Electronic assistive technology for community-dwelling solo-living older adults: A systematic review

Song, Yu; Cammen, Tischa J. M. van der

doi:10.1016/j.maturitas.2019.04.211

Cited by 21 publications

(14 citation statements)

References 47 publications

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“…Furthermore, the intervention mapping approach led to a thorough needs assessment that was both informed by end users and the best available current evidence. The iterative continuous testing along development also helped avoiding the replication of common shortcomings of technology targeting older adults, like unfriendly features to their levels of technology literacy [ 11 ]. Early lab tests, for example, helped to pin-point components that needed adaptation (i.e., registration, links in emails) for older adults’ needs before large scale deployment, preventing later difficulties in the implementation and evaluation stages.…”

Section: Discussionmentioning

confidence: 99%

“…Conversely, it can also create problems, such as decreased sense dignity and stigmatization for the AT user or caregiver, or increased difficulties in performance if the AT does not fit adequately the user or caregiver’s needs which could lead to AT abandonment [ 5 , 6 , 7 ]. Some of these issues can be resolved through post-delivery training and follow-up, leading to better AT use and adoption [ 8 , 9 , 10 , 11 ]. The adoption of AT is behavior-based as it involves learning new behaviors around the use of objects and technology and integrating these learnings in one’s daily living [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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What’s behind the Dashboard? Intervention Mapping of a Mobility Outcomes Monitoring System for Rehabilitation

Auger

Guay

Pysklywec

et al. 2022

IJERPH

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Training and follow-up for older adults who received new assistive technology can improve device use adoption and function, but there is a lack of systematic and coordinated services. To address this gap, the Internet-based MOvIT+™ was designed to provide remote monitoring and support for assistive technology users and their caregivers. This paper presents the intervention mapping approach that was used. In step 1, we established a project governance structure and a logic model emerged from interviews with stakeholders and a systematic review of literature. In step 2, a modified TRIAGE consensus process led to the prioritization of thirty-six intervention components. In step 3, we created use cases for all intervention end users. In step 4, the intervention interface was created through iterative lab testing, and we gathered training resources. In step 5, a two-stage implementation plan was devised with the recruited rehabilitation sites. In step 6, we proposed an evaluation protocol. This detailed account of the development of MOvIT+™ demonstrates how the combined use of an intervention mapping approach and participatory processes with end users can help linking evidence-based, user-centered, and pragmatic reasoning. It makes visible the complexities behind the development of Internet-based interventions, while guiding future program developers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What’s behind the Dashboard? Intervention Mapping of a Mobility Outcomes Monitoring System for Rehabilitation

Auger

Guay

Pysklywec

et al. 2022

IJERPH

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“…The results showed that HA was well adopted (highly accepted and usable) by FI and their families or caregivers [ 18 ]. Moreover, after 6 and 9 months of follow-up, the benefits of measures of self-determination behaviors and IADL scale scores [ 9 , 19 ] were higher in equipped FI [ 18 - 20 ]. For instance, with normalized scores, at 9 months, the equipped group did not change its IADL scale score, whereas the control group lost 1 SD.…”

Section: Discussionmentioning

confidence: 99%

“…First, all the studies published so far on AT lack empirical evidence of their efficacy, mostly because of shortcomings related to study design (small sample size, irrelevant or nonstandardized measures, short follow-up duration, no control group, etc) [ 9 , 10 ]. Second, despite the many technological innovations available to assist older adults in their daily life [ 9 ], their silo-based nature (one technology per need) makes them challenging to integrate, as older adults require more services to assist an increasing number of ADLs because of multiple, various, and evolving task needs, particularly in the frailty context [ 11 ]. Indeed, the restricted capability to perform ADL remains extremely patient-dependent (individual and need variability) [ 6 ].…”

Section: Introductionmentioning

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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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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“…This high ratio of elderly people and increase in life expectancy combined with the fact that most of these people are living alone have made it necessary to develop more sophisticated techniques and technologies to monitor them. In this regard, artificial intelligence (AI) [3] plays an important role in healthcare, particularly in assistive care technologies [4] for old people owing to the spurt in the Internet of Things (IoT)-based technological applications. In assistive care technology, activity detection is one of the key tasks to prevent accidents that might occur to elderly people.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Activity Detection Using Wide-Angle Low-Resolution Infrared Array Sensor

2021

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In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) array sensors. Alongside with the main challenge which is how to further improve the performance of IR array sensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR array sensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors (32×24 pixels), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including "walking", "standing", "sitting", "lying", "falling", and the transition between the activities which is referred to as "action change". This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of activities. Through experiments, we show that the classification accuracy of the ceiling data, wall data, and combined data with the LSTM reach 0.96, 0.95, and 0.97, respectively.

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Electronic assistive technology for community-dwelling solo-living older adults: A systematic review

Cited by 21 publications

References 47 publications

What’s behind the Dashboard? Intervention Mapping of a Mobility Outcomes Monitoring System for Rehabilitation

What’s behind the Dashboard? Intervention Mapping of a Mobility Outcomes Monitoring System for Rehabilitation

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

A Novel Hybrid Deep Learning Model for Activity Detection Using Wide-Angle Low-Resolution Infrared Array Sensor

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