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The CareConekta App | The CareConekta app was built through collaboration between the study team and Jembi Health Systems in Cape Town [ | PMC10238954 | ||
Methods | PMC10238954 | |||
Study Design and Dates | We conducted a prospective, unblinded randomized controlled trial at the Gugulethu Midwife Obstetric Unit (MOU), a public sector clinic providing integrated HIV and peripartum care for pregnant women near Cape Town, South Africa. Full details of the study design can be found in our published protocol [ | PMC10238954 | ||
App Design Specifications | EVENT | To characterize participant mobility during the study period, the CareConekta app was designed to collect 2 GPS location heartbeats per day. In addition, the app was built with a geographic list of health facilities in South Africa so that users could see a map of nearby facilities as they traveled. App connectivity and participant location (marked using anonymized study ID numbers) were viewable to the study team through a password-protected, web-based dashboard. To protect participant privacy, the location was made fuzzy by randomizing the location within a 1-km radius. The MOU was set as the home location from which to begin measuring movement. The mobility history was saved on an encrypted, password-protected server at a South African data center.The app was available for free download through the Google Play Store (Google LLC) but required authentication and registration, so access was restricted to those enrolled in the study. CareConekta was designed such that it would cost the participant nothing: data costs associated with location tracking would be immediately reimbursed through reverse billing. Therefore, the cellular service of 1 of the 4 major mobile providers in South Africa was a requirement for eligibility. In the event of disconnection, the app was designed such that location data would be stored on the phone and uploaded as soon as connectivity resumed. However, reverse billing did not apply for app installation or version updates, so participants installed the app at the clinic using free Wi-Fi, and small data bundles were provided by the study staff for reinstallation and updates, when needed. | PMC10238954 | |
Recruitment and Eligibility | Pregnant women were recruited during routine antenatal care at the MOU. Women were eligible if they were in the third trimester of pregnancy (≥28 weeks); aged ≥18 years; able to speak and understand isiXhosa (the predominant local language) or English; diagnosed with HIV at any time before enrollment; able to demonstrate basic smartphone-level literacy; and willing to participate in all aspects of the study, including randomization and mobility tracking. Eligible participants also needed to own a smartphone that met the technical requirements described in the subsequent section. | PMC10238954 | ||
Smartphone Technical Requirements | For the purpose of this study, a smartphone was defined as a mobile phone device with a touchscreen interface and internet and GPS capabilities. CareConekta was designed for phones using the Android operating system, version 5.0 or later. In our preliminary work, nearly 90% of the smartphones of the women approached for study participation used the Android system [ | PMC10238954 | ||
App Installation and Operation | The app was installed at the study site by connecting the smartphone to the study’s Wi-Fi source. Because the app was available on the Google Play Store, all participants first needed a Google email address to download the app. For the app to function properly after installation, the GPS needed to remain enabled (with location allowed), and the phone needed to have some data or airtime available for reverse billing to work. Participants were asked to contact the study team if they needed to reinstall the app because of changing devices or uninstalling the app. | PMC10238954 | ||
Study Measures | Participant-reported data were collected at enrollment and follow-up—approximately 6 months post partum—directly into REDCap (Research Electronic Data Capture; Vanderbilt University) using tablet computers. REDCap is a secure, encrypted, and web-based software platform designed to support data capture for research studies [ | PMC10238954 | ||
Analysis | We report counts and proportions for categorical variables and medians and IQRs for continuous variables. Data analysis was performed using SAS (version 9.4; SAS Institute). Open-ended responses were reviewed to identify key topics or themes and illustrative quotes. | PMC10238954 | ||
Ethics Approval | This study was approved by the institutional review boards of Vanderbilt University (reference 181640) and the University of California, San Francisco (237757), and the Human Services Research Committee of the University of Cape Town (659/2018). All participants signed a written informed consent form before enrollment, which included specific permission for location tracking. | PMC10238954 | ||
Results | PMC10238954 | |||
Installation Experience | App installation at enrollment was a highly variable experience. The most common reasons for slow experiences at installation were the need to delete items on the phone to make room for another app, the need to create a Google or Gmail account to use the Google Play Store, the need to update the Google Play Store app version, or problems sending and receiving heartbeats after installing the app. The following is a quote from the study staff’s notes about a particularly troublesome installation experience:Had to create a Google account for participant and update Play store for app installation. Downloading took a while still. Had to let participant go and she came back a day later for installation; phone still taking forever. The controls get frozen, cleaned phone for efficiency but it still freezes. Finally, app installed and registered participant. Heartbeat data transmitted after all necessary settings adjusted. | PMC10238954 | ||
Investigator Withdrawals | 6/200 | In total, 7 participants were withdrawn from the study soon after enrollment owing to technical issues. Most (6/200, 3%) were withdrawn at the end of the enrollment visit because of an app installation failure that could not be resolved. In addition, 1 (0.5%) other participant was withdrawn for changing their phone to an ineligible phone within 2 weeks of enrollment. | PMC10238954 | |
Phone Sharing | Approximately one in seven (25/173, 14.4%) participants reported sharing their phone during the study. These participants most often shared their phone with one of their family members (12/25, 48%), their boyfriend or husband (11/25, 44%), or one of their friends who was not their boyfriend or husband (3/25, 12%). Of those who reported phone sharing, 80% (20/25) reported that they had their phone with them most of the day. | PMC10238954 | ||
Version Updates | From December 2019 to December 2020, the CareConekta app moved from version 1 to version 7. At one point, a version change (version 5 to 6) meant that the heartbeat data submitted by older versions were no longer received. From July to August 2020, participants were phoned, notified of the new app version, and assisted with updating the app over the phone (Google Play > CareConekta > update app). In cases where participants struggled to follow the steps over the phone, we offered an option for them to come to the study clinic—especially in cases where they were scheduled for an upcoming visit—where we would update the app for them in person. On a few occasions, to those who experienced difficulty over the phone, we sent the steps to follow via SMS text message or WhatsApp. The participants were offered data (100 MB) to update the app over the phone. | PMC10238954 | ||
Gaps in GPS Heartbeat Signals | A key feasibility measure was the successful transmission of at least 1 data location heartbeat per participant per day. Heartbeat data were observed daily by the study staff via the dashboard and periodically exported as a CSV file for analysis. During the study, we experienced periods when the dashboard was offline and data exports were unavailable, which resulted in missing data. Owing to difficulties with the dashboard and data exports resulting in substantial missing periods of data after March 19, 2021, our analysis of heartbeat data was conducted on all heartbeats received between the start of enrollment (December 2019) and March 2021. The daily GPS heartbeats of none of the participants were received without interruption. The range in heartbeat gaps was 2 to 273 days.Among the 127 participants with gaps of ≥28 days, 55 (43.3%) were participants whose heartbeat transmission stopped altogether; the remaining 72 (57%) were women whose heartbeat transmission had long gaps but heartbeats resumed during the study period.On the basis of 3302 heartbeats with GPS location, the median distance traveled from the study site was 2.4 (IQR 1.5-4.5) km. A total of 104 heartbeats (16 women) picked up a distance >50 km away. Only 3 (1.6%) out of 193 women were >50 km away for >7 days based on the heartbeat data, thus meeting our study definition of “travel.” Of the 3 women, 2 (67%) were in the control arm and received no additional action, and 1 (33%) was in the intervention arm; the participant in the intervention arm had a break in GPS when she reached a rural area in November 2020, but her GPS heartbeats resumed in January 2021, and the intervention protocol was followed.In comparison, self-reported mobility during the follow-up visit indicated 37 trips lasting ≥7 days during the study period. Most of these trips were missed by the CareConekta app data.Gaps in GPS heartbeats received during the CareConekta study. | PMC10238954 | ||
Raffle | HOLIDAYS | In November 2020, to encourage participants to keep the CareConekta app installed on their phone and the GPS function enabled throughout the study period, we implemented an incentive: a weekly raffle of one 200 MB data bundle (worth approximately US $4). Participants were eligible to enter the raffle if their phone sent GPS coordinates at least once a day in the prior week. Among all eligible participants, 1 winner per week was randomly selected. The weekly raffle incentive only applied to the participants (n=86) enrolled from November 2020 onward who had signed version 5.0 or later of the informed consent document. There was no limit to the number of times a participant could win the raffle.From the 41-week period of November 18, 2020, to September 15, 2021, the weekly raffle was drawn 32 times. Four drawings were missed because the CareConekta dashboard was down, and we could not see the GPS data. Two drawings were missed because of holidays. There were no winners for 3 weeks because no participants were eligible. From the 32 drawings, there were 23 unique winners. The same participants won the raffle repeatedly because of the small number of participants who met the eligibility criteria of having consistent heartbeats. In total, 3 participants won the raffle 3 times each during this period. We found that the raffle incentive made no difference to the consistency of GPS heartbeats. | PMC10238954 | |
