The AI-Free Retreat
A structured week away from generative artificial intelligence (AI) for people who work with data: see what the tools are doing for you, practice the fundamentals by hand, let your attention settle, and return with a workflow you chose.
Under construction and not peer reviewed. Research summaries reflect publications available as of November 2026. This guide is general information about work practices and well-being, not medical or mental health advice.
A deliberate week without generative AI
Generative AI is now built into the editors, notebooks, search engines, email, and meeting tools that data teams use every day. Over time it becomes hard to say which parts of a workflow a person still does and which parts a model does, how much checking the model's output takes, and whether skills that used to be routine are still there when the tool is unavailable.
The AI-Free Retreat is a planned week of working without generative AI tools, designed for analysts, data scientists, statisticians, informaticians, data managers, and the people who lead them. Work continues during the week at a pace the retreat plans for. The aims are to see clearly where AI helps you and where it costs you time or skill, to practice core methods by hand, to give your attention and stress response room to settle, and to come back with deliberate choices about which AI uses to keep. For teams and organizations, the retreat also produces a written plan for operating without AI and a standard for documenting outputs.
How the retreat is organized
Before the week
Take an inventory of where you use AI, record a baseline check-in, set ground rules, and arrange coverage for your work.
During the week
Follow a daily practice for data work, body, and reflection, and keep a log of the moments you reach for AI and what you did instead.
For organizations
Build a plan for working without AI, which also serves as a continuity plan, and adopt a documentation standard for every output.
After the week
Decide what to keep, change, or drop, repeat the check-in, and set a follow-up date so the changes outlast the routine of a normal week.
The evidence stage summarizes studies of AI assistance, over-reliance on automation, disconnection, and recovery from work, with the limits of each, and can be read at any point.
Who is taking the retreat?
Team and organization choices add facilitation notes to the daily plan and team language to the message and return plan.
Where your entries are kept
Everything you type is saved only in this browser on this device, so you can leave and come back. Nothing is sent to Boyce Data Science or anyone else. Do not enter protected health information (PHI), details about patients or study participants, or the names of colleagues; describe tasks and people by role. To move your work to another device, save a file and load it there.
Where you use AI in your work
List the tasks where you use AI now. Include the obvious tools, such as chat assistants and coding assistants, and the features built quietly into products you already use: email and document summaries, meeting transcription with automatic notes, autocomplete in notebooks and editors, and AI answers at the top of search results. Describe each task in general terms, for example writing Structured Query Language (SQL) for cohort extracts, explaining an error message, summarizing a protocol, drafting an email to a collaborator, mapping source codes to a standard vocabulary, or making a chart.
What your inventory shows
Baseline check-in
Rate each statement from 1 to 10 for how your recent workweeks have felt. You will repeat these ratings at the end of the retreat and again at a follow-up check-in, and the return plan compares them. These ratings are a personal reflection, not a validated scale.
Ground rules, coverage, and setup
What to pause and what to keep
Decide before the week starts what counts as AI for the retreat, so that your reach log records real choices rather than gray areas.
Paused during the retreat
Still in use
- Your own code, scripts, packages, and templates in R, Python, SQL, SAS, or other languages
- Deterministic tools such as linters, spell check, data validation rules, version control, and continuous integration
- Statistical and machine learning methods that you specify, run, and interpret yourself
- Documentation, vignettes, books, published standards, course notes, and colleagues
- Validated or regulated production processes that include AI, which keep running under their owners
Assistive technology stays on. Screen readers, speech-to-text, captioning, and other accessibility tools remain available even when they use AI, and no one should have to disclose a disability to keep using them.
Coverage and communication
A message for the people who need to know
Set up the week before it starts
If a deliverable with patient safety or regulatory consequences depends on an AI-supported process, keep that process running and talk with its owner. The retreat changes personal and team habits; it does not switch off validated systems.
The retreat, day by day
Each day pairs a data practice with a practice for your body and attention, and closes with a reflection prompt. Work through the days in order; if your week includes a weekend, Days 6 and 7 are designed for it.
For teams: hold brief check-ins at the end of Days 1, 3, and 5. Ask what people reached for, where they got stuck, and what they relearned. Keep sharing voluntary and do not compare or rank people.
