pith. sign in

arxiv: 2604.19574 · v1 · submitted 2026-04-21 · 💻 cs.HC

Remindful: Designing Reminder Systems for Caregiver Interpretation in Dementia Care

Joy Lai , Alex Mihailidis This is my paper

Pith reviewed 2026-05-10 01:19 UTC · model grok-4.3

classification 💻 cs.HC
keywords dementia carereminder systemscaregiver interpretationcontextual sensemakingassistive technologyhome-based carehuman-computer interaction
0
0 comments X

The pith

Reminder systems for dementia care should be designed as tools for caregivers to interpret logs with context rather than as neutral sensors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Remindful, a reminder platform that adds alerts, summaries, and review features for caregivers alongside standard task prompts for people with dementia. Formative interviews and in-home use with two dyads showed these additions can provide reassurance about completion, support household coordination, and track routine changes over time. Yet the same logs prove unreliable without details on household participation, who receives prompts, mismatches with actual habits, access issues, and tech problems. The central argument is that such systems work best when built to maintain uncertainty and help caregivers make situated sense of what the data actually means in lived homes.

Core claim

Reminder systems can support caregiver reassurance, household coordination, and awareness of routines over time, but reminder interaction data is highly context-dependent. Household participation, prompt attribution, routine mismatch, accessibility barriers, and technical failures all shaped what reminder logs could reasonably mean. Reminder systems should not be treated as neutral behavioral sensors, but designed as assistive infrastructures for caregiver interpretation that preserve uncertainty and support contextual sensemaking in real homes.

What carries the argument

Remindful's caregiver-facing alerts, summaries, and review features that convert one-way prompts into shared awareness tools for ongoing interpretation.

If this is right

  • Reminder logs can reassure caregivers that tasks occurred when interpreted with household details.
  • Shared summaries enable coordination by making daily patterns visible to multiple family members.
  • Repeated data review helps track how routines evolve as dementia progresses.
  • Designs that ignore attribution or failures risk caregivers drawing inaccurate conclusions from the logs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adding explicit flags for possible context factors in reminder apps could help caregivers avoid over- or under-estimating independence.
  • The focus on uncertainty preservation may apply to other home monitoring tools like medication trackers where logs are also ambiguous.
  • Broader testing in more homes could reveal whether cultural or layout differences change how logs are read.

Load-bearing premise

That observations from interviews and deployments with only two caregiver and person-with-dementia pairs identify the main factors shaping reminder log interpretation across most dementia care homes.

What would settle it

A larger deployment across varied households where reminder logs are consistently read the same way regardless of participation, attribution, or technical issues would undermine the claim that context must be preserved.

Figures

Figures reproduced from arXiv: 2604.19574 by Alex Mihailidis, Joy Lai.

Figure 1
Figure 1. Figure 1: Example of the Remindful reminder set-up in the home. Floorplan image adapted from RoomSketcher. Caregiver and PLwD interface screenshots are authors’ own. Using the caregiver app, caregivers could create reminders, assign recurrence schedules and priority levels, specify display locations, and optionally attach follow-up questions. When a reminder appeared on an in-home iPad, the PLwD could respond using … view at source ↗
Figure 2
Figure 2. Figure 2: Example PLwD-facing reminder interactions in Remindful. The deployed system also included caregiver-facing reports and alerts, shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example sections of the daily (left) and long [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Diagram of Remindful’s architectural pipeline 3.3 In-home Deployment Study We conducted an in-home deployment study with two caregiver-PLwD dyads to examine how Remindful functioned in practice. The goal of this phase was not to evaluate clinical effectiveness or predictive accuracy, but to understand interaction reliability, caregiver interpretation, workflow fit, and the contextual factors that determine… view at source ↗
read the original abstract

