Healthy lifestyle journal -
EndNote V. Studies on digital intervention are promising and have an enormous scope in the health care domain with information and communication technologies. A systematic literature review produced related older studies, but they are primarily in the theoretical phase, and the practical implementation is very young.
Therefore, to keep our systematic literature review focused, articles related to digital interventions for healthy lifestyle management were included when published between January 1, , and December 15, The search was limited to English literature, humans, digital health intervention methods, and research focused on improving healthy lifestyles.
We aim to include peer-reviewed articles that describe digital intervention methodologies, conceptual models, theories, key challenges, lifestyle recommendations, and research related to healthy lifestyle management focusing on preventing obesity and being overweight with digital means.
Articles are classified into the following groups: quantitative, qualitative, both quantitative and qualitative, and short papers, such as posters, editorials, and commentaries. Quantitative analysis is the factual examination of information gathered by the framework to test explicit speculations.
Qualitative analysis centers around words and implications to investigate thoughts and encounters inside and out.
The selection criteria or specific parameters for the quantitative and qualitative articles were 1 articles associated with a healthy lifestyle, with the main goal of preventing obesity and being overweight using digital interventions or recommendations ; 2 methods, theories, and strategies associated with digital interventions; and 3 challenges of digital interventions for lifestyle change.
We aim to adopt the explicit inclusion and exclusion criteria, as described in Textbox 1 and divide and distribute the articles among authors to complete the screening using the Rayyan collaboration and research tool. After individual screening, the results will be verified by other authors to resolve discrepancies between the reviewers.
Subsequently, eligible peer-reviewed articles will be identified by manual search, quality score, and manual assessment of reference lists of related papers. Initially, titles, keywords, abstracts, and conclusions will be screened for inclusion. Then, we review the screened articles independently and check for individual eligibility for final inclusion.
We then excluded short research articles pages from the previous search list Figure 1. In the prefinal assessment, for data extraction, we maintained an Excel spreadsheet with the following fields: title, reference in the American Medical Association format, author, population size, study duration, target group children, adolescents, adults, and older adults , nature of the paper review, conceptual or methodology, survey, and implementation , year, country of research, key terms, keywords, intervention type, publication channel, technology use, peer-reviewed, key findings outcome, measures, intervention methods, theory, intervention components, effectiveness, and results , nature of assessment qualitative, quantitative, or both , key challenges, and quality score based on SANRA.
The primary outcome indicators extracted from the preliminary research results were digital intervention methods, nutritional intake, physical exercise, and healthy habits consumption of tobacco and alcohol. The quality of the included articles was assessed using the SANRA point scale.
We graded individual papers based on the 6 quality parameters, as defined in Textbox 2. Individual quality parameters were subcategorized into a —point scale. Finally, we calculated the mean score of the 6 quality parameters. Out of studies, 15 had a SANRA score between 1.
Therefore, we selected articles that scored scale 2 based on the SANRA scale Textbox 2. In the final stage, we had articles with grade 2 and cited them as references Textbox 3. In addition, we added 30 articles to the reference list Textbox 3 , including websites accessed URLs , conference and journal papers to describe the overview of this study, risks of lifestyle diseases, and healthy behavior plans.
The complete process of choosing the source for this study is shown in the flowchart in Figure 1. The article selection process consists of four stages—identification, screening, eligibility, and inclusion. Instead of depicting all the included studies as separate tables, we have presented significant findings from the respective studies.
The searches electronic database and manual databases resulted in papers in electronic databases and 85 manually , of which were duplicates. A total of articles were excluded from the study following the exclusion criteria, and articles were screened by reading abstracts, keywords, and conclusion sections.
We selected articles for full-text reading. We decided on articles after a full-text review and checked the paper's length and the full-text availability in the prefinal stage.
The final search produced core peer-reviewed articles eligible for citation 4 from Nature, 49 from Springer, 23 from Elsevier, 1 from IEEE, and 30 from PubMed related to digital intervention for healthy lifestyle management.
Of the papers, 72 Here, n signifies the total distribution of studies in the 4 study groups. Mummah et al [ 32 ] proposed the concept of a conceptual framework with the following 10 phases: empathize with target users, define the target behavior, the basics of behavioral theory, come up with implementation strategies, potential prototype products, gather user feedback, build a real minimum product, a pilot test to evaluate potential efficacy and utility, evaluation of effectiveness in randomized controlled trials, and sharing of interventions and results.
These phases are grouped into 4 overarching categories: integration, design, evaluation, and sharing. Muench et al [ 33 ] proposed an overarching framework to perform digital triggering text messages, emails, and push alerts focusing on individual goals with the following 5 components: who sender , how stimulus type, delivery medium, and heterogeneity , when delivered , how much frequency and intensity , and what trigger target, trigger structure, and trigger narrative.
They showed how user characteristics, conceptual models, and clinical aims help to plan digital interventions and initiate tailoring with product features and user states. Lewis et al [ 34 ] provided an idea to understand human behavior technology engagement to measure digital behavior change interventions DBCIs using a proposed framework.
The proposed framework conceptualizes the 2 basic categories of commitment measured in digital behavior interventions DBIs. The types are committed to health behaviors known as Big E and involvement of DBI known as Small E.
DBI engagement has been further broken down into 2 subclasses: user interactions with intervention features designed to encourage frequent use, such as simple log-in, games, and social interactions and make the user experience attractive, and interactions of the user with the components of a behavior change intervention ie, behavior change techniques that influence determinants of health behaviors and then affect health behaviors.
Wang et al [ 35 ] proposed a holistic TUDER Targeting, Understanding, Designing, Evaluating, and Refining framework to integrate taxonomies into the theory-based digital health behavior intervention model. They showed how digital health behavior intervention is guided and influenced by theoretical concepts, such as behavior theories, behavior change technologies, and persuasive technology.
Lubans et al [ 36 ] proposed a framework for designing and delivering organized physical activity PA sessions for children and adolescents for effective dissemination. Recommended strategies include creating partnerships, presentations, intervention dissemination, scaling up research, and embedding evidence-based interventions.
