Smarthealth technology study protocol to improve relationships between older adults with dementia and family caregivers.

AIM: The aim of this study is to develop a Smarthealth system of monitoring, modelling, and interactive recommendation solutions (for caregivers) for in-home dementia patient care that focuses on caregiver-patient relationships. DESIGN: This descriptive study employs a single-group, non-randomized trial to examine functionality, effectiveness, feasibility, and acceptability of the novel Smarthealth system. METHODS: Thirty persons with Alzheimer's Disease or related dementia and their family caregivers (N = 30 dyads) will receive and install Smarthealth technology in their home. There will be a 1-month observation phase for collecting baseline mood states and a 2-month implementation phase when caregivers will receive stress management techniques for each detected, negative mood state. Caregivers will report technique implementation and usefulness, sent via Ecological Momentary Assessment system to the study-provided smartphone. Caregivers will provide daily, self-reported mood and health ratings. Instruments measuring caregiver assessment of disruptive behaviours and their effect on caregivers; caregiver depressive symptoms, anxiety and stress; caregiver strain; and family functioning will be completed at baseline and 3 months. The study received funding in 2018 and ethics board approval in 2019. DISCUSSION: This study will develop and test novel in-home technology to improve family caregiving relationships. Results from this study will help develop and improve the Smarthealth recommendation system and determine its usefulness, feasibility, and acceptability for persons with dementia and their family caregiver. IMPACT: The Smarthealth technology discussed will provide in-home stress reduction resources at a time when older adults may be experiencing increasingly high rates of isolation and anxiety and caregiver dyads may be experiencing high levels of relationship strain. TRIAL REGISTRATION: This study was registered with Clinical Trials.gov (Identifier NCT04536701).

Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study.

BACKGROUND: The field of dietary assessment has a long history, marked by both controversies and advances. Emerging technologies may be a potential solution to address the limitations of self-report dietary assessment methods. The Monitoring and Modeling Family Eating Dynamics (M2FED) study uses wrist-worn smartwatches to automatically detect real-time eating activity in the field. The ecological momentary assessment (EMA) methodology was also used to confirm whether eating occurred (ie, ground truth) and to measure other contextual information, including positive and negative affect, hunger, satiety, mindful eating, and social context. OBJECTIVE: This study aims to report on participant compliance (feasibility) to the 2 distinct EMA protocols of the M2FED study (hourly time-triggered and eating event-triggered assessments) and on the performance (validity) of the smartwatch algorithm in automatically detecting eating events in a family-based study. METHODS: In all, 20 families (58 participants) participated in the 2-week, observational, M2FED study. All participants wore a smartwatch on their dominant hand and responded to time-triggered and eating event-triggered mobile questionnaires via EMA while at home. Compliance to EMA was calculated overall, for hourly time-triggered mobile questionnaires, and for eating event-triggered mobile questionnaires. The predictors of compliance were determined using a logistic regression model. The number of true and false positive eating events was calculated, as well as the precision of the smartwatch algorithm. The Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation were used to determine whether there were differences in the detection of eating events by participant age, gender, family role, and height. RESULTS: The overall compliance rate across the 20 deployments was 89.26% (3723/4171) for all EMAs, 89.7% (3328/3710) for time-triggered EMAs, and 85.7% (395/461) for eating event-triggered EMAs. Time of day (afternoon odds ratio [OR] 0.60, 95% CI 0.42-0.85; evening OR 0.53, 95% CI 0.38-0.74) and whether other family members had also answered an EMA (OR 2.07, 95% CI 1.66-2.58) were significant predictors of compliance to time-triggered EMAs. Weekend status (OR 2.40, 95% CI 1.25-4.91) and deployment day (OR 0.92, 95% CI 0.86-0.97) were significant predictors of compliance to eating event-triggered EMAs. Participants confirmed that 76.5% (302/395) of the detected events were true eating events (ie, true positives), and the precision was 0.77. The proportion of correctly detected eating events did not significantly differ by participant age, gender, family role, or height (P>.05). CONCLUSIONS: This study demonstrates that EMA is a feasible tool to collect ground-truth eating activity and thus evaluate the performance of wearable sensors in the field. The combination of a wrist-worn smartwatch to automatically detect eating and a mobile device to capture ground-truth eating activity offers key advantages for the user and makes mobile health technologies more accessible to nonengineering behavioral researchers.

Sensing eating mimicry among family members.

Family relationships influence eating behavior and health outcomes (e.g., obesity). Because eating is often habitual (i.e., automatically driven by external cues), unconscious behavioral mimicry may be a key interpersonal influence mechanism for eating within families. This pilot study extends existing literature on eating mimicry by examining whether multiple family members mimicked each other's bites during natural meals. Thirty-three participants from 10 families were videotaped while eating an unstructured family meal in a kitchen lab setting. Videotapes were coded for participants' bite occurrences and times. We tested whether the likelihood of a participant taking a bite increased when s/he was externally cued by a family eating partner who had recently taken a bite (i.e., bite mimicry). A paired-sample t-test indicated that participants had a significantly faster eating rate within the 5 s following a bite by their eating partner, compared to their bite rate at other times (t = 7.32, p < .0001). Nonparametric permutation testing identified five of 78 dyads in which there was significant evidence of eating mimicry; and 19 of 78 dyads that had p values < .1. This pilot study provides preliminary evidence that suggests eating mimicry may occur among a subset of family members, and that there may be types of family ties more prone to this type of interpersonal influence during meals.