<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "journalpublishing.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="2.0" xml:lang="en" article-type="research-article"><front><journal-meta><journal-id journal-id-type="nlm-ta">JMIR Med Educ</journal-id><journal-id journal-id-type="publisher-id">mededu</journal-id><journal-id journal-id-type="index">20</journal-id><journal-title>JMIR Medical Education</journal-title><abbrev-journal-title>JMIR Med Educ</abbrev-journal-title><issn pub-type="epub">2369-3762</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">v12i1e83475</article-id><article-id pub-id-type="doi">10.2196/83475</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Paper</subject></subj-group></article-categories><title-group><article-title>Adoption and Use of Social Media in Health Care Among Medical Residents: Cross-Sectional Study</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name name-style="western"><surname>Zouakia</surname><given-names>Zineb</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Manns</surname><given-names>Aur&#x00E9;lia</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Meyre</surname><given-names>Thomas</given-names></name><degrees>MD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Burgun</surname><given-names>Anita</given-names></name><degrees>MD, PhD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Kadlub</surname><given-names>Natacha</given-names></name><degrees>MD, PhD</degrees><xref ref-type="aff" rid="aff3">3</xref></contrib><contrib contrib-type="author"><name name-style="western"><surname>Tsopra</surname><given-names>Rosy</given-names></name><degrees>MD, PhD</degrees><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="aff" rid="aff4">4</xref></contrib></contrib-group><aff id="aff1"><institution>Clinical Bioinformatics Laboratory, Imagine Institute, Universit&#x00E9; Paris Cit&#x00E9;, INSERM UMR1163</institution><addr-line>24 Boulevard du Montparnasse</addr-line><addr-line>Paris</addr-line><country>France</country></aff><aff id="aff2"><institution>Department of Medical Informatics, H&#x00F4;pital Europ&#x00E9;en Georges Pompidou, H&#x00F4;pital Necker Enfants Malades, AP-HP</institution><addr-line>Paris</addr-line><country>France</country></aff><aff id="aff3"><institution>Department of Maxillofacial Surgery and Plastic Surgery, MAFACE Rare Diseases Reference Centre, Faculty of Medicine, H&#x00F4;pital Necker Enfants Malades, APHP, Universit&#x00E9; Paris Cit&#x00E9;</institution><addr-line>Paris</addr-line><country>France</country></aff><aff id="aff4"><institution>Universit&#x00E9; Paris Cit&#x00E9;, UPPERS, US 007</institution><addr-line>Paris</addr-line><country>France</country></aff><contrib-group><contrib contrib-type="editor"><name name-style="western"><surname>Stone</surname><given-names>Alicia</given-names></name></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name name-style="western"><surname>Ganesh</surname><given-names>Shankar</given-names></name></contrib></contrib-group><author-notes><corresp>Correspondence to Zineb Zouakia, MD, Clinical Bioinformatics Laboratory, Imagine Institute, Universit&#x00E9; Paris Cit&#x00E9;, INSERM UMR1163, 24 Boulevard du Montparnasse, Paris, F-75006, France, 33 668169844; <email>zineb.zouakia@gmail.com</email></corresp></author-notes><pub-date pub-type="collection"><year>2026</year></pub-date><pub-date pub-type="epub"><day>5</day><month>6</month><year>2026</year></pub-date><volume>12</volume><elocation-id>e83475</elocation-id><history><date date-type="received"><day>03</day><month>09</month><year>2025</year></date><date date-type="rev-recd"><day>17</day><month>04</month><year>2026</year></date><date date-type="accepted"><day>17</day><month>04</month><year>2026</year></date></history><copyright-statement>&#x00A9; Zineb Zouakia, Aur&#x00E9;lia Manns, Thomas Meyre, Anita Burgun, Natacha Kadlub, Rosy Tsopra. Originally published in JMIR Medical Education (<ext-link ext-link-type="uri" xlink:href="https://mededu.jmir.org">https://mededu.jmir.org</ext-link>), 5.6.2026. </copyright-statement><copyright-year>2026</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Education, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="https://mededu.jmir.org/">https://mededu.jmir.org/</ext-link>, as well as this copyright and license information must be included.</p></license><self-uri xlink:type="simple" xlink:href="https://mededu.jmir.org/2026/1/e83475"/><abstract><sec><title>Background</title><p>Social media apps are widely used by health care professionals despite security and regulatory risks. Identifying factors associated with this use is important for developing effective risk-reduction strategies.</p></sec><sec><title>Objective</title><p>This study aimed to investigate how medical residents use 6 popular social media apps in professional tasks and to identify factors influencing their adoption in health care, using the validated Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model.</p></sec><sec sec-type="methods"><title>Methods</title><p>An anonymous web-based survey was conducted between June 2024 and November 2024 among medical residents in France. Participants reported demographic characteristics, frequency, and professional contexts of use for 6 apps (Facebook, Instagram, LinkedIn, Messenger, TikTok, and WhatsApp) and completed UTAUT2-based items. The model was adapted by adding a technology trust construct. Descriptive analyses were performed for all apps. With a sufficient sample size, partial least squares structural equation modeling was conducted for WhatsApp to identify factors associated with behavioral intention and use behavior.</p></sec><sec sec-type="results"><title>Results</title><p>A total of 137 residents (n=87, 63.5% female participants) across 40 specialties completed the survey. WhatsApp was the most widely and professionally used app (n=127, 92.7%), with 75.9% (n=104) using it at least many times per week. It was primarily used for patient care, including written transmissions (n=86, 62.8%), case discussions (n=76, 55.5%), and specialist advice (n=86, 62.8%), as well as for professional networking (n=62, 45.3%). Messenger was used by 46.7% (n=64) of participants for similar purposes. Facebook (n=35, 25.6%) and LinkedIn (n=20, 14.6%) were mainly used for education and networking, whereas Instagram (n=11, 8%) was rarely used, and TikTok was not used for professional purposes. Regarding adoption factors, WhatsApp had the highest overall scores, including the highest performance expectancy (mean 5.4, SD 1.12), behavioral intention (mean 5.28, SD 1.15), and use behavior (mean 5.91, SD 1.30), with high effort expectancy (mean 6.82, SD 0.55) and facilitating conditions (mean 6.07, SD 0.85). LinkedIn showed the highest social influence (mean 5.05, SD 1.06), whereas Instagram showed the highest hedonic motivation (mean 6.61, SD 0.51). Technology trust scores were low across all apps, ranging from a mean of 2.23 (SD 1.16) for Facebook to a mean of 3.72 (SD 1.39) for LinkedIn. In the partial least squares structural equation modeling analysis for WhatsApp, habit was the only significant predictor of behavioral intention (<italic>&#x03B2;</italic>=.53; <italic>P</italic>&#x003C;.001) and use behavior (<italic>&#x03B2;</italic>=.45; <italic>P</italic>&#x003C;.001).</p></sec><sec sec-type="conclusions"><title>Conclusions</title><p>WhatsApp dominates professional use among residents despite low trust in its security, and its use is mainly driven by habit. Secure alternatives with features similar to popular social media apps, supported by institutional policies and digital professionalism training, are needed to encourage physicians to better consider safety when using social media.