According to Morgan et al [ 37 ], the limitations of digital health intervention programs include the lack of attention to critical sociocultural factors that affect participation and interventions on research results. Their research provides a conceptual model that illustrates the design and implementation of social and cultural interventions.
Hekler et al [ 38 ] proposed models and theories for DBCI based on international experts' discussions including behavioral, computer, and health scientists and engineers and provided suggestions for developing models and theories that can be learned from DBCI and can provide references.
The proposed framework provides state-space representations to define when, where, for whom, and for the person in which the intervention will have a targeted effect. State refers to an individual's state based on various variables, which define the space in which an action mechanism may affect. The state-space representation can be used to help guide theorization and determine interdisciplinary methodological strategies to improve measurement, experimental design, and analysis so that DBCI can match the complexity of real-world behavior changes.
In this systematic literature review, we identified the following 2 digital intervention approaches for a healthy lifestyle, and they are further structured in Figure 2 : smartphone app—based intervention health monitoring and personalized recommendation generation [ 33 , 39 - 60 , 80 - ] and web-based intervention web-based monitoring and self-management or self-reporting program [ 1 , 61 - 79 , 97 - ].
In this systematic literature review, 35 studies targeted app-based interventions, 18 targeted web-based interventions, and 19 targeted both app-based and web-based interventions. The essential methods associated with both types of intervention approaches are listed in Textbox 4.
In recent years, an increasing number of digital intervention approaches have been implemented to promote a healthy lifestyle in different age groups. This RQ found modest evidence for effective digital interventions to improve PA, diet, and habits to prevent obesity and overweight.
In individuals who are overweight and obese, therapeutic weight control approaches contribute to clinically significant weight losses; however, due to limited access, expense, and time constraints, many people cannot engage in these face-to-face treatments.
The advancement of several digital weight loss services has resulted in technological advances, such as universal access to the internet, expanded use of smartphones, and newer behavioral self-monitoring tools. Verjans-Janssen et al [ 99 ] recognized the importance of implementing a long-term, locally relevant, holistic approach to promoting healthy weight status, stimulating the PA levels of children, and preventing them from wasting unnecessary time throughout school days on sedentary behaviors.
Brigden et al [ ] designed interactive DBI for younger children based on the following characteristics: participation of parents, gaming functionality, additional therapist assistance, behavioral rather than cognitive approaches, and unique feedback and monitoring, shaping knowledge, repetition and substitution, and reward.
Nicklas et al [ ] conceptualized a multi-exposure theory-based motivational theater, which can be an efficient behavior technique to improve preschool children's intake of vegetable dishes that can be conveniently disseminated to a large sample.
Lubans et al [ 36 ] used the Supportive, Active, Autonomous, Fair, and Enjoyable concepts to develop realistic strategies to engage young people with PA sessions to maximize involvement in PA and facilitate physical literacy by optimizing the results of affective, emotional, motivational, and movement skills.
Burrows et al [ 79 ] supported the need for web-based delivery of a balanced lifestyle program that addresses higher nutritional parental issues rather than infant weight. Carrà et al [ 41 ] investigated the Interactive Alcohol Risk Alertness Notifying Network for Adolescents and Young Adults D-ARIANNA , a publicly accessible evidence-based eHealth app to estimate the current health risks by queries and fit-defined risk factors and include an overall risk score in percentage terms, accompanied by relevant images showing the main contributing factors in overview graphs and achievement.
Helle et al [ 57 ] conducted a study and identified 6 main behavioral risk factors as strong determinants of chronic diseases in adolescents risky alcohol consumption, smoking, low diet, physical inactivity, sedentary behavior, and unhealthy sleep patterns. The study revealed that web and mobile technology interventions benefit adolescent participation, scope, and scalability to prevent the identification of health risk behaviors.
Stockwell et al [ 67 ] reported that PA and sedentary behavior are modifiable risk factors for lifestyle diseases and healthy aging; however, most of the older adults remain inadequately active.
DBCIs can reach many older adults to promote PA and reduce sitting time. DBCIs may increase PA and physical function and reduce sedentary lifestyle and systolic blood pressure in older adults, but more high-quality testing is required.
Weegen et al [ 62 ] showed that behavioral approaches were not successful without digital resources, and the integration of behavioral interventions with digital media proved to be an efficient way to stimulate PA.
Geidl et al [ 98 ] performed a recommendation generation study in adults with lifestyle diseases for PA and PA promotion over a week with a guideline of performing at least minutes of aerobic PA with moderate intensity, 75 minutes of aerobic PA with vigorous intensity, or a combination of both.
The PA and PA promotion guidelines advise adults impacted by lifestyle diseases and health providers on how much PA for adults with lifestyle diseases would be ideal. The guidelines provided the best strategies and approaches for growing low PA levels in adults with lifestyle diseases to professionals entrusted with PA promotion.
Gans et al [ 81 ] performed a study on worksite employees, and after 4 months, dietary fat intake decreased significantly with a multimedia-based video intervention strategy.
Individually tailored videos helped office workers minimize dietary fat and increase fruit and vegetable consumption. Recently, to minimize sedentary behavior, technology-enhanced solutions such as mobile apps, activity monitors, prompting apps, SMS text messages, emails, and websites have been exploited.
Step-count sensors can improve walking, helping to tackle physical inactivity pedometers, body-worn trackers, and smartphone apps. Chaudhry et al [ 87 ] assessed the influence of step-count monitors on PA in community-dwelling adults in randomized controlled trials, including longer-term results and discrepancies between step-count monitors and components of the intervention.
Muench et al [ 33 ] performed a multimedia-based text qualitative intervention to show the positive impact of digital triggers such as SMS text messages, emails, and push alerts in adults to change in curative conduct in health interventions. New technology apps for mobile health mHealth are emerging and provide the basis for fundamentally changing medical research, treatment practices, and scope.
Lin et al [ 43 ] conducted a web-based study on adults, collaborating with Quit Genius, an mHealth app focused on cognitive behavioral therapy that helps users quit smoking, to explore the successful nature of an mHealth digital app, which provides its users with substantial benefits and helps them modify their habits for a healthy lifestyle.
