Digital Health Interventions: New opportunities for health science librarians

2021 ◽  
Author(s):  
Jeannette Murphy
Keyword(s):  
Digital Health ◽  
Health Science ◽  
Health Interventions

scholarly journals The role of economic, educational and social resources in supporting the use of digital health technologies by people with T2D: a qualitative study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sophie Turnbull ◽  
Patricia J. Lucas ◽  
Alastair D. Hay ◽  
Christie Cabral
Keyword(s):  
Social Networks ◽  
Social Status ◽  
Digital Health ◽  
Self Care ◽  
Social Resources ◽  
Health Interventions ◽  
Structured Interviews ◽  
Health Technologies ◽  
Digital Skills ◽  
Care Support

Abstract Background Type 2 Diabetes (T2D) is a common chronic disease, with socially patterned incidence and severity. Digital self-care interventions have the potential to reduce health disparities, by providing personalised low-cost reusable resources that can increase access to health interventions. However, if under-served groups are unable to access or use digital technologies, Digital Health Technologies (DHTs) might make no difference, or worse, exacerbate health inequity. Study aims To gain insights into how and why people with T2D access and use DHTs and how experiences vary between individuals and social groups. Methods A purposive sample of people with experience of using a DHT to help them self-care for T2D were recruited through diabetes and community groups. Semi-structured interviews were conducted in person and over the phone. Data were analysed thematically. Results A diverse sample of 21 participants were interviewed. Health care practitioners were not viewed as a good source of information about DHTs that could support T2D. Instead participants relied on their digital skills and social networks to learn about what DHTs are available and helpful. The main barriers to accessing and using DHT described by the participants were availability of DHTs from the NHS, cost and technical proficiency. However, some participants described how they were able to draw on social resources such as their social networks and social status to overcome these barriers. Participants were motivated to use DHTs because they provided self-care support, a feeling of control over T2D, and personalised advice or feedback. The selection of technology was also guided by participants’ preferences and what they valued in relation to DHTs and self-care support, and these in turn were influenced by age and gender. Conclusion This research indicates that low levels of digital skills and high cost of digital health interventions can create barriers to the access and use of DHTs to support the self-care of T2D. However, social networks and social status can be leveraged to overcome some of these challenges. If digital interventions are to decrease rather than exacerbate health inequalities, these barriers and facilitators to access and use must be considered when DHTs are developed and implemented.

scholarly journals Digital health interventions in palliative care: a systematic meta-review

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Anne M. Finucane ◽  
Hannah O’Donnell ◽  
Jean Lugton ◽  
Tilly Gibson-Watt ◽  
Connie Swenson ◽  
...  
Keyword(s):  
Decision Making ◽  
Palliative Care ◽  
Systematic Reviews ◽  
Psychological Symptoms ◽  
Digital Health ◽  
Evidence Synthesis ◽  
Information Provision ◽  
Health Interventions ◽  
Meta Review

AbstractDigital health interventions (DHIs) have the potential to improve the accessibility and effectiveness of palliative care but heterogeneity amongst existing systematic reviews presents a challenge for evidence synthesis. This meta-review applied a structured search of ten databases from 2006 to 2020, revealing 21 relevant systematic reviews, encompassing 332 publications. Interventions delivered via videoconferencing (17%), electronic healthcare records (16%) and phone (13%) were most frequently described in studies within reviews. DHIs were typically used in palliative care for education (20%), symptom management (15%), decision-making (13%), information provision or management (13%) and communication (9%). Across all reviews, mostly positive impacts were reported on education, information sharing, decision-making, communication and costs. Impacts on quality of life and physical and psychological symptoms were inconclusive. Applying AMSTAR 2 criteria, most reviews were judged as low quality as they lacked a protocol or did not consider risk of bias, so findings need to be interpreted with caution.

Effectiveness of Digital Health Interventions on Unintentional Injury, Violence, and Suicide: Meta-Analysis

2020 ◽  
pp. 152483802096734
Author(s):  
Mengtong Chen ◽  
Ko Ling Chan
Keyword(s):  
Interpersonal Violence ◽  
Care Delivery ◽  
Digital Health ◽  
Meta Analysis ◽  
Random Effect ◽  
Unintentional Injury ◽  
Health Interventions ◽  
Effect Sizes ◽  
Future Research ◽  
Face To Face

Digital technologies are increasingly used in health-care delivery and are being introduced into work to prevent unintentional injury, violence, and suicide to reduce mortality. To understand the potential of digital health interventions (DHIs) to prevent and reduce these problems, we conduct a meta-analysis and provide an overview of their effectiveness and characteristics related to the effects. We searched electronic databases and reference lists of relevant reviews to identify randomized controlled trials (RCTs) published in or before March 2020 evaluating DHIs on injury, violence, or suicide reduction. Based on the 34 RCT studies included in the meta-analysis, the overall random effect size was 0.21, and the effect sizes for reducing suicidal ideation, interpersonal violence, and unintentional injury were 0.17, 0.24, and 0.31, respectively, which can be regarded as comparable to the effect sizes of traditional face-to-face interventions. However, there was considerable heterogeneity between the studies. In conclusion, DHIs have great potential to reduce unintentional injury, violence, and suicide. Future research should explore DHIs’ successful components to facilitate future implementation and wider access.