Additional Technical Challenges | On multiple occasions, the staff-facing dashboard was either not accessible or not fully functional, which meant that the team was unable to view or download heartbeat data. During the early study period, these problems often resulted in app revisions and version updates. In some instances, when the dashboard was down, the app did not work either, and heartbeats were not transmitted or recorded. Although the app was originally specified to store heartbeats on the phone and transmit them to the server when the connection was restored, this did not happen. Similarly, the app was designed to be reverse billed so as to not cost participants data for using the app; however, some data or airtime was needed on the device for the app to initiate. | PMC10238954 | ||
Data Expenditure | Overall, for all their cell phone needs, participants reported spending a median of R51 (IQR 30-100; US $2.75, IQR US $1.60-5.40) for data per month, with a similar response for monthly spend on airtime: median R50 (IQR 29-100; US $2.71, IQR US$1.57-5.40). Cellular data in South Africa cost approximately R10 (US $0.50) per 50 MB. | PMC10238954 | ||
Participants’ Understanding of the CareConekta App | SAID | At follow-up, participants were asked, “If you were to explain to a friend what the CareConekta app does, how would you explain it?” Nearly all participants (167/170, 98.2%) mentioned the clinic finder feature:I’d say it’s an app used to search for clinics when I travel to the Eastern Cape so I don’t suffer when I run out of medication. I’d simply just search for a clinic on the app.It is an app that can help you find clinics near you, so you don’t say you did not go to the clinic because you did not know where it was.Only 5 (2.9%) mentioned geolocation tracking or the app knowing the participant’s location, and 2 (1.2%) participants said that they did not know the app’s function. None of the participants mentioned the app notifications or staff contact via WhatsApp or phone. | PMC10238954 | |
Participants’ Acceptability of the CareConekta App | Similar to the participants’ responses regarding their understanding of the app, most of the responses regarding what participants liked about using the app were related to the clinic finder feature:I found it useful because I don’t have to look for clinics should I travel outside Cape Town. The app connects me to the clinics closest to me.What I liked was that we had been looking for a pediatric clinic and got lost in a taxi, I then thought of this app, I used it and it showed me exactly where the clinic was.Some responses indicated that the participants used the clinic finder for a general map too:With this app I know for a fact I’d never get lost when I go somewhere. The app has a GPS function. | PMC10238954 | ||
Initial Efficacy of the Intervention | SECONDARY | Although the initial efficacy of the intervention was a secondary aim of the study, we were unable to assess this because the app did not function as designed. Without receiving regular GPS heartbeats, the study team did not know when a participant was traveling; therefore, the intervention could not be initiated. During the study period, only 1 participant in the intervention arm was flagged as traveling, as defined in our protocol, and received the additional notifications, so it was not possible to assess a statistically meaningful difference between the study arms. | PMC10238954 | |
Discussion | PMC10238954 | |||
Principal Findings | EVENT | This is one of the first GPS-based mHealth studies—if not the first GPS-based mHealth study—targeted at improving HIV care in South Africa, and we found that several key challenges impeded its implementation. This study was designed to test the feasibility and acceptability of the CareConekta app and the initial efficacy of using it as an intervention to improve engagement in care among mobile women living with HIV. Although we were able to accomplish our primary aim of assessing the feasibility and acceptability of the intervention, we were unable to assess the efficacy of the intervention because we did not receive consistent location-tracking data. It is important to note that the app missed picking up on travel that was reported at follow-up. Although this is disappointing, we feel that the lessons learned from the implementation of this ambitious mHealth study are important and will be useful to other researchers considering mHealth interventions for low-resource settings. In designing this study, we made a conscious choice to assess our app under real-world conditions. We briefly considered providing phones—particularly to avoid bias against those who did not own phones and to guarantee a consistent technical level of device—but decided that this would not allow us to interpret the real-world applicability of our results. Similar decisions were made against providing data to all participants. Thus, our results can be viewed as representing implementation in real-world conditions.We developed an initial beta version of the CareConekta app and implemented it in a proof-of-concept trial in 2017. We enrolled 11 participants at the same study site. Among the 11 participants, app installation failed for 7 (64%) individuals. Because the app team was US-based, some requirements of the app did not align with the capabilities of many of the phones in use in South Africa, and we were also unable to offer real-time technological support in the event of installation difficulty. The importance of a local app development team, with available technology support, was one of the key lessons learned from this early work. We were also committed to collaborating with a local development company that works with and knows the mHealth agenda of the South African Department of Health; we wanted to be well poised for broader implementation if our app was successful. In the proof-of-concept trial, we were able to install the app on 4 participants’ phones and deploy the app for 3 months. Of the 4 participants, 1 (25%) lost her phone after approximately 1 month, but the other 3 (75%) produced heartbeats at least weekly, often daily, during the 3-month period. This sufficiently proved the concept for us to proceed with this study.In this study, during the 15 months of intensive data monitoring, no participant had GPS heartbeats every day without interruption, which is a key indicator of study feasibility. Despite specifically designing the app such that mobility data would be stored on the app in the event of interruptions in data, the app did not function correctly and did not provide the missing data. Our attempts to troubleshoot lost GPS signals unexpectedly required substantial staff time; indeed, at least 1 staff member phoned participants every week for a year to ask about missing heartbeats. Most participants did not respond, or if they did, they requested a later callback and then still did not respond. CareConekta was designed to reverse bill, which meant that even if the mobile device had no airtime and no data, the app would still be fully functional. However, through implementation, we found that if a participant had no data at all, the app would not work. That is, for reverse billing to work properly, a small amount of data was first required. This became a major stumbling block for implementation, as the top reason cited by participants for missing GPS heartbeats was a lack of data. In addition, we received numerous reports of purchasing only WhatsApp data, a product that was unfamiliar to the researchers at the time of study design but appears to have grown in popularity during the course of our study. Although the lack of electricity was not mentioned as a reason for lost GPS heartbeats, the study period coincided with regular periods of load shedding—scheduled electricity blackouts to conserve power in South Africa—which would have impacted participants’ ability to keep their phones charged. Future mHealth studies will be wise to consider the high likelihood of the lack of data and electricity during study implementation.Even as early as installation, difficulties arose in using the app. From our preliminary research, we knew that over 90% of our participant population used Android-based phones [Overall, we found that the participant acceptability of CareConekta was high, but we view this finding with caution because it does not align with the numbers of participants who reported losing phones and uninstalling the app or the high frequency of lost GPS heartbeats. It is possible that social desirability bias to report a positive experience to the interviewer influenced responses. In the follow-up responses, all participants understood the participant-facing function of the app—the clinic finder—but seemed to forget or not understand the passive geolocation tracking, despite the great efforts made to be explicit about location tracking at the time of informed consent. Future mHealth intervention developers should note that patient-facing features may be the ones that will be most understood and remembered among participants.The proportion of phone sharing reported at follow-up (15%) is consistent with that reported at the time of enrollment (14%). This frequency of sharing is similar to another recent mHealth study in South Africa that found 11% phone sharing [Ours is not the first smartphone-based study in South Africa to experience substantial feasibility challenges. A South African study conducted from 2015 to 2017 [To our knowledge, this is the first mHealth intervention to use location-based participant tracking in a real-world setting in South Africa. Interventions such as this are becoming increasingly common in the United States and Europe. For example, in a study focused on travel health published in 2016, a total of 101 Swiss adult travel clients planning to travel to Thailand for <5 weeks were provided a smartphone equipped with an app to passively monitor their location and administer a daily questionnaire [Some of the problems experienced with app implementation may have been avoided through clearer communication between the research team and app development or technical team. Although we thought that we had very close communication, some aspects were lost in translation. It is critically important that the research team investigating any mobile app includes someone who fully understands the technical specifications and requirements and can liaise with or translate the research vision to the app development team, as well as caution the research team on potentially problematic areas of design.Our study has some clear limitations, primarily that the app did not function as designed because of a combination of user actions and app malfunction. Another limitation is that we conducted the study at a single site, thus potentially limiting the generalizability of our results. However, given that we attempted to mimic real-world conditions, we anticipate that our study population will be similar to other adults attending public health clinics in South Africa.However, we feel that many important lessons learned through this experience will be useful to other researchers and make the effort worthwhile. One strength of our study is that it was among the first to develop, implement, and clearly document experiences with a GPS-based location-tracking mHealth app in a low-resource setting, particularly in a real-world setting. Thus, our findings are meaningful, even if our intervention was not successful. In addition, we report on several technical factors traditionally unreported in the literature but critical to the feasibility of mHealth interventions. | PMC10238954 | |
