Abstract:An abundant and growing supply of digital health applications (apps) exists in the commercial tech-sector, which can be bewildering for clinicians, patients, and payers. A growing challenge for the health care system is therefore to facilitate the identification of safe and effective apps for health care practitioners and patients to generate the most health benefit as well as guide payer coverage decisions. Nearly all developed countries are attempting to define policy frameworks to improve decision-making, p… Show more
“…Finally, clinical trials, especially randomized controlled trials, will need to be conducted to drive BBCI research and corroborate findings from real-world data analyses such as the study at hand. The resulting evidence base will be crucial to inform decision-making within the growing number of healthcare systems that embrace BBCI [ 45 ]. To conclude, this study adds to a growing body of research on the efficacy of BBCI for weight loss and documents their multifaceted impact per treatment phase in a real-world care setting.…”
Background: Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss. Objectives: This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI. Methods: Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months. Results: Overall relative weight loss was −1.63 kg at 1 month, −3.61 kg at 3 months, −5.28 kg at 6 months, and −6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss. Conclusions: The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.
“…Finally, clinical trials, especially randomized controlled trials, will need to be conducted to drive BBCI research and corroborate findings from real-world data analyses such as the study at hand. The resulting evidence base will be crucial to inform decision-making within the growing number of healthcare systems that embrace BBCI [ 45 ]. To conclude, this study adds to a growing body of research on the efficacy of BBCI for weight loss and documents their multifaceted impact per treatment phase in a real-world care setting.…”
Background: Blended-care behavior change interventions (BBCI) are a combination of digital care and coaching by health care professionals (HCP), which are proven effective for weight loss. However, it remains unclear what specific elements of BBCI drive weight loss. Objectives: This study aims to identify the distinct impact of HCP-elements (coaching) and digital elements (self-monitoring, self-management, and education) for weight loss in BBCI. Methods: Long-term data from 25,706 patients treated at a digital behavior change provider were analyzed retrospectively using a ridge regression model to predict weight loss at 3, 6, and 12 months. Results: Overall relative weight loss was −1.63 kg at 1 month, −3.61 kg at 3 months, −5.28 kg at 6 months, and −6.55 kg at 12 months. The four factors of BBCI analyzed here (coaching, self-monitoring, self-management, and education) predict weight loss with varying accuracy and degree. Coaching, self-monitoring, and self-management are positively correlated with weight losses at 3 and 6 months. Learn time (i.e., self-guided education) is clearly associated with a higher degree of weight loss. Number of appointments outside of app coaching with a dietitian (coach) was negatively associated with weight loss. Conclusions: The results testify to the efficacy of BBCI for weight loss-with particular positive associations per time point-and add to a growing body of research that characterizes the distinct impact of intervention elements in real-world settings, aiming to inform the design of future interventions for weight management.
“…It is estimated that in 2020, over 90,000 new digital health apps were released, with the majority focused on “wellness management” 5 . As many apps in this space make claims that may be easily interpreted as medical 6 , understanding which apps are targeting wellness vs. formal medical management may not always be straightforward for clinicians and patients 7 . For example, an app offering CBT could fall outside the FDA’s scope if it aims to promote or encourage healthy … activities generally related to a healthy lifestyle or wellness , but the same app could qualify for enforcement discretion if it instead offers to help users maintain their behavioral coping skills by providing a “Skill of the Day” behavioral technique 8 .…”
Rapid innovation and proliferation of software as a medical device have accelerated the clinical use of digital technologies across a wide array of medical conditions. Current regulatory pathways were developed for traditional (hardware) medical devices and offer a useful structure, but the evolution of digital devices requires concomitant innovation in regulatory approaches to maximize the potential benefits of these emerging technologies. A number of specific adaptations could strengthen current regulatory oversight while promoting ongoing innovation.
“…United Kingdom and Australia) and more digitally hesitant countries (e.g. United States) (9,10), thus providing a balanced representation of search behaviour across countries at various stages of digital health development.…”
Section: Discussionmentioning
confidence: 99%
“…We extracted weekly relative search volume data for Canada, the United States, the United Kingdom, New Zealand, Australia, and Ireland from 1 February 2019 to 1 August 2021 (n=780 country-weeks). The six countries were chosen because they share English as their dominant language and provide a varied representation in policy landscape regarding digital health (9,10). Google is used for 87% to 93% of the online search queries in the countries under study (32)(33)(34), meaning our data accurately captures the preferences of the population of the countries under study.…”
Section: Keyword Timeframe and Country Selectionmentioning
confidence: 99%
“…For instance, the design process of digital health solutions is indicative of which populations it will be able to reach and, equally importantly, what population groups will experience difficulties in accessing and using the tool (3,9). Policy environments are also vital to laying the foundation of how conducive a health system is to adopting a digital health solution and how health professionals are trained in the field of digital health (3,9,10). Finally, the readiness and willingness of digital health users are crucial elements in adopting digital health solutions (2,8).…”
Background: Due to the emergency responses early in the pandemic, the use of digital health in healthcare increased abruptly, yet it remains unclear whether this introduction was sustainable on the long term. We explore trends in digital health-seeking behaviour as proxy for readiness to adopt digital health as a mainstream form of healthcare.
Methods: We use weekly Google Trends data from February 2019 to August 2021 in Canada, United States, United Kingdom, New Zealand, Australia, and Ireland. We used five keywords to monitor online search interests in Google Trends: online doctor, telehealth, online health, telemedicine, and health app. Data are analysed using an interrupted time-series analysis with break-points on 11 March 2020 and 20 December 2020.
Results: Digital health searches immediately increased in all countries after the pandemic announcement. There was some variance in what keywords were used per country. However, searches declined after this immediate spike, sometimes towards pre-pandemic levels. The exception is the search volume of health app, which showed to either remain stable or gradually increase during the pandemic.
Interpretation: Our findings suggest that digital health-seeking behavioural patterns associated with the pandemic are currently not sustainable. Further building of digital health capacity and development of robust digital governance and literacy frameworks remain crucial to more structurally facilitate digital health transformation across countries.
Funding: Not applicable.
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