How to use the Integrated-Change Model to design digital health programs

Objective Tailored digital health programs can promote positive health-related lifestyle changes and have been shown to be (cost) effective in trials. However, such programs are used suboptimally. New approaches are needed to optimise the use of these programs. This paper illustrates the potential of recommender systems to support and enhance computer-tailored digital health interventions. The aim is threefold, to explore: (1) how recommender systems provide health recommendations, (2) to what extent recommender systems incorporate theoretical models and (3) how the use of recommender systems may enhance the usage of computer-tailored interventions. Methods A scoping review was conducted, using MEDLINE and ScienceDirect, to identify health recommender systems reported in studies between January 2007 and December 2017. Information was subsequently extracted to understand the potential benefits of recommender systems for computer-tailored digital health programs. Titles and abstracts of 1184 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form. Results A total of 26 articles were included for data extraction. General characteristics were reported, with eight studies reporting hybrid filtering. A description of how each recommender system provides a recommendation is described; the majority of recommender systems used messages as recommendation. We identified the potential effects of recommender systems on efficiency, effectiveness, trustworthiness and enjoyment of the digital health program. Conclusions Incorporating a collaborative method with demographic filtering as a second step to knowledge-based filtering could potentially add value to traditional tailoring with regard to enhancing the user experience. This study illustrates how recommender systems, especially hybrid programs, may have the potential to bring tailored digital health forward.

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Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocw166 ◽

2017 ◽

Vol 24 (4) ◽

pp. 867-879 ◽

Cited By ~ 7

Author(s):

Adrienne O’Neil ◽

Fiona Cocker ◽

Patricia Rarau ◽

Shaira Baptista ◽

Mandy Cassimatis ◽

...

Keyword(s):

Systematic Reviews ◽

Digital Health ◽

Reporting Quality ◽

Health Interventions ◽

Health Programs ◽

Quality Of Reporting ◽

Digital Interventions ◽

Meta Review ◽

Research Findings

Abstract Objectives. We conducted a meta-review to determine the reporting quality of user-centered digital interventions for the prevention and management of cardiometabolic conditions. Materials and Methods. Using predetermined inclusion criteria, systematic reviews published between 2010 and 2015 were identified from 3 databases. To assess whether current evidence is sufficient to inform wider uptake and implementation of digital health programs, we assessed the quality of reporting of research findings using (1) endorsement of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, (2) a quality assessment framework (eg, Cochrane risk of bias assessment tool), and (3) 8 parameters of the Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth (CONSORT-eHEALTH) guidelines (developed in 2010). Results. Of the 33 systematic reviews covering social media, Web-based programs, mobile health programs, and composite modalities, 6 reported using the recommended PRISMA guidelines. Seven did not report using a quality assessment framework. Applying the CONSORT-EHEALTH guidelines, reporting was of mild to moderate strength. Discussion. To our knowledge, this is the first meta-review to provide a comprehensive analysis of the quality of reporting of research findings for a range of digital health interventions. Our findings suggest that the evidence base and quality of reporting in this rapidly developing field needs significant improvement in order to inform wider implementation and uptake. Conclusion. The inconsistent quality of reporting of digital health interventions for cardiometabolic outcomes may be a critical impediment to real-world implementation.

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Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol

JMIR Research Protocols ◽

10.2196/11456 ◽

2019 ◽

Vol 8 (5) ◽

pp. e11456 ◽

Cited By ~ 3

Author(s):

Diwakar Mohan ◽

Jean Juste Harrisson Bashingwa ◽

Pierre Dane ◽

Sara Chamberlain ◽

Nicki Tiffin ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Digital Health ◽

Monitoring And Evaluation ◽

Health Programs ◽

Learning Methods ◽

Machine Learning Methods

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Older Adults Engage With Personalized Digital Coaching Programs at Rates That Exceed Those of Younger Adults

Frontiers in Digital Health ◽

10.3389/fdgth.2021.642818 ◽

2021 ◽

Vol 3 ◽

Author(s):

Sarah A. Graham ◽

Natalie Stein ◽

Fjori Shemaj ◽

OraLee H. Branch ◽

Jason Paruthi ◽

...

Keyword(s):

