Development of an Internet of Things Technology Platform (the NEX System) to Support Older Adults to Live Independently: Protocol for a Development and Usability Study

Background Ambient assisted living (AAL) technologies are viewed as a promising way to prolong aging in place, particularly when they are designed as closely as possible to the needs of the end users. However, very few evidence-based results have been provided to support its real value, notably for frail older adults who have a high risk of autonomy loss as well as entering a nursing home. Objective We hypothesized that the benefit from an AAL with a user-centered design is effective for aging in place for frail older adults in terms of everyday functioning (instrumental activities of daily-life scale). In addition, our secondary hypotheses are that such an AAL decreases or neutralizes the frailty process and reduces the rates of institutionalization and hospitalization and that it improves the psychosocial health of participants and their caregivers when compared with the control condition. We also assume that a large proportion of equipped participants will have a satisfactory experience and will accept a subscription to an internet connection to prolong their participation. Methods HomeAssist (HA) is an AAL platform offering a large set of apps for 3 main age-related need domains (activities of daily-living, safety, and social participation), relying on a basic set of entities (sensors, actuators, tablets, etc). The HA intervention involves monitoring based on assistive services to support activities related to independent living at home. The study design is quasi-experimental with a duration of 12 months, optionally extensible to 24 months. Follow-up assessments occurred at 0, 12, and 24 months. The primary outcome measures are related to everyday functioning. Secondary outcome measures include indices of frailty, cognitive functioning, and psychosocial health of the participants and their caregivers. Every 6 months, user experience and attitudes toward HA are also collected from equipped participants. Concomitantly, data on HA use will be collected. All measures of the study will be tested based on an intention-to-treat approach using a 2-tailed level of significance set at α=.05, concerning our primary and secondary efficacy outcomes. Results Descriptive analyses were conducted to characterize the recruited equipped participants compared with the others (excluded and refusals) on the data available at the eligibility visit, to describe the characteristics of the recruited sample at baseline, as well as those of the dropouts. Finally, recruitment at 12 months included equipped participants (n=73), matched with control participants (n=474, from pre-existing cohorts). The results of this study will be disseminated through scientific publications and conferences. This will provide a solid basis for the creation of a start-up to market the technology. Conclusions This trial will inform the real-life efficacy of HA in prolonging aging in place for frail older adults and yield an informed analysis of AAL use and adoption in frail older individuals. International Registered Report Identifier (IRRID) DERR1-10.2196/33351

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“…Therefore, multidomain AAL is still only in the design phase, as recently proposed with the next system study protocol [ 82 ].…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of an Ambient Assisted Living (HomeAssist) Platform for Supporting Aging in Place of Older Adults With Frailty: Protocol for a Quasi-Experimental Study

Sauzéon¹,

Edjolo²,

Amieva³

et al. 2022

JMIR Res Protoc

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“…N−m+1 l . The NEX project [27] developed an integrated IoT system to offer unobtrusive health and wellness monitoring to support older adults living independently in their homes. This involved the use of ambient sensors non-intrusively placed around the home to detect various domestic, in-home activities.…”

Section: Algorithm 1: Compute Periodicity Intensitymentioning

confidence: 99%

“…Collectively, data from these sensors capture the purposeful actions of the participants, i.e., they indicate a deliberate action, thus the opening or closing of doors, the movement of a person and the switching on or off of electrical devices. Sensor data were gathered from the in-home sensors from the 24 participants' homes over periods varying from 6 weeks to 6 months each [27].…”

Section: Algorithm 1: Compute Periodicity Intensitymentioning

confidence: 99%

Periodicity Intensity Reveals Insights into Time Series Data: Three Use Cases

Smeaton

2023

Algorithms

Self Cite

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Periodic phenomena are oscillating signals found in many naturally occurring time series. A periodogram can be used to measure the intensities of oscillations at different frequencies over an entire time series, but sometimes, we are interested in measuring how periodicity intensity at a specific frequency varies throughout the time series. This can be performed by calculating periodicity intensity within a window, then sliding and recalculating the intensity for the window, giving an indication of how periodicity intensity at a specific frequency changes throughout the series. We illustrate three applications of this, the first of which are the movements of a herd of new-born calves, where we show how intensity in the 24 h periodicity increases and decreases synchronously across the herd. We also show how changes in 24 h periodicity intensity of activities detected from in-home sensors can be indicative of overall wellness. We illustrate this on several weeks of sensor data gathered from each of the homes of 23 older adults. Our third application is the intensity of the 7-day periodicity of hundreds of University students accessing online resources from a virtual learning environment (VLE) and how the regularity of their weekly learning behaviours changes throughout a teaching semester. The paper demonstrates how periodicity intensity reveals insights into time series data not visible using other forms of analysis.

show abstract

“…they indicate a deliberate action thus the opening or closing of doors, the movement of a person and the switching on or off of electrical devices. Sensor data was gathered from the in-home sensors from the 24 participants' homes over periods varying from 6 weeks to 6 months each [27].…”

Section: Algorithm 1: Compute Periodicity Intensitymentioning

confidence: 99%