Day 1: Notice the reach
Work as you normally would, minus the paused tools, and pay attention to the moments you reach for AI. The urge itself tells you where AI sits in your workflow.
- Keep the reach log below open and add an entry each time you reach for AI, noting what you wanted it to do and what you did instead.
- Start timing one of the tasks you picked from your inventory.
- Before you start work, and whenever you notice the reach, try a few rounds of exhale-focused breathing (see Body and attention).
- Turn off notifications you will not need during the week.
What did you reach for AI to do most often, and what were you feeling just before you reached?
Day 2: Read the source
Answer your questions from primary sources instead of generated summaries, and keep track of where each answer came from.
- Read the documentation, help page, or vignette for a function or package you use often, including the sections you usually skip.
- Read the data dictionary or codebook for a data set you work with from beginning to end, noting anything that surprises you.
- When a question comes up, search the documentation, a standards site, or the literature directly, and record where you found the answer.
- Take a walk outside, without a phone or podcast if you can.
What did you learn from the source material that a generated answer would likely have left out?
Day 3: Write the code yourself
Build something routine from a blank file and debug it with error messages, documentation, and small tests.
- Write a script for a task you usually generate, such as an extraction query, a cleaning function, or a summary table.
- Debug by reading the error message closely, printing intermediate results, and checking one assumption at a time.
- Comment the code to explain why each step exists as well as what it does.
- Work in single-task blocks with one window and one problem, and stand up and move between blocks.
Where did you get stuck, how did you get unstuck, and what came back to you that you thought you had forgotten?
Day 4: Look at the data by hand
Spend time with the raw records before you look at any summary of them.
- Profile a data set yourself: row counts, distinct values, missingness, ranges, and dates that fall outside the expected period.
- Read a sample of raw records row by row and compare what you see with the data dictionary.
- Sketch the analysis by hand, covering the question, the population, the exposure and outcome definitions, and the variables each one needs.
- Eat a meal away from your desk, and spend some time outdoors or near trees or water if you can.
What did the raw records show you that a summary or a generated description would have hidden?
Day 5: Document and share
Make the week's work reproducible by someone other than you.
- Complete a provenance record for something you produced during the week (see Documenting outputs).
- Write or update the README for a project so that a colleague could rerun it.
- Ask a colleague to follow your documentation without your help and tell you where they got lost.
- End the workday with a shutdown routine: list open items, decide the first task for next time, and close the laptop.
Could someone reproduce your work from what you documented, and what was missing?
Day 6: Rest
Rest is part of the retreat, and recovery research associates physical activity and detachment from work with stronger recovery during time off.
- Leave work alone, including email, code, and reading for work.
- Move in a way you enjoy, spend time outside, see people, and sleep.
- Do something with your hands that has nothing to do with screens, such as cooking, gardening, building, or crafts.
How does your body feel compared with Day 1?
Day 7: Review the week
Turn what you noticed during the week into decisions you can act on.
- Read back through your reach log and timing log.
- Complete the end-of-week check-in in Coming back.
- Draft your keep, change, or drop decision for each task in your inventory.
- Take a longer walk, or a longer session of slow breathing.
What surprised you most, and what do you want to protect when AI tools come back?
Reach log
Add an entry each time you reach for AI. By the end of the week, the log shows which tasks pull hardest and what happened when you did them another way.
Timing log
Pick tasks you do often. Before the week, estimate how long each one takes with AI, including the time you spend checking or fixing the output; during the week, measure how long it takes without AI. In one randomized trial, experienced developers believed AI made them faster on tasks where it made them slower, so a measurement is more useful than an impression.
Settling attention and the stress response
People who oversee a lot of AI output describe mental fog, headaches, and slower decisions; a 2026 survey of US workers by Boston Consulting Group (BCG) calls the pattern AI brain fry. The practices below are low-risk ways to give your attention and your body's stress response room to settle during the retreat and afterward. Each one names the study behind it and how far its finding reaches.
This section is general wellness information, not medical or mental health advice. Stop any breathing practice that makes you dizzy or uncomfortable. If stress, sleep, mood, or anxiety are affecting your daily life, talk with a clinician or your employee assistance program.