scholarly journals Effectiveness, Acceptability, and Feasibility of Digital Health Interventions for LGBTIQ+ Young People: Systematic Review

10.2196/20158 ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. e20158
Author(s):  
Dylan Gilbey ◽  
Helen Morgan ◽  
Ashleigh Lin ◽  
Yael Perry
Keyword(s):  
Young People ◽  
Sexual Health ◽  
Digital Health ◽  
Health Interventions ◽  
Health Concern ◽  
Sufficient Evidence ◽  
Future Research ◽  
Evidence Based ◽  
Bisexual Men ◽  
Targeted Interventions

Background Young people (aged 12-25 years) with diverse sexuality, gender, or bodily characteristics, such as those who identify as lesbian, gay, bisexual, transgender, intersex, or queer (LGBTIQ+), are at substantially greater risk of a range of mental, physical, and sexual health difficulties compared with their peers. Digital health interventions have been identified as a potential way to reduce these health disparities. Objective This review aims to summarize the characteristics of existing evidence-based digital health interventions for LGBTIQ+ young people and to describe the evidence for their effectiveness, acceptability, and feasibility. Methods A systematic literature search was conducted using internet databases and gray literature sources, and the results were screened for inclusion. The included studies were synthesized qualitatively. Results The search identified 38 studies of 24 unique interventions seeking to address mental, physical, or sexual health–related concerns in LGBTIQ+ young people. Substantially more evidence-based interventions existed for gay and bisexual men than for any other population group, and there were more interventions related to risk reduction of sexually transmitted infections than to any other health concern. There was some evidence for the effectiveness, feasibility, and acceptability of these interventions overall; however, the quality of evidence is often lacking. Conclusions There is sufficient evidence to suggest that targeted digital health interventions are an important focus for future research aimed at addressing health difficulties in LGBTIQ+ young people. Additional digital health interventions are needed for a wider range of health difficulties, particularly in terms of mental and physical health concerns, as well as more targeted interventions for same gender–attracted women, trans and gender-diverse people, and people with intersex variations. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020128164; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=128164

scholarly journals Research and Implementation Lessons Learned From a Youth-Targeted Digital Health Randomized Controlled Trial (the ARMADILLO Study)

10.2196/13005 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e13005
Author(s):  
Lianne Gonsalves ◽  
Winnie Wangari Njeri ◽  
Megan Schroeder ◽  
Jefferson Mwaisaka ◽  
Peter Gichangi
Keyword(s):  
Randomized Controlled Trial ◽  
Digital Health ◽  
Controlled Trial ◽  
Text Message ◽  
Health Interventions ◽  
World Health ◽  
Intervention Period ◽  
Implementation Challenges ◽  
Randomized Controlled ◽  
Client Side

Background Evidence is lacking on the efficacy of sexual and reproductive health (SRH) communication interventions for youth (aged 15-24 years), especially from low- and middle-income countries. Therefore, the World Health Organization initiated the Adolescent/Youth Reproductive Mobile Access and Delivery Initiative for Love and Life Outcomes (ARMADILLO) program, a free, menu-based, on-demand text message (SMS, short message service) platform providing validated SRH content developed in collaboration with young people. A randomized controlled trial (RCT) assessing the effect of the ARMADILLO intervention on SRH-related outcomes was implemented in Kwale County, Kenya. Objective This paper describes the implementation challenges related to the RCT, observed during enrollment and the intervention period, and their implications for digital health researchers and program implementers. Methods This was an open, three-armed RCT. Following completion of a baseline survey, participants were randomized into the ARMADILLO intervention (arm 1), a once-a-week contact SMS text message (arm 2), or usual care (arm 3, no intervention). The intervention period lasted seven weeks, after which participants completed an endline survey. Results Two study team decisions had significant implications for the success of the trial’s enrollment and intervention implementation: a hands-off participant recruitment process and a design flaw in an initial language selection menu. As a result, three weeks after recruitment began, 660 participants had been randomized; however, 107 (53%) participants in arm 1 and 136 (62%) in arm 2 were “stuck” at the language menu. The research team called 231 of these nonengaging participants and successfully reached 136 to learn reasons for nonengagement. Thirty-two phone numbers were found to be either not linked to our participants (a wrong number) or not in their primary possession (a shared phone). Among eligible participants, 30 participants indicated that they had assumed the introductory message was a scam or spam. Twenty-seven participants were confused by some aspect of the system. Eleven were apathetic about engaging. Twenty-four nonengagers experienced some sort of technical issue. All participants eventually started their seven-week study period. Conclusions The ARMADILLO study’s implementation challenges provide several lessons related to both researching and implementing client-side digital health interventions, including (1) have meticulous phone data collection protocols to reduce wrong numbers, (2) train participants on the digital intervention in efficacy assessments, and (3) recognize that client-side digital health interventions have analog discontinuation challenges. Implementation lessons were (1) determine whether an intervention requires phone ownership or phone access, (2) digital health campaigns need to establish a credible presence in a busy digital space, and (3) interest in a service can be sporadic or fleeting. Clinical Trial International Standard Randomized Controlled Trial Number (ISRCTN): 85156148; http://www.isrctn. com/ISRCTN85156148

scholarly journals Digital Interventions to Support Adolescents and Young Adults With Cancer: Systematic Review

