---
title: "Research-Grounded Design Principles"
description: "Evidence-backed cognitive-load, executive-function, and accessibility guidelines for Kon."
last_updated: 2026-04-26
---
# Design principles for Kon, grounded in evidence

## The lens: cognitive load and executive dysfunction as a design constraint

Kon serves people whose working memory, initiation, sequencing, and time perception are intermittently or chronically impaired — by ADHD, autism, dyslexia, TBI, stroke, long COVID, ME/CFS, fibromyalgia, perimenopause, depression, anxiety, or burnout. The unifying mechanism is reduced **available cognitive bandwidth** (Sweller's intrinsic load), aggravated by event boundaries that purge volatile thoughts (Radvansky), temporal myopia (Barkley), and shame cycles that make tools themselves into stressors (Tracy & Robins; Corrigan). The right design response is not to "train" capacity back but to act as an **external cognitive system** in the Hutchins/Clark-and-Chalmers sense — a reliable, low-friction extension that reduces intrinsic load (Risko & Gilbert, 2016), supports autonomous motivation (Deci & Ryan, 2000), respects the user's variable capacity (Jason's energy envelope), and earns long-term use by being forgiving rather than punishing (Cochran & Tesser's "what-the-hell" effect). The capability approach (Sen; Toboso, 2011) gives the normative frame: Kon should expand what users can do and be, not measure how close they get to a neurotypical baseline.

---

## Per-feature guidelines

### 1. Voice capture (local Whisper, low-friction thought dumping)

**The evidence.** Speech is materially faster than touchscreen typing — Ruan et al. (2018, IMWUT) found 3× faster English entry and 20% lower error rate. For dyslexic and learning-disabled writers, dictation reliably produces longer, more complex, lower-error texts because it offloads transcription cost (Higgins & Raskind, 1995; Quinlan, 2004 *J Educ Psych*; MacArthur & Cavalier, 2004 *Exceptional Children*). Matre & Cameron's 2022 scoping review confirms positive effects across eight studies. The mechanism transfers: ADHD writers face the same transcription bottleneck (Re, Pedron & Cornoldi, 2007), as do TBI patients with motor fatigue.

**Be honest about two limits.** ADHD-specific dictation RCTs are sparse — the case is largely inferential from working-memory theory and dyslexia studies. Svensson et al.'s (2023) five-year follow-up found long-term STT use *declined* when error-correction friction outweighed input speed. And dictation is contraindicated for severe expressive aphasia (Russo et al., 2017).

**Do.** Make capture launchable in one gesture or hotword; never require unlock or app foreground. Whisper's local processing is correct — privacy materially affects what users will dictate (see local-first below). Allow capture without immediate triage: thought-dumping must not require categorisation. Show a transcript draft but never block the save on accuracy. Permit silent partial-correction later. Support fragmentary, ungrammatical, half-finished thoughts as first-class items.

**Avoid.** Mandatory tagging at capture time. Forcing review before save. Network round-trips that introduce latency or privacy doubt. Treating low-confidence transcripts as failures rather than user-editable artefacts.

### 2. MicroSteps (LLM-decomposed 3–7 actions)

**The evidence.** Task analysis is one of the longest-validated EF supports: Spooner et al. (2012) and the NCAEP review (Steinbrenner et al., 2020) classify it as evidence-based for autism and intellectual disability; visual activity schedules meet EBP criteria across 31 studies (Knight, Sartini & Spriggs, 2015). Goal Management Training (Levine et al., 2000; Stamenova & Levine 2019 meta-analysis) and metacognitive strategy training (Cicerone et al., 2019) are practice standards for TBI executive dysfunction. **Implementation intentions** — explicit if-then phrasing — show d = 0.65 across 94 studies (Gollwitzer & Sheeran, 2006) and bring ADHD children's inhibition to non-ADHD levels (Gawrilow & Gollwitzer, 2008).

**The 3–7 range** is justifiable: Cowan's (2001) revised working-memory limit of ~4 chunks (lower in clinical populations) bounds the *upper* end; below three steps the decomposition adds no scaffold. Cognitive Load Theory (Sweller, 2010) predicts decomposition helps novices but hurts experts via the **expertise reversal effect** (Kalyuga, 2007).

