scholarly journals Systematic review of context-aware digital behavior change interventions to improve health

2020 ◽  
Vol 11 (5) ◽  
pp. 1037-1048
Author(s):  
Kelly J Thomas Craig ◽  
Laura C Morgan ◽  
Ching-Hua Chen ◽  
Susan Michie ◽  
Nicole Fusco ◽  
...  
Keyword(s):  
Systematic Review ◽  
Behavior Change ◽  
Health Behaviors ◽  
Digital Health ◽  
Health Improvement ◽  
Context Aware ◽  
Behavior Change Techniques ◽  
Health Technologies ◽  
Behavior Change Interventions ◽  
Improve Health

Abstract Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013–2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users’ health behaviors.

scholarly journals Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review

2021 ◽  
Vol 3 ◽  
Author(s):  
Jaime Martín-Martín ◽  
Cristina Roldán-Jiménez ◽  
Irene De-Torres ◽  
Antonio Muro-Culebras ◽  
Adrian Escriche-Escuder ◽  
...  
Keyword(s):  
Social Support ◽  
Systematic Review ◽  
Behavior Change ◽  
Sedentary Behavior ◽  
Randomized Clinical Trials ◽  
Digital Health ◽  
Clinical Population ◽  
Outcome Variable ◽  
Behavior Change Techniques ◽  
Behavior Change Interventions

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field.Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681.Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks−3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes.Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

scholarly journals Digital Predictors of Morbidity, Hospitalization, and Mortality Among Older Adults: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 2 ◽  
Author(s):  
Sofia Daniolou ◽  
Andreas Rapp ◽  
Celina Haase ◽  
Alfred Ruppert ◽  
Marlene Wittwer ◽  
...  
Keyword(s):  
Systematic Review ◽  
Older Adults ◽  
Health Outcomes ◽  
Digital Health ◽  
Meta Analysis ◽  
Design Development ◽  
Clinical Implementation ◽  
Care Needs ◽  
Health Technologies ◽  
Improve Health

The widespread adoption of digital health technologies such as smartphone-based mobile applications, wearable activity trackers and Internet of Things systems has rapidly enabled new opportunities for predictive health monitoring. Leveraging digital health tools to track parameters relevant to human health is particularly important for the older segments of the population as old age is associated with multimorbidity and higher care needs. In order to assess the potential of these digital health technologies to improve health outcomes, it is paramount to investigate which digitally measurable parameters can effectively improve health outcomes among the elderly population. Currently, there is a lack of systematic evidence on this topic due to the inherent heterogeneity of the digital health domain and the lack of clinical validation of both novel prototypes and marketed devices. For this reason, the aim of the current study is to synthesize and systematically analyse which digitally measurable data may be effectively collected through digital health devices to improve health outcomes for older people. Using a modified PICO process and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, we provide the results of a systematic review and subsequent meta-analysis of digitally measurable predictors of morbidity, hospitalization, and mortality among older adults aged 65 or older. These findings can inform both technology developers and clinicians involved in the design, development and clinical implementation of digital health technologies for elderly citizens.

Patient-facing Mobile Applications to Support Physiotherapy Care: Protocol for a Systematic Review of Apps (Preprint)

2021 ◽  
Author(s):  
Mark Merolli ◽  
Jillian Francis ◽  
Patrick Vallance ◽  
Kim Bennell ◽  
Peter Malliaras ◽  
...  
Keyword(s):  
Systematic Review ◽  
Behavior Change ◽  
Behaviour Change ◽  
Mobile Applications ◽  
Rating Scale ◽  
Mobile Apps ◽  
Digital Health ◽  
Behaviour Change Techniques ◽  
Behavior Change Techniques ◽  
Care Protocol

