The Development of the Smartphone Proficiency Questionnaire for Chinese Older Adults (SPQ-COA)

Usage of Smartphones in Older Adults

With the development of Internet technology, our reliance on the Internet have pervaded daily communication, education, entertainment, etc. As of December 2022, China’s Internet penetration rate had reached 75.6%, and the size of Internet users reached 1.067 billion (CINIC , 2023). Notably, the proportion of Internet users among older adults burgeoned from 16.7% to 54.5% between 2017 and 2022 (CINIC, 2018; CINIC, 2023; MOCA, PRC, 2023). When it comes to the medium of Internet access, China Internet Network Information Center (CINIC, 2022) released that 99.5% of Chinese older adult Internet users accessed the Internet through smartphones, basically the same as the situation of overall proportion of Internet users (99.8%), while the number of people using computers to access the Internet is declining year by year. As a result of this, the smartphone usage situation of older adults in China can be mirrored by their Internet usage situation. This situation is predictable because mobile devices such as smartphones are more prevalent compared with computers and can provide older individuals with an accessible gateway to the digital realm (Marler, 2018). ¹

For older adults, using a smart mobile device to connect to the Internet moderately can improve their quality of life and bring psychological benefits in several ways. Engagement in online discussions provides a platform for older adults to reaffirm their social identity (K. Lee & Lee, 2018); simultaneously, mobile applications emerge as instrumental tools, catering to the dual purpose of building offline social relationships and enriching leisure activities (Kim et al., 2020). These activities contribute to less feeling of loneliness (Khalaila & Vitman-Schorr, 2017; Liu et al., 2024; Yu et al., 2021), lower level of depressive symptoms (Ji et al., 2023; Li et al., 2023; Yang et al., 2024), and higher satisfaction with life (Kim et al., 2021; Lam et al., 2020; Yang et al., 2024). Beyond these immediate benefits, proficient smartphone use is perceived by older adults as a confidence booster, a source of enjoyment, and a catalyst for a broader spectrum of online activities (Cerda Díez et al., 2023; Kurniawan et al., 2006). Moreover, it is also found that using the Internet appropriately can serve as a protective factor against cognitive decline in older adulthood (Yu & Fiebig, 2020).

However, not all older adults can enjoy those benefits. Despite an overall Internet penetration rate of 75.6%, only 54.5% of individuals aged over 60 in China are Internet users (CINIC, 2023), and older Internet users engage in fewer online activities, primarily focusing on basic functions such as daily communication. Less than 40% of older adult users can complete more advanced operations on their own, such as calling a taxi, booking a ticket, or making a medical appointment (CINIC, 2022). Compared with young adults, older adults lag behind in both scope and intensity of Internet usage (Loges & Jung, 2001; Song et al., 2021). The disparity in Internet usage between different age groups is referred to as the “digital divide.” The origin of the term goes back to an unknown American source in the middle of the 1990s and its concept has changed with the development of related research (Scheerder et al., 2017; Van Dijk, 2006). This discourse has shifted from binary Internet access (the first-level digital divide) to skills and use of the Internet (the second-level digital divide), to a focus on the beneficial outcomes of Internet use (the third-level digital divide) (Scheerder et al., 2017). Over the past 30 years, the phenomenon of the digital divide has become increasingly prominent in China, with a huge gap between older and young age groups in terms of the level of IT acceptance, usage frequency, and knowledge acquisition (Huang, 2020).

The Barriers of Low Usage and the Necessity to Measure Proficiency Level

In a survey of non-Internet users, the primary reason for not accessing the Internet, accounting for 58.2% of the total, is the “lack of knowledge and skills,” with only 13.4% citing “lack of interest or need” (CINIC, 2023). This discrepancy suggests that the reluctance among older adults to embrace smartphones may be more attributable to insufficient proficiency than genuine disinterest. This aligns with the constructs of perceived ease of use and perceived usefulness in the Technology Acceptance Model (Davis, 1989). Lack of knowledge and skills can make older adults feel that using a smartphone is too difficult, thus contributing to low perceived ease of use. Lack of interest or need may make older people feel that a smartphone cannot help them with tasks or for recreational purposes, thus diminishing perceived usefulness. Although both perceived ease of use and perceived usefulness act as barriers to smartphone adoption among older adults, the latter is clearly the primary hindrance according to the data.

To address the proficiency barrier, strategic projects such as the “Implementation Plan for Effectively Addressing Difficulties Faced by Older Adults in Using Smart Technologies” and the “Special Action Plan for Internet Application Age-appropriateness and Accessibility Improvement” have been introduced by the General Office of the State Council and the MIIT (Ministry of Industry and Information Technology) of China. These initiatives aim to simplify usage processes or enhance user skills through targeted training, both emphasizing the crucial role of evaluating the proficiency of older adults.

