Maria Aguiar, Ander Cejudo, Gorka Epelde, Deisy Chaves, Maria Trujillo, Garazi Artola, Unai Ayala, Roberto Bilbao, Itziar Tueros
{"title":"一种在移动医疗应用程序中为没有特定健康状况的用户提供自我数据报告的方法。","authors":"Maria Aguiar, Ander Cejudo, Gorka Epelde, Deisy Chaves, Maria Trujillo, Garazi Artola, Unai Ayala, Roberto Bilbao, Itziar Tueros","doi":"10.1186/s12911-024-02833-4","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objective of this study is to evaluate the effect of gamification features in a mHealth app that includes the most common categories of behavior change techniques for the self-report of lifestyle data. The data reported by the user can be manual (i.e., diet, activity, and weight) and automatic (Fitbit wearable devices). As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect.</p><p><strong>Methods: </strong>In this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. With a first group of users, we perform a comparative analysis in terms of adherence and system usability scale of two versions of the app, both including the most common categories of behavior change techniques but the second version having added gamification features. Then, with a second group of participants and the best mHealth app version, a longer study is carried out and user adherence, the system usability scale and user feedback are analyzed.</p><p><strong>Results: </strong>In the first stage study, results have shown that the app version with gamification features has achieved a higher adherence, as the percentage of days active was higher for most of the users and the system usability scale score is 80.67, which is categorized as rank A. The app also exceeded the expectations of the users by about 70% for the app version with gamification functionalities. In the second stage of the study, an adherence of 76.25% is reported after 8 weeks and 58% at the end of the pilot for the mHealth app. Similarly, for the wearable device, an adherence of 74.32% is achieved after 8 weeks and 81.08% is obtained at the end of the pilot. We hypothesize that these specific wearable devices have contributed to a decreased system usability scale score, reaching 62.89 which is ranked as C.</p><p><strong>Conclusion: </strong>This study evidences the effectiveness of the gamification category of behavior change techniques in increasing the overall user adherence, expectations, and perceived usability. In addition, the results provide quantitative results on the effect of the most common categories of behavior change techniques for the self-report of lifestyle data. Therefore, a higher duration in the study has shown several limitations when capturing lifestyle data, especially when including wearable devices such as Fitbit.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"16"},"PeriodicalIF":3.3000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721516/pdf/","citationCount":"0","resultStr":"{\"title\":\"An approach to boost adherence to self-data reporting in mHealth applications for users without specific health conditions.\",\"authors\":\"Maria Aguiar, Ander Cejudo, Gorka Epelde, Deisy Chaves, Maria Trujillo, Garazi Artola, Unai Ayala, Roberto Bilbao, Itziar Tueros\",\"doi\":\"10.1186/s12911-024-02833-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objective of this study is to evaluate the effect of gamification features in a mHealth app that includes the most common categories of behavior change techniques for the self-report of lifestyle data. The data reported by the user can be manual (i.e., diet, activity, and weight) and automatic (Fitbit wearable devices). As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect.</p><p><strong>Methods: </strong>In this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. With a first group of users, we perform a comparative analysis in terms of adherence and system usability scale of two versions of the app, both including the most common categories of behavior change techniques but the second version having added gamification features. Then, with a second group of participants and the best mHealth app version, a longer study is carried out and user adherence, the system usability scale and user feedback are analyzed.</p><p><strong>Results: </strong>In the first stage study, results have shown that the app version with gamification features has achieved a higher adherence, as the percentage of days active was higher for most of the users and the system usability scale score is 80.67, which is categorized as rank A. The app also exceeded the expectations of the users by about 70% for the app version with gamification functionalities. In the second stage of the study, an adherence of 76.25% is reported after 8 weeks and 58% at the end of the pilot for the mHealth app. Similarly, for the wearable device, an adherence of 74.32% is achieved after 8 weeks and 81.08% is obtained at the end of the pilot. We hypothesize that these specific wearable devices have contributed to a decreased system usability scale score, reaching 62.89 which is ranked as C.</p><p><strong>Conclusion: </strong>This study evidences the effectiveness of the gamification category of behavior change techniques in increasing the overall user adherence, expectations, and perceived usability. In addition, the results provide quantitative results on the effect of the most common categories of behavior change techniques for the self-report of lifestyle data. Therefore, a higher duration in the study has shown several limitations when capturing lifestyle data, especially when including wearable devices such as Fitbit.</p>\",\"PeriodicalId\":9340,\"journal\":{\"name\":\"BMC Medical Informatics and Decision Making\",\"volume\":\"25 1\",\"pages\":\"16\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721516/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Informatics and Decision Making\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12911-024-02833-4\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-024-02833-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
An approach to boost adherence to self-data reporting in mHealth applications for users without specific health conditions.
Background: The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management. The objective of this study is to evaluate the effect of gamification features in a mHealth app that includes the most common categories of behavior change techniques for the self-report of lifestyle data. The data reported by the user can be manual (i.e., diet, activity, and weight) and automatic (Fitbit wearable devices). As a secondary objective, this work aims to explore the differences in the adherence when considering a longer study duration and make a comparative analysis of the gamification effect.
Methods: In this study, the effectiveness of various behavior change techniques strategies is evaluated through the analysis of two user groups. With a first group of users, we perform a comparative analysis in terms of adherence and system usability scale of two versions of the app, both including the most common categories of behavior change techniques but the second version having added gamification features. Then, with a second group of participants and the best mHealth app version, a longer study is carried out and user adherence, the system usability scale and user feedback are analyzed.
Results: In the first stage study, results have shown that the app version with gamification features has achieved a higher adherence, as the percentage of days active was higher for most of the users and the system usability scale score is 80.67, which is categorized as rank A. The app also exceeded the expectations of the users by about 70% for the app version with gamification functionalities. In the second stage of the study, an adherence of 76.25% is reported after 8 weeks and 58% at the end of the pilot for the mHealth app. Similarly, for the wearable device, an adherence of 74.32% is achieved after 8 weeks and 81.08% is obtained at the end of the pilot. We hypothesize that these specific wearable devices have contributed to a decreased system usability scale score, reaching 62.89 which is ranked as C.
Conclusion: This study evidences the effectiveness of the gamification category of behavior change techniques in increasing the overall user adherence, expectations, and perceived usability. In addition, the results provide quantitative results on the effect of the most common categories of behavior change techniques for the self-report of lifestyle data. Therefore, a higher duration in the study has shown several limitations when capturing lifestyle data, especially when including wearable devices such as Fitbit.
期刊介绍:
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.