Exhale-focused breathing
Breathe in through your nose, take a second, shorter breath in to fill your lungs, and then breathe out slowly and fully through your mouth. Repeat for a few minutes at a pace that feels comfortable. The pacer shows one rhythm; follow your own if it suits you better.
What the study found. In a remote randomized trial, a daily five-minute practice of this kind, called cyclic sighing, improved mood and lowered breathing rate more than the same time spent in mindfulness meditation over one month (Balban and colleagues, 2023).
How far it reaches. Volunteers, one month, and outcomes based on self-reported mood and wearable data.
One task at a time
Close everything except the window you are working in, turn off notifications, and gather email and messages into set times rather than answering as they arrive.
What the study found. When information workers went without email for five workdays, they switched windows about half as often, stayed with tasks longer, and showed lower stress measured by heart rate variability (HRV) (Mark, Voida, and Cardello, 2012).
How far it reaches. A small sample at one workplace, and email rather than AI; the study is included as an analog for removing an always-available tool.
Time outdoors
Walk outside, sit near trees or water, or look out at greenery between work blocks.
What the study found. A systematic review and meta-analysis found that exposure to natural environments improved working memory and cognitive flexibility, and less reliably attentional control, with low to moderate effects (Stevenson, Schilhab, and Bentsen, 2018).
How far it reaches. Short-term measures, often taken under laboratory conditions; an earlier review found support on only some attention measures.
Boundaries for your phone
Block mobile internet on your phone during the retreat while keeping calls and texts, or set limits for the apps you open most often.
What the study found. In a preregistered randomized trial, blocking mobile internet for two weeks improved sustained attention, mental health, and subjective well-being, and participants spent more time socializing in person, exercising, and outdoors (Castelo and colleagues, 2025).
How far it reaches. Volunteers who knew the purpose of the study, and many people who signed up never installed the blocker.
Rest that includes movement and detachment
Plan at least one day with no work at all, and include physical activity you enjoy.
What the studies found. Meta-analyses of vacations find that well-being improves during time off and then fades after the return to work, in one analysis within the first week back (de Bloom and colleagues, 2009; Speth, Wendsche, and Wegge, 2023). A 2025 meta-analysis found that how long the benefits last depends on the length of the break and what people do during it, with physical activity and detachment from work associated with stronger recovery (Grant, Buchanan, and Shockley, 2025).
How far it reaches. These studies concern vacations rather than AI-free work weeks. The fade-out finding is the reason the retreat ends with a return plan and recurring practices.
Small habits that support the week
Many people find these steadying, although they are not tied to a specific study here: regular sleep and wake times, meals away from the desk, standing and stretching between work blocks, a walk at the start or end of the workday, and a clear stopping point for work in the evening.
A plan for working without AI
This stage builds a written plan for operating without generative AI, either for a retreat week or as a continuity plan in case a tool becomes unavailable, a policy changes, or a data use agreement restricts sending data to AI services. Fill in what applies and leave the rest blank, then assemble the plan at the bottom of the stage. Use roles rather than names if the plan will be shared widely.
Scope and sponsor
Methods for each task without AI
Each row starts with a common method; edit it to match how your organization works, name the role that owns it, and clear the box for tasks that do not apply.
Capacity and timelines
Quality checks without AI
Skills and shared knowledge
Accessibility and exceptions
Communication
Measures
Compare results with your own baseline and treat them as information for decisions rather than proof. When reporting pulse survey results, do not report any group smaller than about five people, so that individuals cannot be identified.
Documentation standard
Required fields for every output's provenance record (the Documenting outputs stage has a record builder).
Closing review
A documentation standard that holds with or without AI
A provenance record describes how an output was made so that someone else can check it, rerun it, or rely on it. The same record works whether or not AI was involved, which makes the retreat a good time to adopt it. The fields draw on reproducibility practice in computational research and on the disclosure expectations that medical journals place on AI use.
