The Effectiveness of eHealth Interventions on Lifestyle Modification in Patients With Nonalcoholic Fatty Liver Disease: Systematic Review and Meta-analysis

Abstract

Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing in parallel with the epidemic of obesity and metabolic syndrome. Lifestyle modification is a crucial strategy for the treatment of NAFLD, which can lead to a reduction in liver fat with concomitant weight loss. The use of eHealth technologies is an effective approach to improve health outcomes in patients as they do not have any time and space limitations.

Objective: This study aimed to evaluate published eHealth intervention studies for the improvement of lifestyle modifications among patients with NAFLD and to provide recommendations for future studies.

Methods: We conducted a systematic review and meta-analysis. Five electronic databases (PubMed, Cochrane Central, CINAHL, Embase, and Web of Science) were searched for studies reporting the effect of lifestyle modification intervention using eHealth in patients with NAFLD published from inception to November 3, 2022. Study selection, data extraction, and quality assessment were performed by 3 researchers independently. The quality of included studies was assessed using the Cochrane risk of bias tool and the Risk of Bias Assessment Tool for Nonrandomized Studies.

Results: In total, 2688 records were identified, and 41 full-text articles were assessed. Seven studies were included in the systematic review. The participants of all interventions were 1257 individuals with NAFLD, and the mean age ranged from 38.3 to 57.9 years. The duration of the interventions was 3-24 months, and all interventions were categorized into 3 types: internet-based computers, telephones, and mobile apps. Of these, 4 studies were randomized controlled trials and were included in the meta-analysis: 3 studies for body weight and BMI and 4 studies for alanine aminotransferase (ALT) and aspartate aminotransferase (AST). According to the meta-analysis, clear improvements in BMI (P=.02; 95% CI –1.01 to –0.10), AST (P=.02; 95% CI –1.22 to –0.13), and ALT (P=.01; 95% CI –1.28 to –0.15) were observed in the eHealth intervention as compared with the control groups.

Conclusions: Lifestyle modification interventions using eHealth technologies are significantly effective for BMI, AST, and ALT in patients with NAFLD. Future research should conduct interventions with larger sample sizes and evaluate whether these interventions have sustained benefits, and how we can make these eHealth methods most effective on a large scale.

Introduction

Background

Nonalcoholic fatty liver disease (NAFLD) is a growing common cause of chronic disease. The global prevalence of NAFLD, which is associated with metabolic syndrome or other chronic diseases such as diabetes, hyperlipidemia, and hypertension, is approximately 33% [1]. NAFLD is historically defined as the presence of > 5% hepatic steatosis, excluding secondary causes of hepatic fat accumulation, including excessive alcohol consumption, steatogenic medications, or hereditary disorders [2]. Clinically, NAFLD encompasses a broad spectrum of liver conditions ranging from simple steatosis or steatohepatitis to hepatic fibrosis, which may lead to cirrhosis, end-stage liver disease, or hepatocellular carcinoma [3].

NAFLD is usually asymptomatic in the majority of patients and diagnosed by imaging or histology. Since NAFLD is a progressive disease [4], patients with NAFLD have an increased risk of liver-related complications if not managed. They can develop steatohepatitis, liver fibrosis, cirrhosis, and other related complications, including variceal bleeding, ascites, hepatorenal syndrome, hepatic encephalopathy, and hepatocellular carcinoma [5,6]. In addition, if patients experience these complications, it might result in an increased mortality. However, currently, there is no approved pharmacological agent for treating NAFLD, and therefore, its sustainable management is very important.

The treatment of NAFLD has focused on lifestyle modifications related to dietary habits and physical activity leading to body weight loss [2,7]. The current guidelines recommend achieving 7%-10% weight loss as a goal for improving NAFLD conditions [2,8].