Conclusions | AIDS | AIDS | In conclusion, we did not demonstrate the feasibility of using a GPS-based tracking app to characterize mobility and improve engagement in HIV care. Our most common problems that contributed to failure were a lack of mobile phone data, app uninstallations, phone changes, and missing heartbeat data. We are far less motivated to create a novel app in our future research endeavors and instead will use tools that people already are using, particularly WhatsApp, which all of our participants reported as their favorite app [This work was funded by the US National Institute of Mental Health (R34 MH118028) and based on earlier work that was funded by the US National Institutes of Health (NIH) under grant P30 AI110527 provided to the Tennessee Center for AIDS Research. TKP was supported by a Collaborative Initiative for Pediatric HIV Education and Research grant from the International AIDS Society and by grant K43TW011943 jointly supported by the Fogarty International Center and National Institute of Mental Health of the NIH. The funders had no role in the study design, data collection and analysis, or decision to publish or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or other parties.The authors are most grateful to the study participants, without whom this work would not have been possible.Conflicts of Interest: None declared.CONSORT-eHEALTH checklist (V 1.6.1). | PMC10238954 |
Abbreviations | antiretroviral therapymobile healthMidwife Obstetric UnitResearch Electronic Data Capture | PMC10238954 | ||
Background | Preventing addiction through training takes precedence over treatment and plays a crucial role in enhancing the well-being of adolescents. Utilizing inclusive and participatory methods can significantly enhance the effectiveness of education. Numerous studies have demonstrated that gamification, as an interactive and comprehensive approach, has the potential to boost teenagers’ motivation to engage in learning and contributes to better comprehension. | PMC10641928 | ||
Aim | This study aimed to assess the impact of gamification-based training to prevent substance and internet addiction on the knowledge and attitudes of male adolescents. Additionally, the study examined this educational program’s effects on male adolescents’ academic achievement. | PMC10641928 | ||
Methods | This study employed a quasi-experimental design with a control group. One hundred fourteen male adolescents were randomly assigned to the intervention or control groups. They completed a pre-intervention questionnaire assessing addiction-related knowledge, attitudes, and academic achievement. Subsequently, the intervention group received the gamification-based drug and internet addiction prevention training. Post-tests were conducted immediately after the training and again one month later for both groups. | PMC10641928 | ||
Results | Before the intervention, there were no significant differences in knowledge of substance and internet addiction, attitudes toward substances and the Internet, and academic achievement between the intervention and control groups (P > 0.05). However, after the intervention, the intervention group demonstrated significantly higher scores in knowledge of substance and internet addiction, attitudes toward substances and the Internet, and academic achievement compared to the control group (P < 0.001). | PMC10641928 | ||
Conclusion | The current study highlights the positive impact of gamification-based training on enhancing male adolescents’ knowledge, attitudes, and academic achievement. | PMC10641928 | ||
Supplementary Information | The online version contains supplementary material available at 10.1186/s12909-023-04858-1. | PMC10641928 | ||
Keywords | PMC10641928 | |||
Background | anxiety, behavioral dependence, aggression, learning behaviors, impaired social connections, substance addiction, substance abuse | ADVERSE EFFECTS, ABUSE, DISORDERS, WEST | During adolescence, judgment and decision-making skills are developing, and the ability to accurately assess risks and make decisions is underdeveloped [Adolescence is a pivotal developmental stage marked by profound physical, cognitive, emotional, social, and behavioral transformations. The biological and neural changes during this period render adolescents more susceptible to the initiation of substance abuse, the development of substance use disorders, and the experience of long-lasting and severe adverse effects associated with substance misuse [The scientific definition of substance abuse is a chronic, recurring mental disorder of the brain [International organizations have provided significant statistics to highlight the scope of these issues. According to the Substance Abuse Department of the World Health Organization, 18.4% of people over 15 worldwide report heavy alcohol consumption, 15.2% daily tobacco use, 3.8% cannabis and marijuana consumption, 0.77% amphetamine use, 0.37% opioid consumption, and 0.35% cocaine use [While the Internet serves various global purposes, such as education, business, and recreation, problems arise when its use negatively impacts teenagers’ physical, mental, and social health [Today, alongside substance addiction, there are other behaviors, such as Internet addiction, which psychiatrists believe can lead to behavioral dependence and warrant attention [Internet addiction is characterized by the inability to resist the urge to be online, difficulty spending time offline, and experiences of anxiety and aggression. It can also lead to disruptions in family, friends, and school relationships due to uncontrolled Internet use. Internet dependence shares similar characteristics, including an inability to resist the desire to be online and difficulty engaging in face-to-face interactions. This issue can result in anxiety, aggression, strained family relationships, impaired social connections, and a decline in academic performance [A meta-analysis of data from 31 global studies in seven regions found that the prevalence of Internet addiction in the Middle East is 10.9%, while the lowest prevalence is in North and West Europe at 2.6%. This study also revealed an inverse relationship between the prevalence of Internet addiction and quality of life [Education is a vital tool in preventing health problems, and interactive multimedia methods, among various media and virtual approaches, have proven to be more effective in enhancing learning [In contrast, traditional training methods often promote passive learning, neglect individual differences and the specific needs of learners, and fail to address critical thinking and higher-level cognitive skills [In recent years, e-learning techniques, particularly smartphone-based learning, have significantly developed [One innovative and engaging method for teaching adolescents and exploring the world of electronic multimedia is gamification. Gamification involves applying game-like elements to tasks that may not inherently involve play, turning otherwise dull activities or educational lessons into engaging experiences [Zickermann initially described gamification as a process incorporating game thinking and mechanics to engage users in problem-solving. Later, he refined this definition, stating that gamification involves engaging the audience through the application of game design, behavioral economics, and the best practices in programs [Through gamification, teachers can actively engage their audience with natural stimuli, making education exciting and practical while capturing the audience’s attention, promoting interaction, and enhancing learning [Various studies have demonstrated the positive effects of gamification on academic performance, health promotion, attitudes, and learning behaviors [Numerous studies have explored adolescent addiction prevention, each focusing on specific aspects [Furthermore, most of these studies relied on traditional educational methods, paying less attention to innovative training techniques. In response to these gaps, this study aimed to assess the impact of gamification-based training on the knowledge and attitude of male adolescents regarding preventing substance and internet addiction. Additionally, the study explored how this educational program affects their academic achievement. | PMC10641928 |
Materials and methods | PMC10641928 | |||
The study environment and population | substance abuse | This study employed a quasi-experimental design with pre-test and post-test measurements, including a control group. The research population consisted of male adolescents aged 13–15 who attended health centers affiliated with Shiraz University of Medical Sciences in 2021.The authors used MedCalc software version 18 to determine the sample size, with a 95% confidence level, a 5% first type error (α), and an 80% test power (β). After accounting for potential attrition, the sample was 57 individuals per group.Random sampling was used to select participants from 32 comprehensive health centers in Shiraz. Four centers were randomly chosen. Households with male adolescents aged 13 to 15 were identified, listed, and assigned numbers from each of these centers. Randomly generated numbers were matched to select the intervention group’s sample, and the next adolescent on the list was assigned to the control group. This process continued until 114 participants were included.Inclusion criteria required the adolescent and their parents to provide informed consent for the adolescent’s participation. Adolescents were also required to have access to a smartphone and be fluent in Persian. Exclusion criteria included a history of educational rejection, smoking or substance abuse, disconnection from the selected comprehensive health centers’ coverage area, and previous participation in addiction prevention training classes. | PMC10641928 | |