Digital reminder systems are widely used in dementia care to support everyday tasks, but they are typically designed for one-way prompting rather than helping caregivers interpret engagement over time. We present Remindful, a caregiver-informed reminder platform that extends task prompting with caregiver-facing alerts, summaries, and review features to support awareness in home-based dementia care. Drawing on formative caregiver interviews, lived-experience advisor input, and in-home deployments with two caregiver-PLwD dyads, we examine how reminder-based caregiver awareness functions in practice. Our findings show that reminder systems can support caregiver reassurance, household coordination, and awareness of routines over time, but that reminder interaction data is highly context-dependent. Household participation, prompt attribution, routine mismatch, accessibility barriers, and technical failures all shaped what reminder logs could reasonably mean. We argue that reminder systems should not be treated as neutral behavioral sensors, but designed as assistive infrastructures for caregiver interpretation that preserve uncertainty and support contextual sensemaking in real homes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper presents Remindful, a caregiver-informed reminder platform that adds alerts, summaries, and review features to standard task prompting. Drawing on formative interviews, lived-experience advisor input, and in-home deployments with two caregiver-PLwD dyads, the authors find that reminder interaction data is highly context-dependent, shaped by household participation, prompt attribution, routine mismatch, accessibility barriers, and technical failures. They argue that reminder systems should be designed as assistive infrastructures for caregiver interpretation that preserve uncertainty and support contextual sensemaking, rather than treated as neutral behavioral sensors.

Significance. If the results hold, this work offers a useful contribution to HCI research on assistive technologies for dementia care by shifting focus from one-way prompting and behavioral monitoring to supporting caregivers' interpretive practices. The emphasis on preserving uncertainty and contextual sensemaking provides a concrete design orientation that could inform more realistic home-based systems.

major comments (1)
  1. [Abstract and In-Home Deployments] Abstract and In-Home Deployments section: The central design argument—that reminder systems should be designed as assistive infrastructures preserving uncertainty—rests on findings from in-home deployments with only two caregiver-PLwD dyads. The paper does not report saturation, member-checking, or explicit comparison to existing dementia-care literature, leaving open whether the listed factors are primary or whether additional cases would surface different influences that modify the recommendations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback, which identifies a key area for strengthening the manuscript's claims about generalizability. We address the major comment below and describe targeted revisions.

read point-by-point responses

  1. Referee: The central design argument—that reminder systems should be designed as assistive infrastructures preserving uncertainty—rests on findings from in-home deployments with only two caregiver-PLwD dyads. The paper does not report saturation, member-checking, or explicit comparison to existing dementia-care literature, leaving open whether the listed factors are primary or whether additional cases would surface different influences that modify the recommendations.

    Authors: We acknowledge that the in-home deployment involved only two dyads and that this constitutes a limitation for the breadth of the findings. The manuscript's argument draws on the full study process, including formative interviews with additional caregivers and input from lived-experience advisors, which informed the design before the deployments. The deployments themselves provided rich, longitudinal qualitative data on contextual factors such as household participation and prompt attribution. We did not report saturation or member-checking because the work is positioned as exploratory design research rather than theory-building qualitative inquiry; formal saturation assessment was not part of the protocol. For revision, we will add an explicit limitations subsection in the Discussion that (1) states the sample size and its implications, (2) notes the absence of saturation and member-checking, and (3) expands the comparison to prior dementia-care literature on reminder systems and caregiver sensemaking. These additions will better delimit the scope of the design recommendations while preserving the core claim that reminder data is context-dependent in the observed cases. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical qualitative claims from user studies

full rationale

The paper presents qualitative findings from formative caregiver interviews, lived-experience advisor input, and in-home deployments with two caregiver-PLwD dyads. It reports observed patterns in reminder log interpretation (household participation, prompt attribution, routine mismatch, accessibility barriers, technical failures) and argues for designing reminder systems as assistive infrastructures that preserve uncertainty. No equations, fitted parameters, model-based predictions, or derivations exist. No self-citation chains, uniqueness theorems, or ansatzes are invoked to reduce the central design argument to prior inputs by construction. Claims rest on direct empirical observations without self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work relies on established domain assumptions from dementia care and HCI design research without introducing free parameters, new entities, or ungrounded postulates.

