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Internet of things–Enabled technologies as an intervention for childhood obesity: A systematic review

['Ching Lam', 'Department For Biomedical Engineering', 'University Of Oxford', 'United Kingdom', 'Madison Milne-Ives', 'Centre For Health Technology', 'University Of Plymouth', 'Richard Harrington', 'Nuffield Department Of Primary Health Care Services', 'Anant Jani']

Date: 2022-05

Childhood obesity is one of the most serious public health challenges of the 21st century, with consequences lasting into adulthood. Internet of Things (IoT)-enabled devices have been studied and deployed for monitoring and tracking diet and physical activity of children and adolescents as well as a means of providing remote, ongoing support to children and their families. This review aimed to identify and understand current advances in the feasibility, system designs, and effectiveness of IoT-enabled devices to support weight management in children. We searched Medline, PubMed, Web of Science, Scopus, ProQuest Central and the IEEE Xplore Digital Library for studies published after 2010 using a combination of keywords and subject headings related to health activity tracking, weight management, youth and Internet of Things. The screening process and risk of bias assessment were conducted in accordance with a previously published protocol. Quantitative analysis was conducted for IoT-architecture related findings and qualitative analysis was conducted for effectiveness-related measures. Twenty-three full studies are included in this systematic review. The most used devices were smartphone/mobile apps (78.3%) and physical activity data (65.2%) from accelerometers (56.5%) were the most commonly tracked data. Only one study embarked on machine learning and deep learning methods in the service layer. Adherence to IoT-based approaches was low but game-based IoT solutions have shown better effectiveness and could play a pivotal role in childhood obesity interventions. Researcher-reported effectiveness measures vary greatly amongst studies, highlighting the importance for improved development and use of standardised digital health evaluation frameworks.

Obesity is a serious public health concern affecting a growing number of children worldwide and can have long lasting effects on their physical and mental well-being. Health and fitness apps have become an increasingly common means for people to manage their weight and new technologies that can connect to the Internet–such as wearable sensors–are becoming increasingly available. We have conducted a systematic review of studies that described internet-enabled interventions for childhood obesity. In our results and discussion, we provide details of the types of devices, the way they collect, transfer, and analyse data, their aims, and their reported impact. In addition to summarizing the current state of these internet-enabled weight management devices, we discuss areas for future research to improve and better evaluate these devices.

Funding: This manuscript was supported by the Sir David Cooksey Fellowship in Healthcare Translation. The views expressed in the paper belong to the authors and are not necessarily those of the funding body or the authors' University affiliations. The funding body was not involved in the study design, data collection or analysis, or the writing and decision to submit the article for publication.

Copyright: © 2022 Lam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This systematic review aims to synthesize the evidence on the use of IoT-enabled technologies as an intervention for childhood obesity. The objectives of this review include to:

Clinic-based interventions seem to have favourable impacts in promoting dietary and lifestyle changes through behavioural therapies and counselling [ 2 ], but face-to-face counselling can be costly and may not always be possible for children in rural areas [ 3 ]. A potential solution could be the use of network-connected health Internet of Things (IoT) technologies such as smartphones, wearables, sensors, and linked mobile applications (apps) [ 6 ]. Wearables and smartphone apps have been used for tracking physical activity and diet and have shown promise in delivering high-quality care at lower costs [ 7 ]. Recent improvement of wearable technologies such as wristwatches with activity sensors has seen them evolve from providing measures of steps, distance, calories, and sleep to measures of activity minutes, heart rate and goal- and target-oriented designs [ 8 ]. This has allowed more accurate tracking and provides greater insight into the type and form of physical activity undertaken by the user, such as exercise intensity and metabolic rate [ 9 , 10 ]. Combined with the use of wearables, smartphone apps have shown to be useful in increasing physical activity over the short-term but user engagement tends to decline over a longer period of time [ 11 ]. To effectively utilise these technologies for weight management and behaviour change for childhood obesity, network-connected health devices and platforms have been designed to be more appealing, gamified and provide education [ 12 ]. However, there have not been any published studies focused on the system designs of these technologies and how they have been evaluated.