**Do.** Default to four steps; allow user-controlled granularity. Phrase at least one step as an implementation intention ("when the kettle boils, …"). Permit users to edit, reject, collapse, or override AI output — preserving agency directly addresses Spiel et al.'s (2022) and Jamshed et al.'s (2025, ASSETS) critique that ND productivity tools shift the burden of "access-making" onto users. Track mastery and offer to fold familiar routines back into single items (scaffolding fade — Pea, 2004; van de Pol, 2010).

**Avoid.** Locking the step count. Decomposing tasks the user has demonstrated mastery of. Marketing AI decomposition as equivalent to clinical task analysis — there is **no peer-reviewed RCT** comparing LLM-generated to therapist-generated breakdowns; goblin.tools has not been evaluated. State this honestly.

### 3. Buckets (Inbox / Today / Soon / Later)

**The evidence.** Bellotti et al.'s 2004 CHI fieldwork on real to-do behaviour found users ignore explicit P1–P4 priority labels and naturally re-sort by time horizon and recency; long undifferentiated lists demoralise and get abandoned. Whittaker, Bellotti & Gwizdka (2006) explain why: priorities shift, so static labels go stale. Heylighen & Vidal's (2008) analysis of GTD argues opportunistic, context-driven execution outperforms rigid priority queues — though GTD's own RCT base is thin.

**Today as default** is supported by choice architecture (Thaler & Sunstein, 2008; Johnson & Goldstein, 2003 — defaults reliably alter behaviour through inertia and effort-avoidance) and by Iyengar & Lepper's (2000) jam-study evidence that larger choice sets reduce engagement. Cowan's working-memory ceiling makes a 5–10-item Today list cognitively manageable; a 200-item flat list is not.

**Do.** Default to Today. Keep four buckets — adding more re-introduces the categorisation tax that buckets exist to avoid. Allow drag-only re-bucketing; never force a deadline. Treat Inbox as a deliberate triage zone, not a backlog of shame. Make "Soon" and "Later" *visible counts* but not push surfaces — they are deliberately out of immediate attention. Display a single, gentle bucket-position cue, not a percentage-complete bar.

**Avoid.** Numeric priorities. Smart-sort algorithms that override the user's bucket choice. Showing all buckets simultaneously by default. Surfacing overdue counts on app launch (a documented shame trigger — see Challenge A).

### 4. "Match my energy" sort

**The evidence.** Jason's energy envelope theory (Jason et al., 2013; O'Connor et al., 2019) is the strongest empirical anchor: ME/CFS patients who keep expenditure within perceived capacity have better functioning across fatigue, pain, depression, and QoL. NICE NG206 (2021) makes pacing — staying within current limits, never escalating — the recommended approach for ME/CFS and (by extension) long COVID, and explicitly warns against graded escalation. The chronotype × time-of-day **synchrony effect** (Schmidt et al., 2007; 2025 *Chronobiology International* systematic review) shows real but modest performance gains when task demand matches arousal state. ADHD shows altered circadian profiles and greater within-day arousal variability (Coogan & McGowan, 2017), supporting energy-matched scheduling for that population specifically.

**Be honest.** **Spoon theory** (Miserandino, 2003) is a culturally legible metaphor with major patient-community traction but **no peer-reviewed psychometric validation**; cite it as a communication frame, ground the actual mechanic in Jason's envelope. The strict 90-minute ultradian/BRAC cycle popularised by Tony Schwartz and Andrew Huberman is **weakly supported** — Eriksen et al. (1995) found no 90-min periodicity in cognitive performance; LaJambe & Brown (2008) review is sceptical. Mack et al.'s (2022, ASSETS) "consequence-based accessibility" paper is the strongest HCI peg.

**Do.** Allow a quick three-state energy input (high/medium/low) with one-tap update and a "skip" that doesn't penalise. Surface tasks tagged at or below current state. Let users define what high/medium/low *mean for them* — the spoon count is individual.

**Avoid.** Multiple daily prompts (EMA literature: cognitive impairment and fatigue predict lower compliance — Shiffman et al., 2008; Wrzus & Neubauer, 2023). Any feature that suggests the user "do a bit more than yesterday" — that is graded exercise therapy by another name and is contraindicated by NICE NG206. Auto-promoting low-energy tasks to high-energy days.

