Background: In recent years, there has been a rise in the use of conversational agents for lifestyle medicine, in particular for weight-related behaviors and cardiometabolic risk factors. Little is known about the effectiveness and acceptability of and engagement with conversational and virtual agents as well as the applicability of these agents for metabolic syndrome risk factors such as an unhealthy dietary intake, physical inactivity, diabetes, and hypertension.
Objective: This review aimed to get a greater understanding of the virtual agents that have been developed for cardiometabolic risk factors and to review their effectiveness.
Methods: A systematic review of PubMed and MEDLINE was conducted to review conversational agents for cardiometabolic risk factors, including chatbots and embodied avatars.
Results: A total of 50 studies were identified. Overall, chatbots and avatars appear to have the potential to improve weight-related behaviors such as dietary intake and physical activity. There were limited studies on hypertension and diabetes. Patients seemed interested in using chatbots and avatars for modifying cardiometabolic risk factors, and adherence was acceptable across the studies, except for studies of virtual agents for diabetes. However, there is a need for randomized controlled trials to confirm this finding. As there were only a few clinical trials, more research is needed to confirm whether conversational coaches may assist with cardiovascular disease and diabetes, and physical activity.
Conclusions: Conversational coaches may regulate cardiometabolic risk factors; however, quality trials are needed to expand the evidence base. A future chatbot could be tailored to metabolic syndrome specifically, targeting all the areas covered in the literature, which would be novel.
Metabolic syndrome (MetS) is a highly prevalent condition that affects up to approximately 30% of adults aged >65 years worldwide . It consists of multiple symptoms, namely abdominal obesity, glucose intolerance, hypertension, and high cholesterol as well as low high-density lipoprotein [ ]. It is associated with a substantially increased risk of premature morbidity and mortality from diabetes and cardiovascular disease (CVD) [ ]. Low levels of physical activity (PA) are strongly associated with MetS, including obesity and overweight [ ], high blood pressure [ ], and insulin intolerance [ ]. Furthermore, low levels of activity are significantly associated with increased risk of complications of MetS, including diabetes and CVD [ , ]. In addition, research has found that losing weight by approximately 5% to 10% results in significantly reduced MetS-associated markers [ ] in patients with existing disease, highlighting that MetS may be modifiable through lifestyle-related weight interventions. Dietary modifications, including reduced sodium, sugar, and fat intake, are also highly beneficial for reducing the risk of the syndrome and its complications [ ].
In recent years, mobile health (mHealth) has increasingly been used to support behavior changes related to weight loss, including improving dietary intake and physical activity . Research on the use of mHealth interventions has found support for a moderate effect size for assisting with weight loss [ ]. This includes the use of SMS text messaging for behavior change and mHealth apps that target weight loss using a range of behavior change techniques (BCTs) [ ], including self-monitoring, feedback, goal setting, education, tips, personal tailoring, reminders, encouragement, and social and professional support [ ]. mHealth is a form of health care that enables timely accessibility, portability, and personalized medicine tailored to the needs of the user [ , ]. It includes smartphones, PDAs, MP3 players, iPads (Apple Inc), smart clothing, and smart watches [ , ].
Emerging research in the mHealth field has focused on developing conversational agents that can simulate human professional interactions for managing different health problems , including weight issues [ ]. Furthermore, avatars have also been developed to display a conversational coach in addition to written conversational text, simulating real-life interactions with a professional, such as a live fitness coach [ , ]. Having a conversational coach complement or replace metabolic-related health advice from professionals may increase accessibility and enable more timely health monitoring and diagnosis of health conditions [ ] such as MetS if physicians also gain access to patient data. Given that technology in the field is advancing, it is time to determine whether these conversational agents are effective for assisting with MetS-associated risk factors, including overweight, obesity, physical inactivity, and unhealthy dietary intake. There is also a need to better understand what types of weight-related and MetS-related studies have been undertaken using conversational agents and to identify challenges with the technology and future areas of research.
This review aimed to better understand the evidence surrounding the use of conversational coaches for metabolic-related risk factors and biomarkers. Furthermore, this review aimed to determine whether conversational coaches are effective for improving weight-related behaviors and metabolic indicators and whether conversational agents are acceptable for consumers as agents of behavior change.
- Research question (RQ) 1: How effective are conversational agents (chatbots and avatars) for weight-related behaviors, including diet and exercise?
- RQ 2: How effective are conversational agents for improving metabolic risk factors, including blood pressure, cholesterol, abdominal obesity, and glucose (diabetes management)?
- RQ 3: What are consumers’ perspectives on the use of chatbots?
A systematic review of PubMed and MEDLINE was conducted in December 2021 for all relevant studies on conversational coaches for metabolic risk factors published over the last 10 years. Google Scholar was also searched for any additional papers along with manual hand searching.
Inclusion and Exclusion Criteria
This review included studies on chatbots or avatar conservational agents that acted as coaches for improving metabolic health behaviors, including dietary intake (sodium and sugar intake), PA, and weight (including abdominal obesity). Studies that evaluated one or more physiological indicators of metabolic health or risk factors for MetS, such as diabetes, glucose intolerance, hypertension, cholesterol, and serum triglycerides, were also included. Studies must have been published in the English language to be included. Chatbots that were used for survey reasons but not primarily for targeting weight-related or metabolic risk factors were excluded. Studies whose primary focus was not on conversational coaches were excluded (including those that had an avatar element but did not primarily focus on evaluating it). Studies on wearables that did not include avatars or chatbots were excluded. Studies in pregnant women were excluded.
The keywords included word variations for “chatbot,” “virtual assistant,” “virtual coach,” or “avatar”; weight-related behaviors, including “diet,” “exercise,” or “weight”; and metabolic risk factors, including “hypertension,” “cholesterol,” or “diabetes.” The search strategy is shown in.
PubMed search strategy example.
