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Background: Digital health interventions (DHIs) have shown promising results for the management of chronic wounds. However, its effectiveness compared to usual care and whether variability in the type of intervention affects wound outcomes are unclear., Objective: The main objective was to determine the effectiveness of DHIs on wound healing outcomes in adult patients with chronic wounds. The secondary objectives were to assess if there was any variation in wound healing outcomes across the various types of DHIs., Methods: In total, 9 databases were searched for the literature up to August 1, 2023. Randomized controlled trials (RCTs), cohort studies, and quasi-experimental studies comparing the efficacy of DHIs with controls in improving wound outcomes in adult patients with chronic wounds were included. Study selection, data extraction, and risk of bias assessment were conducted independently by 2 reviewers. We assessed the quality of each RCT, cohort study, and quasi-experimental study separately using the Cochrane risk of bias tool, ROBINS-I, and the Joanna Briggs Institute Critical Appraisal tools checklists. Relative risks (RRs) and 95% CIs were pooled using the random effects model, and heterogeneity was assessed by the I 2 statistic. Subgroup analysis and sensitivity analysis were also performed., Results: A total of 25 studies with 8125 patients were included in this systematic review, while only 20 studies with 6535 patients were included in the meta-analysis. Efficacy outcomes in RCTs showed no significant differences between the DHIs and control groups in terms of wound healing (RR 1.02, 95% CI 0.93-1.12; P=.67) and all-cause mortality around 1 year (RR 1.08, 95% CI 0.55-2.12; P=.83). Compared with the control group, the use of DHIs was associated with significant changes in adverse events (RR 0.44, 95% CI 0.22-0.89; P=.02). Subgroup analysis suggested a positive effect of the digital platforms in improving wound healing (RR 2.19, 95% CI 1.35-3.56; P=.002). Although meta-analysis was not possible in terms of wound size, cost analysis, patient satisfaction, and wound reporting rates, most studies still demonstrated that DHIs were not inferior to usual care in managing chronic wounds., Conclusions: The findings of our study demonstrate the viability of adopting DHIs to manage chronic wounds. However, more prominent, high-quality RCTs are needed to strengthen the evidence, and more detailed clinical efficacy research is required., Trial Registration: PROSPERO CRD42023392415; https://tinyurl.com/4ybz6bs9., (©Xinrui Bai, Hongyan Zhang, Yanxia Jiao, Chenlu Yuan, Yuxia Ma, Lin Han. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.07.2024.)

Background: Digital health interventions (DHIs) have been used to improve postoperative functional ability in older patients with hip fractures. However, there is limited information on the characteristics of home-based DHIs, and controversy exists regarding their impact on functional outcomes in this population., Objective: This study aims to provide an overview of the characteristics and effects of home-based DHIs on functional outcomes in older patients with hip fractures after surgery., Methods: We conducted a systematic review and meta-analysis following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Five electronic medical databases (PubMed, Embase, Cochrane, ProQuest, and CINAHL) were searched up until January 3, 2023. We included clinical trials or randomized controlled trials (RCTs) in English involving home-based DHIs for postoperative care among older patients with hip fractures. Excluded studies involved patients not hospitalized, not discharged to home, not directly using DHIs, or with inaccessible full text. The PROSPERO registration number is CRD42022370550. Two independent reviewers screened and extracted data (SP and NB). Disagreements were resolved through discussion and agreement with the third author (KP). Home-based DHIs were characterized in terms of purpose and content, mode of delivery, and health care provider. Functional outcomes assessed included Timed Up and Go (TUG) test, Short Physical Performance Battery (SPPB), and Functional Independence Measure (FIM). Summary measures were calculated using mean differences with 95% CIs. Risk of bias was assessed using the Risk-of-Bias 2 assessment tool for RCTs and ROBINS-I for non-RCTs. The quality of evidence was assessed using GRADE (Grading of Recommendations Assessment, Development and Evaluation)., Results: Of 2125 identified studies, 16 were included in the systematic review, involving 1467 participants. Six studies were included in the meta-analysis (4 for TUG, 4 for SPPB, and 2 for FIM). Home-based DHIs predominantly involved communication and feedback, education, and telerehabilitation. Telephone calls were the most common mode of delivery, followed by web-based software and mobile apps. Physical therapists were the main health care providers. The meta-analysis showed that home-based DHIs improved functional outcomes compared with usual care, with decreased TUG scores (mean difference=-7.89; 95% CI -10.34 to -5.45; P<.001), significantly increased SPPB scores (mean difference=1.11; 95% CI 0.51-1.72; P<.001), and increased FIM scores (mean difference=7.98; 95% CI 5.73-10.24; P<.001)., Conclusions: Home-based DHIs that integrate communication and feedback, education, and telerehabilitation have demonstrated effectiveness in enhancing functional outcomes among older patients recovering from hip fractures after surgery. These interventions are commonly administered by physical therapists, who play a crucial role in facilitating and guiding the rehabilitation process. However, while the existing evidence supports the efficacy of such interventions, further research is needed to enhance our understanding and optimize the implementation of home-based DHIs for this specific population., (©Suphawita Pliannuom, Kanokporn Pinyopornpanish, Nida Buawangpong, Nutchar Wiwatkunupakarn, Poppy Alice Carson Mallinson, Wichuda Jiraporncharoen, Chaisiri Angkurawaranon. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.06.2024.)