What every record includes
- What the output is and the question it answers
- Inputs: the data sets, their versions or extraction dates, and where they came from
- Methods: where the code lives and the version or commit that produced the output
- Software: languages, packages, and their versions
- AI involvement: none, assisted, or generated, with the tool, its version, and its purpose
- Checks: what was verified, how, and by whom
- Limitations: known gaps, assumptions, and data quality issues
- Storage and ownership: where the output and its code are kept, and who maintains them
Statements of AI involvement
Use one statement per output. Journals that follow the recommendations of the International Committee of Medical Journal Editors (ICMJE) ask authors to disclose AI use, name the tool, state its purpose, and confirm that the human authors take responsibility for the content. Check the current requirements of each journal, funder, and institution, since policies continue to change.
None: No generative AI tools were used to produce this output.
Assisted: [Tool and version] was used to [purpose]. [Role] reviewed and verified the result and takes responsibility for it.
Generated: [Which parts] were generated by [tool and version] and then checked by [role] using [method]. [Role] takes responsibility for the final output.
Build a record
Output log
CSV stands for comma-separated values, a plain text format that opens in Excel, Google Sheets, R, and Python.
Reproducibility habits
- Keep track of how every result was produced, as a script or a recorded sequence of steps.
- Avoid editing data by hand, and record any manual change that cannot be avoided.
- Record the exact versions of software and packages.
- Keep all custom code under version control.
- Save intermediate results and the data behind every chart.
- Link each statement in a report to the result that supports it.
Adapted from Sandve and colleagues, Ten simple rules for reproducible computational research (2013).
Shared data sets
For data sets that others will reuse, the FAIR principles (findable, accessible, interoperable, and reusable) describe the identifiers, metadata, and licensing that make reuse possible by people and by software.
Returning with a workflow you chose
Recovery research suggests that well-being gains from time away tend to fade soon after people return to work. The decisions and recurring practices in this stage are meant to keep what the retreat showed you once the usual routine returns.
End-of-week check-in
Rate the same statements for how the retreat week felt.
Choose a date and put it on your calendar.
Follow-up check-in ratings, to complete on the follow-up date
Rate the same statements for how the weeks since the retreat have felt.
How the check-ins compare
Keep, change, or drop
Decide what happens to each task in your inventory now that AI tools are available again. Several studies found that people who used AI for hints, explanations, or conceptual questions kept more of their own skill than people who delegated whole answers (Bastani and colleagues, 2025; Shen and Tamkin, 2026; Liu and colleagues, 2026).
Practices to keep
Questions for a team debrief
- Where did AI help us most, and where did it cost us time, skill, or quality?
- Which outputs do we now want to review differently?
- What will we change in our AI use policy or team norms?
- Which documentation standard will we keep?
- How will we know, at the follow-up check-in, whether the changes held?
What research shows so far
As of November 2026, no published study has tested a structured AI-free week for data professionals. The studies below come from neighboring questions: what AI assistance does to skills, productivity, and workload; how over-reliance on automation develops; and what breaks from constant connectivity and from work do for attention and well-being. Each card names the kind of study, what it found, and the limits on how far the finding reaches. Read them as reasons to run the experiment on your own work and measure the results.
Brief AI help reduced persistence once the help was removed
Liu, Christian, Dumbalska, Bakker, and Dubey (2026), Conference on Language Modeling; preprint on arXiv. Randomized experiments
What it found. Across randomized experiments with 1,222 online participants solving fraction problems and reading comprehension questions, people who had a GPT-5 assistant for roughly ten minutes generally did better while it was available, then did worse and gave up more often once it was removed, compared with people who never had it. Participants who used the assistant for hints rather than complete answers held up better.
How far it reaches. Short tasks, online participants, and outcomes measured immediately; the effect of daily use over months is unknown.
Learning a new library with AI help produced lower quiz scores
Shen and Tamkin (2026), How AI assistance impacts the formation of coding skills, Anthropic. Randomized trial
What it found. Software engineers, most of them junior, learned an unfamiliar Python library. The group with an AI assistant averaged 50% on a follow-up quiz compared with 67% for the group that coded by hand, with the largest gap on debugging questions, and the time saved was not statistically significant. Participants who asked the assistant conceptual questions and requested explanations scored higher than those who delegated code generation.
How far it reaches. One short session with an immediate quiz. The study was conducted by Anthropic, which makes AI products.
