Effects of technology-based interventions on dietary intake or anthropometrics among adolescents and adults in South Asia - A systematic review of intervention studies.

INTRODUCTION: Mobile technology has been increasingly used as part of dietary interventions, but the effects of such interventions have not been systematically evaluated in the South Asian context. The systematic review aimed to determine the effects of technology-based interventions on dietary intake or anthropometrics among adolescents and adults in South Asia. METHODS: Five electronic databases were searched (PubMed, Scopus, Web of Science, Global Health Library and Health Technology Assessment). Studies published in English between 1st January 2011 and 31st December 2021were included. Interventions that evaluated the effects of dietary interventions using technology on dietary outcomes and anthropometrics in adolescents or adults in the age group of 13-44 years (or a broader age group) from South Asia were eligible for inclusion. The risk of bias was assessed using the Cochrane Risk-of-bias 2 tool and ROBINS-I tool. A narrative synthesis was conducted. RESULTS: Twenty-one studies met the inclusion criteria (20,667 participants). Eleven of the 17 randomised controlled trials (RCTs) had a high overall risk of bias. The four non-randomised intervention studies had a serious or critical overall risk of bias. When including studies with low risk or some concern for bias, the interventions had a beneficial effect on at least one dietary outcome in four of the six RCTs that measured changes in diet, and no effect on the anthropometric outcomes in the six RCTs that measured changes in anthropometric outcomes. DISCUSSION: Technology-based dietary interventions have had some positive effects on dietary intake, but no effects on anthropometry in South Asia. More evidence is needed as the overall risk of bias was high in a majority of the studies.

Effects of intervention on lifestyle changes among coronary artery disease patients: A 6-month follow-up study.

AIM: The main aim of the study was to investigate the effects of a nurse-led lifestyle-related risk factor modification intervention on multiple lifestyle behaviours among coronary artery disease patients over six months. DESIGN: A pre-test post-test control group design was conducted in a single clinical centre in Nepal. METHODS: A total of 224 eligible patients were randomly assigned to either the usual care group or the intervention group at baseline. The lifestyle intervention consisted of a brief counselling session supplemented with informational leaflets. Standard questionnaires were used to collect self-reported data from patients on multiple lifestyle behaviours: diet, physical activity, adherence to medication, stress, body mass index, smoking and alcohol consumption. General linear model repeated measure analysis was used to estimate the effect of intervention. RESULTS: A statistically significant effect of study group-by-time interaction for diet, adherence to medication, physical activity, and perceived stress was found at 6-month follow-up. Overall, greater improvement in lifestyle habits was found in the intervention group compared with the control group at 6-month follow-up.

Effects of a lifestyle-related risk factor modification intervention on lifestyle changes among patients with coronary artery disease in Nepal.

OBJECTIVE: To investigate the effect of a lifestyle-related risk factor modification intervention on coronary artery disease (CAD) patients' lifestyle changes. METHOD: A randomized controlled study was conducted in Nepal. A total of 224 CAD patients (112 in each study group) were included at baseline, and 196 patients (98 in each group) completed the one-month follow-up. Patients in the intervention group (IG) received nurse-led intervention in addition to the usual care. Face-to face and telephone interview was conducted using standard questionnaires to collect data on lifestyle-related risk factors; smoking, alcohol consumption, diet, body mass index, stress, adherence to medical therapy, and physical activity. General linear model repeated measure analysis was used to analyse the effects of the intervention. RESULTS: Based on self-reported data we found significant improvement in lifestyle-related risk factor habits in the IG compared with the usual care group with respect to diet (p < 0.001), physical activity (p < 0.001), medication adherence (p < 0.001) and stress (p < 0.001) at one-month follow-up. CONCLUSION: Lifestyle-related risk factor modification intervention can positively influence health risk habits, even when it is less intensive but supplemented with information leaflets. PRACTICAL IMPLICATIONS: Nurse-led one-time intervention may successfully deliver counselling to improve healthy lifestyle among underserved CAD patients.

Lifestyle-related risk factors among patients with coronary artery disease in Nepal.

BACKGROUND: Coronary artery disease (CAD) is the leading cause of death and morbidity globally. South Asia, including Nepal, has higher risks for CAD due to relatively higher exposures to risk factors. This study evaluated the prevalence of lifestyle-related risk factors and the associations of risk factors with socio-demographic variables among CAD patients. METHODS: A cross-sectional study was conducted among CAD patients (n = 224) admitted to a national heart centre in Nepal. Data on dietary habits, smoking, alcohol consumption, stress, physical activity, overweight or obesity and adherence to medication were collected using standard questionnaires. The numbers of risk factors were categorised into three groups (1-2 = low, 3 = medium, 4-7 = high). Data analysis was performed by cross-tabulation and multinomial logistic regression. Prevalence odds ratios (POR) and their 95% confidence intervals (CIs) were used as the measure of the associations. RESULTS: The prevalence of risk factors among patients varied from 23% to 97%, with stress being the most prevalent, and current alcohol consumption the least. The majority of study patients had multiple lifestyle-related risk factors. Male patients (POR for medium vs. low 2.83; 95% CI 1.3, 6.18) and patients with high incomes (POR for high vs. low 2.53; 95% CI 1.10, 5.83) had higher odds of being in the medium- and high-risk group, respectively. CONCLUSIONS: Lifestyle-related risk factors were highly prevalent among CAD patients. Various socio-demographic variables were associated with the risk groups. Intervention studies on lifestyle risk factor modifications among this target group are recommended.