### 5. Local-first / privacy

**The evidence.** Anonymity and perceived privacy reliably increase honest disclosure of stigmatised content: Joinson (1999, 2001), Gnambs & Kaspar's (2017) meta-analysis, the Pennebaker expressive-writing tradition (Frattaroli, 2006 meta-analysis: privacy is a moderator of therapeutic effect). Mental-health apps have a serious privacy problem: Iwaya et al. (2023) found 24/27 apps had critical security risks; O'Loughlin et al. (2019) found only 4% of depression apps had acceptable transparency. Powell et al.'s 2024 CHI paper documents users actively self-censoring honest reporting in cloud-backed mental-health apps. Penney's (2016) Wikipedia traffic analysis demonstrates measurable chilling effects from perceived surveillance.

**Do.** Default to local-only storage; treat any sync as opt-in per data class (transcripts, embeddings, summaries separately). State the data flow in one sentence on the capture screen — privacy *perception* is what drives disclosure, not just the underlying engineering. Allow per-entry redaction before any optional sync. Provide an "incognito capture" mode that bypasses logs entirely.

**Avoid.** Implicit cloud backup. Telemetry on transcript content (even hashed). Required accounts for core features. Any analytics that touch the spoken text. Marketing copy that conflates "encrypted" with "private" — users can tell the difference.

**Honest gap.** No RCT directly compares local-first to cloud-stored journaling apps' effect on disclosure of stigmatised content; the case rests on transitive evidence (anonymity literature + privacy calculus + chilling effects). The inference is solid but not directly tested.

### 6. Custom vocabulary / per-profile language

**The evidence is strong and unambiguous.** Personalised ASR delivers 35–80% relative WER reduction across atypical-speech populations (Shor et al., 2019, Interspeech; Green et al., 2021 — personalised models *outperformed expert human transcribers* on disordered speech). Just five minutes of personalised data captures ~71% of the gain (Shor 2019). Contextual biasing/custom vocabulary cuts WER on rare named entities by 10–48% (Pundak et al., 2018; Kolehmainen et al., 2023). Lea et al. (2023, CHI) document user-driven personalisation as the path for people who stutter; Tomanek et al. (2021) on residual adapters shows efficient on-device personalisation is feasible. De Russis & Corno (2019) find off-the-shelf cloud ASR has WER >50% for many dysarthric speakers — personalisation is **a baseline accessibility requirement, not a luxury**.

**Do.** Treat vocabulary as a first-class object: per-user noun lists (names, jargon, medications, slang), with low-friction in-context add ("learn this word"). Support adapter-based personal acoustic models for users with accents, dysarthria, stutter, post-stroke speech, or atypical prosody (autism). Persist them locally. Make corrections one-tap and feed them back into the model.

**Avoid.** Hard-coded vocabularies the user can't edit. Discarding user corrections. Penalising fragmented or restarted utterances — these are common in cognitive fatigue and dysfluency.

### 7. Dyslexia-friendly fonts, bionic reading, reduce motion

**The evidence here is contested and the developer should be candid in copy.**

**OpenDyslexic.** Repeatedly negative: Wery & Diliberto (2017, *Annals of Dyslexia*); Rello & Baeza-Yates (2013/2016, ACM TACCESS) — dyslexic readers preferred Verdana and Helvetica; Kuster et al. (2018, n=170+147) — null and Arial preferred. Marinus et al. (2016) found a 7% Dyslexie advantage that **disappeared when Arial was given matched spacing** — the benefit is from spacing, not letterforms. The **British Dyslexia Association 2023 style guide does not endorse OpenDyslexic**; the IDA position is that specialty fonts have "no desired effect."

**Lexend** has no independent peer-reviewed RCTs; Shaver-Troup's evidence is a doctoral dissertation and an N=20 promotional study. Its design principles (large x-height, generous spacing) are evidence-based; the brand is not.

**Atkinson Hyperlegible** was designed by the Braille Institute for **low-vision character disambiguation** — don't conflate it with dyslexia.