Unguarded AI tutoring lowered exam scores after access ended
Bastani and colleagues (2025), Generative AI without guardrails can harm learning, Proceedings of the National Academy of Sciences. Randomized field experiment
What it found. Nearly a thousand high school math students in Turkey took part. Students with a standard GPT-4 chat interface did better on practice problems, but once access was removed they scored 17% lower on exams than students who never had access. A version designed to give hints rather than answers largely removed the harm.
How far it reaches. Students rather than professionals, one school, and one subject.
Essay writers using a chatbot showed weaker engagement and ownership
Kosmyna and colleagues (2025), Your brain on ChatGPT, MIT Media Lab; preprint on arXiv. Experiment with electroencephalography (EEG)
What it found. Participants wrote essays with a large language model, with a search engine, or with no tools. EEG showed the weakest brain connectivity in the language model group, whose members also reported the lowest sense of ownership and had trouble quoting their own essays.
How far it reaches. A small sample of 54 people, essay writing only, and posted as a preprint; check for a peer-reviewed version before citing it.
Confidence in AI went with less critical thinking at work
Lee and colleagues (2025), The impact of generative AI on critical thinking, Conference on Human Factors in Computing Systems (CHI), Microsoft Research and Carnegie Mellon University. Survey
What it found. In a survey, 319 knowledge workers described 936 examples of using generative AI at work. Higher confidence in the AI was associated with less critical thinking, and higher confidence in one's own ability with more. Critical thinking shifted toward verifying information, integrating responses, and overseeing the task.
How far it reaches. Self-reported at a single point in time, so it shows associations rather than causes.
Frequent AI use was associated with lower critical thinking scores
Gerlich (2025), AI tools in society: Impacts on cognitive offloading and the future of critical thinking, Societies. Survey and interviews
What it found. Among 666 participants, frequent AI tool use correlated with lower critical thinking scores, and cognitive offloading appeared to mediate the relationship. Younger participants relied on AI more and scored lower.
How far it reaches. Correlational, so it cannot show that AI use caused the lower scores.
Unassisted detection rates fell after routine AI use began
Budzyń and colleagues (2025), Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy, The Lancet Gastroenterology and Hepatology. Observational study
What it found. At four endoscopy centers in Poland, the adenoma detection rate for colonoscopies performed without AI fell from 28.4% before routine AI use to 22.4% after it.
How far it reaches. A before-and-after observational design in a clinical procedure rather than data work. It is included as one of the few real world measurements of skill change among experienced professionals after routine AI use.
Experienced developers were slower with AI and believed they were faster
Becker, Rush, Barnes, and Rein (2025), Measuring the impact of early-2025 AI on experienced open-source developer productivity, Model Evaluation and Threat Research (METR). Randomized trial
What it found. In the trial, 16 experienced open-source developers completed 246 tasks in repositories they knew well. Tasks where AI was allowed took 19% longer. Before the study, developers expected AI to cut their time by 24%, and afterward they estimated it had cut their time by 20%.
How far it reaches. Few developers, large and mature codebases, and early-2025 tools; the authors caution against generalizing to other settings.
AI improved some consulting tasks and worsened another
Dell'Acqua and colleagues (2023), Navigating the jagged technological frontier, Harvard Business School working paper, later published in Organization Science. Randomized field experiment
What it found. The experiment involved 758 BCG consultants. On tasks within the AI's capabilities, consultants with AI completed 12.2% more tasks, 25.1% faster, at higher quality. On a task chosen to fall outside those capabilities, consultants using AI were 19 percentage points less likely to reach a correct answer.
How far it reaches. Consulting tasks with a 2023 model, and the boundary between the two kinds of task was hard to see in advance.
AI tools intensified work rather than reducing it
Ranganathan and Ye (2026), AI doesn't reduce work: it intensifies it, Harvard Business Review. Field study, magazine report
What it found. Researchers at the Berkeley Haas School of Business followed about 200 employees at a US technology company over eight months. Generative AI tools increased the pace and scope of work, with people taking on tasks outside their roles and taking fewer breaks.
How far it reaches. One company, and initial findings from research in progress, published as a magazine article rather than a peer-reviewed paper.