The app's ability to improve users' hedonic well-being and inspire them mentally in their everyday lives was described as essential to help users quit smoking. The findings found that users whose well-being was improved via the app were 1. Korinek et al [ ] revealed that an adaptive phase target plus reward intervention using a mobile app appeared to be a feasible solution to increasing walking activity in overweight adults.
Satisfaction with the app was strong, and the participants enjoyed having variable targets every day. Mummah et al [ 32 ] tested the effect of a mobile app to increase vegetable consumption among overweight adults seeking to sustain weight loss.
The findings showed the effectiveness of a mobile app in increasing the consumption of vegetables among overweight adults. Hanze University [ ] launched a health promotion initiative to enable workers to lead a less sedentary life. The use of an activity tracker for tracking the regular step count of participants was one of the program's measures.
For a fortnightly coaching session, the regular move count acted as feedback. They argued that the use of machine learning in the process of automated personalized coaching might become an invaluable advantage.
Individualized algorithms allow PA to be predicted during the day and provide the ability to intervene in time. Machine learning techniques empower automatic coaching and personalization. Since attending a weight control program, many individuals who are overweight find it difficult to sustain weight loss.
Self-weighing and telephone support are useful tools for weight loss monitoring. Partridge et al [ 82 , 84 ] and Sidhu et al [ 83 ] tested the efficacy of a weight maintenance program based on SMS text messaging to facilitate daily self-weighing in adults and found it to be effective for young men and women.
Ball et al [ 69 ] organized an incentive-based, promising web-based intervention study to increase PA and reduce sitting among adults ACHIEVE: Active Choices IncEntiVE. They explored the effectiveness, appeal, and impact of offering nonfinancial incentives for inactive middle-aged adults to encourage increased PA, decreased sedentary time, decreased BMI, and blood pressure.
Franssen et al [ 90 ] performed a study on consumer wearable activity trackers to promote PA levels. Oosterveen et al [ 72 ] conducted a qualitative analysis of eHealth behavioral interventions aimed at analyzing smoking rates, nutritional habits, alcohol consumption, PA levels, and obesity in young adults and revealed that because of their high level of use of technology, eHealth interventions have potential among young adults.
Therefore, this RQ reveals that digital interventions have the potential to promote a healthy lifestyle regular PA, healthy habits, and proper dietary intake in all age groups, for personal weight management. In therapeutic approaches, self-monitoring is a crucial part of digital intervention [ 73 ].
It is difficult to sustain long-term weight reductions achieved through behavioral therapy, and a different set of skills may be needed for success following interventions [ 1 , 32 , 42 ].
Many teenagers have low diet and PA patterns, which in later life can contribute to the development of lifestyle diseases. Web-based networks provide an affordable means of providing health interventions, but their efficacy is poorly understood.
Investigation of the locations of PA and dietary patterns can promote setting-specific lifestyle interventions and increase knowledge of contextual vulnerabilities to poor health. As future directions for digital weight management, distribution, and policy implications should be emphasized [ - ].
RQ1 helps to identify that behavioral theory, design thinking, evaluation, and the identification of limitations in the existing digital intervention frameworks are essential for successful, healthy lifestyle management.
In contrast, RQ2 reveals different approaches and methods associated with digital health interventions. Digital interventions for healthy lifestyle management have been categorized as discrete usefulness of advanced innovation applied to accomplish well-being goals and is executed inside digital well-being programs and information and communication technology frameworks, including communication channels, such as instant messages SMS text message , alerts, and app-based notifications [ - ].
Digital intervention can motivate and stimulate individuals with self-tracking, goal setting, evaluation, and feedback or recommendation generation to promote a healthy lifestyle [ , ]. Different methods appear to impact health outcomes and usability. It would be interesting to test variants of component design and their impact on health outcomes and usability.
Use of personalization to account for differences in preferences between groups of participants and even within groups of participants is essential in addition to cocreation between intervention developers and the target group [ - ].
Participants who attempted to self-manage their healthy lifestyle found that the most challenging part was to remain motivated [ 39 ]. They require apps that give them power and inspiration [ 39 ].
The study has confirmed that motivation is a multidimensional construct and people have different, sometimes competing, reasons for engaging in activities [ 39 ]. Moreover, human-centered analysis in digital intervention to study the intrinsic interactions of motivation and different regulations must be addressed.
Despite the widespread use of mobile phones, digital literacy barriers are common among vulnerable people [ 39 ]. Participants have different participation levels in various activities, from higher to lower levels of participation. Researchers using traditional user-centered design methods should routinely measure these communication domains in their end-user samples.
Future research should replicate these findings to a larger sample through direct observation, and persuasive prompts may be more effective in providing feedback to those with communication difficulties. RQ3 summarizes the evidence on the importance of digital intervention that were exclusively directed at promoting healthy lifestyles, especially in children, adolescents, adults, and older adults.
In the next section, we discuss the importance of digital intervention on healthy lifestyle promotion elaborately. A healthy lifestyle is a lifestyle that reduces the risk of severe illness or early death.
Not all diseases are preventable, but a large proportion of deaths can be avoided, especially lifestyle or noncommunicable diseases. According to the Harvard Medical School , the key lifestyle factors to be monitored are healthy diet, healthy PA level, healthy body weight, no tobacco consumption, and moderate alcohol intake [ 31 ].
According to RQ3 , digital intervention can have a significant impact on healthy lifestyle management. A study conducted by Steene et al [ 86 ] found significant country- and region-specific variations in PA and sedentary time in the European population, with lower PA levels.
Boys in all age groups were more aggressive and less sedentary. At about 6 to 7 years of age, the initiation of age-related decline or leveling-off of PA and rise in sedentary time begins to become evident [ 86 ].
In children and adolescents, sedentary behavior strategies successfully decrease screen time; however, the scale of the effect tends to be limited [ ]. The potential of digital intervention in older age groups outside of occupational settings and during sedentary leisure time must be examined in future studies.