</p></sec></abstract><kwd-group><kwd>digital health</kwd><kwd>social media</kwd><kwd>messaging</kwd><kwd>WhatsApp</kwd><kwd>Facebook</kwd><kwd>health communication</kwd><kwd>health care professionals</kwd><kwd>physicians</kwd><kwd>medical residency</kwd><kwd>clinical practice</kwd><kwd>technology acceptance</kwd><kwd>UTAUT2</kwd><kwd>Unified Theory of Acceptance and Use of Technology 2</kwd></kwd-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Social media, including messaging apps, are now common tools in health care [<xref ref-type="bibr" rid="ref1">1</xref>]. Their use is boosted by the popularity of smartphones and quick communication, especially among young people [<xref ref-type="bibr" rid="ref2">2</xref>,<xref ref-type="bibr" rid="ref3">3</xref>]. For example, WhatsApp is used by more than 90% of physicians to share clinical information [<xref ref-type="bibr" rid="ref4">4</xref>-<xref ref-type="bibr" rid="ref7">7</xref>]; Facebook is used by more than 70% of health care professionals, primarily for continuing education [<xref ref-type="bibr" rid="ref8">8</xref>]; LinkedIn is used by more than half for career development [<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref10">10</xref>]; and Instagram is popular among the younger users [<xref ref-type="bibr" rid="ref11">11</xref>], with 68% of medical residents and students using it daily [<xref ref-type="bibr" rid="ref12">12</xref>]. These apps are used for a wide range of health care activities [<xref ref-type="bibr" rid="ref1">1</xref>,<xref ref-type="bibr" rid="ref2">2</xref>,<xref ref-type="bibr" rid="ref13">13</xref>]: (1) clinical coordination (eg, shift rotation and clinical updates); (2) peer consultation (eg, case discussion and specialist advice); (3) interaction with patients (eg, responding to questions and providing test result updates), with more than 12% of residents having patients (or patients&#x2019; family members) listed as social media &#x201C;friends&#x201D; [<xref ref-type="bibr" rid="ref14">14</xref>]; (4) professional networking; and (5) continuing education (eg, access to the latest medical information).</p><p>However, the use of such apps raises significant concerns around patient confidentiality and data security, as they fail to comply with data protection standards such as the European General Data Protection Regulation [<xref ref-type="bibr" rid="ref15">15</xref>] or the US Health Insurance Portability and Accountability Act [<xref ref-type="bibr" rid="ref16">16</xref>], making them inappropriate for use in health care [<xref ref-type="bibr" rid="ref17">17</xref>]. To guarantee the security of patient data, secure tools have been developed, such as institutional platforms for patient exchange (eg, intrahospital provider portals; regional or national secure messaging systems [<xref ref-type="bibr" rid="ref18">18</xref>-<xref ref-type="bibr" rid="ref20">20</xref>]); private solutions like Siilo [<xref ref-type="bibr" rid="ref21">21</xref>,<xref ref-type="bibr" rid="ref22">22</xref>], a secure messaging app for medical professionals; or Pixacare, designed for secure medical photography exchange [<xref ref-type="bibr" rid="ref23">23</xref>]. Despite the availability of these secure tools, medical professionals continue to prefer using popular social media apps [<xref ref-type="bibr" rid="ref4">4</xref>], raising questions about the underlying reasons for their continued use.</p><p>Several studies have explored the reasons behind the increasing use of social media apps in health care. For example, McGowan et al [<xref ref-type="bibr" rid="ref24">24</xref>] found that physicians&#x2019; use was driven by the ease of use and usefulness in achieving better outcomes, while Nikolic et al [<xref ref-type="bibr" rid="ref6">6</xref>] pointed out factors such as being free, accessible, widely adopted by peers, and offering features like group chat and data sharing. Additionally, Youssef et al [<xref ref-type="bibr" rid="ref11">11</xref>] reported that orthopedic surgeons viewed social media as a means to boost visibility, connect with patients, stay informed, and advance their careers. However, prior studies have limitations: (1) they focused on single apps (mostly WhatsApp [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref25">25</xref>-<xref ref-type="bibr" rid="ref28">28</xref>]) or specific specialties (mostly surgery; eg, orthopedics [<xref ref-type="bibr" rid="ref11">11</xref>,<xref ref-type="bibr" rid="ref12">12</xref>,<xref ref-type="bibr" rid="ref29">29</xref>-<xref ref-type="bibr" rid="ref32">32</xref>]); (2) they reported general reasons for using social media without examining each app separately [<xref ref-type="bibr" rid="ref5">5</xref>,<xref ref-type="bibr" rid="ref6">6</xref>,<xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref9">9</xref>,<xref ref-type="bibr" rid="ref12">12</xref>,<xref ref-type="bibr" rid="ref24">24</xref>,<xref ref-type="bibr" rid="ref31">31</xref>,<xref ref-type="bibr" rid="ref33">33</xref>-<xref ref-type="bibr" rid="ref36">36</xref>]; and (3) they did not use a validated theoretical model or relied on earlier models [<xref ref-type="bibr" rid="ref24">24</xref>,<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref35">35</xref>,<xref ref-type="bibr" rid="ref37">37</xref>]. For example, the technology acceptance model [<xref ref-type="bibr" rid="ref38">38</xref>] does not account for relevant factors like habit or social influence.</p><p>Here, we aimed to examine the use of social media apps in health care, by investigating how medical residents use 6 popular social media apps in professional contexts, and to identify factors associated with their adoption using the validated Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model [<xref ref-type="bibr" rid="ref39">39</xref>].</p></sec><sec id="s2" sec-type="methods"><title>Methods</title><sec id="s2-1"><title>Study Design</title><p>We conducted a cross-sectional study using an anonymous web-based survey to investigate the professional use of social media apps among medical residents and to identify the factors influencing their adoption in health care. The study was designed and reported in accordance with the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) [<xref ref-type="bibr" rid="ref40">40</xref>] (<xref ref-type="supplementary-material" rid="app4">Checklist 1</xref>).</p></sec><sec id="s2-2"><title>Participants and Recruitment</title><p>Eligible participants were medical residents currently enrolled in postgraduate training programs in France. Participants were recruited between June 25 and November 27, 2024. Recruitment was conducted using a nonprobability convenience sampling strategy. The survey link was disseminated via institutional mailing lists, university networks, and social media through medical residents&#x2019; groups. Participants were also encouraged to share the survey with peers, enabling snowball sampling. The recruitment message included a brief description of the study objectives, eligibility criteria, estimated completion time, and a link to the survey. Participation was voluntary, and no incentives were provided.</p></sec><sec id="s2-3"><title>Survey Development and Administration</title><p>The questionnaire was developed to assess the professional use of social media applications among medical residents and the factors influencing their adoption. It included both descriptive and theory-based components, with the latter based on the UTAUT2 model [<xref ref-type="bibr" rid="ref39">39</xref>], adapted to the health care context. It was administered online using LimeSurvey and was designed to be completed in approximately 15 minutes. Before dissemination, it was pilot-tested on 3 medical residents to ensure clarity, relevance, and usability.</p><p>The survey was structured into 4 sections covering participant characteristics, personal and professional use of social media apps, frequency and contexts of professional use, and factors influencing adoption and use. Participants accessed the survey through a web link and could complete it using a computer or mobile device. They were able to review and modify their responses before submission. No randomization of items was implemented. Adaptive questioning was used based on the apps selected by participants to reduce the number and complexity of questions. To minimize duplicate entries, survey settings were configured to restrict multiple submissions from the same participant.</p></sec><sec id="s2-4"><title>Measures</title><sec id="s2-4-1"><title>Selection of Social Media Apps</title><p>The social media apps included in this study were selected based on data from the Digital 2024 Global Overview Report by DataReportal [<xref ref-type="bibr" rid="ref41">41</xref>], which provides global statistics on digital usage. Apps were chosen according to their number of active users worldwide and to represent different categories. We selected the 3 most widely used multipurpose social media platforms (Facebook, Instagram, and TikTok), the 2 most widely used mobile messaging apps (WhatsApp and Messenger), and the most widely used professional networking platform (LinkedIn). WeChat, although among the most widely used applications globally, was excluded due to its limited use outside of China.</p></sec><sec id="s2-4-2"><title>Participants&#x2019; Characteristics</title><p>Participants were asked to provide demographic information, including age, gender, medical specialty, and whether they used each app in their personal lives.