Method of study | Initially, participants were contacted by phone. The purpose of the study, the methodology, and the commitment to information confidentiality were explained to motivate honest responses from the adolescents. In light of COVID-19 conditions, informed consent forms were collected online from the students and their parents.The authors formed a WhatsApp group to communicate the study’s goals and support participants throughout all stages. Questionnaires were distributed online in a pre-test format.The intervention group received a mobile application for addiction prevention training, incorporating gamification elements like scoring, badges, rewards, and feedback. The program consisted of questions and answers accompanied by educational images and music. Participants couldn’t revisit previous questions. They earned one point for each correct answer and received a star or badge for every five. Incorrect responses prompted the display of the correct answer. Users could engage with the application as often as desired, aiming to improve their scores and assess their progress compared to previous attempts.The educational content covered several topics, including the definition of addiction, understanding the concept and risks of addiction, identifying various drugs and their types, recognizing the concept and types of internet addiction, and understanding addiction’s physical, psychological, social, and academic impacts. The content also addressed signs and symptoms of drug and internet addiction in teenagers, activities to prevent addiction and proper internet usage.The participants’ questions about the content were answered through the WhatsApp group throughout the study. After this initial phase, both groups completed the questionnaire in the immediate post-test and again one month later, all done online. The same educational content was eventually provided to the control group as part of ethical compliance. | PMC10641928 | ||
Data collection and statistical analysis | substance addiction, CVI, substance abuse, anxiety | MINOR | The research instrument included a five-part questionnaire covering demographic information, addiction knowledge, attitudes toward substances and the Internet, and academic achievement.The Addiction Knowledge Questionnaire was designed by the researchers and consisted of 18 true/false questions, with 10 related to substance addiction and 8 related to internet addiction. The questionnaire’s face and content validity were assessed by 10 faculty members from Shiraz University of Medical Sciences specializing in psychometrics, virtual education, and adolescent health. Quantitative methods were employed to determine the content validity of the addiction knowledge questionnaire.Each item was rated on a 3-point scale (necessary, useful but not necessary, unnecessary) for content validity. All items had a content validity ratio (CVR) score higher than 0.8, confirming their validity. Waltz and Basel’s Content Validity Index (CVI) was utilized to assess the relevance of questionnaire items. This four-point Likert scale (1 = not relevant, 2 = needs revision, 3 = relevant with minor revision, 4 = very relevant) was applied by experts to each item. CVI scores were computed by dividing the number of experts rated the item as 3 or 4 by the total number of experts. Items were evaluated against these criteria: CVI > 0.79 (suitable), CVI < 0.79 (requires review and modification), and CVI < 0.70 (unacceptable). The results indicated that all items scored higher than 0.99. Thus, each item’s content validity index (CVI) exceeded 0.99.The questionnaire was administered to 25 teenagers not part of the study group using a test-retest method to assess its reliability. The correlation coefficient for questions related to drug addiction knowledge was 0.847; for internet addiction knowledge questions, it was 0.854; and for the entire questionnaire, it was 0.781.The Attitude to Substances Questionnaire comprises 25 questions divided into five subscales: tendency toward substances (12 questions), reluctance to participate in prevention efforts (5 questions), incorrect belief in positive substance effects (3 questions), lack of active resistance to substances (3 questions), and incorrect belief in the high prevalence of substance abuse (2 questions). Responses are recorded on a five-point Likert scale. Higher scores on this questionnaire indicate a more positive and potentially dangerous attitude toward substances.A Cronbach’s alpha coefficient of 0.875 supports the questionnaire’s reliability. The authors conducted an exploratory factor analysis to assess its construct validity, revealing five factors that explain approximately 61% of the total variance [The Attitude to Internet Questionnaire consists of 40 questions organized according to the Internet Attitude Scale (IAS) model, comprising four subscales: feeling of self-efficacy, recognizing the Internet’s usefulness, Internet anxiety, and Internet enjoyment. Responses are recorded on a five-point Likert scale, with each subscale containing 10 questions. Higher scores on this questionnaire indicate a more favorable attitude toward the Internet.The questionnaire’s reliability was evaluated using internal consistency and test-retest, resulting in an internal consistency coefficient of 0.93 and a retesting coefficient of 0.83. Confirmatory factor analysis was employed to assess construct validity. The criterion and Bartlett’s test (P > 0.889) confirmed sample size sufficiency, making factor analysis appropriate for the data. Subsequently, confirmatory factor analysis was conducted using software and orthogonal rotation [Herman’s Academic Advancement Questionnaire comprises 29 incomplete sentences with four response options each. Scores on this questionnaire range from 29 to 116, with higher scores indicating better academic achievement. The questionnaire demonstrates good reliability, with a Cronbach’s alpha coefficient of 0.84.The authors utilized factor analysis based on the correlation matrix to assess the questionnaire’s construct validity. The analysis revealed general factors such as self-confidence, perseverance, career choice, foresight, and hard work, which collectively explained 40.62% of the overall variance [The data were analyzed using Statistical Software (SPSS) version 18, employing descriptive and analytical statistics. A significance level of P < 0.05 was used for all tests. The normality of the data was assessed using the Kolmogorov-Smirnov test on three occasions in both groups. As the data did not meet the criteria for normal distribution, non-parametric tests were employed. The tests utilized included the Mann-Whitney test and repeated measures analysis using the Friedman test. | PMC10641928 |
Results | substance abuse | The study involved 114 adolescents aged 13 to 15, with an equal distribution of 50% in the intervention group and 50% in the control group. The subjects had a mean age of 14.0 years with a standard deviation of 6.85.Most adolescents in both groups were the first child in their families (50 individuals, 43.8%). Most (46 individuals, 40.3%) came from families with two children. Furthermore, the parents of most adolescents (111 individuals, 97.3%) reported no history of smoking or substance abuse (Table
Comparison of the frequency distribution of demographic characteristics of adolescents in the two groups of intervention and controlBefore the training, no significant differences existed between the intervention and control groups regarding knowledge scores on substance and internet addiction, total attitude scores towards substances and the Internet, their subscales, and academic achievement (P > 0.05).However, following gamification-based training, there was a significant increase in the mean knowledge scores on substance and internet addiction in the intervention group (P < 0.001). This increase in addiction knowledge persisted immediately after training and one month later (P < 0.001) (Table
Comparison of mean scores of (substance abuse and Internet) knowledge in and between the intervention and control groupsAfter the addiction prevention training, significant differences were observed in various aspects for the intervention group compared to the control group. Specifically, the intervention group showed statistically significant improvements in their total attitude scores toward substances and their subscales, total attitude scores toward the Internet and its subscales, and academic achievement (P < 0.001).Furthermore, within the intervention group, the mean score for the tendency toward substance abuse showed a significant increase immediately after the intervention, and this increase was sustained one month later. The intervention group also demonstrated significant improvements in the mean scores for reluctance to actively participate in prevention, incorrect belief in the positive effects of substances, lack of active resistance to substance abuse, and incorrect belief in the high prevalence of substance abuse.Immediately and one month after the intervention, the intervention group demonstrated statistically significant differences compared to the control group, with more pronounced changes. Specifically, the intervention group’s attitude toward substances significantly decreased, indicating the development of a more negative attitude toward substances (P < 0.001).In contrast, the intervention group’s average score for their attitude toward the Internet significantly increased immediately and one month after the intervention, suggesting a more positive attitude. Academic achievement also saw an increase only in the intervention group immediately and one month after training, highlighting the positive impact of the training on individuals’ Internet attitudes and academic performance among adolescents in the intervention group (Tables
Comparison of mean attitudes toward drugs in and between the intervention and control groups
Comparison of the mean score of Internet attitude and academic achievement score in and between the intervention and control groups | PMC10641928 | |
