Adaptive Information Density in Mobile Augmented Reality: A Framework for Enhancing Dual-Task Performance in Older Adults
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThe experimental setup is a strength. The paper cleanly separates the detection layer from the presentation layer, so the comparison across MIN, MOD, and RICH is more controlled than in many AR studies.
However, the central theoretical claim is overstated. The paper proposes the CRDE framework as if it explains performance through a demand-to-capacity relationship, but the key construct, user cognitive-motor / attentional capacity (K), was not directly measured.
The participants were not cognitively characterized in a way that supports this claim. The paper does not report formal screening for MCI or cognitive testing such as MoCA or MMSE. Eligibility was based mainly on physical and general health criteria.
Because of this, CRDE is better described as a hypothesis or conceptual framework rather than a validated explanatory model. The results may be consistent with CRDE, but they do not directly test its core mechanism.
The novelty is also limited. The main idea that too little feedback may be insufficient, moderate feedback may help, and too much feedback may overload users is already well grounded in the theories the paper cites, including Cognitive Load Theory, Motor Learning Theory, and Multiple Resource Theory.
Therefore, CRDE seems more like a reframing or synthesis of existing theory than a truly new theoretical contribution. The stronger contribution is the application of this idea to a mobile AR exercise setting, not the core idea itself.
The paper also makes stronger claims about threshold-like collapse and zone structure than the design can fully support. With only three interface conditions and no direct measure of K, the evidence is not sufficient to establish a true density threshold or equilibrium boundary.
Overall, the study provides a useful empirical result: moderate information density performed best in this older-adult AR task. But the paper should soften claims of theoretical novelty and mechanistic validation.
A more accurate framing would be: the study offers initial evidence consistent with a capacity-based interpretation, but does not directly validate CRDE or measure the cognitive capacity construct it relies on.
Author Response
Comment1: The paper proposes the CRDE framework as if it explains performance through a demand-to-capacity relationship, but the key construct, user cognitive-motor / attentional capacity (K), was not directly measured. The participants were not cognitively characterized in a way that supports this claim. The paper does not report formal screening for MCI or cognitive testing such as MoCA or MMSE. Eligibility was based mainly on physical and general health criteria. Because of this, CRDE is better described as a hypothesis or conceptual framework rather than a validated explanatory model. The results may be consistent with CRDE, but they do not directly test its core mechanism.
Response1: We thank the reviewer for this central and well-founded critique. We agree and have revised the manuscript at multiple locations as follows:
(1) Abstract (Lines 23–30): the interpretation of the CRDE construct was rewritten as “These findings provide preliminary empirical evidence consistent with a CRDE perspective, a conceptual framework proposes performance as a zone-structured function of the demand-to-capacity ratio (D/K). The framework remains tentative and requires further empirical operationalization due to the lack of a direct measure of cognitive capacity (K).”
(2) Section 1, Introduction (Lines 87–90): CRDE is presented with a clear disclaimer: “CRDE is proposed here as a framework, not a validated model. The study tests its core prediction empirically; full validation would require direct measurement of cognitive capacity, which this study does not provide.”
(3) Section 4.1, Participants (Lines 386–389): A new sentence now states: “Formal cognitive screening (e.g., MoCA or MMSE) was not administered; participants were drawn from active senior club members who had been independently engaged in community programs, suggesting functional cognitive status.”
(4) Section 6.2, CRDE Framework (Lines 643–647): The paragraph now explicitly reads: “The framework is presented as a conceptual scaffold for organizing empirical observations and generating testable predictions; it is not a validated explanatory model, as K was not directly measured in this study (see also Sections 6.4 and 7).”
(5) Section 6.4, Limitations (Lines 732–740): A new limitation paragraph states: “First, cognitive capacity (K) was not directly measured. Although K is central to the CRDE framework as the denominator of the D/K ratio, no formal cognitive screening (e.g., MoCA, MMSE) or capacity-specific assessment (e.g., working memory span, dual-task cost) was administered. The framework therefore remains a conceptual scaffold rather than an empirically estimated model, and the zone-based interpretation of MIN, MOD, and RICH should be understood as inference from convergent behavioral and subjective patterns, not direct verification of capacity-relative dynamics. Future work that operationalizes K through validated cognitive measures is required to test the framework’s predictions and to estimate zone boundaries empirically.”