Older Adults ◽

Technology Use ◽

Chronic Conditions ◽

Digital Health ◽

Cost Effective ◽

Health Programs ◽

Care Providers ◽

Younger Adults ◽

Health Technologies ◽

Healthcare Needs

Background: The US population is aging and has an expanding set of healthcare needs for the prevention and management of chronic conditions. Older adults contribute disproportionately to US healthcare costs, accounting for 34% of total healthcare expenditures in 2014 but only 15% of the population. Fully automated, digital health programs offer a scalable and cost-effective option to help manage chronic conditions. However, the literature on technology use suggests that older adults face barriers to the use of digital technologies that could limit their engagement with digital health programs. The objective of this study was to characterize the engagement of adults 65 years and older with a fully automated digital health platform called Lark Health and compare their engagement to that of adults aged 35–64 years.Methods: We analyzed data from 2,169 Lark platform users across four different coaching programs (diabetes prevention, diabetes care, hypertension care, and prevention) over a 12-month period. We characterized user engagement as participation in digital coaching conversations, meals logged, and device measurements. We compared engagement metrics between older and younger adults using nonparametric bivariate analyses.Main Results: Aggregate engagement across all users during the 12-month period included 1,623,178 coaching conversations, 588,436 meals logged, and 203,693 device measurements. We found that older adults were significantly more engaged with the digital platform than younger adults, evidenced by older adults participating in a larger median number of coaching conversations (514 vs. 428) and logging more meals (174 vs. 89) and device measurements (39 vs. 28) all p ≤ 0.01.Conclusions: Older adult users of a commercially available, fully digital health platform exhibited greater engagement than younger adults. These findings suggest that despite potential barriers, older adults readily adopted digital health technologies. Fully digital health programs may present a widely scalable and cost-effective alternative to traditional telehealth models that still require costly touchpoints with human care providers.

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Feasibility and Adoption of a Focused Digital Wellness Program in Older Adults

Geriatrics ◽

10.3390/geriatrics6020054 ◽

2021 ◽

Vol 6 (2) ◽

pp. 54

Author(s):

Eric Tam ◽

Pedro Kondak Villas Boas ◽

Fernando Ruaro ◽

Juliane Flesch ◽

Jennifer Wu ◽

...

Keyword(s):

Older Adults ◽

Health Outcomes ◽

User Satisfaction ◽

Digital Health ◽

The United States ◽

Health Programs ◽

Older Individuals ◽

Wellness Program ◽

Patient Health ◽

Future Work

Digital health programs offer numerous psychological and physical health benefits. To date, digital programs have been aimed broadly at younger participants, yet older individuals may also benefit. Our study sought to demonstrate user feasibility and satisfaction in a digital wellness program for older adults. We conducted a retrospective analysis of 140 participants in a digital health wellness application that integrated guided exercises, nutrition planning and health education. Primary outcomes were active participant retention, engagement in the mobile program and user satisfaction as operationalized by NPS scores. Among 140 participants, median age was 59.82 (50–80), 61% female, in a sample taken in the United States. Engagement was high and sustained, with more than 65% participants engaged, operationalized as at least completing one task activity a month over 17 weeks. Participants were also satisfied with the program, reporting NPS scores of 43 on day 30 of the program. Secondary health outcomes included 3.44 pound weight change during the first month. User feasibility and satisfaction was demonstrated in a sample of older participants for this novel digital health wellness program. Future work focused on older adult users may result in improvements in patient health outcomes and improved preventive medicine strategies.

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Mobile Technology for Community Health in Ghana: what happens when technical functionality threatens the effectiveness of digital health programs?

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-017-0421-9 ◽

2017 ◽

Vol 17 (1) ◽

Cited By ~ 25

Author(s):

Amnesty E. LeFevre ◽

Diwakar Mohan ◽

David Hutchful ◽

Larissa Jennings ◽

Garrett Mehl ◽

...

Keyword(s):

Community Health ◽

Mobile Technology ◽

Digital Health ◽

Health Programs

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Use of Big Data and Machine Learning Methods in the Monitoring and Evaluation of Digital Health Programs in India: An Exploratory Protocol (Preprint)

10.2196/preprints.11456 ◽

2018 ◽

Author(s):

Diwakar Mohan ◽

Jean Juste Harrisson Bashingwa ◽

Pierre Dane ◽

Sara Chamberlain ◽

Nicki Tiffin ◽

...

Keyword(s):

Machine Learning ◽

Big Data ◽

Health Information ◽

Program Effectiveness ◽

Health Workers ◽

Interactive Voice Response ◽

Digital Health ◽

Health Programs ◽

Frontline Health Workers ◽

Applications Of Machine Learning

BACKGROUND Digital health programs, which encompass the subsectors of health information technology, mobile health, electronic health, telehealth, and telemedicine, have the potential to generate “big data.” OBJECTIVE Our aim is to evaluate two digital health programs in India—the maternal mobile messaging service (Kilkari) and the mobile training resource for frontline health workers (Mobile Academy). We illustrate possible applications of machine learning for public health practitioners that can be applied to generate evidence on program effectiveness and improve implementation. Kilkari is an outbound service that delivers weekly gestational age–appropriate audio messages about pregnancy, childbirth, and childcare directly to families on their mobile phones, starting from the second trimester of pregnancy until the child is one year old. Mobile Academy is an Interactive Voice Response audio training course for accredited social health activists (ASHAs) in India. METHODS Study participants include pregnant and postpartum women (Kilkari) as well as frontline health workers (Mobile Academy) across 13 states in India. Data elements are drawn from system-generated databases used in the routine implementation of programs to provide users with health information. We explain the structure and elements of the extracted data and the proposed process for their linkage. We then outline the various steps to be undertaken to evaluate and select final algorithms for identifying gaps in data quality, poor user performance, predictors for call receipt, user listening levels, and linkages between early listening and continued engagement. RESULTS The project has obtained the necessary approvals for the use of data in accordance with global standards for handling personal data. The results are expected to be published in August/September 2019. CONCLUSIONS Rigorous evaluations of digital health programs are limited, and few have included applications of machine learning. By describing the steps to be undertaken in the application of machine learning approaches to the analysis of routine system-generated data, we aim to demystify the use of machine learning not only in evaluating digital health education programs but in improving their performance. Where articles on analysis offer an explanation of the final model selected, here we aim to emphasize the process, thereby illustrating to program implementors and evaluators with limited exposure to machine learning its relevance and potential use within the context of broader program implementation and evaluation. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11456