Discussion | substance abuse | EVANS, STD | Table The results of this study highlight the positive impact of gamification on improving students’ knowledge, attitude, and academic achievement. Gamification enhances the teaching process, making it more efficient and effective. This method can find applications in medical sciences and health education, promoting healthier lifestyles. A systematic review also supports the validity of gamification in teaching health-related programs and enhancing academic achievement among students. Its increased motivation is a compelling reason to consider gamification techniques in educational processes [A study in Korea demonstrated the positive impact of gamification in improving learning outcomes, particularly when game elements are integrated, which fosters positive learning attitudes and behaviors while reducing the risk of internet addiction [Gamification is a valuable learning strategy that can potentially enhance adolescent educational performance [Furthermore, studies by Gabaron et al. (2013) and Harona et al. (2018) concluded that gamification-based training is an accessible and effective intervention model for adolescents compared to traditional methods [The study’s findings, aligned with the research hypotheses, revealed that the intervention group demonstrated a significant increase in addiction knowledge immediately and one month after the training intervention. Additionally, the intervention group showed significantly improved attitudes toward substances and their various subscales, reaching a favorable level immediately and one month after the training. Moreover, the attitude toward the Internet and academic achievement increased compared to before the intervention and achieved favorable values.These changes underscore the effectiveness of educational intervention in enhancing adolescent knowledge, fostering a negative attitude towards substance use, promoting a positive attitude towards the Internet, and increasing motivation for academic achievement.These changes in the intervention group’s attitude towards the Internet following the educational intervention highlight the positive impact of the intervention in fostering a more positive and responsible attitude among adolescents. It’s worth noting that a significant and negative correlation exists between internet addiction and adolescents’ academic achievement, emphasizing the importance of educational efforts to improve adolescents’ motivation and academic performance compared to pre-intervention levels.Evans et al. (2020) indicated that participation in substance abuse prevention programs improved knowledge, self-efficacy, and safer attitudes among students [Joo et al. (2010) assessed the impact of a training program focused on empowerment against addiction, internet games, and stress among junior high school students. They reported higher empowerment scores and lower addiction and stress scores among students in the intervention group after an empowering, addiction prevention, and stress management program [Furthermore, Haruna et al. (2018) demonstrated that gamification-based education in STD prevention programs increased knowledge, improved attitudes, and enhanced student motivation [These findings align with numerous other studies highlighting the positive effects of education on knowledge of addiction, attitudes towards substances and the Internet, and the promotion of academic achievement [ | PMC10641928 |
Conclusion | adolescence | This study demonstrated that gamification-based addiction prevention training is an engaging and effective method for enhancing knowledge, improving attitudes, and boosting adolescent academic achievement. Prevention training during adolescence is not only cost-effective but also superior to treatment.As a recommendation for policymakers and social and health planners, the authors propose using gamification-based training as an attractive, valuable, and cost-effective approach to preventing adolescent addiction.This study, however, has some limitations. The follow-up duration was short; further investigation is needed to assess the intervention’s long-term effects. Cultural considerations limited the inclusion of both genders and future research should aim to conduct comparative studies with reduced cultural barriers, including both male and female participants. | PMC10641928 | |
Acknowledgements | This article was derived from an MSc dissertation in community health nursing by Esmaeel Taghipour and received financial support from the Vice-Chancellor of Shiraz University of Medical Sciences (Grant No: 22445). The authors extend their gratitude to all participating adolescents and their parents. | PMC10641928 | ||
Authors’ contributions | F V, E.T: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Visualization, Investigation, Supervision, Validation, Writing- Reviewing and Editing. N.Z: Conceptualization, Methodology, Investigation, Supervision. | PMC10641928 | ||
Funding | This study received financial support from the Vice-Chancellor of Research, Shiraz University of Medical Sciences (Grant No: 22445). The funder had no involvement in the study’s design, data collection, analysis, interpretation, or manuscript preparation. | PMC10641928 | ||
Data Availability | Upon request from the first author, data is available. For inquiries related to data access, please contact essitaghipour1997@gmail.com. | PMC10641928 | ||
Declarations | PMC10641928 | |||
Ethics approval and consent to participate | All interventions in this study were conducted under the approval of the Ethics Committee of Shiraz University of Medical Sciences (Approval Number: IR.SUMS.REC1399.1299). The study adhered to the fundamental principles outlined in the Helsinki Declaration (2013), and the investigator obtained the commitment of all participating students. Informed consent was diligently obtained from all students who participated in the study, and their parents were also required to consent. The investigator provided comprehensive and clear verbal and written information to the study subjects regarding the study’s nature, objectives, and potential risks and benefits. Informed consent was collected in written and dated form. Any modifications to the study protocol that may impact the study’s execution, student benefit, or student safety will require a formal protocol amendment. Such amendments will be subject to approval by the Vice-Chancellor of Research and the Ethics Committee of Shiraz University of Medical Sciences. | PMC10641928 | ||
Consent for publication | Not applicable. | PMC10641928 | ||
Competing interests | The authors declare no conflicts of interest regarding this research, authorship, or publication. | PMC10641928 | ||
List of abbreviations | Not applicable. | PMC10641928 | ||
References | PMC10641928 | |||
1. Introduction | movement sensations, post-stroke, Stroke, neuromuscular disability, lower-limb impairments, sudden loss of focal neurological function, stroke, urban mobility, visual motor imagery, hemorrhage, neurological diseases, neuromuscular disabilities, kinesthetic motor imagery, MP, infarction | NEUROLOGICAL DISEASE, STROKE, STROKE, EVENT, DISORDERS, HEMORRHAGE, INFARCTION, CORTEX, BRAIN INFARCTION | Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies. While upper-limb recovery has been extensively studied, research on lower limbs is still limited, despite their important role in locomotion, independence, and good quality of life. As the life and social costs due to neuromuscular disability are significant, the relatively low cost, safety, and portability of tDCS devices, combined with low-cost robotic systems, can optimize therapy and reduce rehabilitation costs, increasing access to cutting-edge technologies for neuromuscular rehabilitation. This study explores a novel approach by utilizing the following processes in sequence: tDCS, a motor imagery (MI)-based brain-computer interface (BCI) with virtual reality (VR), and a motorized pedal end-effector. These are applied to enhance the brain plasticity and accelerate the motor recovery of post-stroke patients. The results are particularly relevant for post-stroke patients with severe lower-limb impairments, as the system proposed here provides motor training in a real-time closed-loop design, promoting cortical excitability around the foot area (Cz) while the patient directly commands with his/her brain signals the motorized pedal. This strategy has the potential to significantly improve rehabilitation outcomes. The study design follows an alternating treatment design (ATD), which involves a double-blind approach to measure improvements in both physical function and brain activity in post-stroke patients. The results indicate positive trends in the motor function, coordination, and speed of the affected limb, as well as sensory improvements. The analysis of event-related desynchronization (ERD) from EEG signals reveals significant modulations in Mu, low beta, and high beta rhythms. Although this study does not provide conclusive evidence for the superiority of adjuvant mental practice training over conventional therapy alone, it highlights the need for larger-scale investigations.Stroke is a clinical condition that consists of a sudden loss of focal neurological function due to an infarction or hemorrhage in the central nervous system (CNS) [The social and economic costs directly related to neuromuscular disabilities, such as stroke, are significant. This includes health and rehabilitation services, specific projects aimed at the labor market, education and professional training, social benefits, the distribution of assistive devices, and urban mobility [Studies conducted by Mohammadi, A., 2016 [Another type of innovative tool used to help generate neuroplasticity is a brain-computer interface (BCI). BCI is a communication system that measures the CNS activity and converts it into artificial commands to control end-applications [MI is defined as a dynamic event during which an individual mentally reproduces an action, and it encompasses two fundamental types: kinesthetic motor imagery (KMI) and visual motor imagery (VMI). KMI involves simulating movement sensations without actual execution, and VMI is primarily centered around visualizing movement execution. Both KMI and VMI engage overlapping neural networks found in regions like the primary motor cortex, supplementary motor areas, somatosensory cortex, and cerebellum [Notably, previous studies utilizing tDCS in association with MI-BCI have primarily focused on lower-limb rehabilitation using costly equipment or have centered on studies with healthy individuals. For instance, the investigation by Chew [The proposed method integrates cutting-edge technologies including tDCS, MI-BCI, VR, and a customized MP to facilitate the lower-limb rehabilitation of post-stroke patients. This innovative system also introduces a novel approach to BCI calibration, combining EEG signals from both MI and actual movements [ | PMC10708803 |
2. Materials and Methods | PMC10708803 | |||
2.1. Patient | right hemiparesis of a subacute, post-stroke, stroke, hemorrhagic post-stroke, aphasia | STROKE, SKIN LESIONS, OSTEOPOROSIS | The patient in our research was a 73-year-old male, suffering from right hemiparesis of a subacute (2 months) hemorrhagic post-stroke, who was followed by a multidisciplinary rehabilitation team composed of clinical staff and engineers for 3 weeks.The inclusion criteria to be included in this research were as follows: one participant in the subacute phase to chronic post-stroke; stable medical condition; good understanding and ability to follow instructions according to the score on the Mini-Mental State Examination (MMSE); and good tolerance when standing using or not using support. The exclusion criteria were scores above 2 on the Ashworth Scale; severe aphasia; severe perceptual problems or other neurological conditions; severe osteoporosis; skin lesions at the electrode site; very limited visual ability; more than one stroke episode; and no signed informed consent for experiments. | PMC10708803 |
2.2. Study Design | This study adopted an ATD (quasi-experimental), as suggested by Slijper in 2014 [This research was approved by the Research and Ethical Committees of the UFES/Brazil (registry number CAAE: 46099421.9.0000.5542) and conducted in accordance with the Declaration of Helsinki. | PMC10708803 | ||
