Design Of Digital Interventions Research Articles

Behavior Change Techniques Within Digital Interventions for the Treatment of Eating Disorders: Systematic Review and Meta-Analysis.

Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design. This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included. A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes. Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results. There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions. PROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060.

Closing the Digital Divide in Interventions for Substance Use Disorder.

Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.

How Technologies Can Support Self-Injury Self-Management: Perspectives of Young Adults With Lived Experience of Nonsuicidal Self-Injury.

BackgroundThere is growing interest in the design of digital interventions to improve conditions for young people who engage in high-risk behaviors, like nonsuicidal self-injury (NSSI). However, few studies have focused on how young people self-manage NSSI, or their existing, and historic, use of technologies to support their goals related to NSSI behavior change. Such an understanding has the potential to inform the design of digital interventions that meet this population's unique needs.ObjectivesThis study aims to (a) understand the self-management practices of young adults who engage in NSSI, (b) explore how they currently use technologies for self-injury self-management, and (c) identify the ways they can envision an app-based technology supporting their self-management.Methods and MaterialsTwenty young adults (aged 18–24) with lived experience of NSSI, and who were not currently enrolled in therapy, were recruited from online venues. Participants completed baseline measures to assess mental health and NSSI characteristics, followed by a virtual 1-h semi-structured interview where they were invited to share their experience of self-management, their goals, and their thoughts on supportive technology. Interview scripts were transcribed and analyzed via thematic analysis.ResultsThemes and sub-themes are organized under two broad domain areas: (1) How young adults self-manage NSSI thoughts and behaviors and (2) Opportunities and challenges for digital interventions to assist young adults in their recovery process. We found that young adults had varied experiences with, and goals related to, NSSI. Participants reported a lack of effective strategies to reduce NSSI urges and a desire for an app-based technology to track patterns and deliver personalized suggestions for self-management. Participants reported existing use of technologies as part of self-management, as well as early information and support seeking for NSSI online.ConclusionsThis study contributes a greater understanding of young people's experiences with self-injury, their self-management practices, and their desire to engage with technology. Our findings highlight the need for design flexibility in developing digital interventions that support individual goals, unique presentations of NSSI, and needs at different phases of recovery. Implications for the design of highly personalized and relevant digital interventions to address NSSI are discussed.

Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study.

BackgroundThere is a strong need to improve medication adherence (MA) for individuals with hypertension in order to reduce long-term hospitalization costs. We believe this can be achieved through an artificial intelligence agent that helps the patient in understanding key individual adherence risk factors and designing an appropriate intervention plan. The incidence of hypertension in Sweden is estimated at approximately 27%. Although blood pressure control has increased in Sweden, barely half of the treated patients achieved adequate blood pressure levels. It is a major risk factor for coronary heart disease and stroke as well as heart failure. MA is a key factor for good clinical outcomes in persons with hypertension.ObjectiveThe overall aim of this study is to design, develop, test, and evaluate an adaptive digital intervention called iMedA, delivered via a mobile app to improve MA, self-care management, and blood pressure control for persons with hypertension.MethodsThe study design is an interrupted time series. We will collect data on a daily basis, 14 days before, during 6 months of delivering digital interventions through the mobile app, and 14 days after. The effect will be analyzed using segmented regression analysis. The participants will be recruited in Region Halland, Sweden. The design of the digital interventions follows the just-in-time adaptive intervention framework. The primary (distal) outcome is MA, and the secondary outcome is blood pressure. The design of the digital intervention is developed based on a needs assessment process including a systematic review, focus group interviews, and a pilot study, before conducting the longitudinal interrupted time series study.ResultsThe focus groups of persons with hypertension have been conducted to perform the needs assessment in a Swedish context. The design and development of digital interventions are in progress, and the interventions are planned to be ready in November 2020. Then, the 2-week pilot study for usability evaluation will start, and the interrupted time series study, which we plan to start in February 2021, will follow it.ConclusionsWe hypothesize that iMedA will improve medication adherence and self-care management. This study could illustrate how self-care management tools can be an additional (digital) treatment support to a clinical one without increasing burden on health care staff.Trial RegistrationClinicalTrials.gov NCT04413500; https://clinicaltrials.gov/ct2/show/NCT04413500International Registered Report Identifier (IRRID)DERR1-10.2196/24494

Theory Integration for Lifestyle Behavior Change in the Digital Age: An Adaptive Decision-Making Framework.

