Exploration of verbal repetition in people with dementia using an online symptom-tracking tool

Personalized goals of people living with dementia and family carers: A content analysis of goals set within an individually tailored psychosocial intervention trial

Introduction

Person-centered goals capture individual priorities in personal contexts. Goal Attainment Scaling (GAS) has been used in drug trials involving people living with dementia (PLWD) but GAS has been characterized as difficult to incorporate into trials and clinical practice. We used GAS in a trial of New Interventions for Independence in Dementia Study (NIDUS)-family, a manualized care and support intervention, as the primary outcome and to tailor the interventions to goals set. We aimed to assess the feasibility and content of baseline goal-setting.

Methods

We developed training for nonclinical facilitators to set individualized GAS goals remotely with PLWD and family carer dyads, or carers alone, in the intervention trial, during the COVID-19 pandemic. A qualitative content analysis of the goals set explored participants' priorities and unmet needs, to consider how existing GAS goal domains might be extended in a psychosocial intervention trial context.

Results

Eleven facilitators were successfully trained to set and score GAS goals. A total of 313/328 (95%) participants were able to collaboratively set three to five goals with the facilitators. Of these, 302 randomized participating dyads set 1043 (mean 3.5, range 3 to 5) goals. We deductively coded 719 (69%) goals into five existing GAS domains (mood, behavior, self-care, cognition, and instrumental activities of daily living); 324 (31%) goals were inductively coded into four new domains: carer break, carer mood, carer behavior, and carer sleep. The most frequently set goals pertained to social support. There was little variation in types of goals set based on the context of who set them or level of pandemic restrictions in place.

Discussion

It is feasible for people without clinical training to set GAS holistic goals for PLWD and family carers in the community. GAS has potential to facilitate personalization of care and support interventions, such as NIDUS-family, and facilitate the roll out of more personalized care.

Highlights

Goal Attainment Scaling (GAS) can capture meaningful priorities of people with dementia and their family carers.A psychosocial intervention RCT used GAS as the primary outcome measure and goals were set collaboratively by non-clinically trained facilitators.The findings underscore the feasibility of using GAS as an outcome measure with this population.The content analysis findings unveiled the diversity in experiences and priorities of the study participants.GAS has the potential to support the implementation of more person-centred approaches to dementia care.

Formal caregivers' perceptions of everyday interaction with Deaf people with dementia

OBJECTIVES

Deteriorating interactive ability of people with dementia challenges formal caregivers. In Finland, Deaf people with advanced dementia may live in a nursing home designed for their care where the staff use Finnish Sign Language (FiSL). This study describes the perceptions of formal caregivers, focusing on the challenges, how they solve the challenges, and what support they need to improve interaction with Deaf residents.

METHODS

Semi-structured interviews with 13 formal caregivers who work with Deaf people with dementia were conducted and analyzed using qualitative content analysis. A purposive sampling was used.

RESULTS

Three key themes were challenges in interaction, strategies in supporting interaction, and support for coping. Caregivers perceived challenges in interaction caused by linguistic changes, deteriorating physical mobility and memory, and Deaf residents' behavioral challenges. Caregivers supported Deaf residents by learning to know them and using personal and linguistic strategies. Support for coping comprised supporting family members and other caregivers.

CONCLUSIONS

Efficient skills in sign language (SL) and knowledge of dementia are essential in interacting with Deaf residents and to build interpersonal relationships for care.

CLINICAL IMPLICATIONS

Supporting Deaf residents requires learning the way they interact which can be achieved over time.

Repetitive Behaviors in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Repetitive behaviors (RBs) are a well-known symptom of Alzheimer's disease (AD); however, they have been little studied and have not been the subject of any specific literature review.

OBJECTIVE

To conduct a systematic review of all studies to document RBs in AD.

METHODS

An extensive literature search combining five databases and a meta-analysis were conducted to investigate the frequency, nature, and cognitive correlates of RBs in AD.