axioms (2)
  • domain assumption Caregivers benefit from enhanced awareness and interpretation support regarding PLwD engagement with reminders
    This premise drives the system design and the interpretation of deployment outcomes.
  • domain assumption Qualitative data from small-scale home deployments can yield generalizable design implications for reminder systems
    Invoked when moving from two dyads to broader recommendations about context-dependency.

pith-pipeline@v0.9.0 · 5461 in / 1448 out tokens · 80577 ms · 2026-05-10T01:19:19.130747+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

  1. [1]

    Amershi, S. et al. 2019. Guidelines for human-AI interaction. Conference on Human Factors in Computing Systems - Proceedings (May 2019)

    work page 2019

  2. [2]

    Berridge, C. et al. 2021. Domain Experts on Dementia-Care Technologies: Mitigating Risk in Design and Implementation. Science and Engineering Ethics. 27, 1 (Feb. 2021). https://doi.org/10.1007/s11948-021-00286-w

    work page doi:10.1007/s11948-021-00286-w 2021

  3. [3]

    Brookman, R. et al. 2023. Technology for dementia care: what would good technology look like and do, from carers’ perspectives? BMC Geriatrics. 23, 1 (Dec. 2023). https://doi.org/10.1186/s12877-023-04530-9

    work page doi:10.1186/s12877-023-04530-9 2023

  4. [4]

    Civitarese, G. 2019. Human Activity Recognition in Smart-Home Environments for Health-Care Applications. 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) (2019), 1

    work page 2019

  5. [5]

    Curnow, E. et al. 2021. Exploring the needs of people with dementia living at home reported by people with dementia and informal caregivers: a systematic review and Meta-analysis. Aging and Mental Health. 25, 3 (2021), 397–407. https://doi.org/10.1080/13607863.2019.1695741

    work page doi:10.1080/13607863.2019.1695741 2021

  6. [6]

    Eiband, M. et al. 2018. Bringing transparency design into practice. International Conference on Intelligent User Interfaces, Proceedings IUI (Mar. 2018), 211–223

    work page 2018

  7. [7]

    Frias, C.E. et al. 2020. Informal caregivers’ roles in dementia: The impact on their quality of life. Life. 10, 11 (Nov. 2020), 1–

    work page 2020

  8. [8]

    https://doi.org/10.3390/life10110251

    work page doi:10.3390/life10110251

  9. [9]

    Hao, J. et al. 2016. Cognitive errors detection: Mining behavioral data stream of people with cognitive impairment. ACM International Conference Proceeding Series (Jun. 2016)

    work page 2016

  10. [10]

    Jönsson, K.E. et al. 2019. A reminder system for independence in dementia care: A case study in an assisted living facility. ACM International Conference Proceeding Series (Jun. 2019), 176–185

    work page 2019

  11. [11]

    Lai, J. et al. 2025. From Checking to Sensemaking: A Caregiver-in-the-Loop Framework for AI-Assisted Task Verification in Dementia Care. (Nov. 2025)

    work page 2025

  12. [12]

    Lai, J. et al. Listening before Asking: Lived-Experience Advisors as Methodological Partners in Dementia Caregiving Studies

    work page

  13. [13]

    and Mihailidis, A

    Lai, J. and Mihailidis, A. 2025. Transforming Digital Reminder Systems into Behavioral Anomaly Detectors: A Proof-of- Concept Using LSTM Autoencoders

    work page 2025

  14. [14]

    Lazar, A. et al. 2017. A critical lens on dementia and design in HCI. Conference on Human Factors in Computing Systems - Proceedings (May 2017), 2175–2188

    work page 2017

  15. [15]

    Liao, Q.V. et al. 2020. Questioning the AI: Informing Design Practices for Explainable AI User Experiences. Conference on Human Factors in Computing Systems - Proceedings (Apr. 2020)

    work page 2020

  16. [16]

    Lindsay, S. et al. 2012. Empathy, Participatory Design and People with Dementia. (2012), 3228

    work page 2012

  17. [17]

