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arxiv: 2605.20205 · v1 · pith:LBF4FLST · submitted 2026-04-08 · cs.HC

Challenges in Working Towards Patient Engagement in Developing Technology Prototypes

Fateme Rajabiyazdi , Julie Babione , Doreen M. Rabi , Foroozan Daneshzand , Sheelagh Carpendale This is my paper

Reviewed by Pith2026-05-21 10:11 UTCgrok-4.3pith:LBF4FLSTopen to challenge →

classification cs.HC
keywords patient engagementdigital health interventionmultiple chronic conditionspilot studyCumulative Complexity Modelself-managementusage analyticstechnology design
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The pith

A two-month pilot of MyCareCompass shows how to define and achieve patient uptake and sustained use of digital tools for multiple chronic conditions.

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

This paper investigates why many digital health tools fail to gain traction among people managing several chronic illnesses at once. It uses the Cumulative Complexity Model to frame the problem as a balance between the heavy workload patients already carry and their limited capacity to add new tasks. In a real-world test of the MyCareCompass platform, the authors measured engagement through actual usage data and follow-up comments rather than self-reported intentions. From these observations they extract three practical lessons aimed at helping designers create tools that patients will both start and keep using. The work matters because better engagement could reduce the repeated coordination and tracking burdens that currently dominate these patients' days.

Core claim

In a two-month pilot of the MyCareCompass patient-facing digital health intervention for people living with multiple chronic conditions, engagement is defined as uptake and sustained use, measured via usage analytics and follow-up feedback, which together yield three implementation lessons for designing tools that fit within the workload-capacity balance described by the Cumulative Complexity Model.

What carries the argument

The definition of engagement as patient uptake and sustained use during a two-month pilot, tracked through usage analytics and follow-up feedback, which is interpreted through the Cumulative Complexity Model to produce three implementation lessons.

If this is right

  • Digital health tools succeed only when they reduce rather than add to the visible and invisible treatment work patients already perform.
  • Short pilot data on actual platform use and patient comments can surface concrete barriers that longer studies or surveys might miss.
  • Design choices that respect the workload-capacity balance increase the chance that patients will continue using self-management platforms beyond initial adoption.
  • Lessons drawn from complex chronic care can inform engagement strategies for other patient-facing technologies that require ongoing data tracking and provider coordination.

Where Pith is reading between the lines

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

  • The same workload-capacity lens might help explain low adoption of apps for single-condition management or for caregivers rather than patients.
  • Extending the pilot to six or twelve months would test whether the three lessons support engagement that lasts through changes in disease severity or life circumstances.
  • Combining the lessons with automated reminders or simplified data entry could further lower the coordination burden that the paper identifies as a key obstacle.

Load-bearing premise

The two-month pilot study using usage analytics and follow-up feedback from patients with multiple chronic conditions is sufficient to identify generalizable implementation lessons for designing patient engagement in digital health interventions.

What would settle it

A follow-up study in a comparable patient population that applies the three lessons yet still records low sustained use or high dropout rates would indicate the lessons do not reliably produce engagement.

read the original abstract

Creating supportive technologies for people living with multiple chronic conditions is extremely challenging. These patients are often faced with substantial visible and invisible treatment work as well as their everyday responsibilities, including coordinating across providers, tracking information, and repeating communication in emotionally charged contexts. In the Cumulative Complexity Model (CuCoM), the balance between patient workload and patient capacity shapes what patients can realistically take on, including whether a digital tool can be adopted and sustained. In this paper, we report engagement lessons from implementing MyCareCompass, a patient-facing digital health intervention (DHI) intended to support day-to-day self-management for people living with multiple chronic conditions. We define engagement as patient uptake and sustained use during a two-month pilot study of our platform, drawing on usage analytics and follow-up feedback, and distill three implementation lessons for designing for engagement in complex chronic care.

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

2 major / 2 minor

Summary. The paper reports on a two-month pilot of MyCareCompass, a patient-facing digital health intervention for individuals with multiple chronic conditions. It defines engagement explicitly as uptake and sustained use, draws on usage analytics and follow-up feedback, and distills three implementation lessons for designing for engagement in complex chronic care, grounded in the Cumulative Complexity Model (CuCoM) that balances patient workload and capacity.

