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      Omnichannel Communication to Boost Patient Engagement and Behavioral Change With Digital Health Interventions

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          Abstract

          Digital health interventions are being increasingly incorporated into health care workflows to improve the efficiency of patient care. In turn, sustained patient engagement with digital health interventions can maximize their benefits toward health care outcomes. In this viewpoint, we outline a dynamic patient engagement by using various communication channels and the potential use of omnichannel engagement to integrate these channels. We conceptualize a novel patient care journey where multiple web-based and offline communication channels are integrated through a “digital twin.” The principles of implementing omnichannel engagement for digital health interventions and digital twins are also broadly covered. Omnichannel engagement in digital health interventions implies a flexibility for personalization, which can enhance and sustain patient engagement with digital health interventions, and ultimately, patient quality of care and outcomes. We believe that the novel concept of omnichannel engagement in health care can be greatly beneficial to patients and the system once it is successfully realized to its full potential.

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          Most cited references76

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          The spread of obesity in a large social network over 32 years.

          The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions. Copyright 2007 Massachusetts Medical Society.
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            Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions.

            This paper is one in a series developed through a process of expert consensus to provide an overview of questions of current importance in research into engagement with digital behavior change interventions, identifying guidance based on research to date and priority topics for future research. The first part of this paper critically reflects on current approaches to conceptualizing and measuring engagement. Next, issues relevant to promoting effective engagement are discussed, including how best to tailor to individual needs and combine digital and human support. A key conclusion with regard to conceptualizing engagement is that it is important to understand the relationship between engagement with the digital intervention and the desired behavior change. This paper argues that it may be more valuable to establish and promote "effective engagement," rather than simply more engagement, with "effective engagement" defined empirically as sufficient engagement with the intervention to achieve intended outcomes. Appraisal of the value and limitations of methods of assessing different aspects of engagement highlights the need to identify valid and efficient combinations of measures to develop and test multidimensional models of engagement. The final section of the paper reflects on how interventions can be designed to fit the user and their specific needs and context. Despite many unresolved questions posed by novel and rapidly changing technologies, there is widespread consensus that successful intervention design demands a user-centered and iterative approach to development, using mixed methods and in-depth qualitative research to progressively refine the intervention to meet user requirements.
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              Objective User Engagement With Mental Health Apps: Systematic Search and Panel-Based Usage Analysis

              Background Understanding patterns of real-world usage of mental health apps is key to maximizing their potential to increase public self-management of care. Although developer-led studies have published results on the use of mental health apps in real-world settings, no study yet has systematically examined usage patterns of a large sample of mental health apps relying on independently collected data. Objective Our aim is to present real-world objective data on user engagement with popular mental health apps. Methods A systematic engine search was conducted using Google Play to identify Android apps with 10,000 installs or more targeting anxiety, depression, or emotional well-being. Coding of apps included primary incorporated techniques and mental health focus. Behavioral data on real-world usage were obtained from a panel that provides aggregated nonpersonal information on user engagement with mobile apps. Results In total, 93 apps met the inclusion criteria (installs: median 100,000, IQR 90,000). The median percentage of daily active users (open rate) was 4.0% (IQR 4.7%) with a difference between trackers (median 6.3%, IQR 10.2%) and peer-support apps (median 17.0%) versus breathing exercise apps (median 1.6%, IQR 1.6%; all z≥3.42, all P<.001). Among active users, daily minutes of use were significantly higher for mindfulness/meditation (median 21.47, IQR 15.00) and peer support (median 35.08, n=2) apps than for apps incorporating other techniques (tracker, breathing exercise, psychoeducation: medians range 3.53-8.32; all z≥2.11, all P<.05). The medians of app 15-day and 30-day retention rates were 3.9% (IQR 10.3%) and 3.3% (IQR 6.2%), respectively. On day 30, peer support (median 8.9%, n=2), mindfulness/meditation (median 4.7%, IQR 6.2%), and tracker apps (median 6.1%, IQR 20.4%) had significantly higher retention rates than breathing exercise apps (median 0.0%, IQR 0.0%; all z≥2.18, all P≤.04). The pattern of daily use presented a descriptive peak toward the evening for apps incorporating most techniques (tracker, psychoeducation, and peer support) except mindfulness/meditation, which exhibited two peaks (morning and night). Conclusions Although the number of app installs and daily active minutes of use may seem high, only a small portion of users actually used the apps for a long period of time. More studies using different datasets are needed to understand this phenomenon and the ways in which users self-manage their condition in real-world settings.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                November 2022
                16 November 2022
                : 24
                : 11
                : e41463
                Affiliations
                [1 ] The Institute for Digital Medicine Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore
                [2 ] The N.1 Institute for Health National University of Singapore Singapore Singapore
                [3 ] Department of Biomedical Engineering College of Design and Engineering National University of Singapore Singapore Singapore
                [4 ] Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore
                [5 ] Arcondis Pte Ltd Singapore Singapore
                [6 ] The Bia-Echo Asia Centre for Reproductive Longevity and Equality Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore
                Author notes
                Corresponding Author: Agata Blasiak agata.blasiak@ 123456nus.edu.sg
                Author information
                https://orcid.org/0000-0003-0727-7611
                https://orcid.org/0000-0001-6797-7850
                https://orcid.org/0000-0001-5752-1550
                https://orcid.org/0000-0003-4781-6413
                https://orcid.org/0000-0002-1937-0939
                https://orcid.org/0000-0002-0216-9032
                https://orcid.org/0000-0002-1225-4302
                https://orcid.org/0000-0002-7337-296X
                Article
                v24i11e41463
                10.2196/41463
                9713622
                36383427
                a87eff7e-3b0f-4c1a-b0f4-8f1411f57190
                ©Agata Blasiak, Yoann Sapanel, Dana Leitman, Wei Ying Ng, Raffaele De Nicola, V Vien Lee, Atanas Todorov, Dean Ho. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.11.2022.

                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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 26 July 2022
                : 7 September 2022
                : 27 September 2022
                : 13 October 2022
                Categories
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                Medicine
                digital health intervention,omnichannel engagement,behavioral change,communication channels,personalized engagement,health care,patient care,health care outcome,patient engagement,digital twin,dhi,digital health,ehealth,framework,development

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