Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Background: Falls are a major problem for older people and recurrent fallers are especially prone to severe consequences due to falls. This study investigated the association between chronic conditions and falls. Methods: Responses from 872 older persons (age 65–98) to a health questionnaire were used in the analyses. Characteristics and disease prevalence between recurrent fallers, one-time fallers and non-fallers were compared. A hierarchical clustering method was applied to find combinations of chronic conditions that were associated with recent recurrent falling. Results: The results showed that recurrent fallers had a higher number of diseases (median 4, interquartile range, IQR = 2.0–5.0) compared to non-fallers (median 2, IQR = 1.0–3.0). Eight clusters were formed based on the data. The participants in the low chronic disease cluster were younger, more physically active, not frail, and had fewer geriatric conditions. Multiple chronic disease cluster participants were older, less physically active, overweight (body mass index, BMI > 30), at risk of malnutrition, and had more geriatric conditions. Significantly increased risk of recurrent falls relative to the low chronic cluster was found for respondents in the osteoporosis cluster and multiple chronic disease cluster (OR = 5.65, 95% confidence interval CI: 1.23–25.85, p = 0.026, and OR = 13.42, 95% CI: 2.47–72.96, p = 0.002, respectively). None of the clusters were associated with increased risk of one-time falling. Conclusions: The results implicate that the number of chronic diseases is related with risk of recurrent falling. Furthermore, the results implicate the potential of identifying certain combinations of chronic diseases that increase fall risk by analyzing health record data, although further studies are needed with a larger population sample.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human–computer interaction, data management, and communication in AI implementation projects.

Quality of service (QoS) is becoming increasingly important with the rise of multimedia applications (e.g., voice over IP (VoIP), video conferencing, online gaming, and Internet protocol television (IPTV)) in public data networks. These applications demand real-time service, while the networks should still be able to transfer also other traffic reliably (e.g., data, e- mail, WWW). We have developed a measurement tool, which is able to measure one-way QoS performance statistics. The tool enables passive monitoring of the desired application(s) and gives information about the QoS provided by the network. The tool can be used practically over any kind of network structure as long as IP is supported. In this paper, we focus on detailed end-to-end delay characteristics measured in live HSDPA (high speed downlink packet access) enabled 3G network. HSDPA provides clearly better delay performance than the basic WCDMA (wideband code division multiple access). Interestingly, deterministic long time behavior in delay was observed, and also, discrete jitter levels were found. With argumentation, and finally with a trial, we proved that these phenomena originate from the CBR-type (constant bit rate) VoIP source traffic affecting with 3G network timing mechanisms and mutual clock drifts. As a side product, the study also showed that it is advisable to verify the operation of traffic generation tools before using them.

Mobile technology has been increasingly adopted in promotion of mental health among older people. This study assessed the feasibility of a mobile mental wellness training application for individual use and for group work from the perspectives of older adults and social care professionals. The older individuals recruited for the study were participants in a Circle of Friends group and family caregivers' peer support group offered by the communal senior services. The qualitative and quantitative results of interviews, questionnaires, observation, and application usage were reported. Seven older adults started using the application independently at home in parallel with the group activity. This study revealed new information regarding the barriers to the older adults' full adoption of such mobile technologies. The results indicated that there may be potential in the incorporation of mobile technologies in promotion of mental health of older people at group settings.