Arueyingho Oritsetimeyin, Aprioku Jonah Sydney, Marshall Paul, O'Kane Aisling Ann
University of Bristol, Bristol, United Kingdom.
University of Port Harcourt, Port Harcourt, Nigeria.
JMIR Diabetes. 2024 Aug 21;9:e56756. doi: 10.2196/56756.
A significant percentage of the Nigerian population has type 2 diabetes (T2D), and a notable portion of these patients also live with comorbidities. Despite its increasing prevalence in Nigeria due to factors such as poor eating and exercise habits, there are insufficient reliable data on its incidence in major cities such as Port Harcourt, as well as on the influence of sociodemographic factors on current self-care and collaborative T2D care approaches using technology. This, coupled with a significant lack of context-specific digital health interventions for T2D care, is our major motivation for the study.
This study aims to (1) explore the sociodemographic profile of people with T2D and understand how it directly influences their care; (2) generate an accurate understanding of collaborative care practices, with a focus on nuances in the contextual provision of T2D care; and (3) identify opportunities for improving the adoption of digital health technologies based on the current understanding of technology use and T2D care.
We designed questionnaires aligned with the study's objectives to obtain quantitative data, using both WhatsApp (Meta Platforms, Inc) and in-person interactions. A social media campaign aimed at reaching a hard-to-reach audience facilitated questionnaire delivery via WhatsApp, also allowing us to explore its feasibility as a data collection tool. In parallel, we distributed surveys in person. We collected 110 responses in total: 83 (75.5%) from in-person distributions and 27 (24.5%) from the WhatsApp approach. Data analysis was conducted using descriptive and inferential statistical methods on SPSS Premium (version 29; IBM Corp) and JASP (version 0.16.4; University of Amsterdam) software. This dual approach ensured comprehensive data collection and analysis for our study.
Results were categorized into 3 groups to address our research objectives. We found that men with T2D were significantly older (mean 61 y), had higher household incomes, and generally held higher academic degrees compared to women (P=.03). No statistically significant relationship was found between gender and the frequency of hospital visits (P=.60) or pharmacy visits (P=.48), and cultural differences did not influence disease incidence. Regarding management approaches, 75.5% (83/110) relied on prescribed medications; 60% (66/110) on dietary modifications; and 35.5% (39/110) and 20% (22/110) on traditional medicines and spirituality, respectively. Most participants (82/110, 74.5%) were unfamiliar with diabetes care technologies, and 89.2% (98/110) of those using technology were only familiar with glucometers. Finally, participants preferred seeking health information in person (96/110, 87.3%) over digital means.
By identifying the influence of sociodemographic factors on diabetes care and health or information seeking behaviors, we were able to identify context-specific opportunities for enhancing the adoption of digital health technologies.
尼日利亚相当大比例的人口患有2型糖尿病（T2D），而且这些患者中有相当一部分还患有合并症。尽管由于饮食和运动习惯不良等因素，T2D在尼日利亚的患病率不断上升，但在哈科特港等主要城市，关于其发病率以及社会人口因素对当前自我护理和使用技术的T2D协作护理方法的影响，可靠数据不足。再加上严重缺乏针对T2D护理的特定背景数字健康干预措施，这就是我们开展这项研究的主要动机。
本研究旨在（1）探究T2D患者的社会人口概况，并了解其如何直接影响他们的护理；（2）准确理解协作护理实践，重点关注T2D护理背景下的细微差别；（3）根据对技术使用和T2D护理的当前理解，确定改善数字健康技术采用率的机会。
我们设计了与研究目标一致的问卷，通过WhatsApp（Meta平台公司）和面对面互动获取定量数据。一场针对难以接触到的受众的社交媒体活动促进了通过WhatsApp发放问卷，这也让我们能够探索其作为数据收集工具的可行性。与此同时，我们亲自发放调查问卷。我们总共收集了110份回复：83份（75.5%）来自亲自发放，27份（24.5%）来自WhatsApp方式。使用SPSS高级版（版本29；IBM公司）和JASP（版本0.16.4；阿姆斯特丹大学）软件，通过描述性和推断性统计方法进行数据分析。这种双重方法确保了我们研究的数据收集和分析全面。
为了实现我们的研究目标，结果分为三组。我们发现，与女性相比，患有T2D的男性年龄显著更大（平均61岁），家庭收入更高，且通常拥有更高的学历（P = 0.03）。未发现性别与就诊频率（P = 0.60）或去药房频率（P = 0.48）之间存在统计学上的显著关系，文化差异也不影响疾病发病率。关于管理方法，75.5%（83/110）依赖处方药；60%（66/110）依赖饮食调整；分别有35.5%（39/110）和20%（22/110）依赖传统药物和精神疗法。大多数参与者（82/110，74.5%）不熟悉糖尿病护理技术，在使用技术的参与者中，89.2%（98/110）仅熟悉血糖仪。最后，参与者更喜欢亲自寻求健康信息（96/110，87.3%）而不是通过数字方式。
通过确定社会人口因素对糖尿病护理以及健康或信息寻求行为的影响，我们能够确定增强数字健康技术采用率的特定背景机会。
