Dataset Information

Providing a Smart Healthy Diet for the Low-Income Population: Qualitative Study on the Usage and Perception of a Designed Cooking App.

ABSTRACT:

Background

Health behaviors among low-income groups have become a major issue in the context of increasing social inequalities. The low-income population is less likely to be receptive to nutritional recommendations, but providing cooking advice could be more effective. In this domain, taking advantage of digital devices can be a bonus with its own challenges.

Objective

The aim of this study was to develop and deploy NutCracker, a social network-based cooking app for low-income population, including cooking tips and nutritional advices, aiming at creating small online communities. We further determined the usefulness, perceptions, barriers, and motivators to use NutCracker.

Methods

The smartphone app, designed jointly with beneficiaries of the social emergency services, was implemented in a disadvantaged neighborhood of Magny, (Paris region, France). Once the app became available, 28 subjects, living in the neighborhood, tested the app for a 6-month period. Logs to the app and usages were collected by the software. In total, 12 in-depth, semistructured interviews were conducted among the users and the social workers to analyze their uses and perceptions of the app relative to their interest in cooking, cooking skills, socioeconomic constraints, and social integration. These interviews were compared with 21 supplementary interviews conducted among low-income individuals in the general population.

Results

NutCracker was developed as a social network-based app, and it includes cooking tips, nutritional advice, and Web-based quizzes. We identified barriers to uses (especially technical barriers, lack of knowledge in the field of new technologies and written comprehension, and search for real contacts) and motivators (in particular, good social integration, previous use of social networks, and help of children as intermediaries). Cooking skills were both a barrier and a lever.

Conclusions

Targeting the low-income groups through a cooking app to promote healthier behaviors offers many advantages but has not been fully explored. However, the barriers in low-income milieu remain high, especially among the less socially integrated strata. Lessons from this intervention allow us to identify barriers and possible levers to improve nutrition promotion and awareness in deprived areas, especially in the time of social crisis.

SUBMITTER: Regnier F

PROVIDER: S-EPMC6286424 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Providing a Smart Healthy Diet for the Low-Income Population: Qualitative Study on the Usage and Perception of a Designed Cooking App.

Régnier Faustine F Dugré Manon M Darcel Nicolas N Adamiec Camille C

JMIR mHealth and uHealth 20181123 11

<h4>Background</h4>Health behaviors among low-income groups have become a major issue in the context of increasing social inequalities. The low-income population is less likely to be receptive to nutritional recommendations, but providing cooking advice could be more effective. In this domain, taking advantage of digital devices can be a bonus with its own challenges.<h4>Objective</h4>The aim of this study was to develop and deploy NutCracker, a social network-based cooking app for low-income po ...[more]

PMID: 30470677

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Project description:BACKGROUND:Smoking is one of the most significant factors contributing to low life expectancy, health inequalities, and illness at the worldwide scale. Smoking cessation attempts benefit from social support. Mobile phones have changed the way we communicate through the use of freely available message-oriented apps. Mobile app-based interventions for smoking cessation programs can provide interactive, supportive, and individually tailored interventions. OBJECTIVE:This study aimed to identify emotions, coping strategies, beliefs, values, and cognitive evaluations of smokers who are in the process of quitting, and to analyze online social support provided through the analysis of messages posted to a chat function integrated into a mobile app. METHODS:In this descriptive qualitative study, informants were smokers who participated in the chat of Tobbstop. The technique to generate information was documentary through messages collected from September 2014 through June 2016, specifically designed to support a smoking cessation intervention. A thematic content analysis of the messages applied 2 conceptual models: the Lazarus and Folkman model to assess participant's experiences and perceptions and the Cutrona model to evaluate online social support. RESULTS:During the study period, 11,788 text messages were posted to the chat by 101 users. The most frequent messages offered information and emotional support, and all the basic emotions were reported in the chat. The 3 most frequent coping strategies identified were physical activity, different types of treatment such as nicotine replacement, and humor. Beliefs about quitting smoking included the inevitability of weight gain and the notion that not using any type of medications is better for smoking cessation. Health and family were the values more frequently described, followed by freedom. A smoke-free environment was perceived as important to successful smoking cessation. The social support group that was developed with the app offered mainly emotional and informational support. CONCLUSIONS:Our analysis suggests that a chat integrated into a mobile app focused on supporting smoking cessation provides a useful tool for smokers who are in the process of quitting, by offering social support and a space to share concerns, information, or strategies.

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Dataset Information

Providing a Smart Healthy Diet for the Low-Income Population: Qualitative Study on the Usage and Perception of a Designed Cooking App.

Background

Objective

Methods

Results

Conclusions

Publications

Providing a Smart Healthy Diet for the Low-Income Population: Qualitative Study on the Usage and Perception of a Designed Cooking App.

Similar Datasets

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