<?xml version='1.0' encoding='UTF-8'?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
  <responseDate>2026-03-07T09:57:31Z</responseDate>
  <request metadataPrefix="jpcoar_1.0" verb="GetRecord" identifier="oai:ipsj.ixsq.nii.ac.jp:00239416">https://ipsj.ixsq.nii.ac.jp/oai</request>
  <GetRecord>
    <record>
      <header>
        <identifier>oai:ipsj.ixsq.nii.ac.jp:00239416</identifier>
        <datestamp>2025-01-19T08:17:16Z</datestamp>
        <setSpec>1164:8228:11483:11716</setSpec>
      </header>
      <metadata>
        <jpcoar:jpcoar xmlns:datacite="https://schema.datacite.org/meta/kernel-4/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcndl="http://ndl.go.jp/dcndl/terms/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:jpcoar="https://github.com/JPCOAR/schema/blob/master/1.0/" xmlns:oaire="http://namespace.openaire.eu/schema/oaire/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rioxxterms="http://www.rioxx.net/schema/v2.0/rioxxterms/" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns="https://github.com/JPCOAR/schema/blob/master/1.0/" xsi:schemaLocation="https://github.com/JPCOAR/schema/blob/master/1.0/jpcoar_scm.xsd">
          <dc:title>運動習慣化支援アプリケーションにおける行動要因推定手法の提案と評価</dc:title>
          <dc:title xml:lang="en">Assessing Behavior Factor Estimation Techniques in Exercise Habit Formation Apps</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>山本, 達也</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>松木, 萌</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>早川, 昭二</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>上村, 拓也</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Tatsuya, Yamamoto</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Moe, Matsuki</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Shoji, Hayakawa</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Takuya, Kamimura</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:subject subjectScheme="Other">身体・認知機能へのアプローチ</jpcoar:subject>
          <datacite:description descriptionType="Other">健康維持を目的とした運動支援アプリケーションの研究開発と製品化が活発に行われている．一方で，ユーザの心理状態に寄り添いモチベーションを高め，運動習慣へと導くことは依然として技術的に困難であり，医師やカウンセラーなどの支援に依存している状況である．本研究では，アプリケーションの操作や行動ログの情報から，ユーザに負担なく自己効力感などの行動要因 (行動の源泉となる心理特性) を推定する手法を開発した．行動ログとは，ユーザの計画した運動の種類や強度および運動の実施状況のデータであり，行動要因が反映されやすい特徴的な統計情報を用いて設計した．提案手法は，設計した行動ログに基づき機械学習により行動要因を推定する．結果として，自己効力感を心理測定尺度アンケートで取得した結果に対し高い推定精度 (0.82) を達成し，提案手法の有効性を確認した．また，精度向上のための行動ログの分析と，実践的な活用を目的とした複数の行動要因の組み合わせに対する行動要因推定について議論する．</datacite:description>
          <datacite:description descriptionType="Other">The development of exercise support applications focused on health maintenance is actively progressing. However, effectively enhancing motivation and guiding users toward consistent exercise habits while considering their psychological state remains a technical challenge. Currently, reliance on medical professionals and counselors for support persists. Our research proposes a method to estimate behavioral factors (such as self-efficacy) from application interactions and exercise logs without imposing additional burden on users. These behavior logs capture planned exercise types, intensity, and actual performance data. Our approach utilizes machine learning to predict behavioral factors by leveraging distinctive statistical features from these logs. Our results demonstrate a high accuracy rate (0.82) in estimating self-efficacy compared to psychological questionnaires. Furthermore, we discuss the analysis of behavior logs for accuracy improvement and explore combinations of multiple behavioral factors for practical.</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2024-09-19</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/239416</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">2189-4450</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA1271737X</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>研究報告高齢社会デザイン（ASD）</jpcoar:sourceTitle>
          <jpcoar:volume>2024-ASD-30</jpcoar:volume>
          <jpcoar:issue>5</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>9</jpcoar:pageEnd>
          <jpcoar:file>
            <jpcoar:URI label="IPSJ-ASD24030005.pdf">https://ipsj.ixsq.nii.ac.jp/record/239416/files/IPSJ-ASD24030005.pdf</jpcoar:URI>
            <jpcoar:mimeType>application/pdf</jpcoar:mimeType>
            <jpcoar:extent>1.6 MB</jpcoar:extent>
            <datacite:date dateType="Available">2026-09-19</datacite:date>
          </jpcoar:file>
        </jpcoar:jpcoar>
      </metadata>
    </record>
  </GetRecord>
</OAI-PMH>