Low-effort AI output created extra work for colleagues
Niederhoffer and colleagues (2025), AI-generated workslop is destroying productivity, Harvard Business Review; BetterUp Labs and the Stanford Social Media Lab. Survey
What it found. In a survey of 1,150 full-time US employees, 40% had received AI-generated work in the previous month that looked polished but did not advance the task, and recipients reported spending nearly two hours on average dealing with it. Many recipients afterward judged the sender as less capable, reliable, or trustworthy.
How far it reaches. Self-reported time estimates, from a survey run partly by a coaching company.
Overseeing several AI tools went with mental fatigue and errors
Bedard and colleagues (2026), When using AI leads to brain fry, Harvard Business Review; BCG. Survey
What it found. In a survey of 1,488 full-time US workers at large companies, 14% of those using AI reported AI brain fry, described as mental fog, headaches, and slower decisions. It was associated with more decision fatigue, more major errors, and higher intent to quit, and was most common among people overseeing several AI tools at once. Using AI to remove repetitive tasks was associated with lower burnout.
How far it reaches. Cross-sectional and self-reported, and conducted by a firm that advises companies on AI.
A one-day pause made the habit visible
Atlassian Teamwork Lab (2026), When is AI useful, or just a habit? Company experiment, blog report
What it found. About 123 talent acquisition staff spent one day using AI for everything they could and one day using none. On the pause day, 82% felt the urge to "just ask AI," and afterward 92% said they were more confident about when not to use AI.
How far it reaches. One company's internal experiment with one team over two days, reported on the company blog; Atlassian sells AI products.
Over-reliance on automation affects experts as well as novices
Parasuraman and Manzey (2010), Complacency and bias in human use of automation, Human Factors. Review
What it found. Automation complacency and automation bias appeared in both novice and expert participants, could not be overcome with simple practice, and affected teams as well as individuals. Complacency was most likely when several tasks competed for attention.
How far it reaches. Draws mostly on aviation and process control research that predates generative AI.
Workload and time pressure make over-reliance more likely
Goddard, Roudsari, and Wyatt (2012), Automation bias: a systematic review of frequency, effect mediators, and mitigators, Journal of the American Medical Informatics Association. Systematic review
What it found. Across 74 studies, trust, confidence, and task experience shaped over-reliance on decision support, and heavy workload, task complexity, and time pressure made it more likely. Training, emphasizing user accountability, and presenting information rather than recommendations helped reduce it.
How far it reaches. Focused on clinical decision support systems rather than generative AI.
Blocking mobile internet improved attention and well-being
Castelo, Kushlev, Ward, Esterman, and Reiner (2025), Blocking mobile internet on smartphones improves sustained attention, mental health, and subjective well-being, PNAS Nexus. Preregistered randomized trial
What it found. Blocking mobile internet on smartphones for two weeks, while calls, texts, and computer internet still worked, improved sustained attention, mental health, and subjective well-being, and 91% of participants improved on at least one of the three. Participants spent more time socializing in person, exercising, and outdoors.
How far it reaches. Many people who signed up never installed the blocking app, those who finished started with better mental health and attention, and participants knew the purpose of the study.
Working without email reduced window switching and measured stress
Mark, Voida, and Cardello (2012), A pace not dictated by electrons: an empirical study of work without email, Conference on Human Factors in Computing Systems (CHI). Field experiment
What it found. In the study, 13 information workers went without email for five workdays. They switched between windows about half as often, stayed with tasks longer, and showed lower stress measured by HRV. Some felt more isolated.
How far it reaches. A small sample at one workplace, and email rather than AI; it is included as a close analog for removing an always-available tool.
Digital detox studies show mixed results
Radtke, Apel, Schenkel, Keller, and von Lindern (2022), Digital detox: An effective solution in the smartphone era? Mobile Media and Communication. Systematic review
What it found. Effects of digital detox interventions varied across studies of health and well-being, social relationships, self-control, and performance. Some studies found benefits, some found none, and some found negative effects on well-being.
How far it reaches. Studies defined a detox in different ways, which makes them hard to compare. The review is a reason to treat the retreat as an experiment and measure your own results.