The sustainability of lifestyle changes in a positive direction remains a challenge [ ]. Mobile apps to improve PA in young adults should include customized and personalized feedback and provide a coaching feature [ 58 - 60 ].
It is essential to create a well-oriented and easy-to-use interface with the ability to customize the app. The new area of mHealth is mHealth apps that target willing participants to enhance self-management of chronic conditions [ 58 - 60 ]. However, we found that only a small fraction of the mHealth apps available had been reviewed, and the amount of evidence was of inferior quality [ 58 - 60 ].
Improving the quality of evidence includes supporting prerelease app performance monitoring, designing few experiments, and performing better reviews with a rigorous risk of bias assessments [ 43 , ].
Without enough evidence to back it up, for some time to come, digital intervention and app practicability will stall in their infancy [ 43 , , ]. Evidence suggests that an unhealthy lifestyle is associated with poor health outcomes [ 40 , 63 ].
It can have severe implications for health and well-being at any age, [ 63 , 64 , ]. Therefore, there is a need to review the effects of multicomponent, complex interventions that include effective unhealthy lifestyle reduction strategies.
We must focus on optimizing the effects of an intervention. Future intervention studies should use more rigorous methods to improve the quality of studies, considering larger sample sizes, randomized controlled designs, and valid and reliable lifestyle measurements. An overview of intervention development methods can help researchers understand various existing methods and comprehend the range of actions taken in intervention development before evaluating feasibility or pilot interventions [ 32 , , ].
One way to encourage PA and enhance health is to change the physical environment, but research on intervention efficacy is mixed [ 92 - 94 ].
Theoretical perspectives and conceptual problems are used in evaluative studies, and related literature can contribute to these inconsistencies [ 92 - 94 ].
Environmental and policy initiatives are socially incorporated into the framework and function through it. Therefore, a philosophical viewpoint must be considered and should be understood by evaluators. Future research should aim to explain how interventions function across disciplinary fields by considering these structures, the context in which interventions occur, and the measurable and unmeasurable mechanisms that might work [ 92 - 94 , 98 ].
It can be beneficial to promote health-based actions, such as PA, by using innovative and interactive media-based health education [ 92 - 94 , 98 , ]. Therefore, to successfully influence behavior, it is essential to establish user-based techniques and reinforce the theories and hypotheses of behavioral change based on digital media.
Step-count tracking [ 87 ] leads to improvements in short- and long-term step counts. There is no proof that either wearable sensors or smartphone apps, or extra counseling or incentives have additional benefits over more straightforward approaches focused on the pedometer [ 63 , 64 ].
To overcome the public health issue associated with physical inactivity, basic step-count tracking strategies should be prioritized. In general, it is not clear how self-reported sedentary behavior eg, questionnaires, logs, and momentary ecological evaluations compares with system measurement measures eg, accelerometers and inclinometers [ 63 , 64 ].
Evidence from this study indicates that when compared with system tests, single-item self-report measures typically underestimate sedentary time [ 63 , 64 ].
Therefore, to evaluate the reliability and validity of different self-report measures for evaluating sedentary activity, studies should exercise caution when comparing associations between various self-report and system measures with health performance.
In addition, video and adapting technologies have been effective in diet change measures; however, these methods have never been combined with researching personalized video efficacy [ 81 ]. Theory-based mobile interventions could provide a low-cost, scalable, and efficient approach to improving dietary habits and preventing associated chronic diseases.
To encourage a healthy dietary pattern, nutrition messages or nutrient labeling, offering healthier choices, and portion size management of unhealthy foods have been potentially effective strategies in tertiary education environments [ 75 ].
The reduction in rates and the increased availability of nutritious choices in conjunction with nutrition knowledge have contributed to changes in dietary habits [ 75 ]. Further studies comparing the long-term efficacy of the climate and the combination of environmental policies to improve health outcomes are warranted.
Dietary consumption has increased by increasing the availability of nutritious foods and reducing the portion size of unhealthy foods. In terms of modifying overall dietary patterns, the existing evidence base is misleading, as rising intake of desirable food groups was more effective than reducing unfavorable food habits, and fruit or vegetable intake and sugar-sweetened beverage consumption are the most notable observed changes [ 75 , 79 ].
Social support, followed by a demonstration of conduct, self-monitoring, goal setting, and feedback, is the most popular digital health behavior intervention [ 55 , 67 , 85 , 96 ].
In addition, a customized Facebook-based obesity prevention program for teenagers in Korea Healthy Teens [ 76 ] revealed usability problems in terms of material, appearance, and navigation. Facebook [ 76 , 96 ] has tremendous potential in promoting communication and engagement with immigrant teens, considering its prominence among adolescents.
Interventions focused on social media eg, Facebook [ 66 , 96 ] are productive in facilitating meaningful improvements in adolescent eating habits. However, more research is required to explore effectiveness variations based on component tailoring, best use stimuli to promote behavior change over time, and keep people involved in changing physical health behavior.
The first step is to dismantle digital triggers into their parts and reassemble them according to their goals for improvement. PA, sedentary time, and dietary habits vary across homes, schools, and other locations [ 95 ].
Health habits vary depending on the place or environment in which the participants are [ 95 ]. Although eating habits are typically more beneficial in home or school locations, PA is usually low and sedentary time in these locations is higher [ 95 ].
To optimize health habits in each area, digital interventions that address the various locations in which participants spend time and use location-specific behavior change techniques should be explored [ 95 ]. Among young people, binge drinking is prevalent [ 41 ]. eHealth technologies [ 41 ] are appealing to them and can be useful in raising awareness.
However, to make eHealth apps suitable for longer-term effects, additional components, including daily feedback and repeated administration by different multimedia interventions, may be needed.
Mass media campaigns [ 65 ] for smoking or tobacco programs are also effective over long periods. Digital interventions have been associated with decreased drinking and smoking frequency, with a slight yet persistent impact on teenagers and adults.
Protective effects against alcohol and tobacco [ 65 ] use can be demonstrated through digital initiatives focused on a combination of social maturity and approaches to social influence. Evidence tends to be mixed with internet-based interventions, policy proposals, and incentives, and requires further study.
