A portrait of who seeks sleep help and what changes when they get it — drawn from intake assessments and weekly sleep diary data across 2,745 members enrolled in the program.
The 2,745 members in this analysis arrived at the program after years of disrupted sleep, failed remedies, and diminishing quality of life. This report covers two things: who they are and how they sleep before starting, and how their sleep changes over the course of the program.
Nine in ten reported they had been trying to improve their sleep for a long time without lasting success. By week 4 of the program, nearly three quarters were falling asleep faster than at intake.
About the data: This report draws on two data sources from the same 2,745-member dataset. Sections 02–06 (intake portrait) use responses from the program enrollment questionnaire and describe members before the program begins. Section 07 (outcomes) uses weekly sleep diary entries logged in the app during the program. Where a figure could be read either way, the source is noted.
Members in this dataset span adulthood broadly, with the highest concentration in their 40s and 50s — decades often characterized by compounding demands: careers, young children, hormonal transitions, and rising health complexity.
The overrepresentation of women reflects a well-documented pattern: hormonal transitions, caregiving responsibilities, and anxiety-related sleep disruption disproportionately affect women's sleep quality across the lifespan.
Members in this dataset report an average of 5.7 hours per night at intake — well below the 7–9 hours recommended by the American Academy of Sleep Medicine. More striking is the distribution: 17% report fewer than 4.5 hours on a regular basis.
Beyond total sleep duration, intake data captures the time members spend lying awake — a key driver of sleep anxiety and conditioned insomnia.
90% of the members in this dataset lie awake for 30 minutes or more each night. Nearly one in three spends over two hours awake — a pattern clinically associated with learned sleep anxiety and a core driver of the insomnia cycle.
Difficulty staying asleep is as prevalent as difficulty falling asleep. The data shows a population that rarely sleeps through to morning without interruption.
71% of members in this analysis wake in the night every single night, and 94% do so at least several times a week.
Sleep disruption does not occur in isolation. Intake data captures the life context members connect to their sleep problems — the clinical terrain any effective sleep program must address.
The most frequently reported internal factor is cognitive hyperarousal — the inability to switch off a busy mind when trying to sleep. This is the hallmark feature of psychophysiological insomnia, and it appears across nearly the entire dataset.
Members were asked to identify life circumstances affecting their sleep. Work stress is the most common — but health concerns, hormonal transitions, and parenting demands each affect a meaningful portion of this population.
Most prominent among female members in their 40s and 50s, where perimenopause and menopause intersect directly with sleep architecture changes.
A distinct cohort whose sleep disruption is driven by infant and toddler caregiving — needs that differ significantly from classic insomnia.
Members reporting consistent bedtimes past midnight with difficulty adjusting earlier — a pattern consistent with circadian phase delay.
Intake data also captures habits that compound sleep difficulty — patterns that often begin as coping strategies but reinforce poor sleep over time.
At intake, members identify their primary sleep goals. Three outcomes dominate — reflecting the core phenomenology of chronic insomnia: difficulty maintaining sleep, insufficient depth, and difficulty initiating sleep. The majority select more than one, indicating that sleep difficulty is rarely a single-symptom problem.
Beyond specific sleep outcomes, members identify their overarching program intention. A majority are seeking relief from nighttime anxiety as much as, or in addition to, more hours of sleep.
52% of the members in this dataset enrolled specifically to address both their sleep and their nighttime anxiety — underscoring that effective sleep treatment must engage the nervous system, not just sleep hygiene habits.
At enrollment, 72% of members in this dataset were already using some form of sleep aid — evidence of a population that has attempted pharmacological solutions before arriving at a behavioral program.
Nearly a third of the members in this dataset carry a prior insomnia diagnosis, and 17% a sleep apnea diagnosis. The 56% without a recorded prior diagnosis does not mean they have no sleep disorder — it reflects the significant gap between the lived experience of disordered sleep and formal clinical diagnosis.
Chronic difficulty predating enrollment by months or years — not a recent, situational disruption.
High rates of cognitive and somatic hyperarousal — the hallmark feature of psychophysiological insomnia.
Significant anticipatory anxiety around sleep — a conditioned response that turns bedtime into a source of dread.
Sleep Reset's program is built on Cognitive Behavioral Therapy for Insomnia (CBT-I) — the first-line treatment recommended by the American Academy of Sleep Medicine over medication. Members are paired with a trained sleep coach who personalizes the program and adjusts it weekly based on sleep diary data.
Sleep efficiency — the percentage of time in bed actually spent asleep — is the primary clinical measure of insomnia severity. The 85% threshold is widely used to rule out insomnia. Members in this dataset crossed that threshold on average by week 4 and sustained it through week 8.
Sleep Reset vs. published CBT-I benchmarks: 73% of members in this dataset improved sleep onset within 3 weeks, compared to a ~70% improvement rate reported in published clinical CBT-I trials. These figures are not directly comparable — clinical trials use controlled conditions and standardized measurement — but they suggest the program's outcomes are broadly consistent with the evidence base for CBT-I.
This report draws on a single dataset of 2,745 members who enrolled in the Sleep Reset program and completed the intake questionnaire. The same members' weekly sleep diary entries, logged in the app during the program, form the basis of the outcomes section. Members were not selected based on sleep severity, outcomes, or any other filter.
Intake figures are drawn from the program enrollment questionnaire and reflect member-reported responses at the time of joining. Data covers sleep history, duration, behavioral patterns, life stressors, medical conditions, and treatment goals. This data is self-reported and reflects members' perceptions rather than objective clinical measurements.
Where members selected multiple answers (e.g., multiple life stressors or goals), each response was counted independently — category totals may exceed 100%. Records with blank responses were excluded from that variable's calculation.
Outcomes figures are drawn from daily sleep diary entries logged in the Sleep Reset app, covering sleep latency, total sleep time, awakenings, time awake during the night, sleep efficiency, and self-rated sleep quality.
Week-over-week averages are computed from members with data for that given week. Sample sizes vary: week 1 n≈2,208; week 4 n≈1,082; week 6 n≈720. Percentages for before/after comparisons (e.g., "74% falling asleep faster by week 4") reflect only members with diary data at both time points.
Members who enroll in a behavioral sleep program are, by definition, experiencing sleep difficulty — prevalence figures in this report will exceed those of the general adult population. All intake data is self-reported and subject to recall bias. Outcomes data relies on self-logged sleep diaries, which are subject to the same bias. Outcomes figures reflect members who logged data through the relevant week and may not represent those who disengaged earlier. This report does not constitute a clinical trial; there is no control group.
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