1.Cardiometabolic risk factors
“obesity”[MeSH Terms] OR “obese”[tiab] OR “obesity”[tiab] OR “overweight”[tiab] OR “overweight”[tiab] OR “BMI”[tiab] OR “Body mass index”[tiab] OR “Body mass index”[MeSH Terms] OR “physical activity”[Tiab] OR adiposity [tiab] OR weight gain[tiab] OR body weight[tiab] OR “abdominal visceral fat”[Tiab] OR “adipose tissue”[MeSH Terms] “weight loss”[Mesh] OR “weight loss”[tiab] or “metabolic syndrome”
- Diet and physical activity
diets[tiab] OR “diet”[mesh] OR diet[tiab] OR “energy intake”[tiab] OR nutrition[tiab] OR “diet, food, and nutrition”[MeSH Terms] OR diets[tiab] OR Caloric restriction[tiab]OR “physical activity”[tiab]
hypertension[tiab] OR “Blood Pressure”[tiab] OR Prehypertension[tiab] OR BP[tiab] OR “Systolic blood pressure”[tiab] OR SBP[tiab] OR “Diastolic blood pressure”[tiab] OR DBP[tiab] OR cardiovascular[tiab] OR hypotensive[tiab] OR “Hypertension”[MeSH] OR “Blood Pressure”[MeSH] OR “Prehypertension”[MeSH]
“cholesterol”[MeSH Terms] OR cholesterol[tiab]
“Diabetes Mellitus”[MeSH] or diabetes[tiab] or diabetic[tiab] or prediabetes[tiab] or pre-diabetes[tiab] OR “glucose”[MeSH Terms] OR “glucose”[tiab]
chatbot*[tiab] OR chat bot[tiab] OR chat-bot[tiab] OR chatter bot[tiab] OR chat bots[tiab] OR chat-bots[tiab] OR chatter bots[tiab] OR chatterbot*[tiab] OR smart bot[tiab] OR smartbot[tiab] OR smart bots[tiab] OR smartbots[tiab] OR smart-bot*[tiab] OR virtual agent*[tiab] OR virtual character*[tiab] OR virtual coach*[tiab] OR virtual human[tiab] OR avatar*[tiab] OR embodied agent*[tiab] OR relational agent*[tiab] OR animated character*[tiab])
1 AND 2
Screening and Data Extraction
Titles were screened for relevance to the RQs, followed by abstract and full-text retrieval of eligible studies that met the inclusion criteria. A second reviewer (LL) screened the abstracts and full texts against the inclusion and exclusion criteria to ensure agreement. Quantitative and qualitative data were extracted and summarized in a tabular format, including study characteristics, measures, outcomes, and intervention details.
LL and ME screened the final selected papers individually. A total of 52 full texts were selected [, , - ]; however, after double peer screening, 1 protocol and 1 dated technology were removed. The final number included 50 papers [ , , - , - , ]. Details of the search process and reasons for exclusion are illustrated in [ ].
Most of the studies were feasibility and usability studies. A few studies were qualitative and explored consumer perspectives on conversational agents for weight-related behaviors [, ]. The countries where the studies were conducted included Australia, the United States, Italy, Spain, and Taiwan [ , , - ]. Most of the studies explored virtual agents for diet and exercise, with only 2 (4%) exploring chatbots for hypertension management [ , ]. The majority were conducted among adults, but 3 (6%) were conducted among teenagers and preteens [ , , ]. The study characteristics and results are summarized in .
|Study and year||Location, N, and design||Sex (%)||Age (years)||Health targets and measures||Technology and |
|Echeazarra et al , 2021||Female: 42||Mean 52.1||BPb|
|Griffin et al , 2021||Female: 53||Mean 59 (SD 11)||BP|
|Larbi et al , 2021||Female: 50||Range 18-69||PAd|
|Lin et al , 2021||Female: 53||Mean 21.5; range 18-42||PA (core muscle exercise)|
|Dol et al , 2021||Female: 100||Mean 44.4 (SD 12.86); range 19-70||Emotional eating|
|Lin et al , 2021||Female: 50||Mean 70.39 (SD 6.51); range 60-88||PA perceived exertion|
|Maher et al , 2021||Female: 67||Range 45-75||PA, Mediterranean diet, and weight|
|Hickman et al , 2021||Female: 59||Mean 52 (SD 11)||Hypertension, quality of the physician-patient interaction|
|Napolitano et al , 2021||Female: 100||Mean 27.8 (SD 5.4)||Weight, diet, and PA; exercise self-efficacy|
|Santini et al , 2021||Female: 53.3% wave 1; 51.6% wave 2||Mean 61.9||Health behaviors, diet, and PA|
|Krishnakumar et al , 2021||Female: 31.4||Mean 50.8||Diabetes (blood sugar), diet, PA, and weight (logged)|
|Dhinagaran et al , 2021||Female: 62||Mean 33.7||Diet, PA, sleep, and stress|
|To et al , 2021||Female: 81.9||Mean 49.1 (SD 9.3)||PA|
|Mitchell et al , 2021||Female: 100||Mean 56 (SD 11) intervention; 57 (SD 11) control||Diabetes|
|Strombotne et al , 2021||Female: 11||Mean treatment=58.1; control=57.7||Diabetes and risk factors|
|Alves Da Cruz , 2020||Female 48.1||Mean 63.4 (SD 12.7)||HRl, BP, and RRm|
|Kowalska et al , 2020||Female: 36.5||Mean 65.3 (SD 13.8)||CVDn|
|Piao et al , 2020||Female: 56 intervention; 57 control||Range 20-59||Health behaviors (diet and exercise); SRHIo|
|Naylor et al , 2020||N/Ap||Mean 8.4 (SD 1.3)||VO2 (mL × kg–1 × min–1) using indirect calorimetry questionnaire on liking and motivation|
|Hahn et al , 2020||Female (children): 55.2||Treatment: mean 8.06 (SD 1.10); control: mean 7.5 (SD 1.38)||PA using Fitbit and self-report on motivation for PA||Children wore Fitbit with a personalized dog avatar for socializing and support (digital fitness kiosk); theory informed (social cognitive theory)|
|Navarro et al , 2020||Female: N/A||Mean 20.0 (SD 2.2); range 18-37||Cardiac frequency, step counts, accelerometer, and HR monitor|
|Davis et al , 2020||Female: 68||Mean 56.2 (SD 8); range 45-75||Diet: Mediterranean diet adherence tool. Weekly log for diet and step count; activity tracked using a wrist worn tracker (Garmin) that syncs with Paola. Minutes of moderate to vigorous PA assessed with Active Australia Survey|
et al [ ], 2020
|Female: 100||Mean 31.9 (SD 11.7); range 19-61||PA, IPAQq, self-efficacy to regulate exercise, and PA enjoyment scale (PACESr)|
|Balsa et al , 2020||Female: experts 88.9%; end users 27.3%||Mean 62.62; mean end users 70.9; mean experts 54.3||Usability of the app for diabetes medication adherence and improving lifestyle behaviors, diet, and PA|
|Chin et al , 2020||Female: 60%||Mean 67 (SD 5.84)||PA|
|Fadhil et al , 2019||Female: 42||Mean 28.5; range 19-53||Diet and PA questionnaires via chatbot and motivation (HAPAs)|
|Ahn et al , 2019||Female: 61.19||Mean 11.24 (SD 0.85); range 9-13||PA and basic psychological needs|
|Stephens et al , 2019||Female: 57||Mean 15.2; range 9.7-18.5||Weight management; pre-diabetes|
|Srivastana et al , 2019||Female: 70||Range 44-67||Prediabetes|
|Thompson et al , 2019||Female: 73 (teens)||Range 10-15||Diabetes|
|Thompson et al , 2018||Female: 50||Range 12-14||PA|
|Duncan-Carnesciali et al , 2018||Female: 97.5||Range 26-76||Diabetes|
|Klaassen et al , 2018||Female: 52||Mean 13.9||Diabetes|
|Sinoo et al , 2018||Female: 37||Mean 9.2 (SD 1.1)||Diabetes self-management|
|Tongpeth et al , 2018||Female: 10||Mean 52.2 (SD 10.4)||Cardiovascular: acute coronary syndrome management|
|Friedrichs et al , 2014||Female: 60.4||Mean 42.9 (SD 14.5)||PA; Dutch Short questionnaire|
|Stein et al , 2017||Female: 74.5||Mean 47 (SD 1.8); range 18-76||Weight and dietary intake|
|Thompson , 2016||Female: 50||Range 12-14||Preferences for a PA intervention|
|Behm-Morawitz et al , 2016||Female: 100||Range 18-61||Weight and PA self-efficacy|
|Kuo et al , 2016||Female: 63.15||Mean 21.2||Eating behavior observed in laboratory|
|Ruiz et al , 2016||Female: 0||Mean 64 (SD 7)||Cardiovascular behavioral risk factors (diet and exercise)|
|LeRouge et al , 2015||N/A||Teenagers: 12-17||Perceptions of the avatar for diet and exercise|
|Thomas et al , 2015||Female: 100||Mean 55.0 (SD 8.2)||Weight-related eating behaviors|
|Ruiz et al , 2014||Male: 100||Mean 62 (SD 7.9)||Diabetes (knowledge)|
|Li et al , 2014||Female: 41||Range 9-12||PA attitudes, motivation, and game performance|
|Napolitano et al , 2013||Female: 100||Mean 34.1 (SD 13.0); range 18-60 (phase 1)||Weight, PA , and weight self-efficacy; satisfaction; preferences survey and interviews|
|Bickmore et al , 2013||Female: 61||Mean 33.0 (SD 12.6); range 21-69||Diet (NIHt/NCIu fruit and vegetable scan) and PA (IPAQ)|
|Johnoson-Glenberg et al , 2013||N/A||Grades 4-12 (ages 9-18)||Diet (nutrition and food choice test and knowledge)|
|Ruiz et al , 2012||N/A||N/A||PA|
|Mestre et al , 2011||N/A||Range 19-25||PA enjoyment|
aRCT: randomized controlled trial.