</p></sec><sec id="s2-4-3"><title>Use of Social Media Apps in Health Care</title><p>For each app used, participants reported the frequency of use in their professional practice using a 5-point Likert scale (&#x201C;Many times per day,&#x201D; &#x201C;Many times per week,&#x201D; &#x201C;Less than once a week,&#x201D; &#x201C;Less than once a month,&#x201D; and &#x201C;Never&#x201D;).</p><p>Participants also specified the professional contexts in which each app was used. These were assessed using a predefined list of 12 tasks covering 3 domains: patient care (eg, written transmission, case discussion, specialist advice), medical knowledge communication (eg, consumption or creation of educational content), and professional networking (eg, exchange of opportunities, relationship building). The list of tasks was developed empirically based on the authors&#x2019; clinical experience as medical residents and doctors, informal discussions with peers, and iterative refinement during questionnaire development. This approach aimed to capture the most common real-world uses of social media apps in clinical practice.</p></sec><sec id="s2-4-4"><title>Factors of Acceptance and Use in Health Care</title><p>To investigate the determinants of social media adoption and use in health care, we used the UTAUT2 model from Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>], as it is a validated and widely used model for examining factors that influence technology adoption and use [<xref ref-type="bibr" rid="ref42">42</xref>], including in health care research (eg, telemedicine systems [<xref ref-type="bibr" rid="ref43">43</xref>] and mobile health apps [<xref ref-type="bibr" rid="ref44">44</xref>]).</p></sec><sec id="s2-4-5"><title>Adaptation and Use of the UTAUT2 Model</title><p>The original UTAUT2 model includes 7 key constructs (eg, performance expectancy) that predict 2 constructs representing technology use [<xref ref-type="bibr" rid="ref39">39</xref>]: behavioral intention and use behavior (<xref ref-type="table" rid="table1">Table 1</xref>). Each construct contains 1 or more items (<xref ref-type="table" rid="table1">Table 1</xref>).</p><table-wrap id="t1" position="float"><label>Table 1.</label><caption><p>Description of the constructs used in our model.</p></caption><table id="table1" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Constructs</td><td align="left" valign="bottom">Definition</td><td align="left" valign="bottom">Number of items</td><td align="left" valign="bottom">Source</td><td align="left" valign="bottom">Included in model</td></tr></thead><tbody><tr><td align="left" valign="top">Performance expectancy</td><td align="left" valign="top">Perceived efficiency gain from using the technology</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Effort expectancy</td><td align="left" valign="top">Ease of use</td><td align="left" valign="top">4</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Social influence</td><td align="left" valign="top">Peer influence on the decision to use the technology</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Facilitating conditions</td><td align="left" valign="top">Resources and support available to users</td><td align="left" valign="top">4</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Hedonic motivation</td><td align="left" valign="top">Fun or pleasure derived from using the technology</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Price value</td><td align="left" valign="top">Perceived value relative to the cost of use</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">No</td></tr><tr><td align="left" valign="top">Habit</td><td align="left" valign="top">Degree to which technology use becomes automatic</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Technology trust</td><td align="left" valign="top">Degree to which technology is perceived reliable and secure</td><td align="left" valign="top">3</td><td align="left" valign="top">Casal&#x00F3; et al [<xref ref-type="bibr" rid="ref45">45</xref>],<break/>Yee&#x2010;Loong Chong et al [<xref ref-type="bibr" rid="ref46">46</xref>],<break/>Lebrument et al [<xref ref-type="bibr" rid="ref47">47</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Behavioral intention</td><td align="left" valign="top">Intention to use the technology</td><td align="left" valign="top">3</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr><tr><td align="left" valign="top">Use behavior</td><td align="left" valign="top">Actual use (frequency)</td><td align="left" valign="top">1</td><td align="left" valign="top">Venkatesh et al [<xref ref-type="bibr" rid="ref39">39</xref>]</td><td align="left" valign="top">Yes</td></tr></tbody></table></table-wrap><p>For the 7 constructs and behavioral intention, each item is measured using a 7-point Likert scale of agreement (ranging from 1 [strongly disagree] to 7 [strongly agree]), while for use behavior, it is measured using a 7-point Likert scale of frequency of use (from 1 [never] to 7 [many times per day]) [<xref ref-type="bibr" rid="ref39">39</xref>].</p><p>For the purposes of this study, the model was adapted in several ways (<xref ref-type="table" rid="table1">Table 1</xref>). First, the price value construct was excluded, as all apps assessed were free of charge. Second, a technology trust construct was added to account for perceived reliability and security, which are particularly relevant in the health care context, especially with the sharing of sensitive patient data. This construct and its associated items were added following the guidance of previous studies [<xref ref-type="bibr" rid="ref45">45</xref>-<xref ref-type="bibr" rid="ref47">47</xref>].</p><p>All items were translated into French based on previously validated translations [<xref ref-type="bibr" rid="ref48">48</xref>]. While UTAUT2 constructs were measured using 7-point Likert scales, the frequency of use (use behavior) was initially assessed using a 5-point scale for clarity and interpretability. For the statistical analysis, these values were converted to a 7-point scale using linear interpolation to align with the other constructs.</p><p>Based on the original UTAUT2 hypothesis development [<xref ref-type="bibr" rid="ref39">39</xref>], we built our model with the following hypotheses: (1) performance expectancy, effort expectancy, social influence, technology trust, facilitating conditions, hedonic motivation, and habit positively influence behavioral intention; (2) behavioral intention, facilitating conditions, and habit positively influence use behavior; (3) age and gender serve as control variables. The final model and its hypotheses are presented in <xref ref-type="fig" rid="figure1">Figure 1</xref>.</p><fig position="float" id="figure1"><label>Figure 1.</label><caption><p>Theoretical framework and hypotheses development of our study based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The measurement model, outlined in blue, illustrates the relationship between the items and their respective constructs, while the structural model, outlined in red, shows the relationships between the predictor constructs and the predicted constructs. BI: behavioral intention; EE: effort expectancy; FC: facilitating conditions; H: hypothesis; HM: hedonic motivation; HT: habit; PE: performance expectancy; SI: social influence; TT: technology trust; UB: use behavior.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="mededu_v12i1e83475_fig01.png"/></fig></sec><sec id="s2-4-6"><title>Analysis of the Model According to the Partial Least Squares Structural Equation Modeling Method</title><p>To test the model hypotheses, we used partial least squares structural equation modeling (PLS-SEM), a variance-based approach commonly applied in causal modeling [<xref ref-type="bibr" rid="ref49">49</xref>]. PLS-SEM comprises 2 components: a measurement model, which assesses the relationships between observed items and their corresponding constructs, and a structural model, which evaluates the relationships between constructs and represents the hypothesized paths (<xref ref-type="fig" rid="figure1">Figure 1</xref>). Analyses were conducted in RStudio (version 4.3.3; Posit Software, PBC) using the SEMinR package [<xref ref-type="bibr" rid="ref50">50</xref>,<xref ref-type="bibr" rid="ref51">51</xref>].</p><p>The analysis was performed in 3 steps. First, descriptive statistics were computed to summarize construct scores across apps, using means and SDs.