Background: Conversational agents (CAs) or chatbots are computer programs that mimic human conversation. They have the potential to improve access to mental health interventions through automated, scalable, and personalized delivery of psychotherapeutic content. However, digital health interventions, including those delivered by CAs, often have high attrition rates. Identifying the factors associated with attrition is critical to improving future clinical trials., Objective: This review aims to estimate the overall and differential rates of attrition in CA-delivered mental health interventions (CA interventions), evaluate the impact of study design and intervention-related aspects on attrition, and describe study design features aimed at reducing or mitigating study attrition., Methods: We searched PubMed, Embase (Ovid), PsycINFO (Ovid), Cochrane Central Register of Controlled Trials, and Web of Science, and conducted a gray literature search on Google Scholar in June 2022. We included randomized controlled trials that compared CA interventions against control groups and excluded studies that lasted for 1 session only and used Wizard of Oz interventions. We also assessed the risk of bias in the included studies using the Cochrane Risk of Bias Tool 2.0. Random-effects proportional meta-analysis was applied to calculate the pooled dropout rates in the intervention groups. Random-effects meta-analysis was used to compare the attrition rate in the intervention groups with that in the control groups. We used a narrative review to summarize the findings., Results: The systematic search retrieved 4566 records from peer-reviewed databases and citation searches, of which 41 (0.90%) randomized controlled trials met the inclusion criteria. The meta-analytic overall attrition rate in the intervention group was 21.84% (95% CI 16.74%-27.36%; I 2 =94%). Short-term studies that lasted ≤8 weeks showed a lower attrition rate (18.05%, 95% CI 9.91%- 27.76%; I 2 =94.6%) than long-term studies that lasted >8 weeks (26.59%, 95% CI 20.09%-33.63%; I 2 =93.89%). Intervention group participants were more likely to attrit than control group participants for short-term (log odds ratio 1.22, 95% CI 0.99-1.50; I 2 =21.89%) and long-term studies (log odds ratio 1.33, 95% CI 1.08-1.65; I 2 =49.43%). Intervention-related characteristics associated with higher attrition include stand-alone CA interventions without human support, not having a symptom tracker feature, no visual representation of the CA, and comparing CA interventions with waitlist controls. No participant-level factor reliably predicted attrition., Conclusions: Our results indicated that approximately one-fifth of the participants will drop out from CA interventions in short-term studies. High heterogeneities made it difficult to generalize the findings. Our results suggested that future CA interventions should adopt a blended design with human support, use symptom tracking, compare CA intervention groups against active controls rather than waitlist controls, and include a visual representation of the CA to reduce the attrition rate., Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42022341415; https://www.crd.york.ac.uk/prospero/display&#95;record.php?ID=CRD42022341415., (©Ahmad Ishqi Jabir, Xiaowen Lin, Laura Martinengo, Gemma Sharp, Yin-Leng Theng, Lorainne Tudor Car. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.02.2024.)