bBP: blood pressure.
cGP: general practitioner.
dPA: physical activity.
eVR: virtual reality.
fAI: artificial intelligence.
gBCT: behavior change technique.
hT2D: type 2 diabetes.
iHbA1c: hemoglobin A1c.
jFBG: fasting blood glucose.
kPPBG: postprandial blood glucose.
lHR: heart rate.
mRR: respiratory rate.
nCVD: cardiovascular disease.
oSRHI: Self-Report Habit Index.
pN/A: not applicable.
qIPAQ: International Physical Activity Questionnaire.
rPACES: physical activity enjoyment scale.
sHAPA: Health Action Process Approach.
tNIH: National Institutes of Health.
uNCI: National Cancer Institute.
A few studies evaluated the effects of conversational assistants for weight loss [, - , ]. The study by Maher et al [ ] in Australia found that the conversational assistant (chatbot) Paola assisted with a weight loss of 1.3 kg at 12 weeks follow-up (95% CI –0.1 to –2.5). In addition, there was a mean waist circumference reduction of 2.5 cm at follow-up compared with baseline (95% CI –3.5 to –0.7). The chatbot used a range of BCTs, including goal setting, self-monitoring, education, social support, and feedback to users on PA and the Mediterranean diet [ ]. A study in the United States found that the Lark Weight Loss Coach, an artificial intelligence–powered bot, assisted participants with a weight loss of 2.38% (95% CI –3.75 to 1.0) with a mean use of 15 weeks [ ]. The conversational agent was informed by cognitive behavioral therapy and used a range of BCTs, including education, encouragement, and reminders surrounding dietary and PA targets [ ]. The determinants of weight loss included the duration of using the artificial intelligence program and engaging with it, logging meals, and the number of counseling sessions completed [ ]. A large study in the United States examining the use of an avatar coach that targeted self-efficacy and modelled vicarious experiences for diet and PA (4 weeks) found that women lost an average of 1.6 (SD 1.7) kg at follow-up [ ]. A study in India found that an avatar coaching app with calls from health professionals assisted with a weight loss of 1.39 kg (95% CI –0.63 to –2.01; P<.01) at 16 weeks [ ]. A randomized controlled trial (RCT) with a qualitative component found that avatars increase motivation and PA self-efficacy linked with weight loss [ ]. However, some studies did not report any significant weight loss [ , ].
A few studies evaluated the effects of conversational coaches (chatbots and avatars) on dietary intake and found that overall, the coaches assisted with ameliorating dietary habits and goals [, , , , , , ]. A study in the United States found that healthy dietary intake improved in 30% of participants who were using a conversational weight loss coach [ ]. Another study found that eating behaviors improved in users of a conversational eating coach, which included increases in the mean scores for the perceptions of skills to eat healthily and self-control over their eating habits (0.7 increase in points) as well as confidence to control food consumption in social situations (1.0 increase in points; P<.01) [ ]. The Paola chatbot study found a mean increase in the Mediterranean diet score [ ] of 5.8 points at 12 weeks follow-up [ ]. Similarly, a study of Karen, an animated counselor, found significant increases (F3,103=4.5; P<.01) in fruit and vegetable intake in the diet intervention arm relative to the control group [ ]. A further study found that eating behaviors were shaped by the appearance of the avatar, with healthier eating behavioral patterns in participants who had thinner avatars including reduced portions of ice cream and opting for healthier sugar-free drink alternatives [ ].
A few conversational assistant PA coaches, including chatbots and avatars, were evaluated, and overall, they assisted with increasing PA [, , , , , , , ]. Most of them involved exergames with the avatar. However, one of the studies did not find any improvements in PA among the 2 avatars, attributing improvements only to the web-based part of the intervention [ ], and another study did not find a difference between the web-based intervention and the chatbot (only when considering a standard control) [ ]. A preliminary usability study in Australia found that step count goals increased 59% of the time in users of the chatbot that targeted PA and that participants had a preference for personalization and greater knowledge-based content [ ]. Another pilot study of Paola, the chatbot in Australia, found that it assisted with increasing mean step count by 109 minutes per week at 12 weeks follow-up (95% CI 1.9-217.7) [ ]. A study involving an exergame that used a PA avatar coach in teens found that 75% of the time, participants engaged in 15.88 (SD 5.8) minutes of vigorous PA throughout the game [ ]. Participants also wanted the avatar to have a supportive and nonpatronizing or nondisparaging tone in interactions regarding PA and found that it could motivate older adults when adequately personalized [ ]. Similarly, a study in children also found that they desired the option to personalize the avatar, including controlling and customizing its physical appearance during game play when exercising [ ].