Previous studies have conducted lifestyle interventions of various durations and strategies, and several studies have reported positive effects [9-11]. However, despite these considerable efforts, changing one’s lifestyle behavior is not easy, and the majority of patients still have not reached the treatment goal, with some difficulties related to sustainability of compliance, proper social support, or constraints on time and place [12-14]. Similar to the COVID-19 pandemic, when on-site medical treatment with clinical health professionals is restricted, a more effective strategy is needed to improve patients’ health outcomes.

eHealth is defined as health services and information delivered or enhanced through the internet and related technologies [15]. As an innovative health care delivery alternative to meet the increasing demand for long-term care of chronic conditions and health care costs [16], eHealth has been presented as a progressive strategy in various fields. Meta-analyses indicate the positive effects of eHealth services on patients’ health outcomes [17,18]. This is encouraging evidence that eHealth is a beneficial approach to facilitate the modification of unhealthy behavior in patients with NAFLD. However, many eHealth interventions have reported some limitations among patients with other chronic diseases [19], and only a few studies have reported the effectiveness of lifestyle interventions for NAFLD management. Therefore, health professionals may help identify the effects of eHealth interventions and make decisions regarding intervention development and application [20].

Objective

This systematic review aimed to evaluate the effectiveness of lifestyle modification interventions, using eHealth, in experimental trials involving adults with NAFLD. Additionally, the review compares these eHealth interventions with usual care in terms of key clinical outcome variables.

Methods

Protocol and Registration

This systematic review and meta-analysis was conducted using the Cochrane Handbook for Systematic Reviews of Interventions and was reported following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement guidelines [21]. This review was registered in the International Prospective Register of Systematic Reviews (registration number: CRD42021261553). All quantitative studies reporting the use of eHealth interventions were included. Studies were excluded if they did not report outcome measures or were reviews, commentaries, or qualitative studies.

Data Sources and Search Strategy

We conducted a systematic literature search of the PubMed, Cochrane Central, Embase, CINAHL, and Web of Science databases on November 3, 2022. The search strategy included the following keywords as Medical Subject Headings (MeSH) or Embase Subject Headings terms: (“non-alcoholic fatty liver”) AND (“lifestyle modification” OR “diet” OR “exercise” OR “weight loss” OR “body mass index”) AND (“eHealth”). For example, the search strategy in PubMed is presented in Textbox 1. Multimedia Appendix 1 provides the search strategy in all databases.

An example of the search strategy in PubMed.

Nonalcoholic fatty liver

“non-alcoholic fatty liver” [MeSH Terms] OR “non-alcoholic fatty liver” OR “nonalcoholic fatty liver” OR “non-alcoholic fatty liver” OR “NAFLD” OR “nonalcoholic steatohepatitis” OR “NASH” AND “fatty liver” [MeSH Terms] OR “fatty liver” OR “hepatosteatosis” OR “hepatic steatosis.”

Diet, exercise, weight loss, BMI, and lifestyle modification

“diet” [MeSH Terms] OR “diet” OR “nutrition” [MeSH Terms] OR “nutrition” OR “food intake” [MeSH Terms] OR “food intake” OR “exercise” [MeSH Terms] OR “exercise” OR “physical activity” OR “weight loss” [MeSH Terms] OR “weight loss” OR “body weight” [MeSH Terms] OR “body weight” OR “body weight change” [MeSH Terms] OR “body weight change” OR “lifestyle modification” OR “lifestyle change” OR “body mass index” [MeSH Terms] OR “body mass index” OR “BMI.”

eHealth

“telemedicine” [MeSH Terms] OR “telemedicine” OR “mhealth” OR “mobile health units” [MeSH Terms] OR “mobile health units” OR “cell phone” [MeSH Terms] OR “cell phone” OR “smartphone” [MeSH Terms] OR “smartphone” OR “satellite phone” OR “computers, handheld” [MeSH Terms] OR “computers, handheld” OR “tablet computer” OR “mobile application” OR “mobile app” OR ”apps” OR “eHealth” OR “computers” [MeSH Terms] OR “computers” [All Fields] OR “internet-based intervention” [MeSH Terms] OR “internet-based intervention” OR “internet” OR “internet-based” OR “web-based” OR “text messaging” [MeSH Terms] OR “text messaging” OR “telehealth.”

Textbox 1. An example of the search strategy in PubMed.

Eligibility Criteria

We included all interventional studies published from inception to November 3, 2022, limited to peer-reviewed articles that were written in English and conducted among humans. We considered the inclusion of literature in this review if it met the following criteria: (1) it dealt with all adults (18 years and older) diagnosed with NAFLD using sonography or transient elastography (FibroScan, Echosens); (2) any eHealth interventions for lifestyle modification related to diet or physical activity, delivered through an electronic device with internet connectivity or wireless capacity, were used; and (3) outcomes were reported as any quantifiable measure for evaluating the effect of lifestyle modification (body weight, BMI, alanine aminotransferase [ALT], or aspartate aminotransferase [AST]).