**Bionic Reading.** Strukelj (2024, *Acta Psychologica*) — null at n=32 with adequate power. *Attention, Perception & Psychophysics* (2025) — bolding the first half produced reading **costs**, not gains. Doyon's n=2,074 public test showed 2.6 wpm slower and 5–8% worse comprehension.

**What actually has evidence:** font size ≥18pt (Rello, Pielot & Marcos, 2016, CHI; O'Brien et al., 2005), **inter-letter spacing** (Zorzi et al., 2012, *PNAS* — extra-large spacing produces immediate dyslexic reading gains), avoiding italics, sans-serif preference. The strongest principle is **offering user-adjustable presentation** — UDL (CAST), WCAG 1.4.12 Text Spacing, WCAG 2.3.3 Animation from Interactions.

**Do.** Default to a clean sans-serif at ≥16pt, with size adjustable to 22pt+. Provide adjustable letter-spacing and line-spacing — these have the strongest evidence. Honour `prefers-reduced-motion` *and* expose an in-app toggle (Apple HIG; vestibular literature; autism × migraine comorbidity — Sullivan et al., 2014). Suppress parallax, scaling intros, autoplay carousels.

**Avoid.** Marketing OpenDyslexic, Lexend, or Bionic Reading as "proven for dyslexia" — they aren't. Offer them honestly as **subjective preference options**: "Some users find this comfortable; the evidence base is contested."

---

## Per-challenge guidelines

### A. Post-collapse re-entry

**The evidence.** This is where most productivity tools fail Kon's users. The mechanism is well-mapped. Tracy & Robins (2006) and Tangney & Dearing (2002) show that internal-stable-uncontrollable attributions for failure produce **shame**, which motivates withdrawal; internal-unstable-controllable attributions produce **guilt**, which motivates repair. A full inbox after weeks away triggers the shame route by default. Cochran & Tesser's "what-the-hell effect" (and Polivy et al., 2010) shows a single perceived violation cascades into total abandonment — *belief* of failure, not actual failure, drives disengagement. Loss aversion (Kahneman & Tversky, 1979; Kivetz et al., 2006 goal-gradient) makes streak-based systems disproportionately punishing on break.

The counter-evidence is equally clear. Dai, Milkman & Riis's "fresh start effect" (2014, *Management Science*; 2015, *Psychological Science*) shows temporal landmarks — Mondays, months, "fresh starts" — psychologically segregate the imperfect past self and spike aspirational behaviour. Breines & Chen's (2012) self-compassion experiments show induced self-compassion *increases* self-improvement motivation, time studying after failure, and willingness to repair — directly disconfirming the "compassion breeds complacency" worry. MacBeth & Gumley's (2012) meta-analysis confirms a large inverse association between self-compassion and depression/anxiety/stress.

**Do.** Treat re-entry as a first-class state. On returning after >7 days, trigger a fresh-start frame: "Welcome back. This week starts fresh." Offer one-tap **bankruptcy** — archive everything in Inbox/Today older than X days, no questions asked (the consumer-equivalent of Mann's Inbox Zero bankruptcy; consistent with Cochran & Tesser's long-term-framing prescription, even if Mann himself is a non-peer-reviewed source). Frame missed items as system-attributable ("the inbox overflowed"), never user-attributable ("you forgot"). Offer common-humanity language ("most people return after a long break — that's how this tool is meant to be used"). Default to a small Today list of 1–3 items on re-entry.

**Avoid.** Red badges of overdue counts. "You missed N tasks" notifications. Streak-loss screens. Catch-up flows. Any UI that asks the user to *resolve* the backlog before they can use the app. Reactivation emails framed as concern ("we missed you") — they almost always read as guilt to this population.

### B. Unintrusive dopamine loops

**The evidence.** Most "dopamine UX" writing is junk neuroscience. Schultz (1998, 2016) and Berridge & Robinson (1998, 2016) establish that dopamine codes **reward prediction error** and **incentive salience ("wanting")**, not pleasure ("liking"). After learning, *predictable* rewards produce zero phasic dopamine response — which means predictable, fixed-schedule completion feedback **cannot fuel compulsion loops**, only acknowledgement. That is precisely what Kon should want. Schüll's (2012) ethnography of slot machines and Lindström et al. (2021, *Nature Communications*) show what variable-ratio reinforcement does at scale; Eyal's (2014) *Hooked* explicitly imported this into product design and his own (2019) follow-up partially walked it back.