</p><p>Second, the measurement model was assessed to evaluate the reliability and validity of the constructs. Prior to analysis, sample size adequacy was verified. The minimum required sample size, based on the number of predictor constructs, was 70 participants [<xref ref-type="bibr" rid="ref49">49</xref>,<xref ref-type="bibr" rid="ref52">52</xref>-<xref ref-type="bibr" rid="ref54">54</xref>]. This threshold was met only for WhatsApp (n=127), leading to the exclusion of Messenger (n=64), Facebook (n=35), LinkedIn (n=20), and Instagram (n=11) from the PLS-SEM analysis. The &#x201C;Other&#x201D; gender category (n=2) was excluded due to its very small representation, resulting in a final analytical sample of 125 responses. Measurement model evaluation for WhatsApp included the assessment of internal consistency reliability, convergent validity, and discriminant validity, all of which met the recommended thresholds (<xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>). Indicator reliability was satisfactory for all items except for 2 items within the facilitating conditions construct. However, they were retained because the construct as a whole demonstrated strong internal consistency and convergent validity, and the items captured distinct, theoretically relevant aspects of the construct, thus maintaining content validity [<xref ref-type="bibr" rid="ref55">55</xref>].</p><p>Third, the structural model was assessed to test the hypothesized relationships between constructs. Collinearity was evaluated using variance inflation factors, which were below the recommended thresholds. Path coefficients (<italic>&#x03B2;</italic>) were estimated using a regression-based approach, and their significance was assessed through bootstrapping with 5000 resamples [<xref ref-type="bibr" rid="ref49">49</xref>]. The explanatory power of the model was evaluated using the coefficient of determination (<italic>R</italic>&#x00B2;) (<xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>).</p></sec><sec id="s2-4-7"><title>Free-Text Comments</title><p>In addition to the structured questionnaire items, participants were invited to provide optional free-text comments describing positive and negative perceptions of each app. Comments were analyzed using a thematic approach. Responses were reviewed and grouped into recurring themes. The analysis was conducted by one author (ZZ) and reviewed by another author (RT). Any discrepancies were resolved through discussion until consensus was reached. The frequency of each theme was calculated, and illustrative quotes were selected (<xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). These responses were used to complement the quantitative results concerning factors influencing adoption and use (UTAUT2 analysis), providing additional insights into participants&#x2019; experiences.</p></sec></sec><sec id="s2-5"><title>Statistical Analysis</title><p>Only fully completed questionnaires were included in the analysis. Incomplete responses were excluded to ensure data integrity. All statistical analyses were conducted using RStudio (version 4.3.3; Posit Software, PBC). Descriptive statistics were used to summarize participant characteristics, frequency and context of app use, and UTAUT2 construct scores. Categorical variables were reported as counts and percentages, and continuous variables as means and SDs. To investigate factors influencing technology adoption and use, PLS-SEM was conducted for WhatsApp as described earlier. A 2-sided significance level of <italic>P</italic>&#x003C;.05 was considered statistically significant.</p></sec><sec id="s2-6"><title>Ethical Considerations</title><p>This study was approved by the CERAP-HP Center Ethics Committee (IRB: IORG0010044). Participants were informed about the study objectives, eligibility criteria, and procedures on the survey&#x2019;s introductory page. Informed consent was obtained electronically, as completion of the questionnaire implied consent to use anonymized responses for research purposes. No personally identifiable information was collected. Data were anonymous, stored on secure servers, and scheduled for deletion after publication. No financial or material compensation was provided.</p></sec></sec><sec id="s3" sec-type="results"><title>Results</title><sec id="s3-1"><title>Participants&#x2019; Characteristics</title><p>A total of 137 medical residents were included in the study (4 were excluded from the analysis due to inconsistent responses). Most of them were women (n=87, 63.5%) and were aged between 26 and 40 years (n=102, 74.5%). A total of 40 distinct specialties were represented, with pediatrics (n=18, 13.1%), general medicine (n=12, 8.8%), and radiology and medical imaging (n=10, 7.3%) being the most represented (<xref ref-type="supplementary-material" rid="app3">Multimedia Appendix 3</xref>).</p><p>In private life, the most used apps among the 137 medical residents were WhatsApp (n=133, 97.1%), Messenger (n=132, 96.4%), Facebook (n=120, 87.6%), and Instagram (n=105, 76.6%). LinkedIn (n=10, 7.3%) and TikTok (n=12, 8.8%) were less common.</p></sec><sec id="s3-2"><title>Use of Social Media Apps in Health Care</title><sec id="s3-2-1"><title>Frequency of Use in Health Care</title><p>In professional life, the most used app among the 137 medical residents was WhatsApp (n=127, 92.7%). Messenger, Facebook, LinkedIn, and Instagram were used but very less often (n=64, 46.7%; n=35, 25.5%; n=20, 14.6%; n=11, 8%, respectively). TikTok was never used to achieve professional tasks. WhatsApp was used at least &#x201C;many times per week&#x201D; by 75.9% (n=104) of residents, while Messenger was used by 32.1% (n=44), Facebook by 10.2% (n=14), Instagram by 4.4% (n=6), and LinkedIn by 3% (n=4; <xref ref-type="table" rid="table2">Table 2</xref>).</p><table-wrap id="t2" position="float"><label>Table 2.</label><caption><p>Frequency of professional use by app (N=137).</p></caption><table id="table2" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Frequency</td><td align="left" valign="bottom">Facebook, n (%)</td><td align="left" valign="bottom">Instagram, n (%)</td><td align="left" valign="bottom">LinkedIn, n (%)</td><td align="left" valign="bottom">Messenger, n (%)</td><td align="left" valign="bottom">TikTok, n (%)</td><td align="left" valign="bottom">WhatsApp, n (%)</td></tr></thead><tbody><tr><td align="left" valign="top">Many times per day</td><td align="left" valign="top">3 (2.2)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">15 (11)</td><td align="char" char="." valign="top">0 (0)</td><td align="left" valign="top">64 (46.7)</td></tr><tr><td align="left" valign="top">Many times per week</td><td align="left" valign="top">11 (8)</td><td align="left" valign="top">5 (3.7)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">29 (21.2)</td><td align="char" char="." valign="top">0 (0)</td><td align="left" valign="top">40 (29.2)</td></tr><tr><td align="left" valign="top">Less than once a week</td><td align="left" valign="top">12 (8.8)</td><td align="left" valign="top">4 (2.9)</td><td align="left" valign="top">8 (5.8)</td><td align="left" valign="top">13 (9.5)</td><td align="char" char="." valign="top">0 (0)</td><td align="left" valign="top">17 (12.4)</td></tr><tr><td align="left" valign="top">Less than once a month</td><td align="left" valign="top">9 (6.6)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">8 (5.8)</td><td align="left" valign="top">7 (5.1)</td><td align="char" char="." valign="top">0 (0)</td><td align="left" valign="top">6 (4.4)</td></tr><tr><td align="left" valign="top">Never</td><td align="left" valign="top">102 (74.4)</td><td align="left" valign="top">126 (92)</td><td align="left" valign="top">117 (85.4)</td><td align="left" valign="top">73 (53.2)</td><td align="char" char="." valign="top">0 (0)</td><td align="left" valign="top">10 (7.3)</td></tr></tbody></table></table-wrap></sec><sec id="s3-2-2"><title>Medical Context of Use</title><p>The app use depended on context. For patient care, WhatsApp was the most used app among the 137 medical residents, with 62.8% (n=86) of residents using it for written transmission to colleagues and specialist advice, and 55.5% (n=76) for case discussions. Messenger was also used for these purposes but less often. Facebook, Instagram, and LinkedIn were rarely used for clinical tasks.</p><p>For medical knowledge communication, Facebook was the top app, mostly for consuming medical training content (n=26, 19.0%). All apps showed minimal use for sharing daily professional life and creating content.</p><p>For networking, WhatsApp was the most popular app for exchanging professional opportunities (n=62, 45.3%), followed by Messenger (n=27, 19.7%) and Facebook (n=25, 18.2%). WhatsApp was also the most used app for building professional relationships (n=33, 24.1%), while LinkedIn was used by 12.4% (n=17) of residents for this purpose (<xref ref-type="table" rid="table3">Table 3</xref>).