BACKGROUND Care delivered by physiotherapists aims to facilitate positive health behaviors by patients (e.g. adherence to exercise). However, research suggests that behavioral interventions are frequently omitted from care. Hence, better understanding of strategies that can be used by physiotherapists to support patients to engage in positive behaviors are important and likely to optimise outcomes. Digital health interventions delivered via mobile applications (apps) are garnering attention for their ability to support behavior change. They have the potential to incorporate numerous behavior change techniques to support goals of physiotherapy care; including (but not limited to): self-monitoring, goal setting, and prompts/alerts. Despite their potential to support physiotherapy care, much is still unknown about what apps are available, the behavior change techniques they use, their quality, and their potential to change behaviors. OBJECTIVE The primary aim of this systematic review is to describe what mobile apps intended for use by patients are available to support physiotherapy care, including the behavior change techniques within these apps. The secondary aims are to evaluate the quality and behavior change potential of these apps. METHODS A systematic review of apps in app stores will be undertaken. This will follow recommendations for reviews in line with the PRISMA statement, which has been adapted to suit our app store search. Apple Store and Google Play will be searched with a two-step search strategy, using terms relevant to physiotherapy, physiotherapists, and common physiotherapy care. Key eligibility will be that apps are intended for use by patients, and are self-contained or, stand-alone without the need of additional wearable devices or other add-ons. Included apps will be coded for behaviour change techniques (BCTs) and rated for quality using the Mobile Application Rating Scale (MARS) and potential to change behavior using the App Behavior Change Scale (ABACUS). RESULTS The protocol is registered to PROSPERO. App screening and inclusion has started, and data extraction is expected to commence by March, 2021. CONCLUSIONS Knowledge gained from this review will support clinical practice, as well as informing research, by providing a greater understanding about the quality of currently available mobile apps and their potential to support patient behaviour change goals of physiotherapy care.

Behavior change interventions in patients with type 2 diabetes: a systematic review of the effects on self-management and A1c

2021 ◽  
Author(s):  
Ana Paula Delgado Bomtempo Batalha ◽  
Isabela Coelho Ponciano ◽  
Gabriela Chaves ◽  
Diogo Carvalho Felício ◽  
Raquel Rodrigues Britto ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Behavior Change ◽  
Self Management ◽  
Behavior Change Interventions

scholarly journals Diet Behavior Change Techniques in Type 2 Diabetes: A Systematic Review and Meta-analysis

Diabetes Care ◽  
2017 ◽  
Vol 40 (12) ◽  
pp. 1800-1810 ◽  
Author(s):  
Kevin A. Cradock ◽  
Gearóid ÓLaighin ◽  
Francis M. Finucane ◽  
Rhyann McKay ◽  
Leo R. Quinlan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Type 2 Diabetes ◽  
Behavior Change ◽  
Meta Analysis ◽  
Behavior Change Techniques ◽  
Diet Behavior

scholarly journals Behavioral Intervention Components Associated With Cost-effectiveness: A Comparison of Six Domains

2021 ◽  
Author(s):  
E Beard ◽  
F Lorencatto ◽  
B Gardner ◽  
S Michie ◽  
L Owen ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Behavior Change ◽  
Lower Cost ◽  
Self Regulation ◽  
Cost Effective ◽  
Cost Effectiveness Ratio ◽  
Behavior Change Techniques ◽  
Multiple Health Behaviors ◽  
Behavior Change Interventions ◽  
Multiple Health