Time in natural settings improved some attention measures
Stevenson, Schilhab, and Bentsen (2018), Attention Restoration Theory II, Journal of Toxicology and Environmental Health, Part B. Systematic review and meta-analysis
What it found. Exposure to natural environments improved working memory and cognitive flexibility, and to a less reliable degree attentional control, with low to moderate effect sizes. Real outdoor exposure may work better than virtual exposure.
How far it reaches. Short-term laboratory measures; an earlier review by Ohly and colleagues (2016) found support on only some measures.
Brief exhale-focused breathing improved mood
Balban and colleagues (2023), Brief structured respiration practices enhance mood and reduce physiological arousal, Cell Reports Medicine. Randomized trial
What it found. Over one month, people assigned to a daily five-minute breathing practice improved more in mood than those assigned to mindfulness meditation, and exhale-focused cyclic sighing produced the largest improvement in mood and the largest drop in breathing rate.
How far it reaches. A remote study with volunteers, with outcomes based on self-reported mood and wearable data.
Vacation benefits fade soon after work resumes
de Bloom and colleagues (2009), Do we recover from vacation? Journal of Occupational Health. Meta-analysis
What it found. Vacations had a small positive effect on health and well-being that faded soon after people returned to work.
How far it reaches. A small set of studies, in which vacation activities were rarely measured.
Well-being gains fade within the first week back
Speth, Wendsche, and Wegge (2023), We continue to recover through vacation! European Psychologist. Meta-analysis
What it found. Across 13 studies of 1,428 people, vacations improved well-being, but there were no significant differences from pre-vacation levels after the first week back. Longer vacations did not produce larger benefits, and the authors suggest regular short breaks through the year.
How far it reaches. Mostly self-reported well-being measures.
Activities and detachment shape how long benefits last
Grant, Buchanan, and Shockley (2025), I need a vacation: A meta-analysis of vacation and employee well-being, Journal of Applied Psychology; summarized by the British Psychological Society Research Digest. Meta-analysis
What it found. Combining 32 studies, the authors found that how long benefits last depends on the length of the break and on what people do during it. Physical activity and detachment from work were associated with stronger recovery, and passive activities showed no significant effect.
How far it reaches. Studies varied in design and measures, and the findings concern vacations rather than changes in work practice.
All sources cited in this tool, with links
- Atlassian Teamwork Lab (2026). When is AI useful, or just a habit? atlassian.com
- Balban MY and colleagues (2023). Brief structured respiration practices enhance mood and reduce physiological arousal. Cell Reports Medicine 4(1):100895. doi.org/10.1016/j.xcrm.2022.100895
- Bastani H and colleagues (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences 122(26):e2422633122. doi.org/10.1073/pnas.2422633122
- Becker J, Rush N, Barnes B, Rein D (2025). Measuring the impact of early-2025 AI on experienced open-source developer productivity. METR. arxiv.org/abs/2507.09089
- Bedard J and colleagues (2026). When using AI leads to brain fry. Harvard Business Review. bcg.com
- Budzyń K and colleagues (2025). Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy. The Lancet Gastroenterology and Hepatology 10(10):896-903. doi.org/10.1016/S2468-1253(25)00133-5
- Castelo N, Kushlev K, Ward AF, Esterman M, Reiner PB (2025). Blocking mobile internet on smartphones improves sustained attention, mental health, and subjective well-being. PNAS Nexus 4(2):pgaf017. doi.org/10.1093/pnasnexus/pgaf017
- de Bloom J and colleagues (2009). Do we recover from vacation? Meta-analysis of vacation effects on health and well-being. Journal of Occupational Health 51(1):13-25. doi.org/10.1539/joh.K8004
- Dell'Acqua F and colleagues (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of AI on knowledge worker productivity and quality. Harvard Business School working paper 24-013. ssrn.com/abstract=4573321
- Gerlich M (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies 15(1):6. doi.org/10.3390/soc15010006
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The AI-Free Retreat, Boyce Data Science. Research summaries reflect sources available as of November 2026; to reproduce or update them, follow each link and check for newer versions, corrections, or peer-reviewed publication of preprints.