Various distribution systems can enhance the effects of alcohol or tobacco misuse among teenagers and adults, including interactive platforms and policy initiatives. Adolescents are easily accessible by digital media and can represent a scalable and inexpensive opportunity to engage this audience in changing behavior [ 65 ].
Smartphone-based interventions [ 39 - 59 ] such as apps, SMS text messages, sports, multicomponent interventions, emails, and social media are readily available, inexpensive, and use tools already used by most teenagers.
Therefore, it is essential to perform and publish high-quality academic literature studies and formally evaluate apps that have already been developed to inform the creation of potential interventions to change behavior.
Essential improvements in behavior were also seen when interventions involved schooling, setting goals, self-monitoring, and parents' participation. Digital approaches [ 66 ] that include education, goal setting, self-monitoring, and parental participation can affect adolescents' meaningful health behavior changes.
Most of the evidence relates to goal setting, further research into alternative media is needed, and it is essential to assess longer-term effects. There is a lack of evidence on the cost-effectiveness of digital health initiatives, and these data should be recorded in future trials.
The young population has broadly embraced social media, so health researchers are searching for ways to exploit this social media involvement to deliver programs and health promotion campaigns [ 66 , 96 ]. In young adults, weight gain and suboptimal dietary choices are popular, and social media can be a possible instrument for encouraging and supporting healthy choices.
The dissemination of information is now an appropriate use of social media by young adults. Careful evaluation is needed to use social media effectively for social support, either by private or by public sites, as its efficacy has yet to be demonstrated in experimental designs.
In digital intervention studies aimed at manipulating weight, concerns about public social media use can lead to low engagement with social media [ 66 , 96 , ]. Future research should explore how to use social media to better connect with young adults, how to use social media more efficiently to help young adults, and how to encourage social and peer-to-peer support to make healthy choices.
The systematic literature review has revealed that the identified digital intervention methods affect lifestyle behavior outcomes, focusing on PA, diet, habit, and associated primary and secondary health outcomes, such as fitness, motivation, reduced sedentary bouts, weight augmentation or weight status, blood pressure, glycemic responses, lipid profile, and quality of life in different study groups, as explained in the next section.
Here, n signifies the total number of overlapped studies in which the respective parameters are identified. Digital interventions [ 84 , , ] focused on mobile phone apps may be an acceptable and efficient way of encouraging weight loss in people who are overweight or obese.
Digital health coaching can be a revolutionary approach to reduce barriers to access to much-needed weight loss therapies for obesity, given the ubiquity of mobile phones.
A systematic literature review revealed that a healthy diet, healthy habits, and regular PA are powerful tools for reducing obesity and associated health risks.
These findings bolster the use of digital interventions as a preventive option for obesity and overweight. Therefore, behavior change should be given the highest preference to avoid severe health damage.
Planned digital interventions may potentially change growing negative behavior in humans with the adoption of persuasion, observation, goal evaluation, evidence-based personalized recommendation generation, health risk predictions decision-making , automation, motivation, pragmatism, and trust.
Developing and maintaining an empathetic relationship is perhaps the most critical determinant of successful digital intervention [ , ]. It is essential to know the participant first, and the interaction aspects are challenging owing to the delay in reaction time both ways.
Health care professionals need to ensure both relationship communication and goal-oriented coaching when using such digital intervention solutions.
In the future, the quality of the interaction between the system and the participant will require attention if participants are to fully benefit from collaboration in digital intervention programs. Digital intervention for healthy lifestyle management has great potential as a scalable tool that can improve health and health care delivery by improving effectiveness, efficiency, accessibility, security, and personalization.
Therefore, a knowledge base must be accumulated to provide information for developing and deploying digital health interventions [ , ]. However, the evaluation of digital health interventions poses unique challenges.
Methodological limitations, selection of appropriate intervention methods, evaluation of efficacy, limitations of research on different populations, loss to follow-up, attrition rate, lack of participation in tracking, financial incentives and intervention burdens long term or short term , digital literacy, technical participation, personalization, useful evidence-based automatic tailored lifestyle recommendation generation intervention design , research heterogeneity, meta-analysis, cost-effectiveness technical and financial feasibility analysis , trial selection, trial recruitment, scalability, accessibility, ethics, policy development, cyberbullying, safety, trust, user-centeredness, adoption of health care and collaboration methods that promote cooperation, unsustainable growth in complexity, and efficacy evaluation are some of the existing limitations of digital health interventions that should be overcome in existing research [ , ].
Although new technologies and rapidly changing technologies pose many unsolved problems, the broad consensus is that successful intervention design requires user-centered iterative development methods, hybrid methods, and in-depth qualitative research to gradually improve interventions to satisfy users.
Therefore, conceptual participation effective engagement is essential to understand the relationship between the involvement in digital interventions and required behavioral changes and to achieve population-level benefits.
Interventions must be delivered effectively at scale. Small effect sizes and high dropout rates [ 78 , ] often affect web-based computer-tailored interventions, particularly among people with a low education level.
The results and attractiveness of these remedies can theoretically be enhanced by using videos as a delivery format. The most successful and most appreciated intervention was the web-based video version of the computer-tailored obesity prevention intervention.
Future research needs to analyze whether the results are sustained in the long run and how to maximize the intervention. Digital intervention in health care is the intersection of health care, behavior science, computing, and engineering research and requires methods borrowed from all these disciplines.
Digital interventions have effectively improved many health conditions and health behaviors; besides, they are increasingly being used in different health care fields, including self-management of long-term conditions, prevention of lifestyle diseases, and health promotion.
In low-resource primary care environments, digital health strategies can be useful for preventing obesity. To minimize obesity and chronic disease risk among medically vulnerable adults in the primary care environment, digital health intervention uses an advanced digital health approach.
The lack of user involvement hinders the full potential of digital interventions. There is an urgent need to develop effective strategies to promote user participation in digital interventions. One potential method is to use technology-based reminders or personalized recommendation generation.