The exclusion criteria were as follows: (1) if patients with NAFLD were not the main participants, (2) if NAFLD was not the primary diagnosis, (3) if interventions were only targeted at patients with NAFLD, and (4) if only eHealth techniques were used for evaluating the outcomes.

Study Selection

Study citations were imported and compiled into reference management software (Endnote X9.2; Clarivate Analytics) for selection. Studies were screened and selected by 3 reviewers (JC, OYK, and YJ). For the initial search, after removing duplicates, 2 researchers (JC and OYK) independently screened the titles and abstracts of the studies identified from 5 electronic databases to remove irrelevant studies, using eligibility assessment lists. In the second phase, the researchers checked the study types of all the remaining studies, and the full texts of potentially relevant articles were retrieved. Any disagreements or discrepancies between the 2 authors (JC and OYK) regarding the inclusion of studies were resolved through discussion with a third reviewer (YJ).

Data Extraction

Two researchers (JC and OYK) extracted and cross-checked the data independently. The following information was extracted from each selected study: author, year of publication, study location, intervention and control groups (age, sex, and sample size), details about intervention and control (device, intervention and control type, intervention content, and follow-up duration), and relevant outcomes and results. If the studies reported that data were insufficient for meta-analysis, the lacking data were requested from the corresponding authors. All the researchers checked the extracted data for consistency. Discrepancies were resolved through discussions.

Data Synthesis and Analysis

Descriptive analyses of the data were performed, and the findings were synthesized based on the category of outcomes, intervention type and content, and its effect. The primary outcomes of this review were weight, BMI, and liver enzyme levels. When an original study reported only the total sample size of the follow-up data without the number of each control and intervention group, we estimated the sample size of each group using pooled standard deviation formula with pooled SD of each group and 95% CI [22].

Meta-analysis of the extracted data was conducted using the Review Manager of the Cochrane Collaboration (RevMan 5.3, Cochrane Organization). Standardized mean differences (SMDs) and corresponding 95% CIs were computed as between-group differences in means divided by pooled SDs with Hedges correction [23]. The Cochrane Handbook states that the pooled SMDs are equal to the effect size.

Heterogeneity within studies was calculated using the I² heterogeneity degree, wherein 20%, 50%, and 75% were considered indicative of low, medium, and high heterogeneity, respectively [23]. We used a fixed effect model for meta-analyses. If heterogeneity, as measured by I² heterogeneity degree, was greater than 50% and P < .1, we performed random-effects analysis.

Risk of Bias Assessment

The quality of included studies was assessed by using the Cochrane Collaboration Risk of Bias Tool (RoB II) for included randomized controlled trials (RCT) [23]. For nonrandomized controlled studies, the risk of bias assessment tool for nonrandomized studies developed by the Korean National Evidence-Based Healthcare Collaborating Agency was used to evaluate the risk of bias [24]. Two researchers (JY and OYK) independently completed the bias assessment, and any disagreement was resolved through consultation with a third researcher (YJ).

For RCTs, the following criteria were considered: (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported result. Each domain was evaluated using the Cochrane risk ratings of “low,” “high,” and “some concerns.” The criteria considered for the quasi-experimental trial included (1) the selection of participants, (2) confounding variables, (3) the measurement of exposure, (4) blinding of the outcome assessments, (5) incomplete outcome data, and (6) selective outcome reporting. Risk ratings of “low,” “high,” and “unclear” were assigned for each bias.

Results

Study Selection

Our initial search identified 2688 records from 5 relevant database searches (Figure 1). After excluding duplicates, 2270 articles were screened by title and abstract, resulting in the exclusion of a further 2229. We assessed the full text of 41 potentially eligible papers, and 8 of those met the inclusion criteria. Of those, the studies by Pfirrmann et al [25] and Huber et al [26] used the same intervention program and study population, which was taken into account. Seven intervention studies [25,27-32] were consequently included in this review. All included studies were clinical trials: 4 were RCTs [27-30] and 3 were non-RCTs [25,31,32]. All 7 studies were included in the descriptive synthesis. From among these, 4 studies that were conducted using random allocation were included in the meta-analysis.

Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.

Characteristics of Included Studies

The characteristics of the included studies are summarized in Table 1. The studies were published during 2016-2022 in 6 geographical regions (China, Germany, Iran, Italy, Singapore, and the United States).

This review encompassed 1257 participants [25,27-32]; 498 (39.6%) were recruited from gastrointestinal clinics affiliated to a hospital [27-30,32] and 759 (60.4%) were enlisted from either the center of Metabolic Diseases and Clinical Dietetics or Sports Medicine, which serves as the second-level center [25,31]. The average age of the participants in the studies ranged from 38.3 to 57.9 years, and 65.1% (818/1257) were male. One study recruited only male participants [28]. The duration of the intervention ranged from 3 to 24 months. The mean sample size was 180 individuals, the lowest number being 30, and the highest 716.

The majority of participants had NAFLD, regardless of whether they were overweight, obese, or diabetic. However, Lim et al [30] only recruited patients diagnosed with NAFLD who had a BMI ≥ 23 kg/m².

Table 1. Characteristics of the included studies.

Study	Country	Study design	Study participants, sample size (n)	Participants age (years), mean (SD)	Duration (months)
Dong et al [28], 2016	China	RCTa	260 patients with NAFLDb IGc: n=130 CGd: n=130	IG: 57.9 (5.3) CG: 56.7 (5.7)	24
Fard et al [29], 2016	Iran	RCT	60 patients with NAFLD IG: n=30 CG: n=30	IG: 40.3 CG: 38.3	3
Axley et al [27], 2017	United States	RCT	30 patients with NAFLD IG: n=17 CG: n=13	IG: 54 (2.7) CG: 52 (2.3)	6
Lim et al [30], 2020	Singapore	RCT	108 patients with NAFLD IG: n=55 CG: n=53	IG: 46.8 (11.1) CG: 46.2 (10.1)	6
Mazzotti et al [31], 2018	Italy	Non-RCT	716 patients with NAFLD IG: n=278 CG: n=438	IG: 46 (11.5) CG: 55.1 (12.3)	3
Pfirrmann et al [25], 2019	Germany	Non-RCT	43 patients with NAFLD IG: n=43	IG: 43 (10.9)	3
Tincopa et al [32], 2022	United States	Non-RCT	40 patients with NAFLD IG: n=40	IG: 52.5	6

^aRCT: randomized controlled trial.

^bNAFLD: nonalcoholic fatty liver disease.

^cIG: intervention group.

^dCG: control group.

Intervention Characteristics

The characteristics of the interventions are presented in Table 2. Of the included 7 studies, 3 studies [28,29,31] provided professional counseling of diet and physical activity to both intervention and control group. The intervention group additionally administrated a contact-free visit or web-based contact by health professionals for providing health guidance or following up on recommended information. Two interventions [27,30] operated additional intervention components only to the intervention group, while providing standard care for liver disease in the clinic to the control group. Two studies mainly provided intervention on exercise to the intervention group [25], and one of the studies included nutritional assessment [32].

All studies measured changes in body weight, BMI, and liver enzymes [25,27-32]. Five of the studies used weight loss as the main outcome [25,27,28,31,32], and 1 study reported liver enzymes as the primary outcome [29] and 1, a pilot study, assessed drop and completion rates [32]. Six studies measured the body weight of participants by trained personnel using standardized procedures and equipment, but 1 study did not report the measurement methods [32]. Five of the included studies reported that the effect was statistically significant in the intervention group compared to the control group. One study that used a single-arm design showed significant differences in body weight and BMI compared to baseline [25].

Table 2. Characteristics of interventions and their effect.