Background: Digital health interventions (DHIs) are a central focus of health care transformation efforts, yet their uptake in practice continues to fall short of their potential. In order to achieve their desired outcomes and impact, DHIs need to reach their target population and need to be used. Many factors can rapidly intersect between this dynamic of users and interventions. The application of theories, models, and frameworks (TMFs) can facilitate the systematic understanding and explanation of the complex interactions between users, practices, technology, and health system factors that underpin research questions. There remains a gap in our understanding of how TMFs have been applied to guide the evaluation of DHIs with real-world health system operations., Objective: This study aims to map TMFs used in studies to guide the evaluation of DHIs. The objectives are to (1) describe the TMFs and the constructs they target, (2) identify how TMFs have been prospectively used (ie, their roles) in primary studies to evaluate DHIs, and (3) to reflect on the relevance and utility of our findings for knowledge users., Methods: This scoping review was conducted in partnership with knowledge users using an integrated knowledge translation approach. We included papers (eg, reports; empirical quantitative, qualitative, and mixed methods studies; conference proceedings; and dissertations) if primary insights resulting from the application of TMFs were presented. Any type of DHI was eligible. Papers published from 2000 and onward were mainly identified from the following databases: MEDLINE (Ovid), CINAHL Complete (EBSCOhost), PsycINFO (Ovid), EBM Reviews (Ovid), and Embase (Ovid)., Results: A total of 156 studies published between 2000 and 2022 were included. A total of 68 distinct TMFs were identified across 85 individual studies. In more than half (85/156, 55%) of the included studies, 1 of following 6 prevailing TMFs were reported: Consolidated Framework for Implementation Research (n=39); the Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework (n=17); the Technology of Acceptance Model (n=16); the Unified Theory on Acceptance and Use of Technology (n=12); the Diffusion of Innovation Theory (n=10); and Normalization Process Theory (n=9). The most common intended roles of the 6 TMFs were to inform data collection (n=86), to inform data analysis (n=69), and to identify key constructs that may serve as barriers and facilitators (n=52)., Conclusions: As TMFs are most often reported to be applied to support data collection and analysis, researchers should consider more clearly synthesizing key insights as practical use cases to both increase the relevance and digestibility of their findings. There is also a need to adapt or develop guidelines for better reporting DHIs and the use of TMFs to guide evaluation. Hence, it would contribute to ensuring ongoing technology transformation efforts are evidence and theory informed rather than anecdotally driven., (©Geneviève Rouleau, Kelly Wu, Karishini Ramamoorthi, Cherish Boxall, Rebecca H Liu, Shelagh Maloney, Jennifer Zelmer, Ted Scott, Darren Larsen, Harindra C Wijeysundera, Daniela Ziegler, Sacha Bhatia, Vanessa Kishimoto, Carolyn Steele Gray, Laura Desveaux. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.02.2024.)