2.3. Protocol | In our protocol, before the use of the BCI, tDCS was applied for 20 min, with an intensity of 2 mA, following the dual-tDCS model, with the anode positioned over M1 of the affected hemisphere and the cathode positioned over the cerebellum of the contralateral hemisphere ( | PMC10708803 | ||
2.4. Brain-Computer Interface | MP | The system developed here is a novel MI-BCI [The EEG signal acquisition is achieved using the OpenViBE platform, with an EEG cap used to acquire brain signals through Ag/AgCl electrodes at 8 locations (FC1, FC2, C3, C4, Cz, CP1, CP2, and Pz) positioned according to the International 10–20 system. The A1 Ground (GND) and A2 reference (REF) electrodes are placed on the left and right ears, respectively. The acquired EEG signals are then processed and classified to recognize MI patterns related to pedaling movements. The MI classification is an essential step in translating the user’s intentions into control commands for the MP.The MP is designed to be controlled by the user’s brain signals, where a communication and control interface board facilitates the transmission of classification outputs obtained from the MI recognition phase. In this way, users can effectively command the MP using their brain activity.To provide an immersive experience, the system incorporates a VR device. The movement of the MP is captured through an IMU with a Bluetooth connection, allowing real-time data transfer to a specific computer running the VR environment, created by us. This VR device offers visual feedback to the user, creating an interactive and engaging virtual environment during the rehabilitation process, in order to ensure the user’s motivation. In the virtual environment, there is a first-person avatar seated on a tricycle and a real customized MP.The system employs a script developed in Python3 (Version 3.8.0, Python Software Foundation, Wilmington, DE, USA) and an MQTT protocol for efficient communication between the various components. Notably, OpenViBE (Version 3.5.0, Free Software Foundation, Boston, MA, USA), Python3, and the MQTT broker operate simultaneously on a single laptop, streamlining the system’s performance.The MI-BCI proposed in this study is divided into two phases: the calibration phase and the online phase. During the calibration phase, EEG signals collected during rest, motor imagery, and passive pedaling are processed to create a robust classification model for MI recognition. Subsequently, in the online phase, this model is employed, enabling the user to command the customized motorized pedal accurately.In summary, the developed system composed of MI-BCI and VR with a pedal end-effector offers a promising approach to neurological rehabilitation. By leveraging brain signals to control the MP and providing real-time visual feedback through VR, the system aims to enhance motor function and facilitate effective rehabilitation processes. The integration of these components on a single laptop ensures efficiency and practicality in its implementation.The motor training was conducted immediately after the tDCS application. The pedaling exercise lasted 20 min, and the training sessions were carried out at the same time of the day, five times a week, for 3 weeks. | PMC10708803 | |
2.5. Functional and Somatosensory Outcomes | neuropathic diseases | SENSORY DISORDERS, PBC, BEND | In our protocol, the subject was subjected weekly, and at the end of the training period, to functional and somatosensory assessments by the same evaluator. For function evaluation, we used the following metrics: Fugl-Meyer [We also used the Semmes Weinstein Monofilaments to evaluate the subject’s sensitivity to crude to fine touch in the dermatomes. This type of test is considered an easy-to-use and inexpensive touch threshold test, and it is generally used by clinicians to evaluate sensory disorders in neuropathic diseases. This test is carried out using flexible nylon monofilaments of equal length with different diameters; the thicker the filament becomes, the more pressure is necessary to bend the filament. These filaments are labeled to give a linear scale of perceived intensity, with weights ranging between 0.2 and 300 g [All functional parameters of this study produced quantitative variables, whose comparison in the same subject in different situations was performed using the Friedman test in the R Studio (Version 2022.12.0.0 Posit Software, PBC, USA), Version 19.1.3, with an adjusted level of significance at 0.05. Post hoc comparisons using Bonferroni correction were used to indicate the mean score for different conditions.The Friedman test was chosen because of its non-parametric nature, applicability to related samples, tolerance for ordinal data, and robustness to outliers. All these requirements are aligned with the characteristics of single-subject designs. In our study, we collected data from the same patient under various conditions, often on an ordinal scale. This test also provides the flexibility to perform post hoc tests for pair-wise comparisons when significant differences are detected, enabling one to pinpoint where these differences exist. | PMC10708803 |
2.6. Data Analysis | MP | To investigate the cortical effects using our BCI, we analyzed the significant event-related desynchronization (ERD) patterns in the time-frequency representation of the EEG signals. Additionally, we performed Pearson correlation analyses between cycling velocities in the Mu, low beta, and high beta rhythms.The significant ERD pattern analysis only involved successful pedal activation through MI trials with passive movement feedback. We extracted 7-s segments aligned to the MP’s onset movement (being 0 s the onset). Each segment included a 2-s baseline period preceding MI recognition (−3 to −1 s), a 1-s MI period (−1 to 0 s) for pedal activation, and a 4-s period after MI recognition representing passive movements (0 to 4 s). EEG data from −2.0 to −1.0 s served as a reference or baseline, whereas data from 0 to 3 s represented cortical activity during passive pedaling (PP). The segments were first band-pass filtered from 0.1 to 40 Hz and later analyzed through the t-percentile bootstrap algorithm to determine significant ERD patterns, considering significance levels of 0.01 and 0.20 (confidence intervals of 99% and 80%, respectively). | PMC10708803 | |
3. Results | PMC10708803 | |||
3.1. Functional and Somatosensory Parameters | A summary of the data obtained through functional evaluation is presented in The Semmes Weinstein Monofilaments test was used to evaluate the somatosensory parameter, whose results are described in | PMC10708803 | ||
3.2. Quantitative EEG | The ERD patterns were analyzed in Matlab after each protocol session (We also tested the Pearson correlation between the velocities of cycling rhythms in Mu, low beta, and high beta ( | PMC10708803 | ||
4. Discussion | post-stroke, motor impairments, neuromuscular rehabilitation post-stroke, a loss of motor function, stroke, somatosensory deficits, urinary incontinence, pain, leg motor deficits, attention and motor imagery, MP, Sensory impairments | STROKE, MOTION SICKNESS, URINARY INCONTINENCE, CORTEX | We investigated the sequential use of tDCS and MI-BCI, as well as the use of a VR and an MP device, for neurological recovery in people with a loss of motor function due to stroke. The aim of this research was to measure and compare the effects of tDCS in a post-stroke patient. Overall, we report two important results: first, an improvement in somatosensory parameters; second, a correlation between cycling velocities and the Mu, low beta, and high beta rhythms. However, despite the good results that we have obtained, as our findings are based on only one subject, the results of such analyses should be treated with considerable caution.The existing literature on tDCS has primarily focused on upper-limb rehabilitation [Assessing post-stroke body function and structure is crucial in quantifying motor impairments, predicting outcomes, monitoring recovery and therapy responses, and guiding treatment decisions. Valid and standardized measures are essential in assessing various aspects, including neural systems, gait velocity for leg motor deficits, and somatosensory deficits. The FMA quantitatively evaluates motor and sensory impairment in post-stroke subjects and, in our study, it showed gradual improvements in coordination and movement speed on the hemiparetic side, likely due to active locomotion using our BCI and physical training. We believe that combining this with tDCS in our protocol potentiated brain plasticity. Our results differ from a related study [The Semmes Weinstein Monofilaments test was employed to assess the somatosensory parameter, offering an inexpensive, non-invasive, and user-friendly means of quantifying skin pressure detection thresholds. Our results demonstrated the recovery of protective sensitivity, discrimination of various sensory attributes, and transitions from incapacity to diminished protective sensation at specific sites. Sensory impairments after stroke, spanning various modalities, including tactility, pain, temperature, pressure, vibration, proprioception, stereognosis, and graphesthesia, are closely tied to activity limitations and participation restrictions [Clinical evidence supports tDCS’ efficacy in enhancing muscular strength, motor coordination, and mobility, emphasizing its non-invasive potential in optimizing post-stroke neuromuscular rehabilitation and improving life quality [tDCS is a neuromodulatory technique gaining prominence in the realm of neuromuscular rehabilitation post-stroke. Through the application of low-intensity electrical currents to the cerebral cortex, tDCS engenders neuronal membrane polarization, thereby creating alterations in cortical excitability within specific brain regions. Mainly targeting the motor cortex, tDCS endeavors to induce synaptic plasticity, thereby facilitating the restructuring of neural pathways disrupted by stroke [In the context of EEG analysis during tDCS and BCI application, distinctive neural oscillatory bands are scrutinized for insights into the neural mechanisms at play. Mu band analysis reveals that positioning the anode over the affected hemisphere’s primary motor cortex potentially increases excitability in relevant regions, augmenting desynchronization tendencies, and is possibly linked to bolstered attention and motor imagery processes. Concurrently, cathodal placement over the cerebellum of the unaffected hemisphere might exert inter-hemispheric connectivity effects and cerebellar modulation over motor function, whose concerted influence results in augmented synchronization within the Mu band. Likewise, in the low beta band, anodal tDCS placement appears to modulate cortical excitability, concomitantly raising synchronization levels, possibly associated with enhanced sensory processing. In the high beta band, the combined effect of tDCS and BCI is