</p><table-wrap id="t3" position="float"><label>Table 3.</label><caption><p>Professional tasks by app (N=137).</p></caption><table id="table3" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Tasks</td><td align="left" valign="bottom">Facebook, n (%)</td><td align="left" valign="bottom">Instagram, n (%)</td><td align="left" valign="bottom">LinkedIn, n (%)</td><td align="left" valign="bottom">Messenger, n (%)</td><td align="left" valign="bottom">WhatsApp, n (%)</td></tr></thead><tbody><tr><td align="left" valign="top" colspan="6">Patient care</td></tr><tr><td align="left" valign="top">&#x2003;I make a written transmission to a colleague or another health care professional</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">32 (23.4)</td><td align="left" valign="top">86 (62.8)</td></tr><tr><td align="left" valign="top">&#x2003;I discuss a patient case with my colleagues or another health care professional</td><td align="left" valign="top">6 (4.4)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">31 (22.6)</td><td align="left" valign="top">76 (55.5)</td></tr><tr><td align="left" valign="top">&#x2003;I request a specialized advice from a colleague or another health care professional</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">10 (7.3)</td><td align="left" valign="top">86 (62.8)</td></tr><tr><td align="left" valign="top">&#x2003;I discuss a complex case with a multidisciplinary group of experts</td><td align="left" valign="top">3 (2.2)</td><td align="left" valign="top">1 (1.4)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">13 (9.5)</td></tr><tr><td align="left" valign="top">&#x2003;I discuss with a patient about his or her care management</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">5 (3.7)</td><td align="left" valign="top">12 (8.8)</td></tr><tr><td align="left" valign="top" colspan="6">Medical knowledge communication</td></tr><tr><td align="left" valign="top">&#x2003;I consume medical training content</td><td align="left" valign="top">26 (19)</td><td align="left" valign="top">11 (8)</td><td align="left" valign="top">8 (5.8)</td><td align="left" valign="top">3 (2.2)</td><td align="left" valign="top">7 (5.1)</td></tr><tr><td align="left" valign="top">&#x2003;I share my daily life as a health care professional with the general public</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">4 (2.9)</td><td align="left" valign="top">2 (1.5)</td></tr><tr><td align="left" valign="top">&#x2003;I create training content for health care students</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">1 (0.7)</td></tr><tr><td align="left" valign="top">&#x2003;I create educational content intended for the general public</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">1 (0.7)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">0 (0)</td><td align="left" valign="top">0 (0)</td></tr><tr><td align="left" valign="top" colspan="6">Networking</td></tr><tr><td align="left" valign="top">&#x2003;I exchange professional opportunities with my peers (job, on-call duties, replacement, courses, etc)</td><td align="left" valign="top">25 (18.2)</td><td align="left" valign="top">4 (2.9)</td><td align="left" valign="top">9 (6.6)</td><td align="left" valign="top">27 (19.7)</td><td align="left" valign="top">62 (45.3)</td></tr><tr><td align="left" valign="top">&#x2003;I build professional relationships</td><td align="left" valign="top">10 (7.3)</td><td align="left" valign="top">3 (2.2)</td><td align="left" valign="top">17 (12.4)</td><td align="left" valign="top">16 (11.7)</td><td align="left" valign="top">33 (24.1)</td></tr><tr><td align="left" valign="top">&#x2003;I share advancements in my specialty with the scientific community or with my colleagues</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">4 (2.9)</td><td align="left" valign="top">2 (1.5)</td><td align="left" valign="top">8 (5.8)</td></tr></tbody></table></table-wrap></sec></sec><sec id="s3-3"><title>Factors of Acceptance and Use in Health Care</title><sec id="s3-3-1"><title>Facebook</title><p>Facebook scored high in effort expectancy (mean 6.72, SD 0.48) and facilitating conditions (mean 6.11, SD 0.73), indicating ease of use and adequate support. Residents noted its ease of use for sharing information on shift exchanges, medical seminars, and knowledge within medical communities (Ex1, 2, and 4 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). However, performance expectancy (mean 3.89, SD 1.53) was moderate, suggesting limited professional effectiveness. Some considered it outdated or inappropriate for professional use, citing misinformation and poor moderation (Ex6-8 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). The technology trust score was the lowest among apps (mean 2.23, SD 1.16), reflecting concerns about its security and privacy (Ex10 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Some also criticized it as time-wasting and addictive (Ex9 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>) (<xref ref-type="table" rid="table4">Table 4</xref>).</p><table-wrap id="t4" position="float"><label>Table 4.</label><caption><p>Scores of the model&#x2019;s constructs by app. Scores are reported in mean (SD).</p></caption><table id="table4" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom"/><td align="left" valign="bottom">Facebook (n=35), mean (SD)</td><td align="left" valign="bottom">Instagram (n=11), mean (SD)</td><td align="left" valign="bottom">LinkedIn (n=20), mean (SD)</td><td align="left" valign="bottom">Messenger (n=64), mean (SD)</td><td align="left" valign="bottom">WhatsApp (n=127), mean (SD)</td></tr></thead><tbody><tr><td align="left" valign="top">Performance expectancy</td><td align="left" valign="top">3.89 (1.53)</td><td align="left" valign="top">4.09 (1.45)</td><td align="left" valign="top">4.20 (1.30)</td><td align="left" valign="top">4.67 (1.26)</td><td align="left" valign="top">5.43 (1.12)</td></tr><tr><td align="left" valign="top">Effort expectancy</td><td align="left" valign="top">6.72 (0.48)</td><td align="left" valign="top">6.93 (0.16)</td><td align="left" valign="top">5.47 (1.45)</td><td align="left" valign="top">6.84 (0.36)</td><td align="left" valign="top">6.82 (0.55)</td></tr><tr><td align="left" valign="top">Social influence</td><td align="left" valign="top">3.61 (1.11)</td><td align="left" valign="top">3.70 (1.59)</td><td align="left" valign="top">5.05 (1.06)</td><td align="left" valign="top">3.71 (1.19)</td><td align="left" valign="top">4.19 (1.18)</td></tr><tr><td align="left" valign="top">Technology trust</td><td align="left" valign="top">2.23 (1.16)</td><td align="left" valign="top">2.27 (0.88)</td><td align="left" valign="top">3.72 (1.39)</td><td align="left" valign="top">2.41 (1.36)</td><td align="left" valign="top">3.39 (1.43)</td></tr><tr><td align="left" valign="top">Facilitating conditions</td><td align="left" valign="top">6.11 (0.73)</td><td align="left" valign="top">6.45 (0.56)</td><td align="left" valign="top">5.94 (0.76)</td><td align="left" valign="top">6.14 (1.00)</td><td align="left" valign="top">6.07 (0.85)</td></tr><tr><td align="left" valign="top">Hedonic motivation</td><td align="left" valign="top">4.57 (1.46)</td><td align="left" valign="top">6.61 (0.51)</td><td align="left" valign="top">4.48 (1.22)</td><td align="left" valign="top">4.28 (1.38)</td><td align="left" valign="top">4.01 (1.38)</td></tr><tr><td align="left" valign="top">Habit</td><td align="left" valign="top">3.10 (1.29)</td><td align="left" valign="top">2.42 (1.07)</td><td align="left" valign="top">2.80 (1.43)</td><td align="left" valign="top">3.54 (1.43)</td><td align="left" valign="top">3.97 (1.50)</td></tr><tr><td align="left" valign="top">Behavioral intention</td><td align="left" valign="top">4.05 (1.46)</td><td align="left" valign="top">4.39 (1.62)</td><td align="left" valign="top">5.18 (1.17)</td><td align="left" valign="top">4.36 (1.35)</td><td align="left" valign="top">5.28 (1.15)</td></tr><tr><td align="left" valign="top">Use behavior</td><td align="left" valign="top">4.34 (1.41)</td><td align="left" valign="top">4.82 (1.23)</td><td align="left" valign="top">3.85 (1.45)</td><td align="left" valign="top">5.22 (1.39)</td><td align="left" valign="top">5.91 (1.30)</td></tr></tbody></table></table-wrap></sec><sec id="s3-3-2"><title>Instagram</title><p>Instagram had the highest effort expectancy (mean 6.93, SD 0.16) and hedonic motivation (mean 6.61, SD 0.51) among apps, showing that residents found it very easy to use and enjoyed using it for educational content (Ex11 and 12 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). However, performance expectancy (mean 4.09, SD 1.45) was moderate, indicating limited professional effectiveness. It was also viewed as addictive, inappropriate for professional use, and lacking relevance (Ex15-17 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). The technology trust score (mean 2.27, SD 0.88) was low as well (<xref ref-type="table" rid="table4">Table 4</xref>).