You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Inclusion and exclusion criteria

Complete details of the rationale and design of the MAPEC study are described in previous publications [14, 15, 19, 21–24]. In summary, the sample of the MAPEC study consisted of Spanish individuals ≥18 years of age who adhered to a routine of daytime activity and night-time sleep. Inclusion criteria required participants to be either normotensive, untreated hypertensive or resistant to treatment [26] when ingesting all their prescribed BP-lowering medications upon awakening. Exclusion criteria were pregnancy, drug/alcohol abuse history, night/shift-work employment, AIDS, type 1 diabetes, secondary hypertension, CVD disorders (unstable angina pectoris, heart failure, life-threatening arrhythmia, nephropathy and grade III–IV retinopathy), intolerance to ABPM, and inability to communicate and comply with all study requirements. This prospective single-centre study (ClinicalTrial.gov registration no. NCT00295542) was approved by the state Ethics Committee of Clinical Research. All participants gave written informed consent.

Participants and diagnostic criteria

Between 2000 and 2007, we recruited 3,612 persons fulfilling the inclusion/exclusion criteria, with 3,344 (1,718 men and 1,626 women, 52.6 ± 14.5 [mean ± SD] years of age) providing all required information for study. The remaining 268 individuals were excluded due to inadequate ABPM sampling at baseline and non-consent for additional ABPM evaluations. At the time of recruitment, 688 randomised hypertensive patients already had a diagnosis of type 2 diabetes and, therefore, were excluded from the analyses reported here. Among the remaining 2,656 participants, 644 were normotensive at baseline and, accordingly, were not participants in any aspect of the trial involving randomisation to a treatment-time for ingestion of hypertension medications. The final evaluated population for the hypotheses tested herein thus consisted of 2,012 hypertensive patients without diabetes—976 men and 1,036 women, 52.7 ± 13.6 years of age (Fig. 1).

Fig. 1 Flow diagram of participants in the study Full size image

Hypertension in untreated participants was defined according to current ABPM criteria, i.e., awake systolic (SBP)/diastolic (DBP) BP mean ≥135/85 mmHg or asleep SBP/DBP mean ≥120/70 mmHg [27, 28]. New-onset diabetes was defined as fasting glucose ≥7.0 mmol/l on at least two clinical assessments ≥3 months apart [29]; none of the participants received any glucose-lowering treatment during the course of the study. CKD was defined as either an estimated GFR <60 ml min−1 1.73 m−2, albuminuria (urinary albumin excretion ≥30 mg/24 h urine), or both, on at least two occasions ≥3 months apart [30]. GFR (ml min−1 1.73 m−2) was estimated by the CKD Epidemiology Collaboration (CKD-EPI) equation [31]. Diagnosis of the metabolic syndrome was established by the National Cholesterol Education Program Adult Treatment Panel III revised definition [32].

Study design

The MAPEC study was a prospective, randomised, open-label, blinded endpoint (PROBE) trial. A major goal of the study was to assess the effect of treatment-time regimen of prescribed BP-lowering medications on CVD, new-onset diabetes and CKD outcomes. Thus, patients were randomised either to ingest all BP-lowering medications upon awakening or the complete daily dose of ≥1 of them at bedtime and the remaining ones (if any) upon awakening. Assignment of participants to the two treatment-time regimens was done according to the order of recruitment, following an allocation table constructed by a computerised random-number generator.

The study did not specify or require one unique investigational hypertension medication; rather, participating physicians were given the choice of prescribing, as first-line therapy in previously untreated patients, one medication of any of the recommended therapeutic classes [28]. Randomisation of patients to treatment-time (awakening or bedtime) was done separately for each allowed initial individual hypertension monotherapy (valsartan, telmisartan, olmesartan, ramipril, spirapril, amlodipine, nifedipine GITS, nebivolol, torasemide, doxazosin GITS) to ensure the proportion of patients treated with each medication was similar across the morning- and bedtime-treatment arms. If based on ABPM threshold criteria, the ABP of a given patient remained uncontrolled at any time during follow-up (see below), additional medications could be added in keeping with current clinical practice.

Participants with resistant hypertension were randomised either to: (1) modification of the nature of their treatment by exchanging one hypertension medication with a new one, and thus without alteration of the total number of medications, but retaining the upon-waking ingestion time for all of them (awakening treatment regimen); or (2) the same exchange of one medication with a new one, but prescribing its ingestion at bedtime instead of morning (bedtime-treatment regimen) [21, 24]. If, during follow-up, ABP remained uncontrolled, investigators were permitted to: (1) exchange additional hypertension medications for others of different classes (keeping always the upon-waking ingestion time schedule of all medications) in patients of the awakening treatment regimen group; (2) progressively shift additional hypertension medications (that were being ingested upon awakening) to bedtime in patients of the bedtime-treatment regimen group; or (3) prescribe additional BP-lowering medications in patients of either group.