(6) Section 7, Conclusions (Lines 798–802): The conclusions now state: “direct empirical estimation of cognitive capacity, however, was not undertaken in the present study. These findings are interpreted within the proposed CRDE framework, which organizes density-dependent performance patterns within a capacity-relative conceptual structure rather than a validated explanatory model.” The phrase “consistent with the proposed CRDE provisional conceptual framework” replaces earlier language that implied mechanistic confirmation.
Comment2: The novelty is also limited. The main idea — that too little feedback may be insufficient, moderate feedback may help, and too much feedback may overload users — is already well grounded in the theories the paper cites. CRDE seems more like a reframing or synthesis of existing theory than a truly new theoretical contribution. The stronger contribution is the application of this idea to a mobile AR exercise setting, not the core idea itself.
Response2: We agree with this characterization. The manuscript has been revised to explicitly acknowledge that CRDE is integrative rather than foundational:
(1) Section 6.2, CRDE Framework (Line 639): The opening of the CRDE discussion now frames it as “a synthesis-oriented account” anchored in CLT, MLT, and MRT, replacing the earlier language that implied independent theoretical novelty.
(2) Section 6.1 closing / Section 6.2 transition (Lines 630–637): A new bridging paragraph explicitly states that partial correspondences from CLT, MLT, and MRT are drawn together “as observed patterns rather than confirmed mechanisms,” positioning CRDE as a synthesis-oriented organizational account rather than a new explanatory construct.
(3) Section 6.4, Limitations (Lines 719–724): A dedicated limitation paragraph now reads: “Second, the CRDE framework represents a synthesis of CLT, MLT, and MRT rather than an independent theoretical construct. Its contribution lies in proposing a capacity-relative organizational structure for partially conflicting predictions from these established frameworks, not in introducing fundamentally new theoretical constructs. The novelty of CRDE should accordingly be understood as conceptual organization rather than theoretical reformulation.”
Comment3: The paper also makes stronger claims about threshold-like collapse and zone structure than the design can fully support. With only three interface conditions and no direct measure of K, the evidence is not sufficient to establish a true density threshold or equilibrium boundary.
Response3: We agree and have removed strong threshold-language throughout the manuscript, replacing it with descriptive language tied to the observed pattern.
(1) Throughout Sections 5 and 6: the phrase "threshold-like collapse" was removed in all main-text occurrences. In Section 5.6 (Integrated Performance Profile, lines 586–588), the transition from MOD to RICH is now described as "a marked, not strictly gradual, change in performance," a wording that describes the observed pattern without asserting an inferred threshold.
(2) Section 6.2 overload zone (lines 661–663): rephrased to "representational demand is hypothesized to exceed available integration resources, with the framework anticipating a marked rather than gradual decline in performance."
(3) Figure 8 caption (lines 669–674): rewritten to label the diagram explicitly as conceptual:
"The figure illustrates the framework's proposed three-zone structure as a function of the D/K ratio and is not derived from empirical estimation of K, which was not measured in the present study. […] the positions of MIN, MOD, and RICH along the D/K axis are schematic, not empirically estimated."
(4) Section 6.4 Limitation #4 (lines 755–760): we now explicitly acknowledge the implication of using only three density conditions:
"Only three density conditions (MIN, MOD, RICH) were tested. Although this design isolates presentation-layer density and produces clearly differentiated patterns, three discrete levels cannot delineate the underlying density–performance function in fine detail. Studies sampling additional density levels are needed to characterize the shape and width of the proposed equilibrium zone, the location of the transition into the overload region, and the inter-individual variability around these boundaries."
