematic Review

Digital technologies have greatly developed and impacted several aspects of life, including health and lifestyle. Activity tracking, mobile applications, and devices may also provide messages and goals to motivate adopting healthy behaviors, namely physical activity and dietary changes. This review aimed to assess the effectiveness of digital resources in supporting behavior changes, and thus influencing weight loss, in people with overweight or obesity. A systematic review was conducted according to the PRISMA guidelines. The protocol was registered in PROSPERO (CRD42023403364). Randomized Controlled Trials published from the database’s inception to 8 November 2023 and focused on digital-based technologies aimed at increasing physical activity for the purpose of weight loss, with or without changes in diet, were considered eligible. In total, 1762 studies were retrieved and 31 met the inclusion criteria. Although they differed in the type of technology used and in their design, two-thirds of the studies reported significantly greater weight loss among electronic device users than controls. Many of these studies reported tailored or specialist-guided interventions. The use of digital technologies may be useful to support weight-loss interventions for people with overweight or obesity. Personalized feedback can increase the effectiveness of new technologies in motivating behavior changes.


Introduction
Obesity was classified as a disease as early as 1948, and due to the rising epidemic, the World Health Organization (WHO) has since defined obesity as "abnormal or excessive fat accumulation that may impair health", recognizing the need for action against this epidemic growth [1,2].In the past two decades, the rates of obesity have rapidly increased across the developing world, and new statistics show that the prevalence of obesity is still growing [3].It is also estimated that by 2030, obesity will affect over one billion people worldwide [4,5].The continuous increase in the prevalence of overweight and obesity represents a major public health issue because scientific evidence has demonstrated that these conditions are a risk factor for several diseases, mainly chronic ones, such as diabetes, musculoskeletal disorders, cardiovascular diseases or even some cancers, such as gastroesophageal, breast, endometrial, ovarian, kidney and colon cancer [6][7][8][9].Since the start of the International Obesity Task Force (IOTF) in 1995 [10], obesity has been calculated based on the body mass index (BMI) which is calculated based on the weight (in kg)/height (in m 2 ) ratio [11].This measurement allows us to classify individuals into the "underweight", "normal weight", "overweight", or "obese" category.The WHO often classifies adult obesity in subclasses [Obese I, II, III] using BMI cutoffs [12].This WHO classification is beneficial in distinguishing individuals who may have an increased risk of morbidity and mortality due to obesity [2].Different determinants of health have been associated with obesity, such as individual, socio-economic, lifestyle and environmental factors [13].It is widely acknowledged that there is a strong correlation between socioeconomic status and malnutrition [14].Some authors state that rapid urbanization can lead to "incorrect food choices" due to high consumption of ultra-processed food.The lack of time and education, in combination with the issue of poverty in this fast-paced world, can lead to poor food choices with a lack of nutritional value and quality and excessive sugar intake, along with a lack of physical activity (PA), which can lead to obesity [15,16].Different methods for managing weight loss in individuals with overweight or obesity have been developed.These include different types of diets, pharmacotherapy and lifestyle interventions, alone or in combination.However, there is no one-size-fits-all approach, and new strategies are constantly being developed to keep up with changing population trends [17].Furthermore, notwithstanding their effectiveness in determining weight loss, these methods may be ineffective in long-term body weight maintenance.
The introduction of new technologies has had a huge impact on lifestyle choices and health.In this modern era, in which connectivity and technological innovation are in, smartphones and wearables have rapidly gained popularity.Most of the population have their smartphones on or close to them throughout the day [18,19].This increase in technology use has also contributed to the increasing adoption of sedentary lifestyle and to the consequent decrease in PA, which can be related to premature mortality and morbidity and an increased risk of major noncommunicable diseases [20].On the other hand, many researchers have studied different ways to show how the use of digital eHealth or mHealth and new technology, such as wearable sensors, can actually enhance health promotion and prevention [21].The term mHealth was first invented to describe emerging mobile communications and network technologies for healthcare [22], but later, the WHO defined mHealth as an integral part of eHealth, which refers to the cost-effective and secure use of information and communication technologies in support of health and healthrelated fields [23].Good use of mobile phones and related apps can be effective in the delivery of information and improve the impact of treatment and healthcare delivery processes [24].Likewise, wearable activity trackers such as fitness trackers, activity-tracking smartwatches and pedometers have shown to be very useful tools for overcoming physical inactivity and obesity.Many studies have shown that the use of these devices has been associated with increased PA, since they can support behavior-change techniques like selfmonitoring and goal setting, as well as with improved BMI and lower risk of developing obesity [25][26][27][28][29][30].In 2021, Berry et al. published a systematic review on the effectiveness of digital self-monitoring for weight loss in overweight and obesity, providing positive results in favor of new technologies [31].In order to add further evidence to this field, the present review was performed to systematically analyze the available literature regarding behavioral weight loss interventions which aimed to increase participants' PA level by using digital technologies.

