TY - JOUR A1 - Kernebeck, Sven A1 - Holle, Daniela A1 - Pogscheba, Patrick A1 - Jordan, Felix A1 - Mertl, Fabian A1 - Huldtgren, Alina A1 - Bader, Sebastian A1 - Kirste, Thomas A1 - Teipel, Stefan A1 - Holle, Bernhard A1 - Halek, Margareta T1 - A Tablet App– and Sensor-Based Assistive Technology Intervention for Informal Caregivers to Manage the Challenging Behavior of People With Dementia (the insideDEM Study): Protocol for a Feasibility Study (Preprint) JF - JMIR Res Protoc N2 - BACKGROUND Despite the enormous number of assistive technologies (ATs) in dementia care, the management of challenging behavior (CB) of persons with dementia (PwD) by informal caregivers in home care is widely disregarded. The first-line strategy to manage CB is to support the understanding of the underlying causes of CB to formulate individualized nonpharmacological interventions. App- and sensor-based approaches combining multimodal sensors (actimetry and other modalities) and caregiver information are innovative ways to support the understanding of CB for family caregivers. OBJECTIVE The main aim of this study is to describe the design of a feasibility study consisting of an outcome and a process evaluation of a newly developed app- and sensor-based intervention to manage CB of PwD for family caregivers at home. METHODS In this feasibility study, we perform an outcome and a process evaluation with a pre-post descriptive design over an 8-week intervention period. The Medical Research Council framework guides the design of this feasibility study. The data on 20 dyads (primary caregiver and PwD) are gathered through standardized questionnaires, protocols, and log files as well as semistructured qualitative interviews. The outcome measures (neuropsychiatric inventory and Cohen-Mansfield agitation inventory) are analyzed by using descriptive statistics and statistical tests relevant to the individual assessments (eg, chi-square test and Wilcoxon signed-rank test). For the analysis of the process data, the Unified Theory of Acceptance and Use of Technology is used. Log files are analyzed by using descriptive statistics, protocols are analyzed by using documentary analysis, and semistructured interviews are analyzed deductively using content analysis. RESULTS The newly developed app- and sensor-based AT has been developed and was evaluated until July in 2018. The recruitment of dyads started in September 2017 and was concluded in March 2018. The data collection was completed at the end of July 2018. CONCLUSIONS This study presents the protocol of the first feasibility study to encompass an outcome and process evaluation to assess a complex app- and sensor-based AT combining multimodal actimetry sensors for informal caregivers to manage CB. The feasibility study will provide in-depth information about the study procedure and on how to optimize the design of the intervention and its delivery. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11630 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-173061 VL - 8 IS - 2 ER - TY - JOUR A1 - Amaefule, Chimezie O. A1 - Goerss, Doreen A1 - Halek, Margareta A1 - Kernebeck, Sven A1 - Kirste, Thomas A1 - Teipel, Stefan J. T1 - PREDICTING DAYTIME MANIFESTATIONS OF CHALLENGING BEHAVIOURS IN ADVANCED STAGES OF DEMENTIA USING PRE-DAYTIME ACCELEROMETRY: POST-HOC ANALYSIS OF THE DZNE ROSTOCK INSIDEDEM STUDY JF - Alzheimer's & Dementia Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.jalz.2019.06.4063 SN - 1552-5260 VL - 15 SP - P1451 EP - P1452 ER - TY - JOUR A1 - Kernebeck, Sven A1 - Holle, Daniela A1 - Pogscheba, Patrick A1 - Jordan, Felix A1 - Mertl, Fabian A1 - Huldtgren, Alina A1 - Bader, Sebastian A1 - Kirste, Thomas A1 - Teipel, Stefan A1 - Holle, Bernhard A1 - Halek, Margareta T1 - A Tablet App– and Sensor-Based Assistive Technology Intervention for Informal Caregivers to Manage the Challenging Behavior of People With Dementia (the insideDEM Study): Protocol for a Feasibility Study JF - JMIR Research Protocols N2 - Despite the enormous number of assistive technologies (ATs) in dementia care, the management of challenging behavior (CB) of persons with dementia (PwD) by informal caregivers in home care is widely disregarded. The first-line strategy to manage CB is to support the understanding of the underlying causes of CB to formulate individualized nonpharmacological interventions. App- and sensor-based approaches combining multimodal sensors (actimetry and other modalities) and caregiver information are innovative ways to support the understanding of CB for family caregivers. The main aim of this study is to describe the design of a feasibility study consisting of an outcome and a process evaluation of a newly developed app- and sensor-based intervention to manage CB of PwD for family caregivers at home. In this feasibility study, we perform an outcome and a process evaluation with a pre-post descriptive design over an 8-week intervention period. The Medical Research Council framework guides the design of this feasibility study. The data on 20 dyads (primary caregiver and PwD) are gathered through standardized questionnaires, protocols, and log files as well as semistructured qualitative interviews. The outcome measures (neuropsychiatric inventory and Cohen-Mansfield agitation inventory) are analyzed by using descriptive statistics and statistical tests relevant to the individual assessments (eg, chi-square test and Wilcoxon signed-rank test). For the analysis of the process data, the Unified Theory of Acceptance and Use of Technology is used. Log files are analyzed by using descriptive statistics, protocols are analyzed by using documentary analysis, and semistructured interviews are analyzed