Background: Digital health interventions (DHIs) are effective in improving poor nutrition, physical inactivity, overweight and obesity. There is evidence suggesting that the impact of DHIs may be enhanced by improving user engagement. However, little is known about the overall effectiveness of strategies on engagement with DHIs., Objective: This study aims to assess the overall effectiveness of strategies to improve engagement with DHIs targeting nutrition, physical activity, and overweight or obesity and explore associations between strategies and engagement outcomes. The secondary aim was to explore the impact of these strategies on health risk outcomes., Methods: The MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, Scopus, and Academic Source Complete databases were searched up to July 24, 2023. Eligible studies were randomized controlled trials that evaluated strategies to improve engagement with DHIs and reported on outcomes related to DHI engagement (use or user experience). Strategies were classified according to behavior change techniques (BCTs) and design features (eg, supplementary emails). Multiple-variable meta-analyses of the primary outcomes (usage and user experience) were undertaken to assess the overall effectiveness of strategies. Meta-regressions were conducted to assess associations between strategies and use and user experience outcomes. Synthesis of secondary outcomes followed the "Synthesis Without Meta-Analysis" guidelines. The methodological quality and evidence was assessed using the Cochrane risk-of-bias tool, and the Grading of Recommendations Assessment, Development, and Evaluation tool respectively., Results: Overall, 54 studies (across 62 publications) were included. Pooled analysis found very low-certainty evidence of a small-to-moderate positive effect of the use of strategies to improve DHI use (standardized mean difference=0.33, 95% CI 0.20-0.46; P<.001) and very low-certainty evidence of a small-to-moderate positive effect on user experience (standardized mean difference=0.29, 95% CI 0.07-0.52; P=.01). A significant positive association was found between the BCTs social support (effect size [ES]=0.40, 95% CI 0.14-0.66; P<.001) and shaping knowledge (ES=0.39, 95% CI 0.03-0.74; P=.03) and DHI use. A significant positive association was found among the BCTs social support (ES=0.70, 95% CI 0.18-1.22; P=.01), repetition and substitution (ES=0.29, 95% CI 0.05-0.53; P=.03), and natural consequences (ES=0.29, 95% CI 0.05-0.53; P=.02); the design features email (ES=0.29, 95% CI 0.05-0.53; P=.02) and SMS text messages (ES=0.34, 95% CI 0.11-0.57; P=.01); and DHI user experience. For secondary outcomes, 47% (7/15) of nutrition-related, 73% (24/33) of physical activity-related, and 41% (14/34) of overweight- and obesity-related outcomes reported an improvement in health outcomes., Conclusions: Although findings suggest that the use of strategies may improve engagement with DHIs targeting such health outcomes, the true effect is unknown because of the low quality of evidence. Future research exploring whether specific forms of social support, repetition and substitution, natural consequences, emails, and SMS text messages have a greater impact on DHI engagement is warranted., Trial Registration: PROSPERO CRD42018077333; https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=77333., (©Alice Grady, Nicole Pearson, Hannah Lamont, Lucy Leigh, Luke Wolfenden, Courtney Barnes, Rebecca Wyse, Meghan Finch, Matthew Mclaughlin, Tessa Delaney, Rachel Sutherland, Rebecca Hodder, Sze Lin Yoong. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.12.2023.)

Background: Musculoskeletal disorders (MSDs) are widespread in many countries and their huge burden on the society has necessitated innovative approaches such as digital health interventions. However, no study has evaluated the findings of cost-effectiveness of these interventions., Objective: This study aims to synthesize the cost-effectiveness of digital health interventions for people with MSDs., Methods: Electronic databases including MEDLINE, AMED, CIHAHL, PsycINFO, Scopus, Web of Science, and Centre for Review and Dissemination were searched for cost-effectiveness of digital health published between inception and June 2022 following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. References of all retrieved articles were checked for relevant studies. Quality appraisal of the included studies was performed using the Quality of Health Economic Studies (QHES) instrument. Results were presented using a narrative synthesis and random effects meta-analysis., Results: A total of 10 studies from 6 countries met the inclusion criteria. Using the QHES instrument, we found that the mean score of the overall quality of the included studies was 82.5. Included studies were on nonspecific chronic low back pain (n=4), chronic pain (n=2), knee and hip osteoarthritis (n=3), and fibromyalgia (n=1). The economic perspectives adopted in the included studies were societal (n=4), societal and health care (n=3), and health care (n=3). Of the 10 included studies, 5 (50%) used quality-adjusted life-years as the outcome measures. Except 1 study, all the included studies reported that digital health interventions were cost-effective compared with the control group. In a random effects meta-analysis (n=2), the pooled disability and quality-adjusted life-years were -0.176 (95% CI -0.317 to -0.035; P=.01) and 3.855 (95% CI 2.023 to 5.687; P<.001), respectively. The meta-analysis (n=2) for the costs was in favor of the digital health intervention compared with control: US $417.52 (95% CI -522.01 to -313.03)., Conclusions: Studies indicate that digital health interventions are cost-effective for people with MSDs. Our findings suggest that digital health intervention could help improve access to treatment for patients with MSDs and as a result improve their health outcomes. Clinicians and policy makers should consider the use of these interventions for patients with MSDs., Trial Registration: PROSPERO CRD42021253221; https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=253221., (©Francis Fatoye, Tadesse Gebrye, Chidozie Mbada, Ushotanefe Useh. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 06.07.2023.)