Changes in therapeutic scheme during follow-up in uncontrolled patients (those with ABP above the thresholds provided above) were always based on the results from periodic evaluation by ABPM. Adherence to the time-of-day (awakening or bedtime) schedule of treatment and prescribed medication(s) was enforced, although not measured, at each follow-up visit. Adverse events, including type, duration, seriousness, intensity and possible relation to hypertension treatment, were registered as spontaneously reported by the patient and/or detected by the investigators through non-directive questioning and physical examination.

ABP, wrist activity and other assessments

At inclusion and at every scheduled visit for ABPM during follow-up, the SBP and DBP of each participant were automatically measured every 20 min between 07:00 and 23:00 hours and every 30 min during the night for 48 consecutive hours with a calibrated SpaceLabs 90207 ABPM monitor (SpaceLabs, Issaquah, WA, USA). A 48 h, instead of the most common 24 h, monitoring span was chosen to improve reproducibility of results, as accurate calculation of ABP characteristics (including mean BP values) and dipping classification depends markedly on the duration of ABPM [33]. Participants were instructed to adhere to their usual activities with minimal restrictions but to keep a similar activity–rest schedule and avoid daytime napping during the two consecutive days of ABPM. In keeping with current recommendations [27], BP series were considered invalid for analysis, and thus ABPM repeated within the same week, if ≥30% of the measurements were missing, if data were lacking for an interval of >2 h, if data were obtained when the rest–activity schedule was irregular during the 2 days of monitoring, or if the night-time sleep period was <6 h or >12 h.

All participants also wore an actigraph (Mini-Motion-Logger, Ambulatory Monitoring, Ardsley, NY, USA) on the dominant wrist to record the level of physical activity at 1-min intervals during each 48 h ABPM. Actigraphy data were used to verify absence of daytime napping and to precisely define the commencement and termination of the daytime awake and night-time asleep spans of each participant using dedicated software [34] to accurately derive the respective ABP means.

Blood withdrawal from an antecubital vein was carried out in the clinic between 08:00 and 09:00 hours, after overnight fasting, the same week when each 48 h ABPM session was initiated. The patients collected their urine during the first 24 h of ABPM. Blood and urine samples were analysed using routine automatic techniques in the hospital laboratory. Just before commencing each 48 h ABPM session, the same investigator obtained six consecutive clinic BP measurements using a validated automatic oscillometric device (HEM-705IT, Omron Health Care, Vernon Hills, IL, USA) after the participant had rested in a seated position for ≥10 min.

Follow-up

Participants underwent the same evaluation procedure described above, including conventional daytime clinic BP measurement, 48 h ABPM/wrist activity monitoring and blood/urine analysis, annually or more frequently (3 months after any investigator-ordered change in therapy to improve ABP control). Investigators blinded to the hypertension treatment scheme of the patients and not involved in clinic evaluations, ABP measurement and statistical analyses assessed the development of new-onset diabetes, as defined above, among other outcome variables of interest. Complete clinical records of all enrolled participants, including all periodic laboratory tests performed during follow-up, were reviewed at least annually plus the year following the last ABPM.

Statistical methods

ABPM profiles were edited according to conventional criteria to correct for measurement errors and outliers; SBP readings >250 or <70 mmHg, DBP >150 or <40 mmHg and pulse pressure (PP, SBP minus DBP) >150 or <20 mmHg were automatically discarded. The ‘48 h ABP mean’ was calculated as the average of all valid readings obtained throughout the 48 h assessment span. Awake and asleep ABP means were calculated from the 48 h monitoring as the average of all valid readings obtained during the hours of daytime activity or night-time sleep, respectively, as differentiated by wrist actigraphy. Sleep-time relative BP decline (index of BP dipping), defined as % decrease in mean BP during night-time sleep relative to mean BP during daytime activity, was calculated as: [(awake ABP mean − asleep ABP mean) / awake ABP mean] × 100, incorporating all the data sampled by 48 h ABPM. For comparative purposes, a patient was defined as dipper if the sleep-time relative SBP decline was ≥10%, and as non-dipper otherwise [27, 28].

Demographic and clinical characteristics were compared on an intention-to-treat basis among participants randomised to the two treatment-time regimens—(1) all hypertension medications ingested upon awakening or (2) the entire daily dose of ≥1 BP-lowering medications ingested at bedtime—by two-sided t test (continuous variables) or nonparametric χ2 test (proportions). The Cox proportional-hazard model, adjusted for significant confounding variables, was used to estimate HR, with 95% CI, for events associated with treatment-time regimen and class of hypertension medication. For survival analysis, follow-up was established as either the time to the confirmed diagnosis of diabetes or the time to the last clinical evaluation in non-event participants. Survival curves were generated using the Kaplan–Meier product-limit method and compared by the Mantel logrank test.