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Use of Synchronous Digital Health Technologies for the Care of Children With Special Health Care Needs and Their Families: Scoping Review (Preprint)

10.2196/preprints.15106 ◽

2019 ◽

Author(s):

Marissa Bird ◽

Lin Li ◽

Carley Ouellette ◽

Kylie Hopkins ◽

Michael H McGillion ◽

...

Keyword(s):

Health Care ◽

Scoping Review ◽

Digital Health ◽

Special Health Care Needs ◽

Health Programs ◽

Health Care Needs ◽

Care Needs ◽

Special Health Care ◽

Health Technologies ◽

Special Health

BACKGROUND Use of synchronous digital health technologies for care delivery to children with special health care needs (having a chronic physical, behavioral, developmental, or emotional condition in combination with high resource use) and their families at home has shown promise for improving outcomes and increasing access to care for this medically fragile and resource-intensive population. However, a comprehensive description of the various models of synchronous home digital health interventions does not exist, nor has the impact of such interventions been summarized to date. OBJECTIVE We aim to describe the various models of synchronous home digital health that have been used in pediatric populations with special health care needs, their outcomes, and implementation barriers. METHODS A systematic scoping review of the literature was conducted, guided by the Arksey and O’Malley Scoping Review Framework. MEDLINE, CINAHL, and EMBASE databases were searched from inception to June 2018, and the reference lists of the included systematic reviews and high-impact journals were hand-searched. RESULTS A total of 38 articles were included in this review. Interventional articles are described as feasibility studies, studies that aim to provide direct care to children with special health care needs, and studies that aim to support family members to deliver care to children with special health care needs. End-user involvement in the design and implementation of studies is evaluated using a human-centered design framework, and factors affecting the implementation of digital health programs are discussed in relation to technological, human, and systems factors. CONCLUSIONS The use of digital health to care for children with special health care needs presents an opportunity to leverage the capacity of technology to connect patients and their families to much-needed care from expert health care providers while avoiding the expenses and potential harms of the hospital-based care system. Strategies to scale and spread pilot studies, such as involving end users in the co-design techniques, are needed to optimize digital health programs for children with special health care needs.

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Use of Synchronous Digital Health Technologies for the Care of Children With Special Health Care Needs and Their Families: Scoping Review

JMIR Pediatrics and Parenting ◽

10.2196/15106 ◽

2019 ◽

Vol 2 (2) ◽

pp. e15106 ◽

Cited By ~ 3

Author(s):

Marissa Bird ◽

Lin Li ◽

Carley Ouellette ◽

Kylie Hopkins ◽

Michael H McGillion ◽

...

Keyword(s):

Health Care ◽

Scoping Review ◽

Digital Health ◽

Special Health Care Needs ◽

Health Programs ◽

Health Care Needs ◽

Care Needs ◽

Special Health Care ◽

Health Technologies ◽

Special Health

Background Use of synchronous digital health technologies for care delivery to children with special health care needs (having a chronic physical, behavioral, developmental, or emotional condition in combination with high resource use) and their families at home has shown promise for improving outcomes and increasing access to care for this medically fragile and resource-intensive population. However, a comprehensive description of the various models of synchronous home digital health interventions does not exist, nor has the impact of such interventions been summarized to date. Objective We aim to describe the various models of synchronous home digital health that have been used in pediatric populations with special health care needs, their outcomes, and implementation barriers. Methods A systematic scoping review of the literature was conducted, guided by the Arksey and O’Malley Scoping Review Framework. MEDLINE, CINAHL, and EMBASE databases were searched from inception to June 2018, and the reference lists of the included systematic reviews and high-impact journals were hand-searched. Results A total of 38 articles were included in this review. Interventional articles are described as feasibility studies, studies that aim to provide direct care to children with special health care needs, and studies that aim to support family members to deliver care to children with special health care needs. End-user involvement in the design and implementation of studies is evaluated using a human-centered design framework, and factors affecting the implementation of digital health programs are discussed in relation to technological, human, and systems factors. Conclusions The use of digital health to care for children with special health care needs presents an opportunity to leverage the capacity of technology to connect patients and their families to much-needed care from expert health care providers while avoiding the expenses and potential harms of the hospital-based care system. Strategies to scale and spread pilot studies, such as involving end users in the co-design techniques, are needed to optimize digital health programs for children with special health care needs.

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