ArticleJMIR mHealth and uHealth2019
Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care.
Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Aesthetic Experience in the Acceptance of Wearable Technology for People With Dementia: Critical Interpretive Synthesis.JMIR aging · 2025Pooled it
- Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Wearable and Portable GPS Solutions for Monitoring Mobility in Dementia: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Capturing cognitive capacity in the everyday environment across a continuum of cognitive decline using a smartwatch n-back task and ecological momentary assessment.Neuropsychology · 2025Article
- Human-centred artificial intelligence for mobile health sensing: challenges and opportunities.Royal Society open science · 2023Review
- Remote Patient Monitoring for Neuropsychiatric Disorders: A Scoping Review of Current Trends and Future Perspectives from Recent Publications and Upcoming Clinical Trials.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2022Article
- Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study.JMIR formative research · 2022Article
- Comparisons Between GPS-based and Self-reported Life-space Mobility in Older Adults.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Does Being Ill Improve Acceptance of Medical Technology?-A Patient Survey with the Technology Usage Inventory.International journal of environmental research and public health · 2021Article
- Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia: Feasibility Case Series.JMIR formative research · 2019Article
- Rural-urban disparities of Alzheimer's disease and related dementias: A scoping review.Alzheimer's & dementia (New York, N. Y.)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others.
objectiveThe objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices.
methodsA mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week.
resultsThe evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones.
conclusionsThis study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.