(5) Section 1, lines 75–76: "declines sharply" replaced with "declines substantially". Lines 56 and 197–198 reworded so that older adults are described as a "theoretically informative population for studying capacity-related density effects" and as supporting "a theoretically informative approach for examining the enrichment-to-overload transition," rather than as enabling threshold localization.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis manuscript addresses an important timely topic at the intersection of mobile augmented reality aging populations, cognitive workload, and interface design. The attempt to isolate presentation-layer information density while holding detection-layer computation invariant is a strong conceptual contribution. The manuscript is also commendable for integrating HCI, cognitive psychology, and motor performance perspectives. The topic fits the scope of Informatics and will interest readers working in AR systems, digital health, and human-centered computing. Suggested modifications are: 1) Reframe CRDE as a provisional explanatory model or design heuristic requiring further validation across tasks, populations, and density gradients. The study posits validity but only tests three interface conditions. This supports a preliminary pattern not a generalized theory. 2) Statistical effects appear extremely large for behavioral interface studies and raises questions on measurement aggregation methods, within-subject dependency, etc. Suggest adding fuller reporting on confidence intervals, assumption checks, effect size interpretation caution, and possibly mixed-model confirmation. 3) Cognitive load measurement is limited to self-report. State clearly that overload is inferred, not directly measured.
Comments on the Quality of English LanguageMinor concerns: 1) Several sentences are overly dense and could be simplified. For example portions of the introduction and discussion read more like a grant paper than an empirical paper. 2) Figures are informative but some would benefit from cleaner formatting and clearer labels. Figure 8 (CDRE model) should explicitly be identified as conceptual, not empirically estimated.
Author Response
Comment1: Reframe CRDE as a provisional explanatory model or design heuristic requiring further validation across tasks, populations, and density gradients. The study posits validity but only tests three interface conditions. This supports a preliminary pattern not a generalized theory.
Response1: The specific section where CRDE language was reframed as follows:
(1) Abstract (Lines 23–30): the framework is now introduced as: “These findings provide preliminary empirical evidence consistent with a Capacity-Relative Density Equilibrium (CRDE) perspective, a conceptual framework that proposes performance as a zone-structured function of the demand-to-capacity ratio (D/K). The framework remains tentative and requires further empirical operationalization due to the lack of a direct measure of cognitive capacity (K). From this perspective, we identify three potential design principles: actionable sufficiency, density threshold, and dual-task alignment, as practical heuristics for mobile AR systems targeting older adult populations.
(2) Section 1 (Lines 82–90): CRDE explicitly stated as “a framework, not a validated model.”
(3) Section 5.6, Integrated Performance Profile (Lines 585–594): Results are described as “consistent with the CRDE provisional conceptual framework” rather than as supporting or validating the model.
(4) Section 6.2 (Lines 639, 643–647): CRDE framed as “a synthesis-oriented account” and “a conceptual scaffold… not a validated explanatory model.”
(5) Section 6.3, Design Principles (Lines 703–706): Principles are offered “as practical guidance and as testable propositions for future studies,” not as established guidelines.
(6) Section 7, Conclusions (Lines 800–802): Conclusions now refer to “a capacity-relative conceptual structure rather than a validated explanatory model.”
Comment R2.2:
Statistical effects appear extremely large for behavioral interface studies and raises questions on measurement aggregation methods, within-subject dependency, etc. Suggest adding fuller reporting on confidence intervals, assumption checks, effect size interpretation caution, and possibly mixed-model confirmation.
Response2: We have substantially expanded the statistical reporting in Section 5.1 and the individual results subsections. Specific changes:
(1) Section 5.1, Statistical Analysis Approach (Lines 472–487): A new comprehensive paragraph now reports: Shapiro–Wilk normality tests; Mauchly’s sphericity test (Correctness: W = .96, p = .344; Latency: W = .91, p = .061; NASA-TLX: W = .99, p = .746) confirming sphericity for all three outcomes so Greenhouse–Geisser correction was not required; verification against Friedman tests; Bonferroni correction at α = .0167; and the explicit statement that “mean differences are accompanied by 95% confidence intervals. All tests were two-tailed at α = .05.”