The Proteus effect is a phenomenon wherein individuals embody and emulate the behaviors of their virtual characters such as avatars [, ]. A few studies demonstrated support for the Proteus effect when it came to PA behaviors, although the type of avatar varied. A study in Taiwan found that younger looking avatars were associated with higher levels of PA than older looking avatars but only in women. Male participants had higher levels of PA than female participants who used an older looking avatar, highlighting differences between sexes [ ]. A further study found a higher cardiac output resulting from increased intensity of PA in adult users of an avatar that resembled them and wore gym clothes when compared with avatars that appeared unfamiliar like strangers in regular clothing, which reduced heart rate [ ]. Similarly, a study in Taiwan found increases in physical activity assessed in movements (986.7 points higher) in users of a “normal avatar”, more closely resembling them than the most muscular avatar [ ]. They also found that self-efficacy was higher (0.66 points) for core muscle exercises in female participants assigned to normal avatars relative to their muscular counterparts and male participants assigned to the same standard avatar (0.9 points higher), with P<.05 [ ]. Similarly, dietary behavior was also shaped by thinner embodied avatars in another study [ ].
Most diabetes studies were feasibility studies. The results of diabetes conversational coaches were mixed. A few studies did not have positive findings concerning the applications with avatars for diabetes [, ]. However, one study reported a usability score of 73, which is relatively high. Notably, the study integrated a range of BCTs, including goal setting, feedback, self-monitoring, social support, and counseling [ ]. Low usability scores were reported in a few studies, including one that reported an overall score of 44.58 (SD 21.18) [ ]. Similarly, an RCT of a diabetes coaching avatar did not find that knowledge increased relative to controls, but intervention participants in the computer-based programmed dynamic avatar had higher satisfaction levels (F4=3.11; P=.01) [ ]. Another study in the United States in participants with prediabetes found that 60% of patients had successfully completed the modules and met weight targets during 6 months of use (60% success rate) [ ]. Engagement was also moderately high (50%) in a study in Singapore involving a chatbot, although usability was high along with retention (93%) [ ]. In a study in teenagers, attrition was also low, and 80% of the participants were satisfied with the conversational diabetes coach [ ]. A study that evaluated a coaching application for diabetes found an improvement of –11 mg/dL in fasting blood glucose levels [ ]. However, the intervention also involved phone calls from health professionals [ ]. Similarly, an avatar application with a ketogenic diet program assisted with a reduction in hemoglobin A1c levels of 0.69% (SE 0.168%) [ ]. Qualitative research found that avatars created an environment of social presence that facilitated social support and coherence for patients with diabetes [ ]. In another study of avatars combined with robots, children preferred robots over avatars, but their friendship increased if the two had a greater similarity, which impacted usability [ ].
CVD and Associated Risk Factors
A few studies evaluated the use of conversational coaches for CVD. One of them was a pilot study of the Tensiobot chatbot , a coaching application that teaches users how to properly check their blood pressure using recommended practice guidelines and provides users with graphic feedback and reminders. The study found that the chatbot group did not differ from the control group in terms of adhering to blood pressure measurement recommendations. However, there were significantly higher levels of knowledge (+6.53 points) with regard to checking blood pressure in the chatbot group than in the control group (P<.05) [ ]. Blood pressure (diastolic) was significantly reduced, that is, by 1.43 mm Hg (SE 0.65; 95% CI –2.72 to –0.14; P<.01), in users of an avatar application that also involved a ketogenic diet [ ]. In addition, a mixed methods study with a qualitative component found that users in general were interested in trying a hypertension chatbot for medication management as well as for health communication and self-care [ ]. In addition to these studies, a general diet and PA chatbot study evaluated changes in blood pressure, but these changes were nonsignificant [ ]. A study in Poland found high desirability for a CVD voice technology coach, in addition to accessing phone-based telemedical services by health professionals [ ]. A further study in Brazil evaluated avatars for cardiovascular rehabilitation and found that an avatar with an exergame influenced heart rate, systolic blood pressure, and respiratory rate during the intervention and up to 5 minutes after its completion [ ]. Furthermore, a study found that the avatar intervention increased the intent to improve lifestyle behavioral risk factors in patients relative to controls (P=.01), although confidence did not change [ ]. Finally, a study evaluated a cardiovascular educational avatar application and found that it increased symptom recognition by 24% and knowledge of CVD by 15%, with a high satisfaction rate of 87% among patients [ ].
Several studies found that users were interested in using conversational coaches for lifestyle behaviors [, , ]. Overall, participants enjoyed using the chatbots and avatars or found them helpful for diet, exercise, and hypertension management [ , , , , ]. User-friendliness was reported by 83% of the participants in a study that evaluated a PA social media chatbot [ ]. Similarly, 87.5% of women in a weight loss avatar intervention found it helpful [ ]. With the exception of studies on diabetes conversational coaches, adherence or completion of tasks was high across studies on lifestyle (diet and PA) conversational coaches, ranging from 85% to 90% [ , , , ]. The qualitative study themes were related to the desirability for a conversational coach for hypertension and weight-related behaviors, especially for one that simulates human interaction closely, provides advice and goals for meals when cooking, and provides educational support [ ] including for hypertension management [ , ].
A few tech challenges were brought up across the studies. Although users found that the conversational coach answered basic questions correctly, failure to understand and respond to more complex or spontaneous questions was reported in the studies. The percentage of failure for spontaneous or complex questions was 79% in one study , and participants in another study gave a high ranking for the chatbot’s failure to recognize their input [ ]. Paola chatbot correctly answered spontaneous questions on diet in 4 out of 20 attempts, with a success of 20%, while the percentage of correctly answered simple and predetermined questions and responses was 96% and 97% [ ].
This review aimed to better understand the effectiveness of virtual coaches for managing metabolic health and weight-related risk factors. It appears that virtual coaches hold potential for assisting patients with improving their dietary intake and PA behaviors, leading to subsequent weight loss. However, more studies that are larger and sufficiently powered RCTs are needed to establish a stronger evidence base. RCTs are the gold standard of evidence but are often costly and time-consuming [, ]. Most of the studies were limited, as they were pilot studies. Ideally, it would be of interest to research long-term weight changes and cardiometabolic risk factor modifications over longer periods.
It appears that PA interventions may benefit from using avatars that embody the participant. The Proteus effect is based on the hypothesis that users adjust their behavior by modeling the virtual character with which they interact . Thus, it seems that incorporating an avatar may enhance mHealth chatbot interventions, as it adds the element of user interaction and promotes the modeling of behavior through embodiment [ ]. However, 1 (2%) study did not find that the avatars enhanced the effects of the web-based intervention [ ].
We also found that consumers seemed to be interested in and enthusiastic about trying virtual coaches for managing their weight-related behaviors and blood pressure. Adherence to the intervention was also high throughout the studies, which indicates that this technology is acceptable and usable for patients. However, there is a need to undertake qualitative research on developing a MetS coach to further understand consumer perspectives. The main barrier to consider when developing future virtual agents is that the virtual agents did not always answer correctly to spontaneous responses. As consumers want personalized and tailored mHealth for weight-related behaviors , future applications should ensure that the virtual agents are sufficiently advanced to be able to interact with users in a natural and personalized manner.