Despite the growing popularity of digital health interventions, limitations of traditional behavior change theories and a lack of theory integration hinder theory-driven behavior change applications. In this paper, we aim to review theories relevant to lifestyle behavior change from the broader psychology literature and then integrate these theories into a new theoretical framework called adaptive decision-making to address two specific problems. First, our framework represents lifestyle behaviors at two levels—one of individual daily decisions (action level) and one of larger behavioral episodes (reflection level)—to more closely match the temporal characteristics of lifestyle behaviors and their associated digital data. Second, the framework connects decision-making theories and learning theories to explain how behaviors and cognitive constructs dynamically influence each other, making it a suitable scaffold for building computational models. We map common digital intervention techniques onto the behavioral and cognitive processes in the framework and discuss possible contributions of the framework to both theory development and digital intervention design.

A new age intervention to support medication adherence.

Adherence to pharmacological treatments is a complex behaviour which is influenced by a number of factors throughout a person's medication use journey. No single intervention has yet been shown to effectively address non-adherence long term. Technology, which can support a multifaceted intervention targeted to the needs of a person, may present a feasible solution to optimise adherence. Technological interventions such as mobile applications (apps) have shown early promise, increasing medication adherence rates and providing health care practitioners with an opportunity to monitor and measure adherence. Some of their features include providing informational messages, saving prescription details and sending refill reminders. Their inability to address patient specific factors across the three phases of adherence (initiation, implementation and discontinuation) limit their usefulness in clinical practice. We propose recommendations to guide the design of digital interventions that can support adherence. Interventions should be individualised to address patient specific factors that affect adherence to medications; the features should support individuals across all three phases of adherence. Interventions must integrate within existing prescriber and dispensing software and interconnect all members of an individual's healthcare team. Finally, to ensure optimal outcomes for the individual, all digital interventions should inform the person about why adherence is necessary.

Online Health Information-Seeking Among Older Women With Chronic Illness: Analysis of the Women's Health Initiative.

BackgroundUnderstanding how older patients with chronic illnesses use the internet to obtain health information is relevant for the design of digital interventions aimed at improving the health and well-being of adults aged 65 years and older; this cohort represents the sickest, most expensive, and fastest-growing segment of the US population.ObjectiveThe objective of our study was to describe online health information–seeking behavior among older patients with chronic illnesses and to compare the characteristics of patients who report using the internet to obtain health information with those who do not.MethodsThe study population included 72,806 women aged 65 years and older enrolled in the Women’s Health Initiative (WHI), a national cohort study, who completed a 2014 supplemental questionnaire assessing everyday technology use and internet use for researching health conditions. Comparisons were made between participants with and without a history of chronic illness and between users and nonusers of online sources for health information. Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% CIs.ResultsOf the total, 59% (42,887/72,806) of older women used the internet for health information. Compared with women who did not use the internet to obtain health information, those who used the internet were younger (median age: 76 vs 81 years), more likely to be non-Hispanic white (90% [38,481/42,887] vs 87% [26,017/29,919]), earned a higher income (over $US 50,000: 55% [23,410/42,887] vs 33% [9991/29,919]), achieved a higher educational level (more than high school: 87% [37,493/42,887] vs 75% [22,377/29,919]), and were more likely to live with a partner (52% [22,457/42,887] vs 36% [10,759/29,919]) (all P<.001). Women with Alzheimer disease were least likely to report online health information–seeking compared to those without the disease (OR 0.41, 95% CI 0.38-0.43). In contrast, women with a recent diagnosis of cancer, within the previous 2 years (OR 1.23, 95% CI 1.11-1.36) or 2-5 years ago (OR 1.09, 95% CI 1.00-1.19), were most likely to use the internet for health information.ConclusionsNearly 6 in 10 older women participating in the WHI reported using the internet to obtain health information. Patients recently diagnosed with cancer are more likely to be looking for health information online, even after adjustment for age, suggesting that these patients may have a greater need for digital health resources.

Evaluating the role of digital intervention design in treatment outcomes and adherence to eTherapy programs for eating disorders: A systematic review and meta-analysis.

Despite the existence of reviews which document the effectiveness of online therapies, there is little consensus regarding the exact components of online treatment delivery that are most effective in maintaining user engagement and reducing eating disorder (ED) symptomology. The current review and meta-analysis aimed to determine which components of web-based, ED self-help interventions are associated with lower attrition and improved therapeutic outcome. A systematic search of electronic databases (PsycINFO, Medline, Web of Science, Scopus) was conducted for published studies of web-based interventions for EDs and a meta-analysis of the final included studies was performed. Each intervention was coded across four dimensions, including the multimedia channels employed (e.g., text, audio, videos), degree of user interactivity (e.g., online self-monitoring, skills exercises), level of automated feedback (e.g., reminders, personalized feedback), and technological device through which the program was accessed (e.g., smartphone, computer). A final 23 papers were included. The results of meta-regressions indicated that higher scores on the multimedia subscale were associated with greater ED symptom improvement in treatment groups. Conversely, a higher degree of feedback in the treatment group was associated with a group difference in ED psychopathology favoring the control group. Other intervention components were not associated with treatment outcome or dropout. The results are discussed in relation to limitations of the scale used to quantify digital intervention features, as well as the limited diversity and technological sophistication of available ED digital interventions. Such findings indicate the importance of understanding the digital components, which differentiate online therapeutic programs.

