cultural nuances influenced co-design workshops and artefacts. Adjustments to the rhythms of the workshops and co-design activities enabled "transferability" between rural and urban settings, and across diverse cultures, regardless of whether these settings include members from the same cultures.
CCS CONCEPTS• Human-centered computing → Participatory design; Contextual design; Field studies.
Introduction: Digital health support using mobile and digital technologies, such as MomConnect and WhatsApp, is providing opportunities to improve maternal and child healthcare in low- and middle-income countries. Yet, the perspective of health service providers, pregnant women, and mothers as recipients of digital health support is under-researched in rural areas. Material and Methods: An exploratory-descriptive qualitative research approach was adopted to reflect on the experiences of mothers, community leaders, and community health workers on mobile health opportunities in the context of maternal and child health in rural areas. Purposive sampling was used to select 18 participants who participated in the two focus groups and individual semi-structured interviews for data collection about digital maternal and child health support. The thematic open coding method of data analysis assisted authors in making sense of the given reflections of mothers, community leaders, and healthcare workers about digital health support. Results: Participants commented on different existing digital support apps and their importance for maternal and child health. For example, MoMConnect, Pregnancy+, WhatsApp, and non-digital resources were perceived as useful ways of communication that assist in improving maternal and child health. However, participants reported several challenges related to the use of digital platforms, which affect following the health instructions given to pregnant women and mothers. Conclusions: Participants expressed the significant role of digital support apps in maternal and child health, which is impacted by various challenges. Addressing the lack of digital resources could improve access to health instructions for pregnant women and mothers.
The COVID-19 pandemic reminded us of all the things we have taken for granted, things such as giving an old friend a hug or visiting our beloved grandparents were no longer things we just got to do. No family or industry was prepared for this rude awakening. It thus comes as no surprise that the research community was not an exception to this rule. The pandemic swiftly removed access to both remote and local communities and ceased ongoing studies with many institutions, prohibiting face-to-face in person research [2]. This left many researchers needing to find ways to work around the physical restrictions in order to engage with communities.Traditional research methods such as workshops, in person interviews and focus groups [6] were no longer available as tools that we could readily access and conduct as part of our studies. Online platforms started gaining huge momentum [4] and researchers did what researchers do best -find new ways to conduct research, even in the toughest of circumstances. Not surprisingly many articles started to touch upon how to conduct research during
There is a lack of professional rehabilitation therapists and facilities in low-resource settings such as Bangladesh. In particular, the restrictively high costs of rehabilitative therapy have prompted a search for alternatives to traditional in-patient/out-patient hospital rehabilitation moving therapy outside healthcare settings. Considering the potential for home-based rehabilitation, we implemented a low-cost wearable system for 5 basic exercises namely, hand raised, wrist flexion, wrist extension, wrist pronation, and wrist supination, of upper limb (UL) rehabilitation through the incorporation of physiotherapists' perspectives. As a proof of concept, we collected data through our system from 10 Bangladeshi participants: 9 researchers and 1 undergoing physical therapy. Leveraging the system's sensed data, we developed a diverse set of machine learning models. and selected important features through three feature selection approaches: filter, wrapper, and embedded. We find that the Multilayer Perceptron classification model, which was developed by the embedded method Random Forest selected features, can identify the five exercises with a ROC-AUC score of 98.2% and sensitivity of 98%. Our system has the potential for providing realtime insights regarding the precision of the exercises which can facilitate homebased UL rehabilitation in resource-constrained settings.
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