Study	Description of the study group		Intervention effect
	Intervention group	Control group
Dong et al [28], 2016	Lifestyle counseling for diet and physical activity by 2 professional physicians Phone visit by doctors (10 min) every 3 months, providing health guidance on diet and exercise	Lifestyle counseling for diet and physical activity by 2 professional physicians	Weight: +a (IGb), BMI: + (IG, CGc), ACd: + (IG), ALTe: + (IG), ASTf: −g, NAFLD-FSh: + (IG, CG), liver steatosis grade: + (IG, CG)
Fard et al [29], 2016	Counseling, dietary advice from a nutritionist performing physical activities, face-to-face consultation Telephone call follow-up the recommended diet and physical activities	Counseling, dietary advice from a nutritionist performing physical activities, face-to-face consultation	Weight: + (IG), BMI: + (IG), ALT: + (IG), AST: + (IG)
Axley et al [27], 2017	SMS text messages provided information and education including individual goal, nutrition, exercise, stress management, behavior change, and overcoming barriers every 9 AM	Standard of care for liver disease with detailed instruction in clinic on a healthy diet and daily exercise for weight loss	Weight: + (IG), BMI: + (IG), ALT: + (IG), AST: + (IG), HDLi: −, TGj: + (IG)
Lim et al [30], 2020	Advice on dietary and physical activity modification by a dietician Food diary Self-monitoring of physical activity Real-time feedback and encouragement by a dietitian Peer support chat channel Weekly educational clips	Usual standard care: a single face-to-face session in the clinic	Weight: + (IG), BMI: + (IG), Waist circumference: + (IG, CG), ALT: + (IG), AST: + (IG)
Mazzotti et al [31], 2018	Web-based intervention: group program similar to GBI, divided 4 sessions using a Cloud/SaaS e-learning platform: role-game measuring adherence, motivation to change, and competence by online questionnaires; channel to interact with the clinical center offline	Group-based intervention: group counseling on diet, and habitual physical activity physicians and dietitians	Weight: + (IG), 10% weight loss: −, Waist circumference: + (IG), GGTk: + (IG), ALT: + (IG)
Pfirrmann et al [25], 2019	Web-based platform: Individualized training support (moderate-intensity exercise of 3 sessions per week for 4 weeks, and intensified exercise of 5 sessions per week for the remaining 4 weeks) Regular individual patient feedback Frequent interaction with a counselor and peer support using a discussion board and a chatroom Biweekly group training at the sports center	None	Weight: +, body fat: +
Tincopa et al [32]	Mobile technology-based lifestyle intervention: step count and personalized feedback on physical activity with tailored weekly step count goals using Fitbit, and motivational messages via email Nutrition assessment at baseline and month 3 by phone Overall feedback on physical activity and nutrition over the first 3 months with nutrition assessment at month 3 using phone	None	Weight: −, BMI: −, Liver fat: −, Waist circumference: +, HDL: +, LDL: +, TG: +, HbA1cl: +

^a+: yes.

^bIG: intervention group.

^cCG: control group.

^dAC: abdominal circumference.

^eALT: alanine aminotransferase.

^fAST: aspartate aminotransferase.

^g–: no.

^hNAFLD-FS: nonalcoholic fatty liver disease fibrosis score.

ⁱHDL: high-density lipoprotein.

^jTG: triglyceride.

^kGGT:

References

Lim GEH, Tang A, Ng CH, Chin YH, Lim WH, Tan DJH, et al. An observational data meta-analysis on the differences in prevalence and risk factors between MAFLD vs NAFLD. Clin Gastroenterol Hepatol 2021;S1542-3565(21):01276-3.

Chalasani N, Younossi Z, Lavine JE, Diehl AM, Brunt EM, Cusi K, et al. The diagnosis and management of non-alcoholic fatty liver disease: practice guideline by the American Association for the study of liver diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology 2012;55(6):2005-2023.

Ekstedt M, Nasr P, Kechagias S. Natural history of NAFLD/NASH. Curr Hepatol Rep 2017;16(4):391-397.

McPherson S, Hardy T, Henderson E, Burt AD, Day CP, Anstee QM. Evidence of NAFLD progression from steatosis to fibrosing-steatohepatitis using paired biopsies: implications for prognosis and clinical management. J Hepatol 2015;62(5):1148-1155.

Garcia-Tsao G, Abraldes JG, Berzigotti A, Bosch J. Portal hypertensive bleeding in cirrhosis: risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases. Hepatology 2017;65(1):310-335.

Muthiah MD, Cheng Han N, Sanyal AJ. A clinical overview of non‐alcoholic fatty liver disease: a guide to diagnosis, the clinical features, and complications- what the non‐specialist needs to know. Diabetes Obes Metab 2022;24(suppl 2):3-14.