</p></sec><sec id="s3-3-3"><title>LinkedIn</title><p>LinkedIn scored the highest in facilitating conditions (mean 5.94, SD 0.76), indicating adequate support and resources for professional use. Residents noted strengths in networking and career development (Ex19 and 20 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). However, performance expectancy was moderate (mean 4.20, SD 1.30), and use behavior was the lowest among apps (mean 3.85, SD 1.45), indicating limited practical use despite its professional focus. Residents confirmed that LinkedIn had little relevance to their daily practice (Ex21 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Technology trust (mean 3.72, SD 1.39) was moderate but the highest among apps. Social influence (mean 5.05, SD 1.06) was also the highest, suggesting adoption was largely peer-driven (<xref ref-type="table" rid="table4">Table 4</xref>).</p></sec><sec id="s3-3-4"><title>Messenger</title><p>Messenger scored the highest for effort expectancy (mean 6.84, SD 0.36) and facilitating conditions (mean 6.14, SD 1.00), reflecting speed, ease of use, and convenient contact access (Ex24-26 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). However, the technology trust score (mean 2.41, SD 1.36) was low because of security concerns (Ex30 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Some residents considered it unsuitable for professional use, citing its link to Facebook, its outdated nature, and missing features found in WhatsApp such as message pinning (Ex28-31 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref> and <xref ref-type="table" rid="table4">Table 4</xref>).</p></sec><sec id="s3-3-5"><title>WhatsApp</title><sec id="s3-3-5-1"><title>Average Scores of the Factors Influencing App Use</title><p>WhatsApp scored the highest in performance expectancy (mean 5.43, SD 1.12), behavioral intention (mean 5.28, SD 1.15), and use behavior (mean 5.91, SD 1.30) among apps, making it the most frequently used and effective app for professional use. Effort expectancy (mean 6.82, SD 0.55) and facilitating conditions (mean 6.07, SD 0.85) were also high, reflecting ease of use and practical features like phone number integration (Ex32 and 34 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Habit was moderate but the highest among apps (mean 3.97, SD 1.50), indicating WhatsApp&#x2019;s growing routine use, supported by its popularity and suitability for professional interactions (Ex33 and 37 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref>). Technology trust was also moderate (mean 3.39, SD 1.43) but higher than Facebook, Instagram, and Messenger, with some residents viewing it as relatively more secure despite ongoing privacy concerns (Ex38 and 39 in <xref ref-type="supplementary-material" rid="app2">Multimedia Appendix 2</xref> and <xref ref-type="table" rid="table4">Table 4</xref>).</p></sec><sec id="s3-3-5-2"><title>Factors Influencing Acceptance and Use According to PLS-SEM Analysis</title><p>Overall, the model explained 53% of the variance in behavioral intention and 23% of the variance in use behavior (<xref ref-type="fig" rid="figure2">Figure 2</xref>). The hypothesized relationships were tested (<xref ref-type="table" rid="table5">Table 5</xref>, <xref ref-type="fig" rid="figure2">Figure 2</xref>). Habit was the only construct with significant effects, showing a positive association with both behavioral intention (H7: <italic>&#x03B2;</italic>=.53, <italic>P</italic>&#x003C;.001) and use behavior (H10: <italic>&#x03B2;</italic>=.45, <italic>P</italic>&#x003C;.001).</p><p>All other hypothesized paths were not significant. The control variables (age, gender) also showed weak, nonsignificant effects on behavioral intention and use behavior.</p><fig position="float" id="figure2"><label>Figure 2.</label><caption><p>Summary of the structural model results for WhatsApp. The continuous arrows represent the path coefficients (<italic>&#x03B2;</italic>) from the predictor constructs to the predicted constructs; the dotted arrows represent the <italic>&#x03B2;</italic> from the control variables to the predicted constructs; the arrows in bold represent the significant path coefficients; *<italic>P</italic>&#x003C;.001; the coefficient of determination (<italic>R</italic>&#x00B2;) represents the explanatory power of the model.</p></caption><graphic alt-version="no" mimetype="image" position="float" xlink:type="simple" xlink:href="mededu_v12i1e83475_fig02.png"/></fig><table-wrap id="t5" position="float"><label>Table 5.</label><caption><p>Structural model results for WhatsApp, including path coefficients (<italic>&#x03B2;</italic>), SE, <italic>t</italic> values, and 95% CI for the tested hypotheses.</p></caption><table id="table5" frame="hsides" rules="groups"><thead><tr><td align="left" valign="bottom">Path</td><td align="left" valign="bottom"><italic>&#x03B2;</italic></td><td align="left" valign="bottom"><italic>B</italic><sup><xref ref-type="table-fn" rid="table5fn1">a</xref></sup></td><td align="left" valign="bottom">SE</td><td align="left" valign="bottom"><italic>t</italic> value</td><td align="left" valign="bottom">95% CI</td><td align="left" valign="bottom">Hypotheses (H)</td></tr></thead><tbody><tr><td align="left" valign="top" colspan="7">BI<sup><xref ref-type="table-fn" rid="table5fn2">b</xref></sup></td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>PE<sup><xref ref-type="table-fn" rid="table5fn3">c</xref></sup>&#x2192;BI</td><td align="left" valign="top">.08</td><td align="left" valign="top">.09</td><td align="left" valign="top">0.08</td><td align="left" valign="top">1.03</td><td align="left" valign="top">&#x2013;0.07 to 0.25</td><td align="left" valign="top">H1 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>EE<sup><xref ref-type="table-fn" rid="table5fn4">d</xref></sup>&#x2192;BI</td><td align="left" valign="top">.09</td><td align="left" valign="top">.09</td><td align="left" valign="top">0.10</td><td align="left" valign="top">0.88</td><td align="left" valign="top">&#x2013;0.12 to 0.27</td><td align="left" valign="top">H2 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>SI<sup><xref ref-type="table-fn" rid="table5fn5">e</xref></sup>&#x2192;BI</td><td align="left" valign="top">.06</td><td align="left" valign="top">.06</td><td align="left" valign="top">0.07</td><td align="left" valign="top">0.83</td><td align="left" valign="top">&#x2013;0.08 to 0.20</td><td align="left" valign="top">H3 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>TT<sup><xref ref-type="table-fn" rid="table5fn6">f</xref></sup>&#x2192;BI</td><td align="left" valign="top">.08</td><td align="left" valign="top">.08</td><td align="left" valign="top">0.08</td><td align="left" valign="top">0.96</td><td align="left" valign="top">&#x2013;0.08 to 0.24</td><td align="left" valign="top">H4 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>FC<sup><xref ref-type="table-fn" rid="table5fn7">g</xref></sup>&#x2192;BI</td><td align="left" valign="top">.05</td><td align="left" valign="top">.07</td><td align="left" valign="top">0.08</td><td align="left" valign="top">0.65</td><td align="left" valign="top">&#x2013;0.08 to 0.23</td><td align="left" valign="top">H5 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>HM<sup><xref ref-type="table-fn" rid="table5fn8">h</xref></sup>&#x2192;BI</td><td align="left" valign="top">.11</td><td align="left" valign="top">.12</td><td align="left" valign="top">0.08</td><td align="left" valign="top">1.24</td><td align="left" valign="top">&#x2013;0.05 to 0.28</td><td align="left" valign="top">H6 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>HT<sup><xref ref-type="table-fn" rid="table5fn9">i</xref></sup>&#x2192;BI</td><td align="left" valign="top">.53<sup><xref ref-type="table-fn" rid="table5fn10">j</xref></sup></td><td align="left" valign="top">.52</td><td align="left" valign="top">0.09</td><td align="left" valign="top">5.96</td><td align="left" valign="top">0.34 to 0.69</td><td align="left" valign="top">H7 (supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Age&#x2192;BI</td><td align="left" valign="top">&#x2013;.06</td><td align="left" valign="top">&#x2013;.06</td><td align="left" valign="top">0.06</td><td