It appears that diabetes virtual coaches should be improved to maximize engagement and adherence, as not all studies found that they were helpful. Although outside the scope of this review, we note that some studies used BCTs, which could suggest that future applications may benefit from integrating BCTs [, , , , , , ]. In addition, we identified some studies on blood pressure and CVD management, which demonstrated preliminary improvement in patients with hypertension as well as knowledge of CVD. However, we did not identify any virtual coaches for managing MetS. Therefore, there is a need to develop virtual coaches specifically tailored to this syndrome and its associated risk factors. Such virtual coaches could be integrated into a combined synchronized application that involves diabetes and CVD education and monitoring.
MetS is linked with high blood pressure, which is one of the main hallmarks of the disease. The theoretical mechanisms underpinning the development of hypertension in patients with MetS have included a combination of endothelial dysfunction, systemic inflammation, adiposity, and oxidative stress . Dysfunction in the renin-angiotensin system has also been theorized to be a determinant [ ]. Obesity itself has also been identified as a risk factor for high blood pressure in MetS [ ]. Blood pressure is modifiable to some extent through lifestyle changes previously described, including dietary sodium restriction, PA, stress reduction [ ], and medication [ ]. Future virtual coaches may target hypertension as part of a MetS intervention, and this review found that patients are willing to try chatbots for managing their blood pressure.
MetS is also associated with high glucose levels of at least 100 mg/dL when patients are fasting , which indicates that they are in the prediabetes stage, as diabetes begins at fasting glucose levels of 126 mg/dL [ ]. In a recent longitudinal study, patients who reduced their fasting blood glucose levels decreased their overall risk of diabetes by 54% when compared with their counterparts who did not improve their blood sugar levels (95% CIs exclude 1) [ ]. A recent study found that individuals who consumed high amounts of sugar were 32% more likely to have MetS than their counterparts [ ]. Thus, a future MetS virtual coach could target blood glucose monitoring and offer personalized advice on optimum sugar intake.
In addition to targeting dietary intake, PA is integral to managing this syndrome. A meta-analysis found that the risk of cardiovascular events was reduced by 30% in physically active individuals compared with those who were inactive . A longitudinal study in middle-aged women found that increasing step counts significantly reduced, by 30%, the risk of MetS in this population and that they had clinically improved levels of the protective cholesterol high-density lipoprotein, whereas their serum triglycerides had significantly decreased [ ]. A review found that walking on a daily basis reduced the risk of type 2 diabetes by nearly half [ ]. Furthermore, recent research suggests that sedentary behavior, including sitting time, is an independent and significant risk factor for MetS syndrome [ ]. Thus, PA chatbots and avatars, which were found to increase PA time, steps, and self-efficacy in this review, could be integrated into a comprehensive future MetS interventions.
Given that chatbots and avatars hold potential for increasing PA and reducing sedentary behavior, as well as improving dietary intake, studies are needed to evaluate their effectiveness for managing the symptoms and risk factors associated with MetS specifically.
In addition, stress is often an underlying determinant of maladaptive weight-related behaviors, including binge eating, emotional eating, and an unhealthy dietary intake as well as weight gain [- ]. Future avatar and chatbot interventions for cardiometabolic factors could also consider integrating psychological supportive interventions such as mindfulness-based stress reduction, which assists with weight and stress [ - ], as an element.
In summary, we found that virtual coaches hold promise for regulating diet, PA, weight, and possibly hypertension. However, studies on virtual coaches are few in number; therefore, more research, including RCTs, is needed to confirm the effectiveness of virtual coaches. Overall, most participants in the reviewed studies were interested in using virtual coaches, including chatbots and avatars, for regulating their weight-related behaviors, and study adherence was good. Future interventions could be ameliorated to reduce technical challenges associated with these conversational agents and ensure that they respond correctly to complex and spontaneous questions. Furthermore, future research could involve developing a comprehensive conversational agent for MetS, such as a health coach that simultaneously targets diet (sodium, sugar, and fat intake), exercise, weight (including abdominal obesity), blood pressure, and diabetes, and evaluating it. This would include a health coach that simultaneously targets diet (sodium, sugar, and fat intake), exercise, weight (including abdominal obesity), blood pressure, and diabetes.
Conflicts of Interest
- Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis 2016;5:2048004016633371 [FREE Full text] [CrossRef] [Medline]
- About metabolic syndrome. American Heart Association. 2021. URL: https://wwwheartorg/en/health-topics/metabolic-syndrome/about-metabolic-syndrome [accessed 2022-01-10]
- Wiklund P. The role of physical activity and exercise in obesity and weight management: time for critical appraisal. J Sport Health Sci 2016 Jun;5(2):151-154 [FREE Full text] [CrossRef] [Medline]
- Hegde SM, Solomon SD. Influence of physical activity on hypertension and cardiac structure and function. Curr Hypertens Rep 2015 Oct;17(10):77 [FREE Full text] [CrossRef] [Medline]
- Hamasaki H. Daily physical activity and type 2 diabetes: a review. World J Diabetes 2016 Jun 25;7(12):243-251 [FREE Full text] [CrossRef] [Medline]
- Li J, Siegrist J. Physical activity and risk of cardiovascular disease--a meta-analysis of prospective cohort studies. Int J Environ Res Public Health 2012 Feb;9(2):391-407 [FREE Full text] [CrossRef] [Medline]
- Hoyas I, Leon-Sanz M. Nutritional challenges in metabolic syndrome. J Clin Med 2019 Aug 24;8(9):1301 [FREE Full text] [CrossRef] [Medline]
- Lyzwinski LN. A systematic review and meta-analysis of mobile devices and weight loss with an intervention content analysis. J Pers Med 2014 Jun 30;4(3):311-385 [FREE Full text] [CrossRef] [Medline]
- Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013 Aug;46(1):81-95 [FREE Full text] [CrossRef] [Medline]
- mHealth: new horizons for health through mobile technologie. World Health Organization Global Observatory for eHealth. 2011. URL: http://apps.who.int/iris/bitstream/handle/10665/44607/9789241564250_eng.pdf?sequence=1 [accessed 2022-01-10]
- Lefebvre C. Integrating cell phones and mobile technologies into public health practice: a social marketing perspective. Health Promot Pract 2009 Oct;10(4):490-494. [CrossRef] [Medline]
- Griffin AC, Xing Z, Khairat S, Wang Y, Bailey S, Arguello J, et al. Conversational agents for chronic disease self-management: a systematic review. AMIA Annu Symp Proc 2020;2020:504-513 [FREE Full text] [Medline]
- Maher CA, Davis CR, Curtis RG, Short CE, Murphy KJ. A physical activity and diet program delivered by artificially intelligent virtual health coach: proof-of-concept study. JMIR Mhealth Uhealth 2020 Jul 10;8(7):e17558 [FREE Full text] [CrossRef] [Medline]
- LeRouge C, Dickhut K, Lisetti C, Sangameswaran S, Malasanos T. Engaging adolescents in a computer-based weight management program: avatars and virtual coaches could help. J Am Med Inform Assoc 2016 Jan;23(1):19-28 [FREE Full text] [CrossRef] [Medline]
- Virtual trainer. Charamel. 2021. URL: https://wwwcharamelcom/en/case-studies/virtual-trainer [accessed 2022-09-01]
- Davis CR, Murphy KJ, Curtis RG, Maher CA. A process evaluation examining the performance, adherence, and acceptability of a physical activity and diet artificial intelligence virtual health assistant. Int J Environ Res Public Health 2020 Dec 07;17(23):9137 [FREE Full text] [CrossRef] [Medline]
- Echeazarra L, Pereira J, Saracho R. TensioBot: a chatbot assistant for self-managed in-house blood pressure checking. J Med Syst 2021 Mar 15;45(4):54. [CrossRef] [Medline]
- Fadhil A, Wang Y, Reiterer H. Assistive conversational agent for health coaching: a validation study. Methods Inf Med 2019 Jun;58(1):9-23. [CrossRef] [Medline]
- Griffin AC, Xing Z, Mikles SP, Bailey S, Khairat S, Arguello J, et al. Information needs and perceptions of chatbots for hypertension medication self-management: a mixed methods study. JAMIA Open 2021 Apr;4(2):ooab021 [FREE Full text] [CrossRef] [Medline]
- Larbi D, Gabarron E, Denecke K. Social media chatbot for increasing physical activity: usability study. Stud Health Technol Inform 2021 Oct 27;285:227-232. [CrossRef] [Medline]
- Lin JT, Wu D, Yang J. Exercising with a six pack in virtual reality: examining the proteus effect of avatar body shape and sex on self-efficacy for core-muscle exercise, self-concept of body shape, and actual physical activity. Front Psychol 2021;12:693543 [FREE Full text] [CrossRef] [Medline]
- Napolitano MA, Hayes S, Russo G, Muresu D, Giordano A, Foster GD. Using avatars to model weight loss behaviors: participant attitudes and technology development. J Diabetes Sci Technol 2013 Jul 01;7(4):1057-1065 [FREE Full text] [CrossRef] [Medline]
- Navarro J, Cebolla A, Llorens R, Borrego A, Baños RM. Manipulating self-avatar body dimensions in virtual worlds to complement an internet-delivered intervention to increase physical activity in overweight women. Int J Environ Res Public Health 2020 Jun 05;17(11):4045 [FREE Full text] [CrossRef] [Medline]
- Navarro J, Peña J, Cebolla A, Baños R. Can avatar appearance influence physical activity? User-avatar similarity and proteus effects on cardiac frequency and step counts. Health Commun 2022 Feb;37(2):222-229. [CrossRef] [Medline]
- Stein N, Brooks K. A fully automated conversational artificial intelligence for weight loss: longitudinal observational study among overweight and obese adults. JMIR Diabetes 2017 Nov 01;2(2):e28 [FREE Full text] [CrossRef] [Medline]
- Stephens TN, Joerin A, Rauws M, Werk LN. Feasibility of pediatric obesity and prediabetes treatment support through Tess, the AI behavioral coaching chatbot. Transl Behav Med 2019 May 16;9(3):440-447. [CrossRef] [Medline]
- Tammy Lin JH, Wu DY. Exercising with embodied young avatars: how young vs. older avatars in virtual reality affect perceived exertion and physical activity among male and female elderly individuals. Front Psychol 2021 Oct 25;12:693545 [FREE Full text] [CrossRef] [Medline]
- Thomas JG, Spitalnick JS, Hadley W, Bond DS, Wing RR. Development of and feedback on a fully automated virtual reality system for online training in weight management skills. J Diabetes Sci Technol 2015 Jan;9(1):145-148 [FREE Full text] [CrossRef] [Medline]
- Thompson DI, Cantu D, Callender C, Liu Y, Rajendran M, Rajendran M, et al. Photorealistic avatar and teen physical activity: feasibility and preliminary efficacy. Games Health J 2018 Apr;7(2):143-150 [FREE Full text] [CrossRef] [Medline]
- Ahn SJ, Johnsen K, Ball C. Points-based reward systems in gamification impact children's physical activity strategies and psychological needs. Health Educ Behav 2019 Jun;46(3):417-425 [FREE Full text] [CrossRef] [Medline]
- Alves da Cruz MM, Ricci-Vitor AL, Bonini Borges GL, Fernanda da Silva P, Ribeiro F, Marques Vanderlei LC. Acute hemodynamic effects of virtual reality-based therapy in patients of cardiovascular rehabilitation: a cluster randomized crossover trial. Arch Phys Med Rehabil 2020 Apr;101(4):642-649. [CrossRef] [Medline]
- Balsa J, Félix I, Cláudio AP, Carmo MB, Silva IC, Guerreiro A, et al. Usability of an intelligent virtual assistant for promoting behavior change and self-care in older people with type 2 diabetes. J Med Syst 2020 Jun 13;44(7):130. [CrossRef] [Medline]
- Behm-Morawitz E, Lewallen J, Choi G. A second chance at health: how a 3D virtual world can improve health self-efficacy for weight loss management among adults. Cyberpsychol Behav Soc Netw 2016 Feb;19(2):74-79. [CrossRef] [Medline]
- Bickmore TW, Schulman D, Sidner C. Automated interventions for multiple health behaviors using conversational agents. Patient Educ Couns 2013 Aug;92(2):142-148 [FREE Full text] [CrossRef] [Medline]
- Chin J, Quinn K, Muramatsu N, Marquez D. A user study on the feasibility and acceptance of delivering physical activity programs to older adults through conversational agents. Proc Hum Factors Ergon Soc Annu Meet 2020 Dec;64(1):33-37 [FREE Full text] [CrossRef] [Medline]
- Dhinagaran DA, Sathish T, Soong A, Theng Y, Best J, Tudor Car L. Conversational agent for healthy lifestyle behavior change: web-based feasibility study. JMIR Form Res 2021 Dec 03;5(12):e27956 [FREE Full text] [CrossRef] [Medline]