deductively using content analysis. The newly developed app- and sensor-based AT has been developed and was evaluated until July in 2018. The recruitment of dyads started in September 2017 and was concluded in March 2018. The data collection was completed at the end of July 2018. This study presents the protocol of the first feasibility study to encompass an outcome and process evaluation to assess a complex app- and sensor-based AT combining multimodal actimetry sensors for informal caregivers to manage CB. The feasibility study will provide in-depth information about the study procedure and on how to optimize the design of the intervention and its delivery. DERR1-10.2196/11630 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-173274 SN - 1929-0748 VL - 8 SP - e11630 ER - TY - JOUR A1 - Teipel, Stefan A1 - Heine, Christina A1 - Hein, Albert A1 - Krüger, Frank A1 - Kutschke, Andreas A1 - Kernebeck, Sven A1 - Halek, Margareta A1 - Bader, Sebastian A1 - Kirste, Thomas T1 - Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes—The insideDEM framework JF - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring N2 - IntroductionAssessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes.MethodsWe conducted 4 weeks of multimodal sensor assessment together with real-time observation of 17 residents with moderate to very severe dementia in two nursing care units. Nursing staff received extensive training on device handling and measurement procedures. Behavior of a subsample of eight participants was further recorded by videotaping during 4 weeks during day hours. Sensors were mounted on the participants' wrist and ankle and measured motion, rotation, as well as surrounding loudness level, light level, and air pressure.ResultsParticipants were in moderate to severe stages of dementia. Almost 100% of participants exhibited relevant levels of challenging behaviors. Automated quality control detected 155 potential issues. But only 11% of the recordings have been influenced by noncompliance of the participants. Qualitative debriefing of staff members suggested that implementation of the technology and observation platform in the routine procedures of the nursing home units was feasible and identified a range of user- and hardware-related implementation and handling challenges.DiscussionOur results indicate that high-quality behavior data from real-world environments can be made available for the development of intelligent assistive systems and that the problem of noncompliance seems to be manageable. Currently, we train machine-learning algorithms to detect episodes of challenging behaviors in the recorded sensor data. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-173308 SN - 2352-8729 VL - 8 SP - 36 EP - 44 ER - TY - JOUR A1 - Goerss, Doreen A1 - Hein, Albert A1 - Bader, Sebastian A1 - Halek, Margareta A1 - Kernebeck, Sven A1 - Kutschke, Andreas A1 - Kirste, Thomas A1 - Teipel, Stefan J. T1 - P1‐284: AUTOMATED SENSOR‐BASED DETECTION OF CHALLENGING BEHAVIORS IN ADVANCED STAGES OF DEMENTIA IN NURSING HOMES JF - Alzheimer's & Dementia Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.jalz.2019.06.839 SN - 1552-5260 VL - 15 IS - 7S_Part_7 SP - P351 ER - TY - JOUR A1 - Goerss, Doreen A1 - Hein, Albert A1 - Bader, Sebastian A1 - Halek, Margareta A1 - Kernebeck, Sven A1 - Kutschke, Andreas A1 - Kirste, Thomas A1 - Teipel, Stefan J. T1 - AUTOMATED SENSOR-BASED DETECTION OF CHALLENGING BEHAVIORS IN ADVANCED STAGES OF DEMENTIA IN NURSING HOMES JF - Alzheimer's & Dementia Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.jalz.2019.06.4309 SN - 1552-5260 VL - 15 SP - P151 EP - P152 ER - TY - JOUR A1 - Amaefule, Chimezie O. A1 - Goerss, Doreen A1 - Halek, Margareta A1 - Kernebeck, Sven A1 - Kirste, Thomas A1 - Teipel, Stefan J. T1 - PREDICTING DAYTIME MANIFESTATIONS OF CHALLENGING BEHAVIOURS IN ADVANCED STAGES OF DEMENTIA USING PRE-DAYTIME ACCELEROMETRY: POST-HOC ANALYSIS OF THE DZNE ROSTOCK INSIDEDEM STUDY JF - Alzheimer's & Dementia Y1 - 2019 U6 - http://dx.doi.org/10.1016/j.jalz.2019.06.4340 SN - 1552-5260 VL - 15 SP - P165 EP - P166 ER - TY - JOUR A1 - Goerss, Doreen A1 - Hein, Albert A1 - Bader, Sebastian A1 - Halek, Margareta A1 - Kernebeck, Sven A1 - Kutschke, Andreas A1 - Heine, Christina A1 - Krueger, Frank A1 - Kirste, Thomas A1 - Teipel, Stefan T1 - Automated sensor‐based detection of challenging behaviors in advanced stages of dementia in nursing homes JF - Alzheimer's & Dementia N2 - Sensor‐based assessment of challenging behaviors in dementia may be useful to support caregivers. Here, we investigated accelerometry as tool for identification and prediction of challenging behaviors. We set up a complex data recording study in two nursing homes with 17 persons in advanced stages of dementia. Study included four‐week observation of behaviors. In parallel, subjects wore sensors 24 h/7 d. Participants underwent neuropsychological assessment including MiniMental State Examination and Cohen‐Mansfield Agitation Inventory. We calculated the accelerometric motion score (AMS) from accelerometers. The AMS was associated with several types of agitated behaviors and could predict subject's Cohen‐Mansfield Agitation Inventory values. Beyond the mechanistic association between AMS and behavior on the group level, the AMS provided an added value for prediction of behaviors on an individual level. We confirm that accelerometry can provide relevant information about challenging behaviors. We extended previous studies by differentiating various types of agitated behaviors and applying long‐term measurements in a real‐world setting. Y1 - 2020 U6 - http://dx.doi.org/10.1016/j.jalz.2019.08.193 SN - 1552-5260 VL - 16 SP - 672 EP - 680 ER -