Implantation d’un système de vidéosurveillance intelligente pour détecter les chutes en milieu de vie
Introduction. Aging is associated with an increased risk of fall, which threatens Aging in Place. The numerous and serious consequences of falls on the older adult’s health and independence are reduced with a quick intervention. Yet the informal caregivers, who often intervene in case of a fall are not numerous enough and are often worn out because of the burden related to the care provided for the older adult (Ducharme, 2006; Wolff et al., 2017; World Health Organization, 2015). The development of alternatives to detect and alert in case of a fall becomes essential to facilitate Aging in Place in safety and to maintain a quality of life (van Hoof, Kort, Rutten, & Duijnstee, 2011). Many fall detection systems have been developed. However, they have limits (eg. the recording of personal data), that the intelligent videomonitoring system (IVS) tries to compensate. The IVS is composed of one camera linked to a computer and to the Internet. Based on the computerized analysis of the images, the IVS automatically detects falls and sends an alert to the chosen recipient (eg. the informal caregiver) on his smartphone, computer or tablet. The IVS preserves privacy with its closed circuit functioning: without a fall, the images are destroyed; in case of a fall, an image of the fall can be sent to the recipient. This image can be blurred at the request of the older adult. The 30 seconds before the fall can be recorded to document its causes, if the older adult authorizes it. Previous studies on the IVS show that the IVS has the potential to answer the users’ needs (Lapierre et al., 2016, 2015; Londei et al., 2009; Rougier, St-Arnaud, Rousseau, & Meunier, 2011). However, it is important to validate its technology and explore users’ perception in ecological conditions (at home with older adults at risk of fall) (Atoyebi, Stewart, & Sampson, 2015). Purpose. Based on the Model of Competence explaining the person-environment interactions (Rousseau, 2017), the study aims to explore the feasibility of the IVS implementation to detect falls at home in order to improve the older adult’s quality of life and decrease the caregiver’s burden. Methodology. The thesis follows a development research design (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) in four steps. Step 1 was two scoping reviews (Daudt, Van Mossel, & Scott, 2013) on fall detection technology and on wandering management technology respectively. Many databases have been searched (eg. CINHAL, Medline, Embase). Each step of the study selection, data extraction and analysis have been independently realised by two co-authors. Results were compared and disagreements were solved by consensus or by a third part intervention. Extracted data were descriptively analysed (Fortin & Gagnon, 2015). Step 2 was a multiple case study (Yin, 2014) with six older adults living alone with a risk of fall, on the implementation of a previous version of the IVS, the programmable videomonitoring system. The programmable videomonitoring system was installed for seven nights at home to observe participants walk when they went to the bathroom at night. Semi- structured interviews were realised before and after the experiment. Data were qualitatively analysed (Miles, Huberman, & Saldana, 2014). Step 3 was a proof of concept in two phases: 1) a simulation study in an apartment- laboratory (Contandriopoulos, Champagne, Potvin, Denis, & Boyle, 2005) and 2) a pre-test at home with young adults. Phase 1 implied a simulation of daily living scenarios and falls scenarios to estimate the sensitivity, specificity, error rate and accuracy of the IVS. The pre- test consisted in the implementation of the IVS at home for 28 days to anticipate the technological difficulties related to extended implementation. For the two phases, a logbook was completed to document the IVS functioning, then data were descriptively analysed. Step 4 was a multiple case study (Yin, 2014) with three dyads of older adults/caregivers. The included older adults had a high risk of fall and lived alone. The IVS was implemented for a two-month period with the informal caregiver as the alerts recipient. A semi-structured interview was realised before, at mid-term, and after the experiment. Data were qualitatively analysed (Miles, Huberman, & Saldana, 2014). Results. Results encompass the adaptation of the IVS to explore the feasibility of its implementation at home to detect serious falls. Step 1 highlighted the gaps in the literature, some of which were filled by the thesis project (eg. lack of studies exploring the implementation of ambient system in various homes). This step also enabled us to identify ways to improve the IVS and its implementation process. Step 2 highlighted factors facilitating or hindering the implementation of cameras system at home. Step 3 has enabled us to validate the technology in a similar environment to the older adult’s home and to solve technical difficulties related to the prolonged implementation. Finally, step 4 enabled us to explore the feasibility of the implementation of the IVS at older adults’ home for a two-month period. Discussion. This development research enabled us to adapt the IVS for its implementation by means of four research steps (scoping reviews, proof of concept, multiple case study), and then to show the feasibility of its implementation. Results led to the identification of factors influencing the IVS at home and enabled us to make recommendations in this regard. This thesis is original on three aspects: 1) the implication of a multidisciplinary team, 2) a user-based conception, 3) the implementation of the technology at home. Despite the remaining challenges regarding the implementation (eg. the performance discrepancy between the home and the apartment-laboratory), this study encourages the further development of the VSI. Conclusion. This thesis aimed to address the problematic of falls at home thanks to the implementation of the IVS to automatically alert the informal caregiver. Results from this development research highlight that the IVS may be a promising way to detect serious falls, to alert the caregiver and document the falls causes. Future researches should be involving quantitative designs, more specifically with more various profiles of informal caregivers and a longer period of implementation, to demonstrate the IVS outcomes. The IVS could then become accessible to the older adult to support Aging in place and relieve the caregiver’s burden.
Older adult fall aging-in-place home care caregiver technology video monitoring camera