Compared with no strategy, technology-based strategies can promote participation. However, the findings of this systematic literature review should be understood with prudence, as only a few qualified studies have been identified for review, and the results are heterogeneous.
The number and dates of studies indicate that a digital health intervention strategy is an emerging field. More research is needed to understand what strategic features are useful, their cost-effectiveness, and their applicability to different age groups.
The results of this literature review will help to understand the concepts and parameters behind different DBI methods, thereby developing, testing, and evaluating the performance of a useful digital intervention in the future.
The authors AC and AP thank the coauthors MG and SM for reviewing the paper and providing useful comments to improve its quality.
This research is unique, original, and has not been published or submitted elsewhere. The authors AC and AP divided and distributed the articles to complete the screening using the Rayyan tool.
After individual screening, the results were verified by other authors MG and SM to resolve discrepancies between the reviewers. Edited by R Kukafka, G Eysenbach; submitted org , Skip to Main Content Skip to Footer. Digital Interventions on Healthy Lifestyle Management: Systematic Review Digital Interventions on Healthy Lifestyle Management: Systematic Review Authors of this article: Ayan Chatterjee 1 ; Andreas Prinz 1 ; Martin Gerdes 1 ; Santiago Martinez 2.
Article Authors Cited by 40 Tweetations 7 Metrics. Ayan Chatterjee 1 , MEng ; Andreas Prinz 1 , PhD ; Martin Gerdes 1 , PhD ; Santiago Martinez 2 , PhD 1 Department for Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway 2 Department of Health and Nursing Science, Centre for e-Health, University of Agder, Grimstad, Norway.
Corresponding Author: Ayan Chatterjee, MEng Department for Information and Communication Technologies Centre for e-Health University of Agder Jon Lilletuns Vei 9 Grimstad, Norway Phone: 47 Email: ayan.
chatterjee uia. eHealth ; digital intervention ; lifestyle ; obesity ; challenges ; mobile phone. Inclusion and exclusion criteria for systematic literature review.
Searched article scaling based on quality parameters. Nature of studies in the reference list. Nature of studies Articles and web resources URLs to describe the overview of this study, risks of lifestyle diseases, and healthy behavior plan [ 2 - 31 ] Included studies with the Scale for the Assessment of Narrative Review Articles scale 2 articles [ 1 , 32 - ] Textbox 3.
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Soft drink intake is associated with weight gain, regardless of physical activity levels: The health workers cohort study. Crude trends in prevalence of healthy lifestyle factors A and changes in estimated prevalence of specific numbers of healthy lifestyle factors B are shown.
Data were adjusted for National Health and Nutrition Examination Survey survey weights. Heathy lifestyle was defined as 4 or 5 healthy lifestyle factors. Data were adjusted for National Health and Nutrition Examination Survey NHANES survey weights.
FDR indicates false discovery rate. eTable 1. Healthy Eating Index— Components, Component Points, and Scoring Standards. eTable 2. Number of Participants According to the Number of Healthy Lifestyle Factors Among US Adults 20 Years or Older, to March eTable 3.
Crude Trends in Estimated Prevalence of Healthy Eating Index— Among US Adults 20 Years or Older, to March eTable 4. Crude Trends in Estimated Prevalence of Physical Activity Among US Adults 20 Years or Older, to and to March eTable 5.
Adjusted Trends in Estimated Prevalence of Healthy Lifestyle Factors Among US Adults 20 Years or Older, to March eTable 6. Adjusted Trends in Estimated Prevalence of Healthy Lifestyle Among US Adults 20 Years or Older, to March eTable 7.
Crude Trends in Estimated Prevalence of Healthy Lifestyle by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable 8. Crude Trends in Estimated Prevalence of Never Smoking by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable 9.
Crude Trends in Estimated Prevalence of Moderate or Lighter Alcohol Consumption by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable Crude Trends in Estimated Prevalence of Healthy Diet by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March Crude Trends in Estimated Prevalence of Sufficient Physical Activity by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to Crude Trends in Estimated Prevalence of Sufficient Physical Activity by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March Crude Trends in Estimated Prevalence of Healthy Weight by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March Trends in Self-Reported Adherence to Healthy Lifestyle Behaviors Among US Adults, to March JAMA Netw Open.
Question What were trends in lifestyle factors among US adults from the cycle to the combined to March cycle of the National Health and Nutrition Examination Survey? Findings In this cross-sectional study including 47 adults, improvements were observed in smoking habits, diet quality, and physical activity levels, but with a decrease in healthy weight and no significant change in moderate or less alcohol consumption.
Meaning These findings suggest that efforts are still warranted to improve lifestyle in US adults, with attention on equity. Importance Adherence to a healthy lifestyle is associated with lower risks of adverse outcomes.
However, trends in multiple lifestyle factors and overall healthy lifestyle status among US adults in recent years are unknown. Objective To examine trends in multiple lifestyle factors and overall healthy lifestyle among US adults.
Design, Setting, and Participants This serial cross-sectional study used nationally representative data from 10 National Health and Nutrition Examination Survey NHANES cycles nine 2-year cycles from to and 1 combined cycle from to March among adults 20 years or older.
Data were analyzed from December 10, , to January 11, Results A total of 47 adults were included in this study. The weighted mean [SE] age was From the cycle to the to March cycle, the estimated prevalence of the 5 lifestyle factors showed divergent trends, with increasing prevalence of never smoking from Meanwhile, there was no significant trend in moderate or lighter alcohol consumption.
Overall, the estimated prevalence of at least 4 healthy lifestyle factors increased from Disparities in healthy lifestyle were widened by age group, with little improvement among adults 65 years and older difference, 0.
There were persistent disparities in healthy lifestyle by race and ethnicity, educational level, and income level. Conclusions and Relevance The findings of this cross-sectional study of NHANES data over a year period suggest diverse change patterns across 5 healthy lifestyle factors and a modest improvement in overall lifestyle existed among US adults, with worsening or persistent disparities in lifestyle.
