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Japan's telemedicine use increased from 2020 to 2024 as age gap widened (opens in a new tab)

medicalxpress.com · 2026-10-28

Short answerEvidenceSource

Short answer

Supported

Supported.

The story matches what the study reports.

  • 7 supported

Checked against the study summary. The full text wasn't available, so some details couldn't be settled either way.

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Follow the evidence trail
1
2

NewsLink checks it

Supported

Every claim holds up. All seven claims match what the study reports.

  • 7 supported
Open claim evidence
3
Then inspect each claim

Evidence layer

Claim by claim

Each claim gets a verdict. Expand it to see the evidence directly below.

7 claims in this story

Showing all 7 claimsChoose a verdict to focus the list.

Claim 1 of 7SupportedThe rate of increase differed by population group, rising substantially among younger adults but only modestly among older adults, leading to a widening gap by age group.View evidenceHide evidence

Why this verdict

The abstract-level profile reports that adoption increased across age groups but much less among older adults, with the 70–75 group increasing by +1.0 percentage point versus +13.1 percentage points among 18–29 year olds, yielding a negative DID and supporting a widening age gap. The headline framing is consistent with the body claim and does not outrun the supplied evidence.

1

Study evidence

Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups

“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
Claim 2 of 7SupportedIn a study appearing in the Journal of Medical Internet Research, researchers at the University of Tsukuba followed 10,818 adults aged 18–75 years who participated in both the 2020 and 2024 waves of a nationwide internet survey in Japan to identify changes in telemedicine use by age and socioeconomic factors.View evidenceHide evidence

As stated10,818 adults aged 18–75 years; 2020 and 2024 waves

Why this verdict

The supplied abstract-level profile supports the scientific content of the claim: a nationwide internet-based panel survey in Japan, adults aged 18–75, n=10,818, completion of both 2020 and 2024 waves, and analyses of telemedicine adoption inequalities by age and socioeconomic indicators. The profile does not independently verify the University of Tsukuba attribution or journal metadata, but the core study-design and sample claim is supported.

1

Study evidence

Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups

“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
2

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Claim 3 of 7SupportedThe article concludes that although telemedicine use expanded, there was no clear evidence that disparities in its adoption narrowed over time, and that expansion alone may not be sufficient to reduce age-related and socioeconomic disparities.View evidenceHide evidence

Why this verdict

The profile supports the conclusion that telemedicine use expanded while age-related inequalities did not narrow and widened for older groups, and socioeconomic inequalities did not show evidence of narrowing, including a lag for unemployed participants. The statement that expansion alone may not be sufficient is framed cautiously and is consistent with the reported conclusions and implications for targeted assistance and inclusive design.

1

Study evidence

Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups

“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
2

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Claim 4 of 7SupportedThe proportion of participants who had used telemedicine increased from 2.6% in 2020 to 7.0% in 2024.View evidenceHide evidence

As stated2.6% to 7.0%

Why this verdict

The profile reports unweighted telemedicine adopters of 282/10,818 in 2020 and 758/10,818 in 2024, corresponding to 2.6% and 7.0%.

1

Study evidence

Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups

“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
2

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Claim 5 of 7SupportedThe increase in telemedicine use was also smaller among participants who were unemployed at baseline than among higher-status nonmanual workers.View evidenceHide evidence

Why this verdict

The profile reports that the increase was smaller among participants unemployed at baseline than among upper nonmanual workers, with +2.8 versus +5.7 percentage points and a DID of -2.9 percentage points.

1

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Claim 6 of 7SupportedTrends over the four-year period did not significantly differ by educational attainment, household income, or urban versus rural residence.View evidenceHide evidence

Why this verdict

The profile states that DIDs for educational attainment, urbanicity, and household income were not statistically distinguishable from zero, matching the claim that trends did not significantly differ for these factors.

1

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Claim 7 of 7SupportedBecause the study was based on an internet survey, the findings may not fully represent the experience of individuals with limited or no internet access.View evidenceHide evidence

Why this verdict

The profile identifies the data source as an internet-based panel and notes that this may limit generalizability and select participants with internet access and wave completion. The story’s caveat about limited or no internet access is consistent with that limitation.

1

Study evidence

Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups

“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
2

Study evidence

Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP

“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
Then look for missing context

Context layer

What the story left out

Important study details the story did not include.

  • The outcome was self-reported telemedicine adoption, harmonized as cumulative ever use at each survey wave.

    The story reports telemedicine use/adoption but does not mention that the outcome was self-reported or harmonized as cumulative ever use, which is relevant to interpretation of the prevalence estimates.

    From longitudinal panel survey analysis; Internet-based longitudinal panel analysis with IPW and exposure×wave interaction

  • The estimates came from separate inverse probability-weighted multivariable logistic regression models with exposure-by-wave interactions and predictive-margin difference-in-differences estimates.

    The story describes a longitudinal survey and group changes but omits the abstract’s modeling approach, weighting, adjustment, and DID framework.

    From longitudinal panel survey analysis; Internet-based longitudinal panel analysis with IPW and exposure×wave interaction

  • Sensitivity analyses were reported as leaving the main findings largely unchanged.

    The story does not mention the robustness checks reported in the abstract-level profile.

    From secondary data

  • A limitation is that the analytic sample was restricted to participants who completed both waves, which may introduce selection of completers.

    Although the story notes that participants took part in both waves, it does not present this as a potential selection or generalizability limitation.

