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      Effectiveness of a Digital Lifestyle Change Program in Obese and Type 2 Diabetes Populations: Service Evaluation of Real-World Data

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          Abstract

          Background

          The prevalence of type 2 diabetes mellitus (T2DM) and obesity is increasing, and the way people interact with health care is evolving. People traditionally access advice and support to improve their lifestyle and learn more about the self-management of T2DM in a face-to-face setting. Although these services have a strong evidence base, they have limitations for reaching specific groups of people. Digital programs could provide a new delivery model to help more people access health education and behavior change support, but long-term data supporting these programs are limited.

          Objective

          The purpose of this service evaluation was to analyze the weight change of people who participated in OurPath (also known as Second Nature), a UK-based digital lifestyle change program, for either weight management or diabetes-related weight management and structured education at 6 and 12 months.

          Methods

          Participants either paid to access the program privately (self-funded clients) or were referred by their general practitioner to participate in the program free of charge (funded by the National Health Service). Additional follow-up support was provided to help people to maintain lifestyle changes. To retrospectively assess potential weight loss, the analysis included data from participants who submitted weight readings at baseline and 6 and 12 months after starting the program. Changes in weight after 6 and 12 months were primary outcome measures.

          Results

          For the 896 participants who submitted baseline and 6- and 12-month data, a significant change in mean weight of −7.12 kg (−7.50%; SD 6.37; P<.001) was observed at 6 months. Data from the same participants at 12 months showed a change in mean weight when compared with a baseline of −6.14 kg (−6.48%; SD 6.97; P<.001).

          Conclusions

          The data presented here had several limitations, and there were too many uncertainties to make any reliable conclusions. However, these results suggest that digital lifestyle change programs could provide a new way to help people to access nutritional advice and support to achieve weight loss. Further research into digital education and coaching platforms is needed to establish their effectiveness.

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          Most cited references16

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          Sleep disturbances compared to traditional risk factors for diabetes development: Systematic review and meta-analysis.

          Sleep disturbances [short ( 8 h) sleeping time, insomnia (initiating or maintaining sleep), obstructive sleep apnea (OSA) and abnormal sleep timing] have been associated with increased diabetes risk but the effect size relative to that of traditional risk factors is unknown. We conducted a systematic review and meta-analysis to compare the risk associated with sleep disturbances to traditional risk factors. Studies were identified from Medline and Scopus. Cohort studies measuring the association between sleep disturbances and incident diabetes were eligible. For traditional risk factors (i.e., overweight, family history, and physical inactivity), systematic reviews with or without meta-analysis were included. Thirty-six studies (1,061,555 participants) were included. Pooled relative risks (RRs) of sleep variables were estimated using a random-effect model. Pooled RRs of sleeping ≤5 h, 6 h, and ≥9 h/d were respectively 1.48 (95%CI:1.25,1.76), 1.18 (1.10,1.26) and 1.36 (1.12,1.65). Poor sleep quality, OSA and shift work were associated with diabetes with a pooled RR of 1.40 (1.21,1.63), 2.02 (1.57, 2.61) and 1.40 (1.18,1.66), respectively. The pooled RRs of being overweight, having a family history of diabetes, and being physically inactive were 2.99 (2.42,3.72), 2.33 (1.79,2.79), and 1.20 (1.11,1.32), respectively. In conclusion, the risk of developing diabetes associated with sleep disturbances is comparable to that of traditional risk factors. Sleep disturbances should be considered in clinical guidelines for type 2 diabetes screening.
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            Chunking mechanisms in human learning.

            Pioneering work in the 1940s and 1950s suggested that the concept of 'chunking' might be important in many processes of perception, learning and cognition in humans and animals. We summarize here the major sources of evidence for chunking mechanisms, and consider how such mechanisms have been implemented in computational models of the learning process. We distinguish two forms of chunking: the first deliberate, under strategic control, and goal-oriented; the second automatic, continuous, and linked to perceptual processes. Recent work with discrimination-network computational models of long- and short-term memory (EPAM/CHREST) has produced a diverse range of applications of perceptual chunking. We focus on recent successes in verbal learning, expert memory, language acquisition and learning multiple representations, to illustrate the implementation and use of chunking mechanisms within contemporary models of human learning.
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              Weight loss intervention adherence and factors promoting adherence: a meta-analysis

              Background Adhering to weight loss interventions is difficult for many people. The majority of those who are overweight or obese and attempt to lose weight are simply not successful. The objectives of this study were 1) to quantify overall adherence rates for various weight loss interventions and 2) to provide pooled estimates for factors associated with improved adherence to weight loss interventions. Methods We performed a systematic literature review and meta-analysis of all studies published between January 2004 and August 2015 that reviewed weight loss intervention adherence. Results After applying inclusion and exclusion criteria and checking the methodological quality, 27 studies were included in the meta-analysis. The overall adherence rate was 60.5% (95% confidence interval [CI] 53.6–67.2). The following three main variables were found to impact adherence: 1) supervised attendance programs had higher adherence rates than those with no supervision (rate ratio [RR] 1.65; 95% CI 1.54–1.77); 2) interventions that offered social support had higher adherence than those without social support (RR 1.29; 95% CI 1.24–1.34); and 3) dietary intervention alone had higher adherence than exercise programs alone (RR 1.27; 95% CI 1.19–1.35). Conclusion A substantial proportion of people do not adhere to weight loss interventions. Programs supervising attendance, offering social support, and focusing on dietary modification have better adherence than interventions not supervising attendance, not offering social support, and focusing exclusively on exercise.
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                Author and article information

                Contributors
                Journal
                JMIR Diabetes
                JMIR Diabetes
                JD
                JMIR Diabetes
                JMIR Publications (Toronto, Canada )
                2371-4379
                Jan-Mar 2020
                20 January 2020
                : 5
                : 1
                : e15189
                Affiliations
                [1 ] Division of Medical Sciences & Graduate Entry Medicine University of Nottingham Nottingham United Kingdom
                [2 ] Bath and North East Somerset CCG Bath United Kingdom
                [3 ] OurPath London United Kingdom
                Author notes
                Corresponding Author: Michael Whitman michael.whitman@ 123456ourpath.co.uk
                Author information
                https://orcid.org/0000-0002-7548-8288
                https://orcid.org/0000-0002-9693-5403
                https://orcid.org/0000-0001-6979-0061
                https://orcid.org/0000-0001-7144-7545
                Article
                v5i1e15189
                10.2196/15189
                6997924
                31958064
                9326dec4-04fd-409e-9cc4-f4885a701ee2
                ©Iskandar Idris, James Hampton, Fiona Moncrieff, Michael Whitman. Originally published in JMIR Diabetes (http://diabetes.jmir.org), 20.01.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Diabetes, is properly cited. The complete bibliographic information, a link to the original publication on http://diabetes.jmir.org/, as well as this copyright and license information must be included.

                History
                : 26 June 2019
                : 30 July 2019
                : 24 September 2019
                : 16 December 2019
                Categories
                Original Paper
                Original Paper

                weight loss,mhealth,type 2 diabetes,ourpath,obesity,dietetics,cognitive behavioral therapy,empowerment,well-being,mobile app,behavior change,prevention,digital

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