Adhering to healthy lifestyle factors, including avoiding tobacco use, limiting alcohol consumption, maintaining a healthy diet, engaging in sufficient physical activity, and maintaining healthy body weight, is a cost-effective strategy for preventing noncommunicable diseases NCDs suggested by the World Health Organization.
A number of studies have described the population-level trends in individual lifestyle factors among US adults in recent years, including trends in smoking habits and , 7 , 8 alcohol consumption and , 9 , 10 diet quality , 11 physical activity level and , 12 , 13 and body mass index BMI and Hence, it is essential to understand the current status and long-term changes in overall lifestyle at the population level.
Several studies 16 - 18 have reported trends of overall lifestyle among US adults prior to , but no study has reported trends of overall lifestyle during the past decade since To address these gaps, we used the recently released data from the National Health and Nutrition Examination Survey NHANES to examine trends in multiple lifestyle factors as well as combined healthy lifestyle factors among US adults from the cycle to the combined cycle from to March The NHANES is a series of cross-sectional surveys with a complex, multistage probability sample design conducted by the National Center for Health Statistics NCHS to obtain health-related information about the civilian noninstitutionalized population in the US.
Details of the study design, protocol, and data collection have been described elsewhere. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology STROBE reporting guideline.
In the present analyses, we included adults 20 years or older who had complete data for smoking, alcohol consumption, diet, physical activity, and BMI from the cycle through the to March cycle.
In the main analysis, we only used the first hour dietary recall data to evaluate diet quality and did not use the second hour dietary recall, which was available since the cycle.
This was mainly because distributions of dietary intake were not comparable from single vs multiple dietary recalls, 22 and the survey method was different between the first dietary recall in-person and second recall telephone. Data were collected during the household interview and a study visit in the mobile examination center MEC.
This information was collected to report current status and changes in prevalence of lifestyle factors by race and ethnicity. In line with a previous study conducted in the NHANES, 16 we included 5 lifestyle factors: smoking, alcohol consumption, diet quality, physical activity, and BMI.
Information on smoking was obtained by questions about whether the participant smoked at least cigarettes in life, whether they smoked at the time of the survey, and numbers of cigarettes, pipes, or cigars smoked during the past 30 days. Alcohol consumption was assessed by self-reported drinking frequency and drinking quantity over the past year.
Diet information was assessed using one hour dietary recall conducted in person in the MEC. In the dietary recall interview, the participant reported all foods and beverages consumed during the prior 24 hours. For physical activity, the questionnaire changed from a specific Physical Activity and Physical Fitness Questionnaire before the cycle to the Global Physical Activity Questionnaire thereafter.
Both questionnaires accessed the duration of physical activity from different domains. Briefly, the former assessed minutes of physical activity during the past 30 days from the household, transportation, and moderate to vigorous leisure time; the latter measured minutes of physical activity in a typical week from moderate to vigorous work, transportation, and moderate to vigorous leisure time.
Body weight and height were measured using a digital weight scale and a fixed stadiometer respectively, following standard procedures eg, wearing requirement and standing posture at the MEC.
Details of the measurements of the 5 lifestyle factors can be found elsewhere. The primary outcomes were 5 lifestyle factors and combined healthy lifestyle factors.
To capture more detailed information in lifestyle factors, individual lifestyle factors were first classified into multiple levels.
Physical activity from the household and transportation was defined as moderate activity according to the NHANES guidelines. For each healthy lifestyle factor, the participant who met the criterion for a healthy level received a score of 1; others received 0.
The healthy lifestyle score was defined as the sum of all 5 scores and ranged from 0 to 5, with higher scores indicating healthier lifestyle. Since the number of participants with the highest lifestyle score was small across the 10 cycles ranging from 96 to eTable 2 in Supplement 1 , participants with 4 or 5 healthy lifestyle factors were combined into 1 group and defined as having a healthy lifestyle.
Secondary outcomes were trends in 5 healthy lifestyle factors and healthy lifestyle by major sociodemographic subgroups age, sex, race and ethnicity, educational level, and income assessed by standardized questionnaires.
The subgroups were chosen since previous research in US adults documented the co-occurrence of healthy lifestyle factors among sociodemographic strata.
In all analyses, survey procedures were used to account for dietary sample weights, 37 stratification, and clustering of the complex sampling design to ensure nationally representative estimates.
Characteristics of participants by different cycles were presented as numbers percentages for categorical variables and compared using the Rao-Scott χ 2 test.
Absolute differences in weighted prevalence were calculated between the first and the last cycle. P values for interactions were further adjusted with Benjamini—Hochberg false discovery rate FDR correction.
In sensitivity analyses, to further incorporate the data of 2 dietary recalls since the cycle, trends in diet quality were assessed in adults with 2 valid recalls between the cycle and the to March cycle. Similarly, due to the change in the physical activity questionnaire since the cycle, trends in physical activity were accessed separately from the to cycles and from the cycle to the to March cycle.
All analyses were conducted with SAS, version 9. Of the 47 US adults included in the analyses, the weighted mean SE age was In terms of race and ethnicity, weighted proportion, 8.
Significant differences were observed in the distribution of participants by age and educational level groups over time Table 1. Divergent trends were observed among 5 healthy lifestyle factors Figure 1 A.
From the to the to March cycles, the estimated prevalence of never smoking increased from During this period, the estimated prevalence of moderate or lighter alcohol consumption remained stable from In addition, the cycle had the highest prevalence of healthy diet In sensitivity analyses, a similar increase in healthy diet based on 2 diet recalls was observed from the cycle to the to March cycle eTable 3 in Supplement 1 , and increases in sufficient physical activity remained consistent from the to cycles and from the to the to March cycles eTable 4 in Supplement 1.
Moreover, the adjustment of age or all sociodemographic characteristics did not alter the results eTable 5 in Supplement 1. Trends in the prevalence of multiclass lifestyle factors are shown in Table 2 and eTable 4 in Supplement 1.
From the to the to March cycles, improvements were observed in overall lifestyle Figure 1 B. The trend in healthy lifestyle was largely consistent after the adjustment for age or all sociodemographic characteristics eTable 6 in Supplement 1.