projected to induce cortical inhibition, instigating desynchronization within select cortical regions, potentially related to motor activity regulation. Notably, cathodal placement over the unaffected hemisphere’s cerebellum may facilitate functional integration between pertinent regions, leading to elevated synchronization. To summarize, the combination of tDCS and BCI, with specific montage arrangements, exerts a discernible influence on diverse brain oscillatory patterns, leading to distinctive synchronization and desynchronization responses, as delineated in our research.Despite noticeable progress in functional recovery, statistical significance in terms of both ambulatory and sensory outcomes was not achieved. In addition, an important limitation of this study was the evaluation of only one subject. However, it is important to consider the context in which the study was conducted, during the height of the COVID-19 pandemic, which influenced the chosen methodology. Nevertheless, this preliminary study is still valuable as it provides a foundation for future studies involving a larger and more diverse participant group.A potential pathway to explore for the promotion of a wider range of neurophysiological engagement conducive to enhancing functional recovery is to augment the frequency and duration of the training sessions over several weeks. This strategic intensification may have the potential to enhance the inherent neurological modulatory effects of tDCS, which could lead to more substantial advancements in facilitating rehabilitative progress related to ambulation, for example.Importantly, it should be noted that interruptions occurred during some protocol sessions, as the participant had urinary incontinence and discomfort caused by motion sickness while using the VR glasses. | PMC10708803 |
Author Contributions | J.P.S.L.: conducted the research, analysis, writing, review and editing; L.A.S.: performed the research, analysis, data curation, writing and editing; D.D.-R.: conceptualization, supervision, review and editing; V.F.C.: performed the research; E.M.N.-P.: conceptualization; T.F.B.-F.: conceptualization, supervision, review and editing. All authors have read and agreed to the published version of the manuscript. | PMC10708803 | ||
Institutional Review Board Statement | This research was approved by the Research and Ethical Committees of the UFES/Brazil, under the code number CAAE 46099421.9.0000.5542, and conducted in accordance with the Declaration of Helsinki. | PMC10708803 | ||
Informed Consent Statement | The patient gave his informed consent with a handwritten signature for inclusion before he participated in the study. | PMC10708803 | ||
Data Availability Statement | The data that support the findings of this study are available upon reasonable request from the authors. | PMC10708803 | ||
Conflicts of Interest | The authors declare no conflict of interest. | PMC10708803 | ||
References | MP, discrimination loss | Assembly modeling with the tDCS equipment used in our study, with the anode positioned over M1 and the cathode positioned over the cerebellum of the other hemisphere.Methods: (1) The subject is subjected to brain stimulation through transcranial direct current stimulation (tDCS). (2a) The brain-computer interface (BCI) uses electroencephalography signals to control the on/off and velocity of the motorized pedal. (2b) The BCI processes the brain signals, sending them to turn on the motorized pedal. (2c) The virtual reality (VR) device is used to active motor imagery (MI).Event-related desynchronization (ERD) analysis using the time-frequency representation. The ERD patterns were analyzed in Matlab after each protocol session. The distribution of ERD changes in the time-frequency representation over Cz and topographic maps of the mean ERD power obtained while the patient was pedaling the MP for (Pearson correlation between the velocities of cycling and rhythms 8–12 Hz (Mu), 13–22 Hz (low beta), and 23–35 Hz (high beta). The Pearson correlation coefficient measures how closely two variables are related on a scale from −1 (red) to 1 (blue), where 0 (white) represents no linear correlation.Functional parameters for the assessed subject.Mean evolution of every somatosensory parameter measured over the time course of our study.We can interpret the target in grams (g) in the following way: 0.05 g as normal sensation in the hand and foot; 0.2 g as decreased sensitivity and difficulty discriminating texture (light touch); 2.0 g as decreased protective sensitivity in the hand, inability to discriminate texture, and difficulty discriminating shapes and temperature; 4.0 g as loss of protective sensation in the hand and sometimes the foot, loss of texture discrimination, and inability to discriminate shapes and temperature; 10 g as loss of protective sensation in the foot, texture discrimination loss, and inability to discriminate shapes and temperature; and 300 g as only the feeling of deep pressure remaining. The parameters were evaluated according to the dermatome levels. | PMC10708803 | |
Background | diabetes | DIABETES | In high-resource settings, structured diabetes self-management education is associated with improved outcomes but the evidence from low-resource settings is limited and inconclusive. | PMC9957611 |
Aim | diabetes | TYPE 2 DIABETES, DIABETES | To compare, structured diabetes self-management education to usual care, in adults with type 2 diabetes, in low-resource settings.Research design and methods. | PMC9957611 |
Design | diabetes | TYPE 2 DIABETES, DIABETES | Single-blind randomised parallel comparator controlled multi-centre trial.Adults (> 18 years) with type 2 diabetes from two hospitals in urban Ghana were randomised 1:1 to usual care only, or usual care plus a structured diabetes self-management education program. Randomisation codes were computer-generated, and allotment concealed in opaque numbered envelopes. The intervention effect was assessed with linear mixed models.Main outcome: Change in HbA1c after 3-month follow-up.Primary analysis involved all participants.Clinicaltrial.gov identifier:NCT04780425, retrospectively registered on 03/03/2021. | PMC9957611 |
Results | death, diabetes | RECRUITMENT, DIABETES | Recruitment: 22We randomised 206 participants (69% female, median age 58 years [IQR: 49–64], baseline HbA1c median 64 mmol/mol [IQR: 45–88 mmol/mol],7.9%[IQR: 6.4–10.2]). Primary outcome data was available for 79 and 80 participants in the intervention and control groups, respectively. Reasons for loss to follow-up were death (n = 1), stroke(n = 1) and unreachable or unavailable (n = 47). A reduction in HbA1c was found in both groups; -9 mmol/mol [95% CI: -13 to -5 mmol/mol], -0·9% [95% CI: -1·2% to -0·51%] in the intervention group and -3 mmol/mol [95% CI -6 to 1 mmol/mol], -0·3% [95% CI: -0·6% to 0.0%] in the control group. The intervention effect was 1 mmol/mol [95%CI:-5 TO 8 p = 0.726]; 0.1% [95% CI: -0.5, 0.7], p = 0·724], adjusted for site, age, and duration of diabetes.No significant harms were observed. | PMC9957611 |
Conclusion | diabetes | DIABETES | In low-resource settings, diabetes self-management education might not be associated with glycaemic control. Clinician’s expectations from diabetes self-management education must therefore be guarded. | PMC9957611 |
Supplementary Information | The online version contains supplementary material available at 10.1186/s12913-023-09188-y. | PMC9957611 | ||
Keywords | PMC9957611 | |||
What is already known on this topic? | diabetes | DIABETES |
In high-resource settings, structured diabetes self-management education is associated with improved outcomes. | PMC9957611 |
What this study adds? |
There was no between group difference in mean HbA1c at 3 months following a 6-hour structured DSME intervention.HbA1c decreased by 9 mmol/mol [95%CI:-13 to-5, HbA1c decreased by-3 mmol/mol [95%CI:-6 to 1, | PMC9957611 | ||
What are the implications of the study? | diabetes | DIABETES | In low-resource settings, the effect size of structured diabetes self-management education on glycaemic control may be limited and thus, clinician’s expectations from diabetes education must be guarded. | PMC9957611 |
Background | diabetes, Diabetes | DIABETES, DIABETES | Diabetes is a long-standing epidemic with over half a billion adults affected globally. [Self-care is essential for PLD. This underpins the need for self-management education. In high-income countries, structured diabetes self-management education (DSME) programmes such as, the Diabetes Education and Self-Management for Ongoing and Newly Diagnosed (DESMOND) program, are associated with improved outcomes [Indeed, DSME services are limited in Ghana, a low-middle-income country. | PMC9957611 |
Methods | PMC9957611 | |||
Study design and approval | TYPE 2 DIABETES | A multicentre, parallel-group, single-blind randomised controlled trial was conducted at two hospitals (WGMH and KBTH) in Accra, Ghana. Adults living with type 2 diabetes were randomised 1:1 to structured DSME plus usual care, or usual care only. | PMC9957611 | |
Ethical approval | Ethical approval was provided by the Ghana Health Service Ethics Review Committee (protocol ID no: GHS-ERC 009/11/20), and the Institutional Review Board of KBTH (protocol ID no: KBTH-IRB 000,175/2021). | PMC9957611 | ||
Study participants and study setting | T2DM, chronic kidney disease | SICKLE CELL DISEASE | Eligibility criteria included aged 18 years or above, ability to participate in activities in a group setting, known to have T2DM, and not known to have chronic kidney disease or sickle cell disease.The study was conducted between January-May 2021, at two public primary facilities in Accra, Ghana. Potential participants were identified by searching electronic medical records of the study sites. Using attendance records, trained staff called all potential participants meeting eligibility criteria and invited them to participate. Participants, who expressed interest in the study, were given appointments for a screening visit at the study sites. Participants were recruited from 22Prior to any study procedures, all participants gave written informed consent in person. Participants received reimbursement for travel costs and time. | PMC9957611 |
Randomisation and masking | Participants were randomly assigned either to usual care, or usual care plus intervention. Usual care at KBTH polyclinic consisted of informal brief education given by doctors whilst consulting. At WGMH, usual care consisted of unstructured group education, lasting approximately 30 min, delivered on clinic days; by nurses.Enrolled patients were randomised the same day. Stratified randomisation, by participant age (< 40 years or ≥ 40 years), was carried out in variable blocks with the aid of a centralised computer-generated sequence. Each patient randomised had an electronically generated unique identification number matching the assigned study arm. Allotment was concealed in sequentially numbered opaque envelopes and sealed. Care providers at both hospitals were blinded. | PMC9957611 | ||