Selection Protocol and Search Strategy
This systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines [32].The protocol was then registered in PROSPERO with the number CRD42023403364.The research question of the present systematic review was: "Are digital technologies effective to support weight loss in behavioral interventions for individuals with overweight or obesity?".Thus, the review question was conceived using the "PICOS" Framework (P = Patient, problem or population; I = Intervention; C = Comparison, control or comparator; O = Outcome(s); S = Study type) according to the following eligibility criteria: (P) population: humans with overweight or obesity; (I) intervention: weight loss behavioral intervention based on electronic devices, mobile apps, artificial intelligence or smartphones/watches; (C) comparison: obese and overweight patients who did not undergo weight loss intervention based on electronic devices, mobile apps, artificial intelligence or smartphones/watches; (O) outcome: weight loss, BMI changes, anthropometric measures or body composition; (S) study: clinical trials.After a preliminary assessment of the literature, we decided to restrict the analysis to humans with obesity or overweight without any other comorbidities and to randomized clinical trials in order to obtain more consistent outcomes.Three electronic databases (PubMed, Scopus and Web of Science) were then scrutinized using the following search string: (obesity OR overweight) AND ("artificial intelligence" OR "machine learning" OR "mobile applications" OR "wearable electronic devices" OR smartphone OR smartwatch) AND ("dietary interventions" OR "nutritional status" OR "personalized nutrition" OR "weight control" OR "diet control" OR "weight loss").Table S1 reports the search strategy for PubMed.
All databases were searched by title, abstract, and MeSH terms and keywords.The last search was performed from database inception to 8 November 2023.

Inclusion and Exclusion Criteria
This review was based on the use of electronic devices and new technologies to increase physical activity with the aim of achieving weight loss.In order to be eligible, studies were selected based on the following inclusion criteria: studies must be in English or Italian; weight loss must be associated with the use of electronic devices, mobile apps, artificial intelligence, or the use of a smartphone/smartwatch to manage/promote physical activity.Only randomized clinical trials were included.Furthermore, all studies which included underage individuals (<18 years) or patients who had other comorbidities or did not present with obesity or overweight were excluded from this systematic review.Reviews, metaanalysis, observational studies, case studies, proceedings, qualitative studies, editorials, commentary studies, pilot studies and any other type of article were also excluded.The references of reviews and meta-analyzes regarding the same issue were checked in order to identify further articles that did not come up on the baseline research results.
All results, from the beginning until to 8 November 2023, were then retrieved to reference software Zotero Systematic Review Manager v 6.0.26 for further screening and for the removal of duplicates.Ten authors (A.D.G., S.Z., E.M., F.U., V.V., L.C., M.S., G.D.A., I.P., A.H.) then proceeded with the selection of studies by Title and Abstracts according to the selection criteria listed above.All full texts were then read, independently, by the same authors and discussed further.Doubts and disagreements were settled by the other three authors (C.P., F.G., F.V.).

Data Extraction and Quality Assessment
Data were extracted from the selected studies by ten authors (A.D.G., S.Z., E.M., F.U., V.V., L.C., M.S., G.D.A., I.P., A.H.), according to specific characteristics which were previously approved by all authors.The data extraction table was constructed as follows: author, year, country, study design, study population, sample size, type of device, type of intervention, duration, frequency, comparison, main outcomes and secondary outcomes and results.These data were then arranged according to the type of study and the confounding factors.
Each included article was assessed using the Checklist to Evaluate a Report of a Nonpharmacological Trial (CLEAR NPT) [33].This checklist has been specifically developed for measuring the quality of randomized clinical trials assessing nonpharmacological treatments.Indeed, the evaluation of nonpharmacological treatments such as technical devices, behavioral or psychological therapy involves some specific methodological considerations.For example, in nonpharmacological treatment trials, it is frequently impossible to carry out the blinding of care providers and participants, and the success of the treatment often depends on the experience and skill of the care providers.Besides, this kind of study is difficult to standardize [33].Thus, according to several systematic reviews evaluating nonpharmacological treatment [34][35][36][37], the CLEAR NPT checklist was used [33].This checklist contains 10 parameters, and for each item the choice was between "Yes", "No" or "Unclear".By adding up the answers, all authors could attribute a score.The score was between 10 and 8 for a low risk of bias, between 7 and 5 for a median risk of bias and lower than 5 for a high risk of bias.

