Wallman-Jones et al. (2023) — Leave the screen
Open access (CC BY). Seventy University of Bern students wore thigh-mounted accelerometers for seven days; the sensor triggered a smartphone prompt after ten minutes of light activity or thirty minutes of uninterrupted sitting (random prompts otherwise, capped at ten per day, 07:30–21:30). At each prompt they rated the last fifteen minutes on the body subscale of the State Mindfulness Scale and named the predominant activity. Baseline session: height, weight, questionnaires, and a three-trial Schandry counting task.
The wiki’s first source on exercise, sedentary behaviour or screen time, and its first attempt to measure interoception repeatedly in ordinary life.
The finding the paper is named for, and the finding that matters more
The title result is the screen-time contrast, and it is clean: against screen time as reference, every other category scored higher except sleeping, including two sedentary categories. Sitting is not the problem; sitting at a screen is. The authors read this through Pennebaker & Lightner’s (1980) competition-of-cues hypothesis — attention to one source depletes it for others — and note that non-screen social interaction scored higher than solitary non-screen behaviour, which they connect to interoception’s social origins via Oldroyd et al. and Palmer & Tsakiris (2018).
But the result that reorganizes things here is one the authors treat as a puzzle in their discussion: the within-person and between-person effects of physical activity have opposite signs. Move now, and self-reported interoception goes up. Be someone who moves a lot, and it is lower. Same measure, same participants, same model. See state-vs-trait-interoception for what that does to the wiki’s construct pages, and physical-activity-and-interoception for the mechanisms on offer.
The exploratory interaction, and why it is the most interesting thing in the paper
Baseline heartbeat-counting accuracy did two things at once.
As a main effect it predicted higher daily self-reported interoception (B = 4.50, β = 0.21, p = .007). This wiki has recorded the accuracy/sensibility dissociation as one of its most robust patterns — meditators attend without detecting (does-mindfulness-enhance-interoceptive-accuracy), confidence generalizes where performance does not, the interoceptive-taxonomy exists because the two come apart. Here they correlate. Not a contradiction (different sensibility instrument; a state measure aggregated over a week rather than a trait questionnaire; N = 70 with a wide CI, 1.31–7.69), but it is friction worth holding, and it is recorded as such on interoceptive-taxonomy rather than smoothed away.
As a moderator it reversed the activity effect. Split into tertiles, the low and medium groups’ self-reported interoception rose with acute activity; the high group’s fell (Fig. 1). The authors offer two readings — habituation (familiar signals are less perceptually salient, so a trained perceiver notices less at the same intensity) and a ceiling effect — and cannot separate them. Either way this is the same shape as their between-person result one level down, which is the strongest internal support the paper has for the habituation account: the person who reads their body well, and the person who moves a lot, both report noticing it less.
That gives is-more-interoceptive-awareness-better an unusual row. Most rows there read one verdict off one association. This one, like Banellis et al., produces opposite verdicts inside a single sample — and it is now the second such row, which makes the timescale problem structural rather than an oddity of one study.
What the mechanism section can and cannot support
Two mechanisms are proposed throughout and never separated:
- Salience. Sympathetic activation during activity makes bodily signals louder (Craig 2006); Easterbrook’s (1959) cue-utilization narrowing lets them dominate consciousness.
- Attention. Goal-directed exercise directs attention to the body (pacing breath, placing limbs; Toner et al. 2016) where automatized daily activity does not.
The exercise > daily-life-PA gap is offered as evidence for attention — but the authors concede immediately that exercise is also more intense, and intensity was not recorded, so the gap is equally a salience effect. The paper’s own conclusion (“we can only speculate”) is the right one, and the wiki should not resolve it on their behalf. This is the same undecidability physical-activity-and-interoception carries as its central open question.
The sedentary story leans on an “untrained pathway” argument from the group’s own theory paper (Wallman-Jones et al. 2021): absent exercise-induced perturbation, brain–body communication degrades toward a mismatch between actual and perceived bodily state. The supporting evidence is all held at one remove and none of it is longitudinal — reduced heartbeat-evoked potentials in sedentary vs trained young adults (Perakakis et al. 2017, a bioRxiv preprint), attenuated anterior insula response to inspiratory loading in elite athletes (Paulus et al. 2012), BMI–interoception correlations (Robinson et al. 2021). Note that the Paulus finding is cited twice, for opposite purposes: as evidence that sedentariness is bad, and later as the explanation for why habitually active people report less interoception. It cannot be both without an account of when attenuation is efficiency and when it is decay, and the paper does not supply one.
For screen time specifically the evidence is thinner still: two resting-state connectivity studies (Paik et al. 2019 on bedtime smartphone use and the insula; Kwon et al. 2022 on problematic smartphone use and cingulate connectivity), plus a theoretical piece from outdoor-education (Puk 2021). The screen-time claim in this paper’s title rests on this paper.
What it contributes as a method
It is the wiki’s first use of accelerometry-triggered ambulatory assessment — prompts fired by measured behaviour rather than by clock or randomness — which is what makes the within-person activity contrast estimable at all. See experience-sampling, where it now sits alongside Tong & Jia’s beeped EMA, MacCormack’s Day Reconstruction Method and MDES as a fourth point on the same spectrum.
Its instrument problem is worth stating plainly, because it is the field-level gap rather than this paper’s failing. There is no validated state measure of interoception. The authors reached for a mindfulness scale, checked its multilevel reliability properly (ω_within = 0.92, ω_between = 0.97 by MLCFA), and then said in their limitations that the field needs the instrument they did not have. That is the same bottleneck Chen et al. name for non-verbal populations and interoceptive-taxonomy records for regulation: a construct with four competing partitions and, in this case, no clock.
Provenance notes
The corresponding address is Bern but the corresponding e-mail is UCSF — Wallman-Jones had moved by publication. No funding declared, no competing interests, and an explicit statement that no generative AI was used in preparation. Data available on request; six supplementary files (activity-category mapping, MLCFA loadings, interaction models, tertile descriptives, sedentary-cutoff sensitivity analyses) are referenced but not in raw/, which matters for the cut-off sensitivity result above — it is known here only through the authors’ summary of it.
One typographical error in the results-to-discussion transition (“The results of the present study vuild on findings from previous laboratory-based studies”), recorded in the same spirit as the Pollatos statistics and the Lyons transposed table: production errors are common in this literature and worth noticing when they appear near load-bearing sentences. This one is not — it is a verb.