RESULTS

Ten studies were included in the review. Seven studies out of ten investigated the frequency of RBs in patients with AD, which ranged from 52.3% to 87%. A meta-analysis showed an overall frequency of 66.3% (95% CI: 55.5; 77.1) of patients exhibiting RBs in AD, but important heterogeneity was observed between studies. Three studies investigated the predominant nature of RBs in AD. Verbal RBs, complex behavioral stereotypies, and simple motor stereotypies have been identified to different degrees depending on the level of dementia. Most verbal RBs are underpinned by episodic memory impairment, while simple motor stereotypies and complex behavioral stereotypies are mostly underpinned by executive dysfunction.

CONCLUSIONS

The current review seems to suggest that there are two types of mechanisms underpinning RBs involved in AD. The first is observed especially in the mild stages of the disease and is mediated by episodic memory impairment. The second occurs later and is mediated by executive impairment. Additional studies should be conducted to improve the knowledge about RBs in AD and thus improve their management.Systematic review registration number: PROSPERO 2022: CRD42022310027.

A simulated experiment to explore robotic dialogue strategies for people with dementia

Introduction

Persons with dementia (PwDs) often show symptoms of repetitive questioning, which brings great burdens on caregivers. Conversational robots hold promise of helping cope with PwDs’ repetitive behavior. This paper develops an adaptive conversation strategy to answer PwDs’ repetitive questions, follow up with new questions to distract PwDs from repetitive behavior, and stimulate their conversation and cognition.

Methods

We propose a general reinforcement learning model to interact with PwDs with repetitive questioning. Q-learning is exploited to learn adaptive conversation strategy (from the perspectives of rate and difficulty level of follow-up questions) for four simulated PwDs. A demonstration is presented using a humanoid robot.

Results

The designed Q-learning model performs better than random action selection model. The RL-based conversation strategy is adaptive to PwDs with different cognitive capabilities and engagement levels. In the demonstration, the robot can answer a user’s repetitive questions and further come up with a follow-up question to engage the user in continuous conversations.

Conclusions

The designed Q-learning model demonstrates noteworthy effectiveness in adaptive action selection. This may provide some insights towards developing conversational social robots to cope with repetitive questioning by PwDs and increase their quality of life.

Patterns of Symptom Tracking by Caregivers and Patients With Dementia and Mild Cognitive Impairment: Cross-sectional Study

BACKGROUND

Individuals with dementia and mild cognitive impairment (MCI) experience a wide variety of symptoms and challenges that trouble them. To address this heterogeneity, numerous standardized tests are used for diagnosis and prognosis. myGoalNav Dementia is a web-based tool that allows individuals with impairments and their caregivers to identify and track outcomes of greatest importance to them, which may be a less arbitrary and more sensitive way of capturing meaningful change.

OBJECTIVE

We aim to explore the most frequent and important symptoms and challenges reported by caregivers and people with dementia and MCI and how this varies according to disease severity.

METHODS

This cross-sectional study involved 3909 web-based myGoalNav users (mostly caregivers of people with dementia or MCI) who completed symptom profiles between 2006 and 2019. To make a symptom profile, users selected their most personally meaningful or troublesome dementia-related symptoms to track over time. Users were also asked to rank their chosen symptoms from least to most important, which we called the symptom potency. As the stage of disease for these web-based users was unknown, we applied a supervised staging algorithm, previously trained on clinician-derived data, to classify each profile into 1 of 4 stages: MCI and mild, moderate, and severe dementia. Across these stages, we compared symptom tracking frequency, symptom potency, and the relationship between frequency and potency.

RESULTS

Applying the staging algorithm to the 3909 user profiles resulted in 917 (23.46%) MCI, 1596 (40.83%) mild dementia, 514 (13.15%) moderate dementia, and 882 (22.56%) severe dementia profiles. We found that the most frequent symptoms in MCI and mild dementia profiles were similar and comprised early hallmarks of dementia (eg, recent memory and language difficulty). As the stage increased to moderate and severe, the most frequent symptoms were characteristic of loss of independent function (eg, incontinence) and behavioral problems (eg, aggression). The most potent symptoms were similar between stages and generally reflected disruptions in everyday life (eg, problems with hobbies or games, travel, and looking after grandchildren). Symptom frequency was negatively correlated with potency at all stages, and the strength of this relationship increased with increasing disease severity.