Results
A total of 1762 studies were retrieved from the following databases: PubMed, Web of Science, and Scopus.Of these, 796 duplicates were removed and 966 were screened by title and abstract.After the full-text assessment of the 133 articles that remained, 102 articles were excluded, 42 of them because they did not pertain to our question, 12 because the individuals were affected by other comorbidities, 16 because they were a different type of study from RCT, 7 because they considered a young age population (<18 years), 4 because did not have control groups, and 21 because they did not consider the assessment of changes in PA.Finally, we included 31 articles that met the inclusion criteria (Figure 1) .The main characteristics and findings of the interventions, as well as the primary and secondary weight-related outcomes assessed alongside weight loss, are shown in Table 1.In the conventional group, app group, and combi group, BMI decreased significantly (p = 0.004, p = 0.005, and p < 0.001, respectively), no significant decrease was found in the control group.A significant time x group effect was found for BMI (p = 0.006), with the control group being significantly different compared with all other intervention groups.No significant differences were found between the conventional group and the app group and between the conventional group and the combi group (p = 0.41).However, the combi group had significantly higher decrease in BMI compared with the app group (p = 0.03).3-month intervention with counseling, smartphone app and smart band (Mi Band 2, Xiaomi).
After 7 days, subjects were trained to use the device and the app to allow the dietary intake to be self-reported daily and PA data were collected automatically from the smart band.Once all of the daily information was collected, the app integrated the data to create personalized recommendations based on the subjects' characteristics and specific objectives and goals for weight loss.
In concern to quality assessment, 14 studies were considered with a "Low Bias Risk", 12 with a "Medium Bias Risk" and 5 with a "High Bias Risk".
Many of the evaluated studies used smartphone apps to carry out the intervention, matched with other procedures such as motivational phone calls [41] and text messages [50,62], and a good number of them also assessed the use of wearable devices such as smartwatches, smart bands or accelerometers [44,48,51,54,[56][57][58]63,67].
The majority of the studies included a specific duration of each session and frequency of intervention, with a minimum of 8 weeks [40] and a maximum of 24 months [51] for the duration, and with frequency varying from three times daily [42] to monthly [51], except for a few where these characteristics were kept generic, specifying neither duration nor frequency [47,58,59,64].
All but one [48] of the studies were aimed at achieving weight loss through improvements in both diet and PA.
In six studies, no activity was assigned to the control group [39,47,49,50,59,61], and in two studies, the control group had the only task of self-monitoring [42,51].
The adoption of new technologies is rapidly spreading in several areas of our lives, such as in health promotion and control [69].In this context, several devices and applications have been proposed as digital solutions to improve health-related behaviors, such as PA and diet, especially since the beginning of the COVID-19 pandemic [70].As for PA, nowadays, the use of even more sophisticated wearable devices goes beyond the mere tracking of steps or other movements and may help users to reach their activity goals, increase their PA levels and reduce health risk related to inactivity [71].The integration of gamification and/or social support elements can increase their effectiveness in movement promotion, both in adults and children [72][73][74].
With regard to diet monitoring and management, several digital technologies have been developed and evaluated in different subgroups, with inconsistent results [75,76].Digital resources can reach many people at a low cost and have the potential to support lifestyle changes, enabling individuals to self-regulate their behaviors [77][78][79].As for employing these technologies for weight loss, a systematic review and meta-analysis published by Berry et al. in 2021 analyzed the potential role of a digital diet and PA self-monitoring in supporting weight loss among adults with overweight or obesity [31].Their results showed a statistically significant effect of digital self-monitoring in weight loss, moderate PA increase and calorie intake reduction.Furthermore, they reported that tailored interventions were significantly more effective than nontailored ones, highlighting the importance of tailored advice.In line with this, the review by Irvin et al., which was aimed at examining the status of digital exercise program delivery, found that apps may be useful for a low-intensity approach and can improve adherence to programs through self-monitoring [70].However, the authors stated that tailored interventions can produce significant findings for weight loss and that individuals need specialist support to achieve their weight goals.Interestingly, this has also been proven for digital interventions used in studies aimed at dietary behavior change [80].Although it was established that digital interventions have the potential to determine proper changes in the eating behavior of individuals, the efficiency of these interventions increases when coupled with tailored feedback and counseling.This should be considered in the perspective of the long-term maintenance of healthy habits after the conclusion of weight loss interventions.