Background: Digital diabetes prevention programs (digital-DPPs) are being implemented as population-based approaches to type 2 diabetes mellitus prevention in several countries to address problems with the uptake of traditional face-to-face diabetes prevention programs. However, assessments of digital-DPPs have largely focused on clinical outcomes and usability among those who have taken them up, whereas crucial information on decision-making about uptake (eg, whether a user downloads and registers on an app) and engagement (eg, the extent of use of an app or its components over time) is limited. Greater understanding of factors that influence uptake and engagement decisions may support large-scale deployments of digital-DPPs in real-world settings., Objective: This study aimed to explore the key influences on uptake and engagement decisions of individuals who were offered the National Health Service Healthier You: Digital Diabetes Prevention Programme (NHS-digital-DPP)., Methods: A qualitative interview study was conducted using semistructured interviews. Participants were adults, aged ≥18 years, diagnosed with nondiabetic hyperglycemia, and those who had been offered the NHS-digital-DPP. Recruitment was conducted via 4 providers of the NHS-digital-DPP and 3 primary care practices in England. Interviews were conducted remotely and were guided by a theoretically informed topic guide. Analysis of interviews was conducted using an inductive thematic analysis approach., Results: Interviews were conducted with 32 participants who had either accepted or declined the NHS-digital-DPP. In total, 7 overarching themes were identified as important factors in both decisions to take up and to engage with the NHS-digital-DPP. These were knowledge and understanding, referral process, self-efficacy, self-identity, motivation and support, advantages of digital service, and reflexive monitoring. Perceptions of accessibility and convenience of the NHS-digital-DPP were particularly important for uptake, and barriers in terms of the referral process and health care professionals' engagement were reported. Specific digital features including health coaches and monitoring tools were important for engagement., Conclusions: This study adds to the literature on factors that influence the uptake of and engagement with digital-DPPs and suggests that digital-DPPs can overcome many barriers to the uptake of face-to-face diabetes prevention programs in supporting lifestyle changes aimed at diabetes prevention., (©Jamie Ross, Sarah Cotterill, Peter Bower, Elizabeth Murray. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.02.2023.)

Background: Despite the growth of web-based interventions for HIV, viral hepatitis (VH), and sexually transmitted infections (STIs) for key populations, the evidence for the effectiveness of these interventions has not been reported. Objective: This study aimed to inform the World Health Organization guidelines for HIV, VH, and STI prevention, diagnosis, and treatment services for key populations by systematically reviewing the effectiveness, values and preferences, and costs of web-based outreach, web-based case management, and targeted web-based health information for key populations (men who have sex with men, sex workers, people who inject drugs, trans and gender-diverse people, and people in prisons and other closed settings). Methods: We searched CINAHL, PsycINFO, PubMed, and Embase in May 2021 for peer-reviewed studies; screened abstracts; and extracted data in duplicate. The effectiveness review included randomized controlled trials (RCTs) and observational studies. We assessed the risk of bias using the Cochrane Collaboration tool for RCTs and the Evidence Project and Risk of Bias in Non-randomized Studies of Interventions tools for non-RCTs. Values and preferences and cost data were summarized descriptively. Results: Of 2711 records identified, we included 13 (0.48%) articles in the effectiveness review (3/13, 23% for web-based outreach; 7/13, 54% for web-based case management; and 3/13, 23% for targeted web-based health information), 15 (0.55%) articles in the values and preferences review, and 1 (0.04%) article in the costs review. Nearly all studies were conducted among men who have sex with men in the United States. These articles provided evidence that web-based approaches are as effective as face-to-face services in terms of reaching new people, use of HIV, VH, and STI prevention services, and linkage to and retention in HIV care. A meta-analysis of 2 RCTs among men who have sex with men in China found increased HIV testing after web-based outreach (relative risk 1.39, 95% CI 1.21-1.60). Among men who have sex with men in the United States, such interventions were considered feasible and acceptable. One cost study among Canadian men who have sex with men found that syphilis testing campaign advertisements had the lowest cost-per-click ratio on hookup platforms compared with more traditional social media platforms. Conclusions: Web-based services for HIV, VH, and STIs may be a feasible and acceptable approach to expanding services to key populations with similar outcomes as standard of care, but more research is needed in low-resource settings, among key populations other than men who have sex with men, and for infections other than HIV (ie, VH and STIs). J Med Internet Res 2022;24(12):e40150 doi:10.2196/40150 [ABSTRACT FROM AUTHOR]