CONCLUSIONS

Our results emphasize the importance of patient-centricity in MCI and dementia studies and illustrate the valuable real-world evidence that can be collected with digital tools. Here, the most frequent symptoms across the stages reflected our understanding of the typical disease progression. However, the symptoms that were ranked as most personally important by users were generally among the least frequently selected. Through individualization, patient-centered instruments such as myGoalNav can complement standardized measures by capturing these infrequent but potent outcomes.

Development of a symptom menu to facilitate Goal Attainment Scaling in adults with Down syndrome-associated Alzheimer's disease: a qualitative study to identify meaningful symptoms

Background

As life expectancy of people with Down syndrome (DS) increases, so does the risk of Alzheimer’s disease (AD). Identifying symptoms and tracking disease progression is especially challenging whenever levels of function vary before the onset of dementia. Goal Attainment Scaling (GAS), an individualized patient-reported outcome, can aid in monitoring disease progression and treatment effectiveness in adults with DS. Here, with clinical input, a validated dementia symptom menu was revised to facilitate GAS in adults living with Down Syndrome-associated Alzheimer’s disease (DS-AD).

Methods

Four clinicians with expertise in DS-AD and ten caregivers of adults living with DS-AD participated in semi-structured interviews to review the menu. Each participant reviewed 9–15 goal areas to assess their clarity and comprehensiveness. Responses were systematically and independently coded by two researchers as ‘clear’, ‘modify’, ‘remove’ or ‘new’. Caregivers were encouraged to suggest additional items and recommend changes to clarify items.

Results

Median caregiver age was 65 years (range 54–77). Most were female (9/10) with ≥15 years of education (10/10). Adults with DS-AD had a median age of 58 years (range 52–61) and either a formal diagnosis (6/10) or clinical suspicion (4/10) of dementia. The initial symptom menu consisted of 67 symptoms each with 2–12 descriptors (589 total). The clinicians’ adaptation yielded 58 symptoms each with 4–17 descriptors (580 total). Of these 580 descriptors, caregivers identified 37 (6%) as unclear; these were reworded, and one goal area (4 descriptors) was removed. A further 47 descriptors and one goal area were added to include caregiver-identified concepts. The final menu contained 58 goal areas, each with 7–17 descriptors (623 total).

Conclusions

A comprehensive symptom menu for adults living with DS-AD was developed to facilitate GAS. Incorporating expert clinician opinion and input from caregivers of adults with DS-AD identified meaningful items that incorporate patient/caregiver perspectives.

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach

BACKGROUND

SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified.

OBJECTIVE

We aimed to develop a supervised machine learning algorithm to classify dementia stages based on tracked symptoms.

METHODS

We employed clinical data from 717 people from 3 sources: (1) a memory clinic; (2) long-term care; and (3) an open-label trial of donepezil in vascular and mixed dementia (VASPECT). Symptoms were captured with SymptomGuide Dementia. A clinician classified participants into 4 groups using either the Functional Assessment Staging Test or the Global Deterioration Scale as mild cognitive impairment, mild dementia, moderate dementia, or severe dementia. Individualized symptom profiles from the pooled data were used to train machine learning models to predict dementia severity. Models trained with 6 different machine learning algorithms were compared using nested cross-validation to identify the best performing model. Model performance was assessed using measures of balanced accuracy, precision, recall, Cohen κ, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). The best performing algorithm was used to train a model optimized for balanced accuracy.

RESULTS

The study population was mostly female (424/717, 59.1%), older adults (mean 77.3 years, SD 10.6, range 40-100) with mild to moderate dementia (332/717, 46.3%). Age, duration of symptoms, 37 unique dementia symptoms, and 10 symptom-derived variables were used to distinguish dementia stages. A model trained with a support vector machine learning algorithm using a one-versus-rest approach showed the best performance. The correct dementia stage was identified with 83% balanced accuracy (Cohen κ=0.81, AUPRC 0.91, AUROC 0.96). The best performance was seen when classifying severe dementia (AUROC 0.99).

CONCLUSIONS

A supervised machine learning algorithm exhibited excellent performance in identifying dementia stages based on dementia symptoms reported in an online environment. This novel dementia staging algorithm can be used to describe dementia stage based on user-reported symptoms. This type of symptom recording offers real-world data that reflect important symptoms in people with dementia.