For ADHD specifically, Söderlund's "moderate brain arousal" model (2007 *J Child Psychology and Psychiatry*; 2007 *Psychological Review*) and Nigg et al.'s (2024) meta-analysis show white/pink noise produces a small but real benefit (g ≈ 0.22, moderate-confidence GRADE) on attention — though Rijmen & Wiersema (2024, 2026) have challenged the stochastic-resonance mechanism. Brain.fm's amplitude-modulated music (Woods et al., 2024, *Communications Biology*) shows modest attention benefit but is **industry-funded with no independent replication**. Garcia-Argibay et al.'s (2019) binaural beats meta-analysis is positive (g = 0.45, anxiety stronger than attention) but later well-controlled studies (Robison et al., 2022) are sceptical. The **Mozart effect is debunked** (Pietschnig et al., 2010 meta-analysis).

For audio design itself: Brewster's earcon work (1993, 1998); Garzonis et al. (2009) — auditory icons beat earcons on intuitiveness; Williams et al. (2021) on autism + hyperacusis — ~50–70% prevalence of impaired sound tolerance.

**Do.** Use **fixed-schedule, completion-contingent** feedback: every finished task → predictable, brief, low-frequency-friendly acknowledgement. Keep audio cues ≤1.5s, soft attack envelope (≥10–20ms), avoid >4kHz peaks. Provide multimodal redundancy (audio + haptic + visual) so users can disable any channel without losing the cue. Expose a calm/energising/silent intensity axis — Dunn's sensory profile quadrants vary, and many users sit in both "sensation seeking" (ADHD) and "sensitivity" (autism comorbidity) at once. If you offer ambient sound, frame pink/white noise honestly (modest evidence, opt-in) and avoid pseudoscientific language about "neural phase-locking" or "binaural entrainment."

**Avoid.** Variable-ratio reward animations. Surprise rewards. Confetti for ordinary completion. Streak counters as feedback (see D). Marketing copy invoking "dopamine hits." Forced sound on completion. Anything that resembles Gray et al.'s (2018) dark-pattern strategies — nagging, forced action, interface interference.

### C. Capture-to-action gap

**The evidence.** The "thought lives in the head until externalised" intuition is one of the most strongly supported in the brief. Risko & Gilbert's (2016, *Trends in Cognitive Sciences*) review of cognitive offloading defines and validates the core mechanism: physical action that alters information-processing demand. Gilbert et al. (2020, *JEP:General*; 2023 review) show external reminders consistently improve prospective memory; the cost is small relative to benefit. Storm & Stone (2015, *Psychological Science*) demonstrate **saving-enhanced memory** — saving information *improves* learning of subsequent material because resources are freed. Sweller's CLT explains why: working memory is severely limited and externalising reduces intrinsic load. Clark & Chalmers (1998) and Hutchins (1995) provide the philosophical/ethnographic ground for treating reliable tools as cognitive extensions.

The doorway effect (Radvansky & Copeland, 2006; Pettijohn & Radvansky, 2016) operationalises the mechanism: **event boundaries actively purge volatile representations**. Be honest — McKerracher et al. (2021) failed to replicate the specific magnitude in complex VR tasks, and Sparrow et al.'s (2011) "Google effect" failed Many Labs replication. The broader event-boundary literature is robust; the dramatic headlines are not.

**Do.** Optimise for **time-to-first-syllable** as the headline metric. Capture must work from lock screen, in any app, with one input. Permit nameless, untyped, untagged thought-dumps as first-class items (Bellotti et al., 2004 — users abandon tools that demand classification at capture). Buffer constantly: any app return should preserve in-progress dictation. Time-stamp and (optionally) place-stamp captures — Godden & Baddeley's (1975) context-dependent memory has a real if modest effect (Smith & Vela, 2001 meta d ≈ 0.25; replication caveats noted by Murre, 2021). Treat the transcript as the canonical artefact; allow re-listen for verification but don't require it.

**Avoid.** Modal dialogs at capture time. Required categorisation. Network checks. Login prompts. Auto-summarisation that displaces the original — users need to find their own words.