Background: Digital health interventions have revolutionized multiple sclerosis (MS) care by supporting people with MS to better self-manage their disease. It is now understood that the technological elements that comprise this category of digital health interventions can influence participant engagement in self-management programs, and people with MS can experience significant barriers, influenced by these elements, to remaining engaged during a period of learning. It is essential to explore the influence of technological elements in mitigating attrition.Objective: This study aimed to examine the study design and technological elements of documented digital health interventions targeted at people with MS-digital health interventions that were intended to support a program of engagement over a defined period-and to explore how these correlated with attrition among participants of randomized controlled trials (RCTs).Methods: We conducted a systematic review and meta-analysis of RCTs (n=32) describing digital health self-management interventions for people with MS. We analyzed attrition in included studies, using a random-effects model and meta-regression to measure the association between potential moderators.Results: There were no measured differences in attrition between the intervention and control arms; however, some of the heterogeneity observed was explained by the composite technological element score. The pooled attrition rates for the intervention and control arms were 14.7% and 15.6%, respectively.Conclusions: This paper provides insight into the technological composition of digital health interventions designed for people with MS and describes the degree of attrition in both study arms. This paper will aid in the design of future studies in this area, particularly for digital health interventions of this type. [ABSTRACT FROM AUTHOR]

Background: Digital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs.Objective: This study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used.Methods: We conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain.Results: We identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy.Conclusions: Although many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective. [ABSTRACT FROM AUTHOR]

Background: An increasing number of eHealth interventions aim to support healthy behaviors that facilitate weight loss. However, there is limited evidence of the effectiveness of the interventions and little focus on weight loss maintenance. Knowledge about end user values and needs is essential to create meaningful and effective eHealth interventions, and to identify persuasive system design (PSD) principles and behavior change techniques (BCTs) that may contribute to the behavior change required for successful long-term weight loss maintenance.Objective: This study aimed to provide insight into the design of eHealth interventions supporting behavior change for long-term weight maintenance. The study sought to identify the values and needs of people with obesity aiming to maintain weight after weight loss, and to identify PSD principles, BCTs, and design requirements that potentially enable an eHealth intervention to meet end user values and needs.Methods: This study presents the concept of integrating PSD principles and BCTs into the design process of eHealth interventions to meet user values and needs. In this study, individual interviews and focus groups were conducted with people with obesity (n=23) and other key stakeholders (n=27) to explore end user values and needs related to weight loss maintenance. Design thinking methods were applied during the focus group sessions to identify design elements and to explore how eHealth solutions can support the needs to achieve sustainable weight loss maintenance. The PSD model and behavior change taxonomy by Michie were used to identify PSD principles and BCT clusters to meet end user values and needs.Results: A total of 8 key end user values were identified, reflecting user needs for weight loss maintenance support: self-management, personalized care, autonomy, feel supported, positive self-image, motivation, happiness, and health. Goals and planning, feedback and monitoring, repetition and substitution, shaping knowledge, social support, identity, and self-belief were some of the BCT clusters identified to address these concepts, together with PSD principles such as personalization, tailoring, self-monitoring, praise, and suggestions.Conclusions: The process of translating end user values and needs into design elements or features of eHealth technologies is an important part of the design process. To our knowledge, this is the first study to explore how PSD principles and BCTs can be integrated when designing eHealth self-management interventions for long-term weight loss maintenance. End users and other key stakeholders highlighted important factors to be considered in the design of eHealth interventions supporting sustained behavior change. The PSD principles and BCTs identified provide insights and suggestions about design elements and features to include for supporting weight loss maintenance. The findings indicate that a combination of BCTs and PSD principles may be needed in evidence-based eHealth interventions to stimulate motivation and adherence to support healthy behaviors and sustained weight loss maintenance.Trial Registration: ClinicalTrials.gov NCT04537988; https://clinicaltrials.gov/ct2/show/NCT04537988. [ABSTRACT FROM AUTHOR]