Musso G, Cassader M, Rosina F, Gambino R. Impact of current treatments on liver disease, glucose metabolism and cardiovascular risk in non-alcoholic fatty liver disease (NAFLD): a systematic review and meta-analysis of randomised trials. Diabetologia 2012;55(4):885-904.

European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), European Association for the Study of Obesity (EASO). EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease. Obes Facts 2016;9(2):65-90.

Eckard C, Cole R, Lockwood J, Torres DM, Williams CD, Shaw JC, et al. Prospective histopathologic evaluation of lifestyle modification in nonalcoholic fatty liver disease: a randomized trial. Therap Adv Gastroenterol 2013;6(4):249-259.

Wong VW, Chan RS, Wong GL, Cheung BH, Chu WC, Yeung DK, et al. Community-based lifestyle modification programme for non-alcoholic fatty liver disease: a randomized controlled trial. J Hepatol 2013;59(3):536-542.

Keating SE, Hackett DA, George J, Johnson NA. Exercise and non-alcoholic fatty liver disease: a systematic review and meta-analysis. J Hepatol 2012;57(1):157-166.

Vilar-Gomez E, Martinez-Perez Y, Calzadilla-Bertot L, Torres-Gonzalez A, Gra-Oramas B, Gonzalez-Fabian L, et al. Weight loss through lifestyle modification significantly reduces features of nonalcoholic steatohepatitis. Gastroenterology 2015;149(2):367-378.e5.

Thomas N, Alder E, Leese G. Barriers to physical activity in patients with diabetes. Postgrad Med J 2004;80(943):287-291

Whitsett M, VanWagner LB. Physical activity as a treatment of non-alcoholic fatty liver disease: a systematic review. World J Hepatol 2015;7(16):2041-2052

Eysenbach G. What is e-health? J Med Internet Res 2001;3(2):e20

Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet 2009;374(9696):1196-1208.

Wang Y, Xue H, Huang Y, Huang L, Zhang D. A systematic review of application and effectiveness of mHealth interventions for obesity and diabetes treatment and self-management. Adv Nutr 2017;8(3):449-462.

Stratton E, Lampit A, Choi I, Calvo RA, Harvey SB, Glozier N. Effectiveness of eHealth interventions for reducing mental health conditions in employees: a systematic review and meta-analysis. PLoS One 2017;12(12):e0189904.

Wang Y, Min J, Khuri J, Xue H, Xie B, A Kaminsky L, et al. Effectiveness of mobile health interventions on diabetes and obesity treatment and management: systematic review of systematic reviews. JMIR Mhealth Uhealth 2020;8(4):e15400

Metzger MJ, Flanagin AJ. Using web 2.0 technologies to enhance evidence-based medical information. J Health Commun 2011;16(suppl 1):45-58

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol 2021;134:103-112.

Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.

Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Chichester, UK: John Wiley & Sons; 2019.

Kim S, Park J, Seo H, Lee Y, Jang B, Son H, et al. NECA's Guidance for Undertaking Systematic Reviews and Meta-analyses for Intervention. Seoul: National Evidence-based Healthcare Collaborating Agency; 2011.

Pfirrmann D, Huber Y, Schattenberg JM, Simon P. Web-based exercise as an effective complementary treatment for patients with nonalcoholic fatty liver disease: intervention study. J Med Internet Res 2019;21(1):e11250

Huber Y, Pfirrmann D, Gebhardt I, Labenz C, Gehrke N, Straub BK, et al. Improvement of non-invasive markers of NAFLD from an individualised, web-based exercise program. Aliment Pharmacol Ther 2019;50(8):930-939.

Axley P, Kodali S, Kuo YF, Ravi S, Seay T, Parikh NM, et al. Text messaging approach improves weight loss in patients with nonalcoholic fatty liver disease: a randomized study. Liver Int 2018;38(5):924-931.

Dong F, Zhang Y, Huang Y, Wang Y, Zhang G, Hu X, et al. Long-term lifestyle interventions in middle-aged and elderly men with nonalcoholic fatty liver disease: a randomized controlled trial. Sci Rep 2016;6:36783.

