The Potential Role of Digital Health in Obesity Care

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Abstract

Obesity is a complex, multi-factorial, chronic condition which increases the risk of a wide range of diseases including type 2 diabetes mellitus, cardiovascular disease and certain cancers. The prevalence of obesity continues to rise and this places a huge economic burden on the healthcare system. Existing approaches to obesity treatment tend to focus on individual responsibility and diet and exercise, failing to recognise the complexity of the condition and the need for a whole-system approach. A new approach is needed that recognises the complexity of obesity and provides patient-centred, multidisciplinary care which more closely meets the needs of each individual with obesity. This review will discuss the role that digital health could play in this new approach and the challenges of ensuring equitable access to digital health for obesity care. Existing technologies, such as telehealth and mobile health apps and wearable devices, offer emerging opportunities to improve access to obesity care and enhance the quality, efficiency and cost-effectiveness of weight management interventions and long-term patient support. Future application of machine learning and artificial intelligence to obesity care could see interventions become increasingly automated and personalised.

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Most cited references 56

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Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies

Prospective Studies Collaboration, George Smith (2009)

Summary Background The main associations of body-mass index (BMI) with overall and cause-specific mortality can best be assessed by long-term prospective follow-up of large numbers of people. The Prospective Studies Collaboration aimed to investigate these associations by sharing data from many studies. Methods Collaborative analyses were undertaken of baseline BMI versus mortality in 57 prospective studies with 894 576 participants, mostly in western Europe and North America (61% [n=541 452] male, mean recruitment age 46 [SD 11] years, median recruitment year 1979 [IQR 1975–85], mean BMI 25 [SD 4] kg/m2). The analyses were adjusted for age, sex, smoking status, and study. To limit reverse causality, the first 5 years of follow-up were excluded, leaving 66 552 deaths of known cause during a mean of 8 (SD 6) further years of follow-up (mean age at death 67 [SD 10] years): 30 416 vascular; 2070 diabetic, renal or hepatic; 22 592 neoplastic; 3770 respiratory; 7704 other. Findings In both sexes, mortality was lowest at about 22·5–25 kg/m2. Above this range, positive associations were recorded for several specific causes and inverse associations for none, the absolute excess risks for higher BMI and smoking were roughly additive, and each 5 kg/m2 higher BMI was on average associated with about 30% higher overall mortality (hazard ratio per 5 kg/m2 [HR] 1·29 [95% CI 1·27–1·32]): 40% for vascular mortality (HR 1·41 [1·37–1·45]); 60–120% for diabetic, renal, and hepatic mortality (HRs 2·16 [1·89–2·46], 1·59 [1·27–1·99], and 1·82 [1·59–2·09], respectively); 10% for neoplastic mortality (HR 1·10 [1·06–1·15]); and 20% for respiratory and for all other mortality (HRs 1·20 [1·07–1·34] and 1·20 [1·16–1·25], respectively). Below the range 22·5–25 kg/m2, BMI was associated inversely with overall mortality, mainly because of strong inverse associations with respiratory disease and lung cancer. These inverse associations were much stronger for smokers than for non-smokers, despite cigarette consumption per smoker varying little with BMI. Interpretation Although other anthropometric measures (eg, waist circumference, waist-to-hip ratio) could well add extra information to BMI, and BMI to them, BMI is in itself a strong predictor of overall mortality both above and below the apparent optimum of about 22·5–25 kg/m2. The progressive excess mortality above this range is due mainly to vascular disease and is probably largely causal. At 30–35 kg/m2, median survival is reduced by 2–4 years; at 40–45 kg/m2, it is reduced by 8–10 years (which is comparable with the effects of smoking). The definite excess mortality below 22·5 kg/m2 is due mainly to smoking-related diseases, and is not fully explained. Funding UK Medical Research Council, British Heart Foundation, Cancer Research UK, EU BIOMED programme, US National Institute on Aging, and Clinical Trial Service Unit (Oxford, UK).

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Personalized Nutrition by Prediction of Glycemic Responses.

David Zeevi, Tal Korem, Niv Zmora … (2015)

Elevated postprandial blood glucose levels constitute a global epidemic and a major risk factor for prediabetes and type II diabetes, but existing dietary methods for controlling them have limited efficacy. Here, we continuously monitored week-long glucose levels in an 800-person cohort, measured responses to 46,898 meals, and found high variability in the response to identical meals, suggesting that universal dietary recommendations may have limited utility. We devised a machine-learning algorithm that integrates blood parameters, dietary habits, anthropometrics, physical activity, and gut microbiota measured in this cohort and showed that it accurately predicts personalized postprandial glycemic response to real-life meals. We validated these predictions in an independent 100-person cohort. Finally, a blinded randomized controlled dietary intervention based on this algorithm resulted in significantly lower postprandial responses and consistent alterations to gut microbiota configuration. Together, our results suggest that personalized diets may successfully modify elevated postprandial blood glucose and its metabolic consequences. VIDEO ABSTRACT.

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Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

Inbal Nahum-Shani, Shawna Smith, Bonnie J Spring … (2017)

Background The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual’s changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual’s state can change rapidly, unexpectedly, and in his/her natural environment. Purpose Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention We clarify the scientific motivation for the Just-In-Time Adaptive Interventions, define its fundamental components, and discuss key design principles for each component.

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Author and article information

Contributors

Nigel Hinchliffe:

ORCID: http://orcid.org/0000-0001-9284-4566

nigel.hinchliffe@contemporaryhealth.co.uk

Journal

Journal ID (nlm-ta): Adv Ther

Journal ID (iso-abbrev): Adv Ther

Title: Advances in Therapy

Publisher: Springer Healthcare (Cheshire )

ISSN (Print): 0741-238X

ISSN (Electronic): 1865-8652

Publication date (Electronic): 4 August 2022

Publication date PMC-release: 4 August 2022

Pages: 1-16

Affiliations

[1 ]The College of Contemporary Health, Technopark, 90 London Road, London, SE1 6LN UK

[2 ]Rotherham Institute for Obesity, Rotherham, UK

[3 ]GRID grid.7943.9, ISNI 0000 0001 2167 3843, University of Central Lancashire, ; Preston, UK

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Nigel Hinchliffe http://orcid.org/0000-0001-9284-4566

Article

Publisher ID: 2265

DOI: 10.1007/s12325-022-02265-4

PMC ID: 9362065

PubMed ID: 35925469

SO-VID: df1788bf-ae5f-4ffb-af4e-24dbcec98dbe

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Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.

The Potential Role of Digital Health in Obesity Care

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Most cited references 56

Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies

Personalized Nutrition by Prediction of Glycemic Responses.

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support

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