### D. Streaks vs momentum

**The evidence is, for this population, decisively against streaks.** Deci, Koestner & Ryan's (1999, *Psych Bulletin*) meta-analysis of 128 experiments shows tangible, expected, performance-contingent rewards undermine intrinsic motivation — the **overjustification effect**. Cerasoli et al.'s (2014) 40-year meta-analysis (k = 183, N > 200,000) confirms incentives crowd out intrinsic motivation when directly performance-tied. Six et al.'s (2021, *JMIR Mental Health*) meta-analysis of 38 mental-health gamification studies found **gamification did not significantly improve depression outcomes** over non-gamified counterparts. Cheng et al. (2019) document gamification in mental-health apps applied without theoretical grounding; rewards can have negative mood effects in users feeling they're "not achieving enough" (Alqahtani et al., 2021, qualitative).

Streak mechanics specifically combine three documented harms: loss aversion (Kahneman & Tversky), goal-gradient escalation (Kivetz et al., 2006), and the what-the-hell effect (Cochran & Tesser; Polivy et al., 2010) where one break cascades into abandonment. For users with executive collapse cycles built into their condition, this is a designed-in failure mode.

**Be honest about weak claims.** Most "Duolingo streak research" is internal A/B-test marketing, not peer-reviewed. **Rejection sensitive dysphoria** as Dodson describes it is a clinical assertion lacking peer-review; cite **rejection sensitivity** (Downey & Feldman, 1996, *JPSP*) and **emotional dysregulation in ADHD** (Shaw et al., 2014, *Am J Psychiatry*; Beheshti et al., 2020 meta-analysis) instead. James Clear's "identity-based habits" is rhetorical synthesis; the underlying habit-identity correlation is mixed (Verplanken & Sui, 2019).

**Do.** Replace streaks with **non-quantified momentum**: a soft "you've been using Kon this week" indicator without numbers. Use brief reflection prompts (Frattaroli's 2006 expressive-writing meta gives modest but real effects, r ≈ 0.075–0.15) — never enforced. Offer implementation-intention coaching ("when X, then Y") which has d = 0.65 (Gollwitzer & Sheeran, 2006). Frame returns as fresh starts, not catch-ups. Where you must show progress, default to monthly or quarterly time-ranges, not daily.

**Avoid.** Streak counters. Streak-freeze monetisation. "Don't break the chain" framing. Public leaderboards. Badge systems contingent on consecutive use. Notifications triggered by inactivity.

### E. Notifications and nudges

**The evidence.** Kushlev, Proulx & Dunn (2016, CHI) showed that notifications alone produce significantly elevated ADHD-symptom scores in *non-ADHD* users — the implication for users already symptomatic is severe. Stothart et al. (2015) found even *receiving* a notification (without interaction) degrades attention. Mark et al. (2016, CHI) found longer email duration predicts higher measured stress (HR), and **batching does not reduce stress** in their data — but Fitz et al. (2019, *CHB*) RCT found three daily batches improved well-being over both as-they-arrive and total-disable. Pielot & Rello (2017) found total-disable increases anxiety and disconnection. The sweet spot is batching with user control.

**Calm Technology** (Weiser & Brown, 1995; Case, 2015) is a heuristic, not an empirically tested framework — Rogers (2006, UbiComp) critiques it directly. Use it for vocabulary; don't claim it as evidence. Mark's "23 minutes to refocus" figure is widely *mis*quoted — the original measured time to *return to* a task after intervening tasks, not full cognitive recovery. The strongest empirically grounded principle is Leroy's (2009) **attention residue**: unfinished tasks persist cognitively into the next.

The **nudge** literature is in the middle of a serious replication crisis. Maier et al. (2022, *PNAS*) re-analysed Mertens et al.'s positive meta-analysis using publication-bias correction and found **no overall evidence of reliable nudge effects**; DellaVigna & Linos (2022) found field nudges ~6× smaller than published academic nudges; Hu et al. (2025) second-order meta found d collapses from 0.27 to 0.004 after correction. **Don't over-promise behaviour change from copy tweaks.**

For sensory profile: Williams et al. (2021) on autism × hyperacusis (50–70% prevalence); Tomchek & Dunn (2007) — 95% of autistic children show atypical sensory processing.