An expandable approach for design and personalization of digital, just-in-time adaptive interventions.

ObjectiveWe aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables.Materials and MethodsWe propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.ResultsWe evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.ConclusionWhile the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.

Using an Analysis of Behavior Change to Inform Effective Digital Intervention Design: How Did the PRIMIT Website Change Hand Hygiene Behavior Across 8993 Users?

BackgroundIn designing digital interventions for healthcare, it is important to understand not just whether interventions work but also how and for whom—including whether individual intervention components have different effects, whether a certain usage threshold is required to change behavior in each intervention and whether usage differs across population subgroups.PurposeWe investigated these questions using data from a large trial of the digital PRimary care trial of a website based Infection control intervention to Modify Influenza-like illness and respiratory tract infection Transmission) (PRIMIT) intervention, which aimed to reduce respiratory tract infections (RTIs) by increasing hand hygiene behavior.MethodBaseline and follow-up questionnaires measured behaviors, intentions and attitudes in hand hygiene. In conjunction with objective measures of usage of the four PRIMIT sessions, we analysed these observational data to examine mechanisms of behavior change in 8993 intervention users.ResultsWe found that the PRIMIT intervention changed behavior, intentions and attitudes, and this change was associated with reduced RTIs. The largest hand hygiene change occurred after the first session, with incrementally smaller changes after each subsequent session, suggesting that engagement with the core behavior change techniques included in the first session was necessary and sufficient for behavior change. The intervention was equally effective for men and women, older and younger people and was particularly effective for those with lower levels of education.ConclusionsOur well-powered analysis has implications for intervention development. We were able to determine a ‘minimum threshold’ of intervention engagement that is required for hand hygiene change, and we discuss the potential implications this (and other analyses of this type) may have for further intervention development. We also discuss the application of similar analyses to other interventions.

Preconception and pregnancy: opportunities to intervene to improve women's diets and lifestyles.

Recently, large-scale trials of behavioural interventions have failed to show improvements in pregnancy outcomes. They have, however, shown that lifestyle support improves maternal diet and physical activity during pregnancy, and can reduce weight gain. This suggests that pregnancy, and possibly the whole periconceptional period, represents a 'teachable moment' for changes in diet and lifestyle, an idea that was made much of in the recent report of the Chief Medical Officer for England. The greatest challenge with all trials of diet and lifestyle interventions is to engage people and to sustain this engagement. With this in mind, we propose a design of intervention that aims simultaneously to engage women through motivational conversations and to offer access to a digital platform that provides structured support for diet and lifestyle change. This intervention design therefore makes best use of learning from the trials described above and from recent advances in digital intervention design.

Theory-based strategies for enhancing the impact and usage of digital health behaviour change interventions: A review.

There is growing evidence that digital interventions can successfully effect meaningful changes in health-related behaviour. However, optimisation of digital intervention delivery is challenged by low usage, high attrition and small effect sizes. Whilst a number of conceptual frameworks and models exist to guide intervention planning and development, insufficient attention has been paid to how existing psychological theory could inform the optimal implementation and delivery of the design features commonly used in digital health behaviour change interventions. This paper provides a critical review of psychological theories and models in order to consider their implications for the design of digital interventions. The theories reviewed include theories of: persuasion and attitude change; motivation; volition and self-regulation; patient preferences for participation in medical decision making; and social support. A set of theory-based guidelines is provided to inform the development of future interventions.

The Development of Balance Retraining: An Online Intervention for Dizziness in Adults Aged 50 Years and Older.

This article outlines the rationale and development process for an online intervention based on vestibular rehabilitation therapy (VRT). The intervention aims to assist adults aged 50 years and older t o self-manage and reduce dizziness symptoms. The intervention was developed according to the person-based approach to digital intervention design focused on accommodating perspectives of target users. A prototype version of the intervention was provided to 18 adults (11 women, 7 men) aged 50 years and older with dizziness. These adults were invited to use the intervention over a 6-week period and, during this time, took part in a think-aloud session. This session sought to understand users' perceptions of how acceptable, engaging, and easy to use they found the online intervention. Users were extremely positive regarding how easy to navigate, visually appealing, and informative they found the intervention. Think-aloud sessions provided valuable data for informing small amendments to further enhance acceptability of the intervention for target users. Informed by these development-phase data, a finalized version of the intervention is now being investigated in a primary care-based randomized controlled trial. Results should provide an understanding of whether VRT can be effectively-especially, cost-effectively-delivered via an online intervention to adults aged 50 years and older.