Background: Digital health interventions (DHIs) are increasingly being adopted globally to address various public health issues. DHIs can be categorized according to four main types of technology: mobile based, web based, telehealth, and electronic health records. In 2006, Norman and Skinner introduced the eHealth literacy model, encompassing six domains of skills and abilities (basic, health, information, scientific, media, and computer) needed to effectively understand, process, and act on health-related information. Little is known about whether these domains are assessed or accounted for in DHIs., Objective: This study aims to explore how DHIs assess and evaluate the eHealth literacy model, describe which health conditions are addressed, and which technologies are used., Methods: We conducted a scoping review of the literature on DHIs, based on randomized controlled trial design and reporting the assessment of any domain of the eHealth literacy model. MEDLINE, CINAHL, Embase, and Cochrane Library were searched. A duplicate selection and data extraction process was performed; we charted the results according to the country of origin, health condition, technology used, and eHealth literacy domain., Results: We identified 131 unique DHIs conducted in 26 different countries between 2001 and 2020. Most DHIs were conducted in English-speaking countries (n=81, 61.8%), delivered via the web (n=68, 51.9%), and addressed issues related to noncommunicable diseases (n=57, 43.5%) or mental health (n=26, 19.8%). None of the interventions assessed all six domains of the eHealth literacy model. Most studies focused on the domain of health literacy (n=96, 73.2%), followed by digital (n=19, 14.5%), basic and media (n=4, 3%), and information and scientific literacy (n=1, 0.7%). Of the 131 studies, 7 (5.3%) studies covered both health and digital literacy., Conclusions: Although many selected DHIs assessed health or digital literacy, no studies comprehensively evaluated all domains of the eHealth literacy model; this evidence might be overlooking important factors that can mediate or moderate the effects of these interventions. Future DHIs should comprehensively assess the eHealth literacy model while developing or evaluating interventions to understand how and why interventions can be effective., (©Mariam El Benny, Tamar Kabakian-Khasholian, Fadi El-Jardali, Marco Bardus. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.06.2021.)

Background: Engagement emerges as a predictor for the effectiveness of digital health interventions. However, a shared understanding of engagement is missing. Therefore, a new scale has been developed that proposes a clear definition and creates a tool to measure it. The TWente Engagement with Ehealth Technologies Scale (TWEETS) is based on a systematic review and interviews with engaged health app users. It defines engagement as a combination of behavior, cognition, and affect.Objective: This paper aims to evaluate the psychometric properties of the TWEETS. In addition, a comparison is made with the experiential part of the Digital Behavior Change Intervention Engagement Scale (DBCI-ES-Ex), a scale that showed some issues in previous psychometric analyses.Methods: In this study, 288 participants were asked to use any step counter app on their smartphones for 2 weeks. They completed online questionnaires at 4 time points: T0=baseline, T1=after 1 day, T2=1 week, and T3=2 weeks. At T0, demographics and personality (conscientiousness and intellect/imagination) were assessed; at T1-T3, engagement, involvement, enjoyment, subjective usage, and perceived behavior change were included as measures that are theoretically related to our definition of engagement. Analyses focused on internal consistency, reliability, and the convergent, divergent, and predictive validity of both engagement scales. Convergent validity was assessed by correlating the engagement scales with involvement, enjoyment, and subjective usage; divergent validity was assessed by correlating the engagement scales with personality; and predictive validity was assessed by regression analyses using engagement to predict perceived behavior change at later time points.Results: The Cronbach alpha values of the TWEETS were .86, .86, and .87 on T1, T2, and T3, respectively. Exploratory factor analyses indicated that a 1-factor structure best fits the data. The TWEETS is moderately to strongly correlated with involvement and enjoyment (theoretically related to cognitive and affective engagement, respectively; P<.001). Correlations between the TWEETS and frequency of use were nonsignificant or small, and differences between adherers and nonadherers on the TWEETS were significant (P<.001). Correlations between personality and the TWEETS were nonsignificant. The TWEETS at T1 was predictive of perceived behavior change at T3, with an explained variance of 16%. The psychometric properties of the TWEETS and the DBCI-ES-Ex seemed comparable in some aspects (eg, internal consistency), and in other aspects, the TWEETS seemed somewhat superior (divergent and predictive validity).Conclusions: The TWEETS performs quite well as an engagement measure with high internal consistency, reasonable test-retest reliability and convergent validity, good divergent validity, and reasonable predictive validity. As the psychometric quality of a scale is a reflection of how closely a scale matches the conceptualization of a concept, this paper is also an attempt to conceptualize and define engagement as a unique concept, providing a first step toward an acceptable standard of defining and measuring engagement. [ABSTRACT FROM AUTHOR]