align="left" valign="top">&#x2013;1.00</td><td align="left" valign="top">&#x2013;0.18 to 0.07</td><td align="left" valign="top">&#x2014;<sup><xref ref-type="table-fn" rid="table5fn11">k</xref></sup></td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Gender&#x2192;BI</td><td align="left" valign="top">&#x2013;.08</td><td align="left" valign="top">&#x2013;.08</td><td align="left" valign="top">0.06</td><td align="left" valign="top">&#x2013;1.28</td><td align="left" valign="top">&#x2013;0.21 to 0.04</td><td align="left" valign="top">&#x2014;</td></tr><tr><td align="left" valign="top" colspan="7">UB<sup><xref ref-type="table-fn" rid="table5fn12">l</xref></sup>&#x2003;</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>BI&#x2192;UB</td><td align="left" valign="top">.03</td><td align="left" valign="top">.02</td><td align="left" valign="top">0.11</td><td align="left" valign="top">0.31</td><td align="left" valign="top">&#x2013;0.19 to 0.23</td><td align="left" valign="top">H8 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>FC&#x2192;UB</td><td align="left" valign="top">.02</td><td align="left" valign="top">.04</td><td align="left" valign="top">0.13</td><td align="left" valign="top">0.12</td><td align="left" valign="top">&#x2013;0.22 to 0.29</td><td align="left" valign="top">H9 (not supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>HT&#x2192;UB</td><td align="left" valign="top">.45<sup><xref ref-type="table-fn" rid="table5fn10">j</xref></sup></td><td align="left" valign="top">.45</td><td align="left" valign="top">0.10</td><td align="left" valign="top">4.34</td><td align="left" valign="top">0.24 to 0.65</td><td align="left" valign="top">H10 (supported)</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Age&#x2192;UB</td><td align="left" valign="top">.01</td><td align="left" valign="top">.01</td><td align="left" valign="top">0.09</td><td align="left" valign="top">0.13</td><td align="left" valign="top">&#x2013;0.15 to 0.18</td><td align="left" valign="top">&#x2014;</td></tr><tr><td align="left" valign="top"><named-content content-type="indent">&#x00A0;&#x00A0;&#x00A0;&#x00A0;</named-content>Gender&#x2192;UB</td><td align="left" valign="top">&#x2013;.08</td><td align="left" valign="top">&#x2013;.07</td><td align="left" valign="top">0.08</td><td align="left" valign="top">&#x2013;0.95</td><td align="left" valign="top">&#x2013;0.22 to 0.10</td><td align="left" valign="top">&#x2014;</td></tr></tbody></table><table-wrap-foot><fn id="table5fn1"><p><sup>a</sup><italic>B</italic>: bootstrap path coefficient. </p></fn><fn id="table5fn2"><p><sup>b</sup>BI: behavioral intention.</p></fn><fn id="table5fn3"><p><sup>c</sup>PE: performance expectancy.</p></fn><fn id="table5fn4"><p><sup>d</sup>EE: effort expectancy.</p></fn><fn id="table5fn5"><p><sup>e</sup>SI: social influence.</p></fn><fn id="table5fn6"><p><sup>f</sup>TT: technology trust.</p></fn><fn id="table5fn7"><p><sup>g</sup>FC: facilitating conditions.</p></fn><fn id="table5fn8"><p><sup>h</sup>HM: hedonic motivation.</p></fn><fn id="table5fn9"><p><sup>i</sup>HT: habit.</p></fn><fn id="table5fn10"><p><sup>j</sup><italic>P</italic>&#x003C;.001.</p></fn><fn id="table5fn11"><p><sup>k</sup>Not applicable.</p></fn><fn id="table5fn12"><p><sup>l</sup>UB: use behavior.</p></fn></table-wrap-foot></table-wrap></sec></sec></sec></sec><sec id="s4" sec-type="discussion"><title>Discussion</title><sec id="s4-1"><title>Principal Findings</title><p>This study examined the professional use of 6 popular social media platforms among 137 French medical residents across 40 different specialties. Regarding the context of professional use, over 90% of residents reported using WhatsApp professionally, particularly for written transmissions, patient case discussions, and seeking specialist advice. Messenger was used less often for similar purposes. Surprisingly, WhatsApp was also the top app for professional networking, far ahead of LinkedIn, which was used by less than 15% of residents. About 25% of residents used Facebook, mostly as the top app for medical training. Conversely, Instagram was rarely used, mainly for education, and TikTok was never used. Regarding the factors influencing adoption and use, WhatsApp ranked highest overall, leading in performance expectancy, behavioral intention, and use behavior, and showing high effort expectancy, indicating it was perceived as highly effective, easy to use, and likely to remain in use. LinkedIn ranked highest for social influence (peer effect), while Instagram ranked highest for hedonic motivation (entertainment value). Technology trust was low across apps, particularly for Facebook, Instagram, and Messenger, reflecting common security and privacy concerns. Notably, Habit was the only construct significantly associated with both the intention to use and actual use of WhatsApp, suggesting that routine use may outweigh security concerns. These results suggest that informal, noncompliant apps are widely used in clinical practice, mainly because of habit and convenience, even when trust in their security is low.</p></sec><sec id="s4-2"><title>Comparison With Prior Work</title><p>Regarding the context of professional use, our study found WhatsApp to be the most used app among residents (127/137, 93%), a result that is similar to O&#x2019;Sullivan et al [<xref ref-type="bibr" rid="ref7">7</xref>], who reported 100% use among Irish hospital residents. In contrast, Van Ravenswaay et al [<xref ref-type="bibr" rid="ref8">8</xref>] found Facebook to be the most popular platform among health care professionals (71%) in a survey conducted by the Clinical Education Alliance, based in the United States. In our study, WhatsApp was mainly used for patient care, including transmissions and specialist advice (86/137, 63%) and case discussions (55%), which is similar to the findings in Australia, where 85% of physicians use it for clinical communication (eg, patient management details, imaging reports) [<xref ref-type="bibr" rid="ref6">6</xref>]. Similarly, Cherrez-Ojeda et al [<xref ref-type="bibr" rid="ref4">4</xref>] reported a 93% WhatsApp usage rate for physician communication in Ecuador, without detailing clinical contexts. Surprisingly, our study found WhatsApp to be the top app for professional networking, surpassing LinkedIn, which is specifically designed for this purpose. Youssef et al [<xref ref-type="bibr" rid="ref11">11</xref>] also found that German surgeons preferred messenger apps (34%) over employment platforms (27%). Lastly, our results found limited and mostly passive use of social media for education, consistent with previous studies [<xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref11">11</xref>,<xref ref-type="bibr" rid="ref12">12</xref>].</p><p>Regarding the factors influencing adoption and use, our study showed that Habit significantly influenced both the intention to use and the actual use of WhatsApp among medical residents, a finding consistent with another study using elements of the technology acceptance model and UTAUT2 [<xref ref-type="bibr" rid="ref35">35</xref>]. Additionally, other factors such as perceived usefulness [<xref ref-type="bibr" rid="ref24">24</xref>,<xref ref-type="bibr" rid="ref35">35</xref>] and ease of use [<xref ref-type="bibr" rid="ref24">24</xref>] have also been identified as predictors of social media use. However, comparing with other studies is challenging because they differ in terms of studied population (eg, physicians, pharmacists, allied employees [<xref ref-type="bibr" rid="ref35">35</xref>]); theoretical models used (eg, technology acceptance model [<xref ref-type="bibr" rid="ref24">24</xref>,<xref ref-type="bibr" rid="ref25">25</xref>,<xref ref-type="bibr" rid="ref35">35</xref>], UTAUT2 [<xref ref-type="bibr" rid="ref35">35</xref>]); or factors examined (eg, habit is not systematically studied [<xref ref-type="bibr" rid="ref24">24</xref>,<xref ref-type="bibr" rid="ref25">25</xref>]). The absence of significant effects of other factors in our study may reflect our population: medical residents, a digitally fluent group likely in a late-stage adoption phase, where WhatsApp use is already routine. In such contexts, technology use often becomes automatic, and habit can outweigh the influence of early adoption factors such as perceived usefulness or ease of use, a pattern well documented in postadoption research [<xref ref-type="bibr" rid="ref56">56</xref>-<xref ref-type="bibr" rid="ref58">58</xref>].