Evidence map›Paper›PMID 37717116›Full record

Trial reportHypertension research : official journal of the Japanese Society of Hypertension2024

Daily self-reported behavioural efficacy records on hypertension digital therapeutics as digital metrics associated with the reduction in morning home blood pressure: post-hoc analysis of HERB-DH1 trial.

Fumi Hisaki, Mitsuharu Aga, Naoko Tomitani, Yukie Okawara, Noriko Harada, Kazuomi Kario

Abstract readClinical Trial
PubMed Publisher
In one paragraph

Trial report in Hypertension research : official journal of the Japanese Society of Hypertension, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. The role of digital rehabilitation therapy in enhancing upper limb function recovery following surgery in breast cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
  2. Predicting measurement continuity in home blood pressure monitoring using machine learning.Hypertension research : official journal of the Japanese Society of Hypertension · 2026
    Article
  3. Digital hypertension - what we need for the high-quality management of hypertension in the new era.Hypertension research : official journal of the Japanese Society of Hypertension · 2025
    Review
  4. Article
  5. Digital therapeutics in hypertension: How to make sustainable lifestyle changes.Journal of clinical hypertension (Greenwich, Conn.) · 2024
    Review
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Fumi HisakiCureApp, Inc. Medical Division, Tokyo, Japan.
Mitsuharu AgaCureApp, Inc. Medical Division, Tokyo, Japan.
Naoko TomitaniDivision of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Shimotsuke, Tochigi, Japan.
Yukie OkawaraDivision of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Shimotsuke, Tochigi, Japan.
Noriko HaradaDivision of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Shimotsuke, Tochigi, Japan.
Kazuomi KarioDivision of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Shimotsuke, Tochigi, Japan. kkario@jichi.ac.jp.
Jichi Medical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital therapeutics for hypertension, proven effective in the HERB-DH1 trial, enable patients to record and track their daily actions and achievements to improve their lifestyles using an app. However, the association between recording daily behaviours and blood pressure (BP) reduction has not yet been investigated. We conducted a post-hoc analysis to investigate the relationship between them in the HERB-DH1 trial. We defined the counts of daily records of antihypertensive behaviour taken by the patient into the app as daily self-reported behavioural efficacy records (SER). SER was categorised into quartiles, and the trend of changes from baseline to week 12 in morning home systolic blood pressure (SBP), salt intake checklist score, and body weight was assessed. A total of 156 patients with hypertension were included in the analysis. A higher total count of SER was associated with greater SBP reduction (P for trend: 0.049). Patients with a higher SER for salt intake and weight reduction showed reductions in SBP (P for trend: 0.034 and 0.027, respectively). Furthermore, patients with higher salt intake SER exhibited a decrease in the salt intake checklist scores, and patients with greater weight reduction SER experienced a reduction in body weight (P for trend: 0.001 and 0.007, respectively). SER during digital therapeutics is associated with a reduction in morning home SBP in patients with hypertension. Enhancing patients' intrinsic motivation and self-efficacy, as evaluated by the SER, can play an important role in reducing BP by promoting lifestyle improvement. Daily self-reported behavioural efficacy records (SER) defined as the number of patient's app inputs of recall of day-by-day activity of behaviours at the end of the day, is partially affected by self-efficacy and affinity of app, resulting in the effectiveness of digital therapeutics.

Indexed as

HypertensionHypotensionAntihypertensive AgentsBlood PressureBody WeightHumansSelf EfficacySelf ReportSodium Chloride, DietaryWeight LossAntihypertensive AgentsSodium Chloride, DietaryDigital therapeuticsHypertension, Intrinsic motivationLifestyle modificationSelf-efficacy

Identifiers

PMID37717116
OpenAlexW4386796755

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.