JMIR Cancer ◽  
10.2196/12071 ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. e12071 ◽  
Author(s):  
Lisa McCann ◽  
Kathryn Anne McMillan ◽  
Gemma Pugh
Keyword(s):  
Young Adults ◽  
Cancer Diagnosis ◽  
Subjective Experience ◽  
Emotional Health ◽  
Digital Health ◽  
Health Interventions ◽  
Adolescents And Young Adults ◽  
Research Papers ◽  
Digital Interventions ◽  
Diagnosis Of Cancer

Background The last decade has seen an increase in the number of digital health interventions designed to support adolescents and young adults (AYAs) with cancer. Objective The objective of this review was to identify, characterize, and fully assess the quality, feasibility, and efficacy of existing digital health interventions developed specifically for AYAs, aged between 13 and 39 years, living with or beyond a cancer diagnosis. Methods Searches were performed in PubMed, EMBASE, and Web of Science to identify digital health interventions designed specifically for AYA living with or beyond a cancer diagnosis. Data on the characteristics and outcomes of each intervention were synthesized. Results A total of 4731 intervention studies were identified through the searches; 38 interventions (43 research papers) met the inclusion criteria. Most (20/38, 53%) were website-based interventions. Most studies focused on symptom management and medication adherence (15, 39%), behavior change (15, 39%), self-care (8, 21%), and emotional health (7, 18%). Most digital health interventions included multiple automated and communicative functions such as enriched information environments, automated follow-up messages, and access to peer support. Where reported (20, 53% of studies), AYAs’ subjective experience of using the digital platform was typically positive. The overall quality of the studies was found to be good (mean Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields scores >68%). Some studies reported feasibility outcomes (uptake, acceptability, and attrition) but were not sufficiently powered to comment on intervention effects. Conclusions Numerous digital interventions have been developed and designed to support young people living with and beyond a diagnosis of cancer. However, many of these interventions have yet to be deployed, implemented, and evaluated at scale.

scholarly journals Co-creating a large-scale adolescent health survey integrated with access to digital health interventions

2020 ◽  
Author(s):  
Roshini Peiris-John ◽  
Lovely Dizon ◽  
Kylie Sutcliffe ◽  
Kristy Kang ◽  
Theresa Fleming
Keyword(s):  
Adolescent Health ◽  
Health Survey ◽  
Large Scale ◽  
Service Providers ◽  
Digital Health ◽  
Health Interventions ◽  
Future Research ◽  
Health Providers ◽  
Health And Wellbeing ◽  
The Impact

Aim This paper describes how we engaged with adolescents and health providers to integrate access to digital health interventions as part of a large-scale secondary school health and wellbeing survey in New Zealand. Methods We conducted nine participatory, iterative co-design sessions involving 29 adolescents, and two workshops with young people (n = 11), digital and health service providers (n = 11) and researchers (n = 9) to gain insights into end-user perspectives on the concept and how best to integrate digital interventions in to the survey. Results Students’ perceived integrating access to digital health interventions into a large-scale youth health survey as acceptable and highly beneficial. They did not want personalized/normative feedback, but thought that every student should be offered all the help options. Participants identified key principles: assurance of confidentiality, usability, participant choice and control, and language. They highlighted wording as important for ease and comfort, and emphasised the importance of user control. Participants expressed that it would be useful and acceptable for survey respondents to receive information about digital help options addressing a range of health and wellbeing topics. Conclusion The methodology of adolescent-practitioner-researcher collaboration and partnership was central to this research and provided useful insights for the development and delivery of adolescent health surveys integrated with digital help options. The results from the ongoing study will provide useful data on the impact of digital health interventions integrated in large-scale surveys, as a novel methodology. Future research on engaging with adolescents once interventions are delivered will be useful to explore benefits over time.

scholarly journals Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Vincent Bremer ◽  
Philip I Chow ◽  
Burkhardt Funk ◽  
Frances P Thorndike ◽  
Lee M Ritterband
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Behavioral Therapy ◽  
Digital Health ◽  
Area Under The Curve ◽  
Prediction Performance ◽  
Health Interventions ◽  
Drop Out ◽  
Support Vector ◽  
Boosted Decision Trees

BACKGROUND User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. OBJECTIVE The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. METHODS Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. RESULTS Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. CONCLUSIONS The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.

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