Background: The prevalence of mental health disorders continues to rise, with almost 4% of the world population having an anxiety disorder and almost 3.5% having depression in 2017. Despite the high prevalence, only one-third of people with depression or anxiety receive treatment. Over the last decade, the use of digital health interventions (DHIs) has risen rapidly as a means of accessing mental health care and continues to increase. Although there is evidence supporting the effectiveness of DHIs for the treatment of mental health conditions, little is known about what aspects are valued by users and how they might be improved.Objective: This systematic review aimed to identify, appraise, and synthesize the qualitative literature available on service users' views and experiences regarding the acceptability and usability of DHIs for depression, anxiety, and somatoform disorders.Methods: A systematic search strategy was developed, and searches were run in 7 electronic databases. Qualitative and mixed methods studies published in English were included. A meta-synthesis was used to interpret and synthesize the findings from the included studies.Results: A total of 24 studies were included in the meta-synthesis, and 3 key themes emerged with descriptive subthemes. The 3 key themes were initial motivations and approaches to DHIs, personalization of treatment, and the value of receiving personal support in DHIs. The meta-synthesis suggests that participants' initial beliefs about DHIs can have an important effect on their engagement with these types of interventions. Personal support was valued very highly as a major component of the success of DHIs. The main reason for this was the way it enabled individual personalization of care.Conclusions: Findings from the systematic review have implications for the design of future DHIs to improve uptake, retention, and outcomes in DHIs for depression, anxiety, and somatoform disorders. DHIs need to be personalized to the specific needs of the individual. Future research should explore whether the findings could be generalized to other health conditions. [ABSTRACT FROM AUTHOR]

Background: Previous data have validated the benefit of digital health interventions (DHIs) on weight loss in patients following acute coronary syndrome entering cardiac rehabilitation (CR).Objective: The primary purpose of this study was to test the hypothesis that increased DHI use, as measured by individual log-ins, is associated with improved weight loss. Secondary analyses evaluated the association between log-ins and activity within the platform and exercise, dietary, and medication adherence.Methods: We obtained DHI data including active days, total log-ins, tasks completed, educational modules reviewed, medication adherence, and nonmonetary incentive points earned in patients undergoing standard CR following acute coronary syndrome. Linear regression followed by multivariable models were used to evaluate associations between DHI log-ins and weight loss or dietary adherence.Results: Participants (n=61) were 79% male (48/61) with mean age of 61.0 (SD 9.7) years. We found a significant positive association of total log-ins during CR with weight loss (r2=.10, P=.03). Educational modules viewed (r2=.11, P=.009) and tasks completed (r2=.10, P=.01) were positively significantly associated with weight loss, yet total log-ins were not significantly associated with differences in dietary adherence (r2=.05, P=.12) or improvements in minutes of exercise per week (r2=.03, P=.36).Conclusions: These data extend our previous findings and demonstrate increased DHI log-ins portend improved weight loss in patients undergoing CR after acute coronary syndrome. DHI adherence can potentially be monitored and used as a tool to selectively encourage patients to adhere to secondary prevention lifestyle modifications.Trial Registration: ClinicalTrials.gov (NCT01883050); https://clinicaltrials.gov/ct2/show/NCT01883050. [ABSTRACT FROM AUTHOR]

Background: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making.Objective: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes.Methods: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model.Results: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms.Conclusions: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts. [ABSTRACT FROM AUTHOR]