In 2016, more than 1.9 billion adults, 39% of the world’s adult population, were overweight or obese [ 1 ]. This is leading to increased risks of physical and mental health non-communicable diseases (NCDs) such as type 2 diabetes, hypertension, cardiovascular disease, musculoskeletal conditions, depression and certain types of cancers [ 2 ]. Overweight and obese children are five times more likely to be overweight and obese in adulthood [ 3 ]. Childhood obesity has become one of the most serious global public health challenges of the 21 st century affecting children in both developing and developed countries. In addition to the downstream health effects, evidence is also growing that childhood obesity is associated with an increased risk of developing obesity-related NCDs before adulthood. As well as adverse health outcomes and burdens on health systems, childhood obesity can also result in burdens on the individuals and families, adversely affecting quality of life, mental well-being, and education [ 4 , 5 ].

Results

Study selection After the removal of duplicates, 2474 studies were identified from the original database search. Two additional records were identified through reading the references of articles. In total, 23 studies met the eligibility criteria for inclusion in this review. Where the data could be represented numerically, meta-analyses were employed; where the data were heterogeneous, qualitative assessment was conducted. Supporting information includes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart for study selection (S1 Fig), the PRISMA checklist (S1 PRISMA Checklist), and the list of included studies (S1 Table).

A sensory layer: devices or ‘things’ The sensory layer are devices or ‘things’ that contain sensors which can be used to collect data [37]. This section discusses the device types and the data collected in the included studies.

Device types Five device types were identified in the included studies: smartwatch/activity tracker bands, smart garments, smartphone/mobile apps, Near Field Communication (NFC) tags and computer/web services. Some studies employ multiple modalities, for example NFC tags or activity trackers; Fig 2 provides a summary of the number of studies employing each type of device. PPT PowerPoint slide

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TIFF original image Download: Fig 2. Device types in included studies*. * The total numbers are higher than the study totals in the review because of overlaps of device type use in studies. https://doi.org/10.1371/journal.pdig.0000024.g002 Eighteen of the included studies used smartphone embedded sensors or mobile apps as the main intervention. It was noted that some participants did not own a smartphone and had to be provided devices for the study. For instance, in the pilot test conducted in Mexico by Vazquez-Briseno et al. [35], 2 out of 15 children in the test group had never used or owned a mobile phone before. Smart watches and activity tracker bands worn on arms, wrists or ankles were the most popular wearables used in the included studies [13,15–25,27,29–30,32,33] (Fig 2). Out of the 8 studies that used commercially available devices, four used a Fitbit (Fitbit, Inc., United States) [25,28,29,32], one used Sensewear Armband (BodyMedia, United States) [30], one used Microsoft Band (Microsoft Corporation, United States) [21], one used Walkie + D Coffee (Green Cross Healthcare Inc., South Korea) and one used Tractivity activity monitor (Kineteks Corporation, United States). The other ten studies used proprietary physical tracking wearables developed for the study. Only one study employed the use of a smart garment which was worn by the user during physical activity [16]. NFC tags were used in one of the studies to enhance a mobile game into an exergame [21], which combines video games with physical exercises [38]. The last type of device in the included studies were computers/web services where users were required to self-report data onto a web portal.

B Network layer: data transfer The network layer of the IoT architecture serves as an inter-operation layer for devices to communicate over the internet [39]. For this review, data transfer methods used in the included studies were extracted and are summarised in Fig 5. Data transfer through mobile applications, wireless connection to the internet (via Bluetooth and other unspecified wireless methods) were the most popular options for transfer of data in the included studies. PPT PowerPoint slide

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TIFF original image Download: Fig 5. Data transfer methods in included studies. https://doi.org/10.1371/journal.pdig.0000024.g005 Only one of the studies used radio-frequency identification (RFID) technology. This method was used for the transfer of TV viewing data, which was collected by detecting nearby television usage from a smart wearable device to a data processing board for further data processing [13].

E Behavioural change theories Various behaviour change theories were used in the included studies and are summarized in Table 3. Monitoring (91%) and feedback (45%) were the most commonly used behavioural change techniques for the included studies, followed by goal setting and action planning (31.8%) and positive reinforcement (e.g. rewards and challenges; 31.8%). Monitoring refers to the use of self-reports or trackers to monitor user habits such as screen time, physical activity and diet. Monitoring can be done by the child themselves or by other stakeholders, such as other family members, teachers, researchers, and health professionals. Feedback in the context of the included studies refers to technology generated feedback such as weekly progress reports. PPT PowerPoint slide

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TIFF original image Download: Table 3. Use cases for infants/ children/ teenagers. https://doi.org/10.1371/journal.pdig.0000024.t003

[END]

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