Fard SJ, Ghodsbin F, Kaviani MJ, Jahanbin I, Bagheri Z. The effect of follow up (telenursing) on liver enzymes in patients with nonalcoholic fatty liver disease: a randomized controlled clinical trial. Int J Community Based Nurs Midwifery 2016;4(3):239-246

Lim SL, Johal J, Ong KW, Han CY, Chan YH, Lee YM, et al. Lifestyle intervention enabled by mobile technology on weight loss in patients with nonalcoholic fatty liver disease: randomized controlled trial. JMIR Mhealth Uhealth 2020;8(4):e14802

Mazzotti A, Caletti MT, Brodosi L, Di Domizio S, Forchielli ML, Petta S, et al. An internet-based approach for lifestyle changes in patients with NAFLD: two-year effects on weight loss and surrogate markers. J Hepatol 2018;69(5):1155-1163.

Tincopa MA, Lyden A, Wong J, Jackson EA, Richardson C, Lok AS. Impact of a pilot structured mobile technology based lifestyle intervention for patients with nonalcoholic fatty liver disease. Dig Dis Sci 2022;67(2):481-491.

Ng CH, Xiao J, Lim WH, Chin YH, Yong JN, Tan DJH, et al. Placebo effect on progression and regression in NASH: evidence from a meta‐analysis. Hepatology 2022;75(6):1647-1661.

Lau Y, Chee DGH, Chow XP, Cheng LJ, Wong SN. Personalised eHealth interventions in adults with overweight and obesity: a systematic review and meta-analysis of randomised controlled trials. Prev Med 2020;132:106001.

Wu X, Guo X, Zhang Z. The efficacy of mobile phone apps for lifestyle modification in diabetes: systematic review and meta-analysis. JMIR Mhealth Uhealth 2019;7(1):e12297

Martin CK, Gilmore LA, Apolzan JW, Myers CA, Thomas DM, Redman LM. Smartloss: a personalized mobile health intervention for weight management and health promotion. JMIR Mhealth Uhealth 2016;4(1):e18.

Ryan K, Dockray S, Linehan C. A systematic review of tailored eHealth interventions for weight loss. Digit Health 2019;5:2055207619826685.

Kani AH, Alavian SM, Esmaillzadeh A, Adibi P, Azadbakht L. Effects of a novel therapeutic diet on liver enzymes and coagulating factors in patients with non-alcoholic fatty liver disease: a parallel randomized trial. Nutrition 2014;30(7-8):814-821.

Razavi Zade M, Telkabadi MH, Bahmani F, Salehi B, Farshbaf S, Asemi Z. The effects of DASH diet on weight loss and metabolic status in adults with non-alcoholic fatty liver disease: a randomized clinical trial. Liver Int 2016;36(4):563-571.

St George A, Bauman A, Johnston A, Farrell G, Chey T, George J. Effect of a lifestyle intervention in patients with abnormal liver enzymes and metabolic risk factors. J Gastroenterol Hepatol 2009;24(3):399-407.

Rodrŕguez-Hernández H, Cervantes-Huerta M, Rodrŕguez-Moran M, Guerrero-Romero F. Decrease of aminotransferase levels in obese women is related to body weight reduction, irrespective of type of diet. Ann Hepatol 2011;10(4):486-492.

Al-Jiffri O, Al-Sharif FM, Abd El-Kader SM, Ashmawy EM. Weight reduction improves markers of hepatic function and insulin resistance in type-2 diabetic patients with non-alcoholic fatty liver. Afr Health Sci 2013;13(3):667-672.

Balducci S, Cardelli P, Pugliese L, D’Errico V, Haxhi J, Alessi E, Italian Diabetes Exercise Study (IDES) Investigators. Volume-dependent effect of supervised exercise training on fatty liver and visceral adiposity index in subjects with type 2 diabetes: the italian diabetes exercise study (IDES). Diabetes Res Clin Pract 2015;109(2):355-363.

Keating SE, Hackett DA, Parker HM, O’Connor HT, Gerofi JA, Sainsbury A, et al. Effect of aerobic exercise training dose on liver fat and visceral adiposity. J Hepatol 2015;63(1):174-182

Zelber-Sagi S, Buch A, Yeshua H, Vaisman N, Webb M, Harari G, et al. Effect of resistance training on non-alcoholic fatty-liver disease a randomized-clinical trial. World J Gastroenterol 2014;20(15):4382-4392.

Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64(1):73-84.