Characterizing the symptom of misplacing objects in people with dementia: findings from an online tracking tool

OBJECTIVES

Misplacing objects is often reported as a clinically important symptom in dementia. Here we explored misplacing objects in relation to dementia type and stage in an online sample of individuals with dementia and their caregivers.

METHODS

Participants were recruited from www.dementiaguide.com, a web-based tracker for common dementia symptoms. Users provided information about symptoms that they selected as important for monitoring. We analysed cross-sectional data from respondents who tracked at least three symptoms, which allowed for staging dementia severity.

RESULTS

Of 2,775 users with three-plus symptoms, 787 (28%) identified misplacing objects for symptom tracking. Misplacing objects was monitored by users across all stages of dementia, but was more prevalent in mild and severe dementia. Three common clinical subtypes of misplacing were investigated: lost & found (forgetting the location of items), hidden away (hiding items so others would not find them), and odd places (putting items in usual spots). Of the 787, 96% targeted lost & found, the most frequent type. Odd places (targeted in 56%) significantly increased with dementia severity (p < 0.001). Misplacing objects was most strongly associated with the symptoms of interaction with strangers (OR 4.60, 95% CI: 3.20-6.62), reading (3.68: 2.86-4.73), shopping (3.55: 2.73-4.61) and travel/vacationing (3.31: 2.54-4.31).

CONCLUSIONS

Misplacing objects was most often selected for tracking in mild and severe stages of dementia. As disease advances, misplacing more often reflects odd placement of objects rather than their simple loss. Misplacing objects may be a clinically important therapeutic target for improving patients' quality of life and lessening caregiver burden.

The Symptoms Targeted for Monitoring in a Web-Based Tracking Tool by Caregivers of People With Dementia and Agitation: Cross-Sectional Study

BACKGROUND

In people with dementia, neuropsychiatric symptoms (NPSs), especially agitation, are associated with worse quality of life and caregiver burden. As NPSs may vary with illness severity, knowledge of how people with dementia and their caregivers describe and rate the importance of agitation symptoms can improve the understanding of the clinical meaningfulness of the manifestations of agitation. The internet provides new opportunities to better understand patient experiences, as patients and caregivers increasingly look to Web-based platforms as a means of managing symptoms.

OBJECTIVE

The aim of this study was to examine Web-based reports from a dementia symptom website to better understand the symptoms of agitation and explore how they are being targeted for monitoring by caregivers of people with dementia.

METHODS

The Dementia Guide website hosts a Web-based database used by caregivers (97%) and people with dementia (3%). From its 61 dementia symptoms, users can select relevant symptoms that they deem important to monitor or track the effects of treatment. We employed a staging algorithm to determine if individuals had mild cognitive impairment (MCI) or mild, moderate, or severe dementia. Agitation was defined using terms consistent with the International Psychogeriatrics Association's provisional consensus definition. We compared the proportion of people with NPSs and agitation across stages of dementia severity and studied how many agitation-defining descriptors were selected, and how often they occurred, by stage.

RESULTS

As of March 2017, 4121 people had used the tracking tool, of whom 2577 provided sufficient data to allow disease severity staging. NPSs were tracked by 2127/2577 (82.54%) and agitation by 1898/2577 (73.65%). The proportion in whom agitation was tracked increased with increasing cognitive impairment: 68.5% (491/717) in people with MCI, and 72.50% (754/1040), 73.3% (378/516), and 90.5% (275/304) in mild, moderate, and severe dementia, respectively (χ²₃=54.9; P<.001). The number of NPS and agitation descriptors selected also increased with severity (median number of NPSs=1, 2, 2, and 3 for MCI, mild, moderate, and severe dementia, respectively, Kruskal-Wallis H Test H₃=250.47; P<.001; median number of agitation descriptors=1, 2, 3, and 4, H₃=146.11; P<.001).

CONCLUSIONS

NPSs and agitation are common targets for tracking over the course of dementia and appear more frequently with increasing disease severity. These common and distressing symptoms represent clinically meaningful targets in treating people with dementia.