    McGoldrick, C. et al. 2021. MindMate: A single case experimental design study of a reminder system for people with dementia. Neuropsychological Rehabilitation. 31, 1 (2021), 18–38. https://doi.org/10.1080/09602011.2019.1653936

    work page doi:10.1080/09602011.2019.1653936 2021

  18. [18]

    Mettouris, C. et al. 2023. eSticky: An Advanced Remote Reminder System for People with Early Dementia. SN Computer Science. 4, 4 (Jul. 2023). https://doi.org/10.1007/s42979-023-01768-3

    work page doi:10.1007/s42979-023-01768-3 2023

  19. [19]

    Mort, M. et al. 2013. Ageing with telecare: Care or coercion in austerity? Sociology of Health and Illness. 35, 6 (Jul. 2013), 799–812. https://doi.org/10.1111/j.1467-9566.2012.01530.x

    work page doi:10.1111/j.1467-9566.2012.01530.x 2013

  20. [20]

    Na, S.H. et al. 2012. Wandering Detection and Activity Recognition for Dementia Patients Using Wireless Sensor Networks. JOURNAL OF INTERNET TECHNOLOGY. 13, 1 (2012), 115–125

    work page 2012

  21. [21]

    Norton, L.E. et al. 2001. The Impact of Behavioral Symptoms on Activities of Daily Living in Patients With Dementia. Am J Geriatr Psychiatry. 9, 1 (2001), 41–48. 13

    work page 2001

  22. [22]

    and Campos, P.F

    Peres, B. and Campos, P.F. 2024. A systematic review of reminder and guidance systems for Alzheimer’s Disease and Related Dementias patients: context, barriers and facilitators. Disability and Rehabilitation: Assistive Technology. 19, 6 (2024), 2133–

    work page 2024

  23. [23]

    https://doi.org/10.1080/17483107.2023.2277821

    work page doi:10.1080/17483107.2023.2277821 2023

  24. [24]

    Procter, R. et al. 2016. Telecare Call Centre Work and Ageing in Place. Computer Supported Cooperative Work: CSCW: An International Journal. 25, 1 (Feb. 2016), 79–105. https://doi.org/10.1007/s10606-015-9242-5

    work page doi:10.1007/s10606-015-9242-5 2016

  25. [25]

    Sanchez, A.A. et al. 2024. Enhancing communication and autonomy in dementia through technology: Navigating home challenges and memory aid usage. Gerontechnology. 23, 1 (2024), 1–11. https://doi.org/10.4017/gt.2024.23.1.880.06

    work page doi:10.4017/gt.2024.23.1.880.06 2024

  26. [26]

    Shahid, Z.K. et al. 2023. Unsupervised Forecasting and Anomaly Detection of ADLs in single-resident elderly smart homes. Proceedings of the ACM Symposium on Applied Computing (Mar. 2023), 607–610

    work page 2023

  27. [27]

    Shneiderman, B. 2020. Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction. (2020), 109–124. https://doi.org/10.17705/1thci.00131

    work page doi:10.17705/1thci.00131 2020

  28. [28]

    Simonsson, S. et al. 2023. Use of Clustering Algorithms for Sensor Placement and Activity Recognition in Smart Homes. IEEE Access. 11, (2023), 9415–9430. https://doi.org/10.1109/ACCESS.2023.3239265

    work page doi:10.1109/access.2023.3239265 2023

  29. [29]

    Snowball, E. et al. 2024. Engaging people with lived experience of dementia in research meetings and events: insights from multiple perspectives. Frontiers in Dementia. 3, (Jul. 2024). https://doi.org/10.3389/frdem.2024.1421737

    work page doi:10.3389/frdem.2024.1421737 2024

  30. [30]

    Tay, N.C. et al. 2023. A Review of Abnormal Behavior Detection in Activities of Daily Living. IEEE Access. 11, (2023), 5069–5088. https://doi.org/10.1109/ACCESS.2023.3234974

    work page doi:10.1109/access.2023.3234974 2023