Procedures | PMC9957611 | |||
Intervention | coronavirus infectious disease-2019, Diabetes | DIABETES | The intervention tested was a structured DSME program which had been adapted from DESMOND: EXTENDing availability of self-management structured education programmes for people with type 2 Diabetes in low-to-middle income countries (EXTEND). EXTEND has been piloted in Malawi and Mozambique [We further culturally adapted EXTEND to the Ghanaian community; citing local cuisine and contextualising examples [Five community health nurses and one medical officer were trained virtually, by DESMOND trainers to deliver the intervention. The intervention was delivered in-person, while observing all coronavirus infectious disease-2019 (COVID-19) protocols. The intervention consisted of one session of structured DSME, delivered by two educators to groups of six to ten participants in one day, over 6 h. The delivery of the intervention was completed within 2 weeks of randomisation. The intervention was delivered by providers not directly involved in patient care. | PMC9957611 |
Follow-up intervals and assessments | The first 206 patients were consecutively randomised 1:1 either to structured DSME plus usual care or usual care only (Fig. Trial profileDespite prior acceptance of the invitation to participate in endline data collection, some participants failed to show up by the trial end date. Specifically, 71 participants in the intervention group and 78 in the control group completed follow-up at 3 months. Ten participants completed follow-up between 3 to 5 months: eight in the intervention and two in the control group. | PMC9957611 | ||
Outcomes | diabetes-related distress, diabetes-related emotional distress | ADVERSE EVENTS | The primary outcome was change in HbA1c after 3-month follow-up. HbA1c was assessed centrally in an accredited laboratory, adhering to international criteria, set out according to International Organisation for Standardisation standards (ISO Standard 15,189:2012). HbA1c measurement was conducted using the turbidimetric inhibition immunoassay method, with a ROCHE COBAS intergra 400 plus analyser.Secondary outcomes were, changes in clinical, psychological, and self-care variables. Specifically, the clinical outcomes were change in weight, waist circumference, and blood pressure respectively; the psychological outcomes were changes in diabetes-related distress scores[The SDSCA scale assesses the level of self-care in five domains, namely diet, exercise, glucose monitoring, foot care, and smoking. The WHO Qol Bref instrument assess the quality of life. We assessed diabetes-related distress with, the problem areas in diabetes-5 (PAID-5) scale. Increasing scores indicate increasing distress; scores of 8 or more suggest diabetes-related emotional distress [At follow-up, we assessed adverse events using a standardised questionnaire. | PMC9957611 |
Statistical analysis | PMC9957611 | |||
Sample size calculation | RECRUITMENT | Mean reductions in HbA1c, between baseline and follow-up, were assumed to be 0 mmol/mol (0·0%) in the usual care group, and 4·8 mmol/mol (0·5%) in the intervention group [At the time of recruitment, the COVID-19 epidemic was unfolding with vaccines not yet available. Considering the uncertainties surrounding the epidemic, we decided to assess all potentially eligible participants. Subsequently we consecutively randomised the first 206 of those meeting the eligibility criteria. | PMC9957611 | |
Baseline Characteristics | The baseline characteristics show high WHO Qol scores, despite low incomes, low literacy, and high unemployment levels (Table | PMC9957611 | ||
Secondary outcomes | SECONDARY | Similarly, there was insufficient evidence that the intervention had an effect on any of the secondary outcomes except for an improvement in physical health. The difference in physical health between the intervention and control was 3 ( | PMC9957611 | |
Adverse events | ASYMPTOMATIC HYPOGLYCAEMIA | No significant harms were observed. One participant however, had to be treated for symptomatic hypoglycaemia during delivery of the intervention. The participant’s medications included human insulin. | PMC9957611 | |
Discussion | irregular diabetes, diabetes | DISEASE, BLIND, COMPLICATIONS, DIABETES | Our aim was to study the association between structured DSME, and glycaemic control. Our results show that, in people living with diabetes (PLD) in resource-constrained settings, structured DSME may not be associated with change in HbA1c at 3-months. A clinically relevant reduction in HbA1c was observed in the intervention group but not in the control group.Reduction in HbA1c is associated with less micro-vascular complications and may also reflect better self-management routines. In sub-Saharan Africa, many PLD have poor self-management skills and poor diabetes outcomes [There are multiple and complex barriers to self-management in low-resource settings. These barriers include irregular diabetes supplies, financial constraints, low health literacy and culture [The DSME intervention we tested was culturally tailored for the Ghanaian population, and linguistically suited to low-literacy levels. This may potentially explain the clinically relevant and significant reduction in HbA1c within the intervention group. HbA1c is dependent on self-management routines. The changes in the summary of diabetes self-care activities scores suggest an improvement in self-management routines within the intervention group.Our findings align with findings from most previous studies [The studies included in this systematic review share some characteristics with our study and this may contribute to the congruence in the findings [The baseline mean HbA1c was over 10% in Hailu et al.’s study among participants in Ethiopia. Additionally, 50% of their study population had lived with diabetes for over 10 years and participants received six sessions of DSME over a 9-month period [In summary, despite the case-mix variation between studies there is homogeneity in the estimates of the association between structured DSME and glycaemic control in PLD in Africa [Our findings of a null association between structured DSME and glycaemic control are inconsistent with studies from Kuwait, Nigeria, and our recently published systematic review of structured DSME in low- middle-income countries[Our findings on HbA1c are consistent with those of Davies et al. in a study undertaken in the UK[In resource constrained-settings intervention contact time can limit sustainability and scalability of a structured education program. It is not clear what the relation between intervention contact time and reduction in HbA1c is. DSME Interventions with contact time greater or equal to 10 h, have been shown to be associated with significant reductions in HbA1c in a systematic review of over 100 varieties of DSME programs [For most participants in our study, health literacy was low, and the monthly cost of care was greater than half of their monthly income. The average cost of managing one person with diabetes in a clinic in Accra in 2009 was about US$ 28 monthly [Our inability to standardise usual care between groups is also a potential source of bias. Due to the nature of the intervention, it was not possible to blind assessors, and this might be responsible for ascertainment bias. However, the primary outcome, change in HbA1c was an objective outcome thus limiting the risk of bias. Higher baseline HbA1c values in the intervention group, may indicate that those in the intervention group had more advanced disease. This difference could also have biased our estimate. Reassuringly these differences were not statistically significant; pointing to a robust randomization. Stratification on baseline HbA1c would have resulted in more balanced groups. At the time of randomisation however,, baseline HbA1c values were not known.The randomised controlled trial design, and the use of a culturally adapted intervention are strengths of this study. This design increases the internal validity of the estimate of the effect of structured education on glycaemic control in the population.The study was set in Ghana, in two public health facilities, where the national health insurance scheme is the main means of healthcare financing. Our findings may therefore not be generalisable to community-based interventions or private facilities. We excluded participants who were not ambulant, could not participate in group activities and who were not primarily responsible for their care, thus further limiting the generalisability of our findings. | PMC9957611 |
Conclusion | Glycaemic control was not associated with DSME in this study, although the reduction in HbA1c was larger in the intervention group compared to usual care. Ideally, DSME equips individuals with skills for decision making and taking action. We thus recommend larger cluster randomised studies, with longer duration of follow-up, which focus on enumerating the effect of structured DSME on glycaemic control, in resource-constrained settings. | PMC9957611 | ||
Acknowledgements | Diabetes | DIABETES | We acknowledge the support of MDS-Lancet laboratories Ghana, Leicester Diabetes Centre, National Institute for Health Research Applied Research Collaboration-East Midlands, Leicester General Hospital, Leicester, U.K and National Institute for Health Research (NIHR) Leicester Biomedical Research Centre, Leicester, UK for providing the intervention materials, Sedzro Kojo Mensah, for managing the generation of the randomisation codes. | PMC9957611 |
Authors' contributions | MH, MJD | RL led in conceptualization of the study, drafting protocol, and data collection. RL wrote the first draft of the report with input from MAC, KKG, GOA, MMB, DD, and MH. MAC, KKG, RG, GOA, FA, MC, MH, MJD, IA, AY reviewed the protocol and submitted it for ethical clearance. DD participated in delivery of the intervention and data collection. RL prepared the analysis plan. Prior to seeing the data, MMB modified the analysis plan together with RL to increase the robustness of the analysis. SI then made additional modifications to the analysis plan together with RL. SI subsequently conducted the analysis. RL, GOA, SI, MAC, and KKG interpreted the analysis. All authors made corrections to the manuscript and approved the final document. All authors had full access to all the data in the study and have full responsibility for the decision to submit for publication. RL and DD have verified the data. | PMC9957611 | |
Funding | RL is supported by the UMC Utrecht Global Health Support PhD programme. This study was supported in part by Novo Nordisk. They had no role in the study design, collection, analysis, interpretation of data, writing of the report or decision to submit the article for publication. | PMC9957611 | ||
Availability of data and materials | The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. | PMC9957611 |
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