Keeping this in mind, the evidence coming from our review underlines the usefulness of digital technologies in supporting weight loss, since two-thirds of the analyzed studies showed that their usage resulted in significantly greater weight loss.Furthermore, eighteen of the included studies reported tailored interventions, and only four of these did not find significant differences between participants and controls [42,47,50,63].In addition, only three [48,50,63] out of the eleven interventions which involved specialists in their implementation reported non-significant differences.The study published by Jakicic et al. was the only reporting that the digital technologies employed for physical activity monitoring and feedback did not offer an advantage over standard behavioral approaches, since the weight reduction observed in its intervention group, although significant, was lower than that observed in controls [51].Notably, this intervention was not tailored or specialist-driven.
Digital self-monitoring enables individuals to monitor their health behaviors, either through the input of their own data or through the automatic tracking of sensors or wearable technology.Such solutions can allow individuals to receive tailored, automated and real-time feedback.The integration of these systems into usual weight management services may also inform obesity treatment and address service provision, increasing their effectiveness in weight loss and long-term maintenance [31].
However, some considerations are needed in this regard.In general, internal (i.e., motivation and self-efficacy), social (i.e., supporters and saboteurs) and environmental (i.e., an obesogenic environment) factors have been shown to influence the outcomes of a weight loss program, as well as the acceptability of the intervention [81].Considering the barriers to exercise and PA that people with overweight or obesity may encounter, digital solutions have the potential to provide convenient and equitable support in weight loss based on behavior change [70].However, as evidence shows that individualized and interactive tools may improve adherence to intervention and facilitate behavior change, those factors which can drive or hinder the use of digital technologies should be also considered when designing a digital-based intervention.In 2022, Jakob et al. reported that userfriendly and technically stable app design, customizable push notifications, personalized app content, passive data tracking, integrated app tutorials, gratuitousness and personal support represent intervention-related characteristics, which can positively influence adherence to mHealth apps for preventing or managing noncommunicable diseases [82].As for individual-related factors, lack of technical competence, low health literacy, low self-efficacy, a low education level, mental health burden, lack of experience with mHealth apps, privacy concerns, low expectations of the app, low trust in healthcare professionals conducting the intervention, lack of time, age, gender and pre-existing conditions were the user characteristics frequently associated with low mHealth app adherence [82].
In addition, due to the availability of different technological solutions, it should also be considered that some of them can be more effective in supporting certain categories than others in behavior change.In a review published in 2018, Cheatham et al. assessed the efficacy of wearable activity tracking technology in assisting behavior change and weight loss, showing that its use in short-term interventions may lead to better results in middle-aged and older adults, but not in younger adults [83].Belegoli et al. showed that web-based digital health interventions can be more effective in short-term but not in long term weight loss and lifestyle habit changes interventions with respect to offline interventions for overweight and obese adults [84].
Therefore, further research in this field should focus on the individualization of digitalbased interventions based on subjects' characteristics.This could imply the choice of the most adequate behavior change technique to motivate people, but also the implementation of educational interventions to increase their digital literacy, and subsequently their adherence to the weight loss program.
This review has some limitations.First of all, the heterogeneity of the studies examined was high due to the characteristics of the interventions and, in particular, due to the variety of technologies employed and the type of activity (or non-activity) assigned to controls.This did not allow us to compare the studies and to perform a meta-analysis of their results.Furthermore, it should be noted that, in a part of the studies, digital technologies were used to address participants' dietary behaviors together with PA, while in other interventions, diet was only self-reported or in some cases not controlled at all.This may limit the reliability of the findings related to the effectiveness of each technology in determining a specific behavior change and then weight loss, due to possible confounding bias.Moreover, it should be noted that participants in the studies showed differences in gender, age and health conditions.Although we selected only those studies which involved healthy subjects, it is possible that different categories of subjects, mainly those who perceived themselves as at risk for some disease, complied differently with the intervention and this may have influenced the outcomes.In order to obtain stronger evidence about the effectiveness of technology in weight loss, future research should be focused on specific population subgroups and type of device/application.However, it is also possible to highlight the strengths related to this review.In particular, the analysis was specifically focused on randomized controlled studies involving healthy subjects in order to obtain more reliable evidence.Furthermore, this review was intended to explore the possible employ of digital technology in the context of behavioral interventions aimed at reducing body weight, besides the exclusive use of monitoring devices such as activity trackers.

Conclusions
As the development of digital technologies advances, their use in healthcare settings increases.Electronic devices and mobile applications may be useful to support weight loss lifestyle-based interventions for people with overweight or obesity.However, evidence suggests that tailored automated feedback or specialists' advice can increase the effectiveness of these resources by enhancing individuals' motivation to change their behaviors.

Table 1 .
Characteristics of the included studies.