**Do.** Default to **silent, batched, user-summoned** notifications. Offer 1–3 daily digest moments with user-set times. Use compassionate, behaviour-focused language that cues *guilt-repair* rather than *shame-withdraw* (Tracy & Robins, 2006; Breines & Chen, 2012). Honour OS quiet hours and sensory profile (text-only / haptic-only / silent variants). For time-blindness countermeasures (Barkley, 1997, 2001), externalise time visually (see Gaps).

**Avoid.** Push notifications by default. Red badges. "You haven't opened Kon in N days." Inactivity-triggered messages. "Should" or "must" language. Sound on by default. Sharp/high-frequency tones. Persuasive nudges presented as if behaviour-change-proven.

### F. Identity framing

**The evidence.** Phillips & Zhao's (1993) foundational AT-abandonment study found **29.3% of devices abandoned**, with non-involvement of users in selection and divergence between user goals and device logic among the strongest predictors. Scherer's Matching Person & Technology research (1998, 2005) shows uptake is predicted by mood, self-esteem, motivation, and **self-determination** as strongly as by feature-fit. Corrigan's self-stigma model (Corrigan & Watson, 2002; Corrigan, Larson & Rüsch, 2009) maps the awareness → agreement → application → harm cascade and the resulting "why try" effect. Bandura's (1997) self-efficacy work establishes that mastery experiences — not external validation — are the strongest builder of agency. The capability approach (Sen, 1999; Nussbaum, 2011; Toboso, 2011 applied to ICT; MacLachlan et al., 2025 ATA-C study) recommends evaluating tools by *what they let users do and be*, not by how close they bring users to a non-disabled norm.

The neurodiversity paradigm (Walker, 2021; Botha et al., 2024 — community-developed) argues against pathology framing. Shakespeare's (2006) sympathetic critique of the strict social model is also relevant: pure social-model framing under-recognises real cognitive limits the user experiences, which can itself feel invalidating.

**No RCT directly compares prosthetic vs training framings**, but the convergent evidence supports a clear hierarchy:

**Do.** Use **capability/scaffolding** language as primary: "Kon helps you do the things you want to do." Permit **prosthetic** framing for users who self-identify as disabled — "use it as long as you want, like glasses" — without imposing it. Show users their own work (reviewable transcripts, user-curated buckets) to build mastery experiences. Make it possible to use Kon forever without that feeling like failure.

**Avoid.** Cure/training framing ("graduate from Kon," "build your executive function"). Streaks framed as growth. Onboarding that pathologises ("Do you struggle with…?"). Marketing that implies the user is broken. Quizzes that diagnose. Any copy that implies success means needing Kon less.

### G. Gaps the literature surfaces

The most important Kon-relevant gaps are externalised time, body doubling, transition support, and structured implementation-intention scaffolding. Treated in detail in the next section.

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## Gaps: features the literature suggests Kon should consider

**1. Externalised time visualisation.** Barkley's (1997, 2001) work establishes time as a *core* ADHD deficit (temporal myopia, time reproduction errors at long durations). Janeslätt et al.'s (2018) RCT of time-skill training plus Time Assistive Devices (visual timers, electronic schedules) — the strongest RCT evidence in this space — significantly improved daily time management. Kon currently captures, decomposes, and sorts but does not make time *visible*. A disappearing-disc visual on the active MicroStep, or an ambient "elapsed since started" indicator, would directly address the most-evidenced ADHD-specific scaffold. Avoid prescriptive Pomodoro cycles — Biwer et al. (2023, *BJEP*) found Pomodoro breaks *accelerated* fatigue and motivation loss vs self-regulated breaks.

**2. Body-doubling / co-presence layer.** Eagle, Baltaxe-Admony & Ringland's (2024, *ACM TACCESS*) survey of 220 neurodivergent participants — the first formal academic study of body doubling — found many users depend on it for basic activities. The mechanism is grounded in Zajonc's (1965) social facilitation (well-replicated for well-learned tasks). Evidence is emerging rather than strong: Lee et al.'s 2025 VR preprint suggests AI body doubles produce comparable outcomes to human ones. An async "I'm working too" presence layer, or scheduled silent-coworking sessions, fills a gap that solo capture/decomposition cannot.