(2) Section 5.4, Cognitive Load (Lines 538–543): A new paragraph reports the NASA-TLX sub-scale dissociation: “Sub-scale analyses in Table 3 revealed a clear dissociation. Physical workload did not differ significantly across conditions, whereas all five cognitive sub-scales Mental, Temporal, Performance, Effort, and Frustration showed a significant effect of density. This dissociation between invariant physical workload and substantial cognitive sub-scale effects is consistent with the interpretation that observed performance differences reflect cognitive rather than physical load.”
(3) Section 6.4, Limitations (Lines 761–769): A new limitation paragraph reads: “Fifth, the effect sizes observed for latency (η²p = 0.893) and workload (η²p = 0.781) are large by behavioral-research standards and should be interpreted in the context of the within-subject design, the automated signal-based measurement of latency, and the relatively homogeneous community-dwelling sample. These conditions reduce measurement noise and inter-individual variability and are likely to inflate effect-size estimates relative to between-subject designs or to more heterogeneous populations. The magnitudes reported here should not be treated as population-level estimates, and replication in broader samples with between-subject components is necessary to evaluate external generalizability.”
Comment3: Cognitive load measurement is limited to self-report. State clearly that overload is inferred, not directly measured.
Response3: We have added explicit statements in section 4.3 and 6.4:
(1) Section 4.3, Outcome Measures (Lines 425–427): explicit operational note added: “Note that cognitive load in this study is operationalized through self-report (NASA-TLX) and behavioral indicators (correctness, error correction latency). It is inferred rather than directly measured.”
(2) Section 6.4, Limitations (Lines 748–754): A dedicated limitation paragraph states: “Third, cognitive workload was inferred from self-report (NASA-TLX) and behavioral indicators (correctness, error correction latency) rather than directly measured. Although the dissociation between invariant physical sub-scale scores and substantial cognitive sub-scale effects (Section 5.4) is consistent with a cognitive locus of the density effect, this conclusion remains an inference. Future work should incorporate objective physiological measures such as pupillometry, EEG-derived workload indices, or heart rate variability to provide convergent validation.”
Comment4 (English Quality): Several sentences are overly dense and could be simplified. For example, portions of the introduction and discussion read more like a grant paper than an empirical paper.
Response4: We have simplified several key passages. The revised text appears with yellow highlighting in the manuscript:
(1) Section 1, Introduction (Lines 67–70, 74–77, 91–98): Competing theoretical predictions are now stated in plain language; the two research constructs (density threshold, dual-task alignment) are introduced as brief bullet-like sentences; and the four study contributions are listed in a compact paragraph rather than a nested clause structure.
(2) Section 1, Introduction (Lines 222–245): The research gap synthesis paragraph and the closing sentence introducing the CRDE framework have been condensed and simplified, removing nominalization-heavy constructions.
(3) Section 6.1, Table 6 note (Lines 623–626) and Section 6.2 opening (Lines 642–646): The table footnote now uses one direct sentence per framework; the transition paragraph from Section 6.1 to 6.2 has been shortened to remove redundant hedging clauses.
Comment5: Figures are informative but some would benefit from cleaner formatting and clearer labels. Figure 8 (CRDE model) should explicitly be identified as conceptual, not empirically estimated.
Response5: The Figure 8 caption has been updated (Lines 668–673) to:
“Figure 8. Conceptual diagram of the CRDE framework. The figure illustrates the framework’s proposed three-zone structure as a function of the D/K ratio and is not derived from empirical estimation of K, which was not measured in the present study. Within this conceptualization, performance (P) is hypothesized to be preserved within the equilibrium zone (D ≤ K) and to decline markedly in the overload zone (D > K). The shaded region indicates the proposed capacity buffer; the positions of MIN, MOD, and RICH along the D/K axis are schematic, not empirically estimated.”
Author Response File: Author Response.pdf