Abstract Background To help implement behavior change interventions (BCIs) it is important to be able to characterize their key components and determine their effectiveness. Purpose This study assessed and compared the components of BCIs in terms of intervention functions identified using the Behaviour Change Wheel Framework (BCW) and in terms of their specific behavior change techniques (BCTs) identified using the BCT TaxonomyV1, across six behavioral domains and the association of these with cost-effectiveness. Methods BCIs in 251 studies targeting smoking, diet, exercise, sexual health, alcohol and multiple health behaviors, were specified in terms of their intervention functions and their BCTs, grouped into 16 categories. Associations with cost-effectiveness measured in terms of incremental cost-effectiveness ratio (ICER) upper and lower estimates were determined using regression analysis. Results The most prevalent functions were increasing knowledge through education (72.1%) and imparting skills through training (74.9%). The most prevalent BCT groupings were shaping knowledge (86.5%), changing behavioral antecedents (53.0%), supporting self-regulation (47.7%), and providing social support (44.6%). Intervention functions associated with better cost-effectiveness were those based on training (βlow = −15044.3; p = .002), persuasion (βlow = −19384.9; p = .001; βupp = −25947.6; p < .001) and restriction (βupp = −32286.1; p = .019), and with lower cost-effectiveness were those based on environmental restructuring (β = 15023.9low; p = .033). BCT groupings associated with better cost-effectiveness were goals and planning (βlow = −8537.3; p = .019 and βupp = −12416.9; p = .037) and comparison of behavior (βlow = −13561.9, p = .047 and βupp = −30650.2; p = .006). Those associated with lower cost-effectiveness were natural consequences (βlow = 7729.4; p = .033) and reward and threat (βlow = 20106.7; p = .004). Conclusions BCIs that focused on training, persuasion and restriction may be more cost-effective, as may those that encourage goal setting and comparison of behaviors with others.

Digital health technologies for long term self-management of osteoporosis: a systematic review and meta-analysis (Preprint)

2021 ◽  
Author(s):  
Ghada Alhussein ◽  
Leontios Hadjileontiadis
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Digital Health ◽  
Meta Analysis ◽  
Self Management ◽  
Current Status ◽  
Future Research ◽  
Common Chronic Disease ◽  
Health Technologies ◽  
Management Interventions

BACKGROUND Osteoporosis is the fourth most common chronic disease in the world. Adopting preventative measures and effective self-management interventions help in improving bone health. Mobile health (mHealth) technologies can play a key role in osteoporosis patient care and self- management. OBJECTIVE This study presents a systematic review and meta-analysis of the currently available mHealth applications targeting osteoporosis self-management, aiming to determine the current status, gaps and challenges the future research could address, proposing appropriate recommendations. METHODS In this systematic review and meta-analysis, we searched PubMed, Scopus, EBSCO, Web of Science, and IEEExplore databases between Jan 1, 2010 and May 31, 2021, for all English publications that describe apps dedicated to or being useful for osteoporosis, targeting self-management, nutrition, physical activity, risk assessment, delivered on smartphone devices for young and older adults. In addition, a survey of all osteoporosis-related apps available in iOS and Android app stores as of May 31, 2021 was also conducted. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. RESULTS In total, 3906 unique articles were identified. Of these, 32 articles met the inclusion criteria and were reviewed in depth. The 32 studies were comprising 14 235 participants, of whom on average 69.5% were female, with a mean age of 49.8 years (SD 17.8). The app search identified 23 relevant apps for osteoporosis self-management. The meta-analysis revealed that mHealth supported interventions resulted in a significant reduction in pain (Hedge’s g -1.09, 95%CI -1.68 to -0.45) and disability (Hedge’s g -0.77, 95%CI -1.59 to 0.05). The post-treatment effect of the digital intervention was significant for physical function (Hedge’s g 2.54, 95%CI -4.08 to 4.08); yet nonsignificant for wellbeing (Hedge’s g 0.17, 95% CI -1.84 to 2.17), physical activity (Hedges’ g 0.09, 95%CI -0.59 to 0.50), anxiety (Hedge’s g -0.29, 95%CI -6.11 to 5.53), fatigue (Hedge’s g -0.34, 95%CI -5.84 to 5.16), calcium (Hedge’s g -0.05, 95%CI -0.59 to 0.50) and vitamin D (Hedge’s g 0.10, 95% CI -4.05 to 4.26) intake, and trabecular score (Hedge’s g 0.06, 95%CI -1.00 to 1.12). CONCLUSIONS Osteoporosis apps have the potential to support and improve the management of the disease and its symptoms; they also appear to be a valuable tool for patients and health professionals. However, the majority of the apps that are currently available lack clinically validated evidence of their efficacy and they most focus on a limited number of symptoms. A more holistic and personalized approach, within a co-creation design ecosystem, is needed.

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