**3. Implementation-intention coaching.** Kon decomposes into 3–7 steps but does not currently *phrase* them as implementation intentions. Gollwitzer & Sheeran's (2006) meta-analysis of 94 studies shows d = 0.65 for if-then planning; Gawrilow & Gollwitzer (2008) show it brings ADHD inhibition to non-ADHD level. Have the LLM generate at least one step in "when X, then Y" form, anchoring the action to an existing cue.

**4. Transition support and re-orientation.** Monsell (2003) on switch costs and Leroy (2009) on attention residue establish the cognitive cost of moving between tasks. Hume et al.'s (2021) third-generation EBP review classifies visual schedules as evidence-based for autism transitions. Kon should provide a brief "where was I?" re-orientation when returning to an interrupted MicroStep — a one-line summary of the last completed step plus the next one — and an optional gentle pre-warning before bucket switches.

**5. Coach/partner loop (optional).** Wilson et al.'s (2001, *JNNP*) NeuroPage RCT showed task-completion rose from 55% to 74% with paged reminders; Fish et al. (2008) found severe EF impairment moderates self-programming success — users with the deepest deficits benefit most when *someone else* sets the reminders. Janeslätt's RCT involved parent/teacher integration. An optional, granular sharing layer (single-task, time-bounded) for partners, coaches, or therapists addresses this without compromising local-first defaults. Frame as scaffold, not surveillance.

---

## Honest limitations

**Where the evidence is contested or absent, say so in the product, not just the docs.**

**Direct comparisons missing.** No RCT compares LLM-generated to therapist-generated task decomposition; goblin.tools and similar tools have not been peer-evaluated. No RCT compares local-first to cloud-stored journaling apps' effect on disclosure of stigmatised content — the case rests on transitive evidence from anonymity, privacy calculus, and chilling-effects literatures. No study isolates the Time Timer brand specifically; visual-timers-as-a-class have RCT support (Janeslätt, 2018).

**Popular concepts with weak empirical bases.** OpenDyslexic, Lexend, and Bionic Reading lack the evidence their marketing implies (Wery & Diliberto, 2017; Strukelj, 2024). Pomodoro is widely endorsed but Biwer et al. (2023) found self-regulated breaks outperform it. Tiny Habits / Fogg Behavior Model is a useful design heuristic with thin RCT support (Duarte et al., 2025 BMC scoping review). Calm Technology (Weiser & Brown) and "neuro-acoustic stimulation" (Brain.fm) are heuristics or industry-funded findings, not independently replicated science. Binaural beats have a positive meta (Garcia-Argibay, 2019, g=0.45) but later well-controlled studies on sustained attention are sceptical. The Mozart effect is debunked (Pietschnig et al., 2010). RSD as Dodson defines it is not peer-reviewed; rejection sensitivity (Downey & Feldman, 1996) and ADHD emotional dysregulation (Shaw et al., 2014) are. Spoon theory is a culturally legible metaphor (Miserandino, 2003) without psychometric validation; cite as communication frame, not clinical model.

**Replication caveats.** Sparrow et al.'s "Google effect" failed Many Labs replication. The doorway effect's specific magnitude is sensitive to task complexity (McKerracher et al., 2021) though event-boundary theory is robust. Mark's "23 minutes to refocus" is widely misquoted — it measured task return, not cognitive recovery. The nudge literature's overall effect collapses under publication-bias correction (Maier et al., 2022; Hu et al., 2025).

**Population gaps.** Most cognitive-offloading and dictation evidence generalises from healthy or LD populations. **ME/CFS, long COVID, fibromyalgia, perimenopausal cognitive symptoms, and depression-related cognitive impairment are essentially absent from the dictation, decomposition, and offloading literatures.** Most application to these groups is by extrapolation from TBI, ADHD, and autism research. Kon's design choices for these users are reasonable inferences, not validated interventions.

**Body doubling, AI decomposition for ADHD, LLM coaching for autism, and personalised acoustic ASR for dysfluency** are all areas where Kon could plausibly contribute primary evidence — well-designed in-app studies (with consent, opt-in, local analytics) would advance the field, not just the product. The honest framing for the developer to defend in public: "We've built Kon on the strongest available evidence; some of our choices are design intuition pending empirical validation; we will say which is which."