Trends in healthy lifestyle across subgroups are shown in Figure 2 and eTable 7 in Supplement 1. From the to the to March cycles, a greater change in healthy lifestyle was observed among younger vs older adults.
The estimated prevalence of healthy lifestyle increased among young adults aged 20 to 34 years difference, 8. In addition, there were no significant trends among groups with relatively high prevalence, including non-Hispanic White adults and adults with the highest income level.
Trends in individual healthy lifestyle factors across subgroups appear in eTables 8 to 13 in Supplement 1. Among adults 65 years or older, the estimated prevalence of never smoking and healthy diet was not significantly changed, and with a significant decrease in healthy weight. From the to the to March cycles, increases were observed in never smoking, healthy diet, and sufficient physical activity, but a decrease in healthy weight was also observed.
Meanwhile, there was no significant change in moderate or lighter alcohol consumption. An improvement in healthy lifestyle was identified, with widening disparity by age group and persistent disparities by race and ethnicity, educational level, and income level. However, similar to the present findings, both studies indicated relatively low prevalence and small net change.
The increase in healthy diet is consistent with prior reports 11 , 22 that observed improvements in the American Heart Association diet score from to and the mean of HEI during to However, the improvement in healthy diet mainly accumulated before the cycle; the exact reasons were unclear, and future studies are needed.
In a diverse population, physical activity domains other than the leisure-time domain ie, occupation, household, and transportation could be important sources of physical activity level. Although the increase in obesity appeared to slow down or level off during to , 46 studies covering more recent years or a longer period still observed a significant increase.
To the best of our knowledge, the present study provides the most comprehensive evaluation of trends in lifestyle factors in the past 22 years. The increase in healthy lifestyle during the year study period was not consistent with the trends reported between and , when the prevalence of healthy lifestyle showed significant declines or little net change.
Our study observed widened disparity in healthy lifestyle by age group with relatively stable prevalence in healthy lifestyle among adults 65 years and older. The smaller improvement in lifestyle among old adults was reflected in no significant change in never smoking and healthy diet with a decrease in healthy weight.
Weight gain among old adults was associated with higher all-cause mortality in 2 meta-analyses, 55 , 56 and further studies are still needed to explain the weight gain among old adults given that skeletal muscle declines already happen in old adults. Nevertheless, this study has several limitations.
First, data about smoking, alcohol consumption, diet, and physical activity were self-reported and subjected to recall bias.
However, the bias might be minimized through trained interviewers and computer-assisted personal interview systems. Second, only 1 valid dietary recall was used in our main analyses. However, we further evaluated the trend in diet quality using 2 dietary recalls and observed similar results.
Third, as described, the physical activity questionnaire had changed to Global Physical Activity Questionnaire since the cycle.
However, we used sufficient physical activity to construct the healthy lifestyle score, and the consistent increases in sufficient physical activity for the and the to March cycles provide some support for a modest effect of this change.
Fourth, the lifestyle score was derived from the number of healthy lifestyle factors, which may not reflect the unequal effect of individual healthy lifestyle factors.
In this cross-sectional study from to March , we observed different change patterns across 5 healthy lifestyle factors and a modest improvement in overall lifestyle among US adults. Medical care alone is not enough to improve health overall 61 ; preventive care is an indispensable component.
Changes in food, physical, and policy environments are still needed to improve lifestyle, with attention on old adults and persistent disparity in healthy lifestyle by race and ethnicity and socioeconomic levels. Future studies are warranted to validate our results using other US national surveys.
Published: July 14, Open Access: This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. Corresponding Author: Zhilei Shan, MD, PhD zhileishan hust.
cn and An Pan, PhD panan hust. cn , School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan , China. Author Contributions: Drs Shan and Pan had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Messrs Li and Xia contributed equally to this work. Acquisition, analysis, or interpretation of data: Li, Xia, Geng, Tu, Y. Zhang, Yu, J. Zhang, Guo, Yang, Liu, Pan. Critical revision of the manuscript for important intellectual content: All authors.
Conflict of Interest Disclosures: None reported. Dr Liu was supported by grants and from the National Natural Science Foundation of China, grant CFA from the Hubei Province Science Fund for Distinguished Young Scholars, and grant GCRC from the Fundamental Research Funds for the Central Universities.
Data Sharing Statement: See Supplement 2. full text icon Full Text. Download PDF Comment. Top of Article Key Points Abstract Introduction Methods Results Discussion Conclusions Article Information References. Figure 1.
Trends in Estimated Prevalence of Healthy Lifestyle Factors Among US Adults 20 Years or Older, to March View Large Download. Figure 2. Table 1. Characteristics of US Adults 20 Years or Older, to March Table 2. Crude Trends in Estimated Prevalence of Lifestyle Factors Among US Adults 20 Years or Older, to March Supplement 1.
Healthy Eating Index— Components, Component Points, and Scoring Standards eTable 2. Number of Participants According to the Number of Healthy Lifestyle Factors Among US Adults 20 Years or Older, to March eTable 3. Crude Trends in Estimated Prevalence of Healthy Eating Index— Among US Adults 20 Years or Older, to March eTable 4.
Crude Trends in Estimated Prevalence of Physical Activity Among US Adults 20 Years or Older, to and to March eTable 5. Adjusted Trends in Estimated Prevalence of Healthy Lifestyle Factors Among US Adults 20 Years or Older, to March eTable 6.
Adjusted Trends in Estimated Prevalence of Healthy Lifestyle Among US Adults 20 Years or Older, to March eTable 7. Crude Trends in Estimated Prevalence of Healthy Lifestyle by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable 8. Crude Trends in Estimated Prevalence of Never Smoking by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable 9.
Crude Trends in Estimated Prevalence of Moderate or Lighter Alcohol Consumption by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable Crude Trends in Estimated Prevalence of Healthy Diet by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable Crude Trends in Estimated Prevalence of Sufficient Physical Activity by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to eTable Crude Trends in Estimated Prevalence of Sufficient Physical Activity by Age Group, Sex, Race and Ethnicity, Educational Level, and Income, to March eTable Supplement 2.
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