- Dol A, Bode C, Velthuijsen H, van Strien T, van Gemert-Pijnen L. Application of three different coaching strategies through a virtual coach for people with emotional eating: a vignette study. J Eat Disord 2021 Jan 14;9(1):13 [FREE Full text] [CrossRef] [Medline]
- Duncan-Carnesciali J, Wallace BC, Odlum M. An evaluation of a diabetes self-management education (DSME) intervention delivered using avatar-based technology: certified diabetes educators' ratings and perceptions. Diabetes Educ 2018 Jun;44(3):216-224. [CrossRef] [Medline]
- Hahn L, Rathbun SL, Schmidt MD, Johnsen K, Annesi JJ, Ahn SJ. Using virtual agents and activity monitors to autonomously track and assess self-determined physical activity among young children: a 6-week feasibility field study. Cyberpsychol Behav Soc Netw 2020 Jul;23(7):471-478 [FREE Full text] [CrossRef] [Medline]
- Hickman RL, Clochesy JM, Alaamri M. Effects of an eHealth intervention on patient-provider interaction and functional health literacy in adults with hypertension. SAGE Open Nurs 2021;7:23779608211005863 [FREE Full text] [CrossRef] [Medline]
- Johnson-Glenberg MC, Hekler EB. "Alien health game": an embodied exergame to instruct in nutrition and MyPlate. Games Health J 2013 Dec;2(6):354-361. [CrossRef] [Medline]
- Klaassen R, Bul KC, Op den Akker R, van der Burg GJ, Kato PM, Di Bitonto P. Design and evaluation of a pervasive coaching and gamification platform for young diabetes patients. Sensors (Basel) 2018 Jan 30;18(2):402 [FREE Full text] [CrossRef] [Medline]
- Kowalska M, Gładyś A, Kalańska-Łukasik B, Gruz-Kwapisz M, Wojakowski W, Jadczyk T. Readiness for voice technology in patients with cardiovascular diseases: cross-sectional study. J Med Internet Res 2020 Dec 17;22(12):e20456 [FREE Full text] [CrossRef] [Medline]
- Krishnakumar A, Verma R, Chawla R, Sosale A, Saboo B, Joshi S, et al. Evaluating glycemic control in patients of south Asian origin with type 2 diabetes using a digital therapeutic platform: analysis of real-world data. J Med Internet Res 2021 Mar 25;23(3):e17908 [FREE Full text] [CrossRef] [Medline]
- Kuo H, Lee C, Chiou W. The power of the virtual ideal self in weight control: weight-reduced avatars can enhance the tendency to delay gratification and regulate dietary practices. Cyberpsychol Behav Soc Netw 2016 Feb;19(2):80-85. [CrossRef] [Medline]
- Li BJ, Lwin MO, Jung Y. Wii, myself, and size: the influence of proteus effect and stereotype threat on overweight children's exercise motivation and behavior in exergames. Games Health J 2014 Feb;3(1):40-48. [CrossRef] [Medline]
- Mestre DR, Ewald M, Maiano C. Virtual reality and exercise: behavioral and psychological effects of visual feedback. Stud Health Technol Inform 2011;167:122-127. [Medline]
- Mitchell S, Bragg A, Gardiner P, De La Cruz B, Laird L. Patient engagement and presence in a virtual world world diabetes self-management education intervention for minority women. Patient Educ Couns 2022 Apr;105(4):797-804 [FREE Full text] [CrossRef] [Medline]
- Napolitano MA, Harrington CB, Patchen L, Ellis LP, Ma T, Chang K, et al. Feasibility of a digital intervention to promote healthy weight management among postpartum African American/Black women. Int J Environ Res Public Health 2021 Feb 23;18(4):2178 [FREE Full text] [CrossRef] [Medline]
- Naylor JB, Patton BJ, Barkley JE. VO2, liking, and relative reinforcing value of cooperative and competitive exergame play in young children. Int J Exerc Sci 2020;13(5):1501-1511 [FREE Full text] [Medline]
- Piao M, Ryu H, Lee H, Kim J. Use of the healthy lifestyle coaching chatbot app to promote stair-climbing habits among office workers: exploratory randomized controlled trial. JMIR Mhealth Uhealth 2020 May 19;8(5):e15085 [FREE Full text] [CrossRef] [Medline]
- Ruiz JG, Andrade AD, Anam R, Aguiar R, Sun H, Roos BA. Using anthropomorphic avatars resembling sedentary older individuals as models to enhance self-efficacy and adherence to physical activity: psychophysiological correlates. Stud Health Technol Inform 2012;173:405-411. [Medline]
- Ruiz JG, Andrade AD, Anam R, Lisigurski M, Karanam C, Sharit J. Computer-based programmed instruction did not improve the knowledge retention of medication instructions of individuals with type 2 diabetes mellitus. Diabetes Educ 2014;40(1):77-88. [CrossRef] [Medline]
- Ruiz JG, Andrade AD, Karanam C, Krishnamurthy D, Niño L, Anam R, et al. The communication of global cardiovascular risk by avatars. Stud Health Technol Inform 2016;220:341-344. [Medline]
- Santini S, Stara V, Galassi F, Merizzi A, Schneider C, Schwammer S, et al. User requirements analysis of an embodied conversational agent for coaching older adults to choose active and healthy ageing behaviors during the transition to retirement: a cross-national user centered design study. Int J Environ Res Public Health 2021 Sep 14;18(18):9681 [FREE Full text] [CrossRef] [Medline]
- Sinoo C, van der Pal S, Blanson Henkemans OA, Keizer A, Bierman BP, Looije R, et al. Friendship with a robot: children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management. Patient Educ Couns 2018 Jul;101(7):1248-1255. [CrossRef] [Medline]
- Srivastava P, Verma A, Geronimo C, Button TM. Behavior stages of a physician- and coach-supported cloud-based diabetes prevention program for people with prediabetes. SAGE Open Med 2019;7:2050312119841986 [FREE Full text] [CrossRef] [Medline]
- Strombotne KL, Lum J, Ndugga NJ, Utech AE, Pizer SD, Frakt AB, et al. Effectiveness of a ketogenic diet and virtual coaching intervention for patients with diabetes: a difference-in-differences analysis. Diabetes Obes Metab 2021 Dec;23(12):2643-2650 [FREE Full text] [CrossRef] [Medline]
- Thompson D, Callender C, Gonynor C, Cullen KW, Redondo MJ, Butler A, et al. Using relational agents to promote family communication around type 1 diabetes self-management in the diabetes family teamwork online intervention: longitudinal pilot study. J Med Internet Res 2019 Sep 13;21(9):e15318 [FREE Full text] [CrossRef] [Medline]
- Thompson D, Cullen KW, Redondo MJ, Anderson B. Use of relational agents to improve family communication in type 1 diabetes: methods. JMIR Res Protoc 2016 Jul 28;5(3):e151 [FREE Full text] [CrossRef] [Medline]
- To QG, Green C, Vandelanotte C. Feasibility, usability, and effectiveness of a machine learning-based physical activity chatbot: quasi-experimental study. JMIR Mhealth Uhealth 2021 Nov 26;9(11):e28577 [FREE Full text] [CrossRef] [Medline]
- Tongpeth J, Du HY, Clark RA. Development and feasibility testing of an avatar-based education application for patients with acute coronary syndrome. J Clin Nurs 2018 Oct;27(19-20):3561-3571. [CrossRef] [Medline]