</p></sec><sec id="s4-3"><title>Implications for the Future</title><p>Our study showed the widespread use of social media in health care, despite security and regulation concerns. Restrictive policies cannot be a solution to prevent security breaches and may even be counterproductive. Instead, various strategies should be adopted at different levels:</p><p>IT level: Secure health care apps should be developed with the desirable features of current social media apps while incorporating strong security safeguards. As shown in a prior study [<xref ref-type="bibr" rid="ref59">59</xref>], required features should include usability (eg, easy and fast), interoperability with electronic health records, safety (eg, secure authentication and secure certified hosting), and functionalities, such as &#x201C;instant messaging,&#x201D; &#x201C;group discussion,&#x201D; &#x201C;notification,&#x201D; or &#x201C;data storage in electronic health records&#x201D; [<xref ref-type="bibr" rid="ref59">59</xref>]. Additionally, the Medical Informatics community has recognized the importance of human factors in the safe and effective adoption of new technologies [<xref ref-type="bibr" rid="ref60">60</xref>]. As suggested by Kushniruk and Kaufman [<xref ref-type="bibr" rid="ref61">61</xref>] in 2024, this requires a multilevel perspective, from the individual user to the complex social and organizational context. In parallel, the American Medical Association stressed the need to integrate physicians&#x2019; perspectives throughout development so that digital solutions enhance care, fit into workflows, and address concerns such as liability, transparency, and trust [<xref ref-type="bibr" rid="ref62">62</xref>].</p><p>Educational level: Training in digital health, including security, patient confidentiality [<xref ref-type="bibr" rid="ref63">63</xref>-<xref ref-type="bibr" rid="ref65">65</xref>], and regulations [<xref ref-type="bibr" rid="ref66">66</xref>,<xref ref-type="bibr" rid="ref67">67</xref>], should be implemented in medical curricula [<xref ref-type="bibr" rid="ref59">59</xref>]. Digital professionalism (ie, responsible use of social media and professional integrity) should be taught as well [<xref ref-type="bibr" rid="ref68">68</xref>]. Evidence shows that targeted digital professionalism programs improve knowledge of appropriate online behavior, professional conduct, and confidence in privacy and security practices [<xref ref-type="bibr" rid="ref69">69</xref>]. Innovative approaches, such as interactive workshops reviewing social media content, can lead to meaningful behavior changes [<xref ref-type="bibr" rid="ref70">70</xref>]. Likewise, promoting critical thinking in digital health [<xref ref-type="bibr" rid="ref71">71</xref>] and interprofessional team-based training [<xref ref-type="bibr" rid="ref72">72</xref>] can further strengthen competencies, enhance safety culture, and prepare physicians to practice effectively in a digital health care environment.</p><p>Institutional level: Hospitals and health institutions should establish clear, enforceable policies for digital communication, including acceptable tools [<xref ref-type="bibr" rid="ref59">59</xref>]. They should also provide professional smartphones to separate personal and work use, thereby reducing security risks [<xref ref-type="bibr" rid="ref3">3</xref>]. Additionally, they should implement and promote the adoption of secure apps, focusing on a bottom-up approach (eg, direct engagement with frontline clinicians and designation of an experienced colleague to lead adoption), which has been proven to increase adoption [<xref ref-type="bibr" rid="ref73">73</xref>,<xref ref-type="bibr" rid="ref74">74</xref>]. Finally, institutions should allocate resources to secure communication as part of broader efforts to improve patient safety, efficiency, and regulatory compliance. Financial investment is essential to enable practice change and technology adoption. For example, Sevick et&#x202F;al [<xref ref-type="bibr" rid="ref75">75</xref>] demonstrated that implementing a digital tool not only improved provider and patient satisfaction but also reduced per-patient costs, illustrating that such investments can be both impactful and cost-effective.</p></sec><sec id="s4-4"><title>Limitations</title><p>This work has several limitations. First, it focused on French medical residents, which may limit the generalizability to other professional groups, health care systems, or cultural contexts. However, residents are a relevant group as they are heavily involved in clinical work and represent the next generation of physicians likely to use social media in clinical practice. The sample size (N=137) is relatively small, but it spanned 40 specialties, enabling analysis across various disciplines. Due to the sample size, structural modeling (PLS-SEM) could not be performed for all apps [<xref ref-type="bibr" rid="ref55">55</xref>]. However, we still conducted descriptive factor analysis for each app and used free-text responses to ensure a broad interpretative scope despite the analytical constraints.</p><p>Second, reliance on self-reported data may introduce &#x201C;selection bias,&#x201D; possibly overrepresenting residents more engaged with social media [<xref ref-type="bibr" rid="ref76">76</xref>], which may have been further influenced by the use of social media as one of the recruitment channels, potentially favoring more active users. Responses may also be affected by &#x201C;social desirability bias,&#x201D; as residents may have described behaviors that do not fully reflect their actual practices due to a desire to appear more responsible or avoid judgment [<xref ref-type="bibr" rid="ref77">77</xref>]. To limit this bias, the survey was anonymous, which encouraged participants to answer honestly. Likewise, &#x201C;recall bias&#x201D; might occur [<xref ref-type="bibr" rid="ref78">78</xref>], as participants may not remember exactly how they used each app. We mitigated this by designing a structured, detailed questionnaire, breaking down distinct dimensions of use (frequency, professional tasks, and adoption factors), encouraging residents to reflect more precisely on their practices.</p><p>Finally, we limited the analysis to 6 adoption factors (performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit), as defined by the validated and widely used UTAUT2 model for technology acceptance and use behavior. We also added a technology trust factor to address health care&#x2013;specific concerns regarding data privacy and security. However, other factors, such as institutional factors (eg, hospital management policies [<xref ref-type="bibr" rid="ref25">25</xref>]), could also be considered in further studies.</p></sec><sec id="s4-5"><title>Conclusion</title><p>This study found that medical residents overwhelmingly used WhatsApp for professional communication, particularly in patient care and networking, with Messenger serving similar functions at a lower frequency. Facebook, Instagram, and LinkedIn were used far less often and mainly for limited purposes, such as education and career development. The analysis of factors influencing adoption and use showed that <italic>habit</italic> was the only significant factor influencing WhatsApp&#x2019;s use, highlighting the dominance of routine over security concerns. Secure alternatives with similar features to popular social media apps, along with proactive institutional policies and digital professionalism training, are needed to help physicians better consider security when using social media apps.</p></sec></sec></body><back><ack><p>While preparing this manuscript, the authors used ChatGPT-4o (OpenAI) for language correction, editing, and formatting to improve the clarity of the language. The authors reviewed and revised the content as needed and take full responsibility for the accuracy and integrity of the paper.</p></ack><notes><sec><title>Funding</title><p>The authors declared that no financial support was received for this work.</p></sec><sec><title>Data Availability</title><p>The datasets generated and analyzed during this study are not publicly available due to restrictions imposed by the ethics committee to protect participants' confidentiality.</p></sec></notes><fn-group><fn fn-type="con"><p>Conceptualization: ZZ, AM, NK, RT</p><p>Data curation: ZZ</p><p>Formal analysis: ZZ, TM</p><p>Investigation: ZZ</p><p>Methodology: ZZ, AM, RT</p><p>Supervision: RT</p><p>Visualization: ZZ</p><p>Writing &#x2013; original draft: ZZ</p><p>Writing &#x2013; review &#x0026; editing: ZZ, RT, NK, AB</p></fn><fn fn-type="conflict"><p>None declared.</p></fn></fn-group><glossary><title>Abbreviations</title><def-list><def-item><term id="abb1">CHERRIES</term><def><p>Checklist for 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