Significance. If the lessons prove robust, the work could offer practical value for HCI researchers and designers developing DHIs for multi-morbid patients by highlighting real-world engagement barriers tied to treatment burden. The explicit linkage to CuCoM and use of pilot analytics provide a concrete starting point for future studies, though the short timeframe limits claims about long-term sustainability.

major comments (2)
  1. [Abstract / Pilot Study Description] Abstract and pilot description: The central claim that three implementation lessons on sustained engagement can be distilled rests on usage analytics and feedback from a two-month pilot. This duration is unlikely to distinguish sustained adoption from novelty effects or temporary capacity spikes, as the CuCoM itself emphasizes fluctuating workload-capacity balance in multi-morbidity; without explicit mapping of usage drops to capacity changes or comparison to longer baselines, the lessons' generalizability is not demonstrated.
  2. [Results / Lessons] Results / Lessons section: The manuscript states that lessons were derived from the pilot data, but provides no specific quantitative usage metrics, participant numbers, exclusion criteria, or qualitative feedback excerpts tied to each lesson. This absence makes it impossible to evaluate whether the distilled lessons are supported by the evidence rather than post-hoc interpretation.
minor comments (2)
  1. [Methods] Clarify the exact definition and operationalization of 'sustained use' (e.g., minimum login frequency or feature usage thresholds) in the methods.
  2. [Discussion] Add a limitations subsection explicitly addressing the short observation window and its implications for claims about sustained engagement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify the scope and evidentiary basis of our pilot study on MyCareCompass. We respond to each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract / Pilot Study Description] Abstract and pilot description: The central claim that three implementation lessons on sustained engagement can be distilled rests on usage analytics and feedback from a two-month pilot. This duration is unlikely to distinguish sustained adoption from novelty effects or temporary capacity spikes, as the CuCoM itself emphasizes fluctuating workload-capacity balance in multi-morbidity; without explicit mapping of usage drops to capacity changes or comparison to longer baselines, the lessons' generalizability is not demonstrated.

    Authors: We agree that the two-month pilot duration limits strong claims about sustained engagement beyond potential novelty effects and that the dynamic workload-capacity balance central to CuCoM makes longer-term mapping desirable. The lessons are presented as preliminary insights from this pilot rather than robust generalizable findings. In revision we will update the abstract and add an explicit limitations discussion that acknowledges the short timeframe, notes the possibility of novelty or temporary capacity effects, and reframes the lessons as context-specific observations tied to the pilot data without asserting broad generalizability. We will also incorporate any available time-series usage patterns and participant feedback that can be linked to capacity-related factors. revision: yes

  2. Referee: [Results / Lessons] Results / Lessons section: The manuscript states that lessons were derived from the pilot data, but provides no specific quantitative usage metrics, participant numbers, exclusion criteria, or qualitative feedback excerpts tied to each lesson. This absence makes it impossible to evaluate whether the distilled lessons are supported by the evidence rather than post-hoc interpretation.

    Authors: The referee correctly identifies that the current manuscript does not supply the quantitative metrics, participant details, or tied qualitative excerpts needed to evaluate the evidential support for each lesson. We will revise the Results and Lessons sections to include participant numbers, inclusion/exclusion criteria, key usage analytics (e.g., active users, interaction frequencies, and any observed changes over the two months), and anonymized feedback excerpts explicitly connected to each of the three lessons. This addition will make the derivation of the lessons transparent and allow readers to assess their grounding in the data. revision: yes

Circularity Check

0 steps flagged

Empirical pilot study report with explicit data-driven definitions shows no circularity

full rationale

The paper reports engagement lessons from a two-month pilot of MyCareCompass for patients with multiple chronic conditions. It explicitly defines engagement as patient uptake and sustained use measured via usage analytics and follow-up feedback, then distills three implementation lessons directly from those observations. No equations, fitted parameters, or derivation steps are present that reduce by construction to the inputs. The cited CuCoM model is treated as an external framework rather than a self-referential justification. The central claims rest on empirical pilot data and patient feedback, making the contribution self-contained against external benchmarks with no load-bearing self-citation chains or self-definitional loops.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on standard assumptions in HCI pilot studies and the Cumulative Complexity Model as background without introducing new free parameters or invented entities.

axioms (1)
  • domain assumption Usage analytics and follow-up feedback reliably capture patient engagement levels in complex chronic care
    The paper defines engagement using these sources and bases the three lessons directly on them.

pith-pipeline@v0.9.0 · 5692 in / 1204 out tokens · 47747 ms · 2026-05-21T10:11:55.402104+00:00 · methodology

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Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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Reference graph

Works this paper leans on

6 extracted references · 6 canonical work pages

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