    From Internet-based longitudinal panel analysis with IPW and exposure×wave interaction

7 things the story did carry across
  • The study used a nationwide internet-based longitudinal panel survey in Japan of 10,818 adults aged 18–75 who completed both the 2020 baseline and 2024 follow-up surveys.
  • Overall telemedicine adoption increased from 2.6% in 2020 to 7.0% in 2024.
  • Age-related inequalities widened: older adults had smaller increases than younger adults, including +1.0 percentage point for ages 70–75 versus +13.1 percentage points for ages 18–29.
  • Employment-class inequality did not narrow; unemployed participants at baseline had a smaller increase than upper nonmanual workers.
  • Educational attainment, urbanicity, and household income had DIDs that were not statistically distinguishable from zero.
  • The study is observational, so results support changes and associations in adoption patterns rather than causal effects of age or socioeconomic status.
  • A limitation is that the sample came from an internet-based panel, which may limit generalizability, especially for people with limited or no internet access.
Then read the study layer

Study layer

Study at a glance

Scan the study first. Expand only the parts you want to inspect.

Pieces of work

3

Evidence read

study summary

Lead result

secondary data

1Lead resultsecondary dataAssess how age-related inequalities in telemedicine adoption in Japan changed between 2020 and 2024 using longitudinal panel data (including DID-style interaction estimates).longitudinal panel survey analysisExpand

In plain English

Longitudinal analysis of a nationwide internet-based panel (n=10,818, ages 18–75) linked 2020 baseline to 2024 follow-up found that cumulative self-reported telemedicine adoption rose overall but increased less among older adults: the 70–75 year group had a +1.0 percentage-point increase versus +13.1 PP in 18–29 year olds (DID -12.1 PP, 95% CI -18.2 to -5.9). Estimates derive from inverse probability-weighted multivariable logistic regression with exposure×wave interaction and predictive-margin probability-scale DIDs, adjusted for baseline demographic, socioeconomic, and health characteristics.

Key findings

  • Although cumulative telemedicine adoption increased overall from 2020 to 2024, increases were substantially smaller among older adults: 70–75 year olds showed a +1.0 percentage-point change vs +13.1 PP for 18–29 year olds, yielding a DID of -12.1 PP (95% CI -18.2 to -5.9) in adjusted IPW analyses.-12.1 PP (95% CI -18.2 to -5.9) for DID comparing 70–75 vs 18–29 age groups
“We used data from a nationwide, internet-based panel survey of the general population in Japan.”
2secondary dataAssess how socioeconomic inequalities (eg, employment class, education, income, urbanicity) in telemedicine adoption in Japan changed between 2020 and 2024 using longitudinal panel data.Internet-based longitudinal panel analysis with IPW and exposure×wave interactionExpand

In plain English

Using a nationwide internet-based longitudinal panel (same cohort completing 2020 baseline and 2024 follow-up; N=10,818), the authors examined changes in socioeconomic inequalities in cumulative ever telemedicine use between 2020 and 2024. Separate inverse-probability-weighted multivariable logistic regression models were fit for each socioeconomic indicator (employment class, education, household income, urbanicity) including exposure, survey wave, and their interaction; predictive margins were used to estimate adjusted prevalence and probability-scale difference-in-differences (DIDs). Telemedicine adoption rose overall (unweighted 2.6% in 2020 to 7.0% in 2024). The increase was smaller among participants who were unemployed at baseline versus upper nonmanual workers (increase +2.8 vs +5.7 percentage points; DID -2.9 PP, 95% CI -4.6 to -1.2). DIDs for educational attainment, urbanicity, and household income were not statistically distinguishable from zero. Findings were reported to be robust to several sensitivity analyses.

Key findings

  • Increase in telemedicine adoption between 2020 and 2024 was smaller among participants unemployed at baseline compared with upper nonmanual workers.-2.9 PP (95% CI -4.6 to -1.2) DID (unemployed vs upper nonmanual workers); increases: +2.8 PP vs +5.7 PP
  • No statistically detectable change in inequalities by educational attainment, urbanicity, or household income between 2020 and 2024.
“For each age and socioeconomic indicator, we fitted a separate inverse probability-weighted multivariable logistic regression model incorporating the exposure, survey wave, and their interaction”
What this piece can’t prove
  • Outcome based on self-reported cumulative ever telemedicine use (as stated in abstract).
  • Sample drawn from an internet-based panel and restricted to participants who completed both the 2020 and 2024 waves, which may limit generalizability and introduce selection of completers.
  • Abstract does not provide full modeling details (e.g., specific covariates included, IPW specifications, or point estimates/CIs for all SES contrasts).
3secondary dataTest robustness of the main inequality-change findings via sensitivity analyses (reported as largely unchanged).Expand

In plain English

The authors report that several sensitivity analyses were performed using the same 2020–2024 nationwide internet panel and that these analyses left the main inequality-change findings (difference-in-differences estimates for telemedicine adoption by age and socioeconomic indicators) largely unchanged.

Key findings

  • Sensitivity analyses reported to leave the main inequality-change findings largely unchanged.
“The findings remained largely unchanged after several sensitivity analyses.”
What this piece can’t prove
  • Abstract gives only a qualitative statement about sensitivity analyses; lacks specification of alternative models, weighting schemes, sample handling, or numerical results.

2 further details could not be confirmed from the summary.

Finally, the search trail

Method layer

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NewsLink checks the story. Tessa is where you inspect the paper, authors, evidence, and research context.

Papers considered

The selected paper, plus nearby candidates.

PubMed, Europe PMC, Crossref · 15 candidate papers

Candidate

Trends in Age and Socioeconomic Inequalities in Telemedicine Adoption in Japan, 2020-2024: Longitudinal Panel Study (Preprint)

2026 · Crossref

Source (opens in a new tab)

And 9 more candidates considered.