- Friederichs S, Bolman C, Oenema A, Guyaux J, Lechner L. Motivational interviewing in a web-based physical activity intervention with an avatar: randomized controlled trial. J Med Internet Res 2014 Feb 13;16(2):e48 [FREE Full text] [CrossRef] [Medline]
- Hahn L, Schmidt MD, Rathbun SL, Johnsen K, Annesi JJ, Ahn SJ. Using virtual agents to increase physical activity in young children with the virtual fitness buddy ecosystem: study protocol for a cluster randomized trial. Contemp Clin Trials 2020 Dec;99:106181 [FREE Full text] [CrossRef] [Medline]
- Brown SJ, Lieberman DA, Germeny BA, Fan YC, Wilson DM, Pasta DJ. Educational video game for juvenile diabetes: results of a controlled trial. Med Inform (Lond) 1997;22(1):77-89. [CrossRef] [Medline]
- Thompson D, Cantu D, Rajendran M, Rajendran M, Bhargava T, Zhang Y, et al. Development of a teen-focused exergame. Games Health J 2016 Oct;5(5):342-356 [FREE Full text] [CrossRef] [Medline]
- Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009 Jul 21;6(7):e1000097 [FREE Full text] [CrossRef] [Medline]
- Aparicio-Ugarriza R, Cuenca-García M, Gonzalez-Gross M, Julián C, Bel-Serrat S, Moreno LA, et al. Relative validation of the adapted Mediterranean diet score for adolescents by comparison with nutritional biomarkers and nutrient and food intakes: the healthy lifestyle in Europe by nutrition in adolescence (HELENA) study. Public Health Nutr 2019 Sep;22(13):2381-2397. [CrossRef] [Medline]
- Stavropoulos V, Pontes HM, Gomez R, Schivinski B, Griffiths M. Proteus effect profiles: how do they relate with disordered gaming behaviours? Psychiatr Q 2020 Sep;91(3):615-628. [CrossRef] [Medline]
- Dauchot N. Introduction to the proteus effect. Medium. 2018. URL: https://medium.com/uxxr/the-proteus-effect-aeb46d6dfd86 [accessed 2022-09-01]
- Hariton E, Locascio J. Randomised controlled trials - the gold standard for effectiveness research: study design: randomised controlled trials. BJOG 2018 Dec;125(13):1716 [FREE Full text] [CrossRef] [Medline]
- Bondemark L, Ruf S. Randomized controlled trial: the gold standard or an unobtainable fallacy? Eur J Orthod 2015 Oct;37(5):457-461. [CrossRef] [Medline]
- Yee N, Bailenson J. The proteus effect: the effect of transformed self-representation on behavior. Human Comm Res 2007 Jul;33(3):271-290 [FREE Full text] [CrossRef]
- Lyzwinski LN, Caffery LJ, Bambling M, Edirippulige S. Consumer perspectives on mHealth for weight loss: a review of qualitative studies. J Telemed Telecare 2018 May;24(4):290-302. [CrossRef] [Medline]
- Yanai H, Tomono Y, Ito K, Furutani N, Yoshida H, Tada N. The underlying mechanisms for development of hypertension in the metabolic syndrome. Nutr J 2008 Apr 17;7:10 [FREE Full text] [CrossRef] [Medline]
- Franklin SS. Hypertension in the metabolic syndrome. Metab Syndr Relat Disord 2006;4(4):287-298. [CrossRef] [Medline]
- Sparrenberger F, Cichelero FT, Ascoli AM, Fonseca FP, Weiss G, Berwanger O, et al. Does psychosocial stress cause hypertension? A systematic review of observational studies. J Hum Hypertens 2009 Jan;23(1):12-19. [CrossRef] [Medline]
- Nguyen Q, Dominguez J, Nguyen L, Gullapalli N. Hypertension management: an update. Am Health Drug Benefits 2010 Jan;3(1):47-56 [FREE Full text] [Medline]
- Swarup S, Goyal A, Grigorova Y, Zeltser R. Metabolic syndrome. In: StatPearls. Treasure Island, FL, USA: StatPearls Publishing; 2021.
- Lee M, Han K, Kim MK, Koh ES, Kim ES, Nam GE, et al. Changes in metabolic syndrome and its components and the risk of type 2 diabetes: a nationwide cohort study. Sci Rep 2020 Feb 11;10(1):2313 [FREE Full text] [CrossRef] [Medline]
- Seo EH, Kim H, Kwon O. Association between total sugar intake and metabolic syndrome in middle-aged Korean men and women. Nutrients 2019 Sep 01;11(9):2042 [FREE Full text] [CrossRef] [Medline]
- Zając-Gawlak I, Pelclová J, Groffik D, Přidalová M, Nawrat-Szołtysik A, Kroemeke A, et al. Does physical activity lower the risk for metabolic syndrome: a longitudinal study of physically active older women. BMC Geriatr 2021 Jan 06;21(1):11 [FREE Full text] [CrossRef] [Medline]
- Bankoski A, Harris TB, McClain JJ, Brychta RJ, Caserotti P, Chen KY, et al. Sedentary activity associated with metabolic syndrome independent of physical activity. Diabetes Care 2011 Feb;34(2):497-503 [FREE Full text] [CrossRef] [Medline]
- Moyer AE, Rodin J, Grilo CM, Cummings N, Larson LM, Rebuffé-Scrive M. Stress-induced cortisol response and fat distribution in women. Obes Res 1994 May;2(3):255-262 [FREE Full text] [CrossRef] [Medline]
- Kandiah J, Yake M, Jones J, Meyer M. Stress influences appetite and comfort food preferences in college women. Nutr Res 2006 Mar;26(3):118-123 [FREE Full text] [CrossRef]
- Torres SJ, Nowson CA. Relationship between stress, eating behavior, and obesity. Nutrition 2007;23(11-12):887-894. [CrossRef] [Medline]
- Wichianson JR, Bughi SA, Unger JB, Spruijt-Metz D, Nguyen-Rodriguez ST. Perceived stress, coping and night-eating in college students. Stress Med 2009 Aug;25(3):235-240 [FREE Full text] [CrossRef]
- Yau YH, Potenza MN. Stress and eating behaviors. Minerva Endocrinol 2013 Sep;38(3):255-267 [FREE Full text] [Medline]
- Chiesa A, Malinowski P. Mindfulness-based approaches: are they all the same? J Clin Psychol 2011 Apr;67(4):404-424. [CrossRef] [Medline]
- Chiesa A, Serretti A. Mindfulness-based stress reduction for stress management in healthy people: a review and meta-analysis. J Altern Complement Med 2009 May;15(5):593-600. [CrossRef] [Medline]
- Labee EE. Psychology Moment by Moment: A Guide to Enhancing Your Clinical Practice with Mindfulness and Meditation. Oakland, CA, USA: New Harbinger Publications; 2011.
- O'Reilly GA, Cook L, Spruijt-Metz D, Black DS. Mindfulness-based interventions for obesity-related eating behaviours: a literature review. Obes Rev 2014 Jun;15(6):453-461 [FREE Full text] [CrossRef] [Medline]
- Olson KL, Emery CF. Mindfulness and weight loss: a systematic review. Psychosom Med 2015 Jan;77(1):59-67. [CrossRef] [Medline]
|BCT: behavior change technique|
|CVD: cardiovascular disease|
|MetS: metabolic syndrome|
|mHealth: mobile health|
|PA: physical activity|
|RCT: randomized controlled trial|
|RQ: research question|
Edited by L Buis, M Dorsch; submitted 16.05.22; peer-reviewed by N Maglaveras, YC Wang; comments to author 12.08.22; revised version received 04.10.22; accepted 23.12.22; published 25.05.23Copyright
©Lynnette Nathalie Lyzwinski, Mohamed Elgendi, Carlo Menon. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 25.05.2023.
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