In practice, the assessment of proficiency level can identify the specific operational difficulties faced by older adults. There is significant variation in proficiency among older adults, and some researchers have classified them into three levels: basic, proficient, and advanced users (Rosales & Fernández-Ardèvol, 2019). For example, the basic users may only use the camera or call friends while advanced users may be familiar with all the features other than video games. If we are to optimize interfaces or design tailored training programs, such assessment is inevitable. Besides, using a tool to evaluate proficiency levels at the commencement and conclusion of interventions provides insights into their efficacy.

In theory, conducting assessments can help us further understand the influencing factors of usage proficiency in future research. For example, previous studies have shown that computer usage proficiency is influenced by factors such as age, experience, education level, and emotions (Boot et al., 2015; Zhang et al., 2017), while Internet usage is influenced by age, income, education level, and gender (Elena-Bucea et al., 2021; Loges & Jung, 2001). In China, information literacy among middle-aged and older individuals is associated with education and income levels, with higher education and income leading to stronger information literacy (Chinese Academy of Social Sciences [CASS] & Tencent Center for Society, 2018). Employing statistical analyses like such studies enables a targeted long-term intervention strategy by focusing efforts on specific influencing factors.

Tools to Measure Mobile Device Proficiency of Older Adults

Aligned with the need to assess the operational proficiency of older adults, previous researchers have developed related questionnaires: the Computer Proficiency Questionnaire (CPQ, Boot et al., 2015) and the Mobile Device Proficiency Questionnaire (MDPQ, Roque & Boot, 2018). The CPQ focuses on investigating computer usage skills, covering six dimensions: computer basics, printing, communication, Internet, scheduling software, and multimedia use. Derived and modified from the CPQ, the MDPQ encompasses eight dimensions, including mobile device basics, communication, data and file storage, Internet, calendar, entertainment, privacy, troubleshooting, and software management. Despite the apparent suitability of the MDPQ for our purposes, a meticulous examination of its items reveals its inadequacy for direct application to the Chinese context, even after translation.

First, Chinese older adults are special in that they directly transitioned to the mobile age, rather than transferring from a desktop era as older adults in developed countries. Many of the concepts in a mobile phone such as “homepage,” “wallpaper,” and “installing an app” are inherited from computers. As a result, there is a positive relationship between computer use and the uptake of other mobile devices (Morris et al., 2006), and computer proficiency can serve as a predictive variable for proficiency in smartphones. Hence, the MDPQ and the CPQ have high similarity and relevance in terms of content. However, most Chinese older adults directly became a smartphone user before they had a chance to interact with a computer. Data underscores this phenomenon, with 99.5% of older Chinese Internet users accessing the Internet through smartphones, while computer and other device usage stands at less than 20% (CINIC, 2022). Therefore, we need a new tool that can independently measure proficiency in smartphones.

Second, disparities in preferences for smartphone functions across different countries necessitate a tailored approach. The functions encompassed in the MDPQ fail to capture those commonly used by Chinese older adults. The most frequently used functions among Chinese older Internet users are instant messaging, online video, online government services, online news, and online payment (CINIC, 2022). For example, in 2021, 84.8% of Chinese older Internet users watched online videos (CINIC, 2022), compared to less than 30% in the U.S. in 2022 (Vorhaus Advisors, 2022). When it comes to online news, only about 45% of older users in the U.S. got news on their smartphones (Vorhaus Advisors, 2022), while 77.9% of older users in China often got news this way (CINIC, 2022). Moreover, by the end of December 2022, more than 70% of Chinese residents had helped their parents or other older relatives to use mobile payment (Payment & Clearing Association of China, 2022), but only 28% of older smartphone users in the U.S. purchased products or services online at least once a week (Vorhaus Advisors, 2022). This stark contrast in usage patterns underscores the need for a culturally sensitive measurement tool.

In addition to the aforementioned questionnaires, other questionnaires targeting older adults mainly focus on issues other than technological proficiency. For example, Chen and Lou (2020) gauged older adults’ acceptance of technology, while Anderberg et al. (2019) measured older adults’ attitudes toward technology. These tools also cannot fulfill our specific requirements.

To surmount these limitations, this study endeavors to develop a measurement tool similar to the MDPQ but tailored for evaluating smartphone usage proficiency among Chinese older adults. Beyond fundamental psychometric indicators such as reliability and discrimination, the proposed questionnaire will undergo validation through an examination of its relationship with five predictors: (a) age; (b) daily use duration; (c) years of use; (d) monthly income; and (e) education level. With reference to previous studies, we proposed the following hypotheses to assess the validity of the Smartphone Proficiency Questionnaire for Chinese Older Adults (SPQ-COA).

Hypothesis 1 (H1): If the questionnaire is valid, the scores of the young adult group should be significantly higher than those of the older adult group.

Development of the Questionnaire

Participants

During the formal survey session, 552 questionnaires were distributed and collected, comprising 452 paper-based responses from older adults aged 60–94 (M = 68.53, SD = 6.42) and 100 online responses from young adults aged 18–30 (M = 21.95, SD = 2.38). This study received approval from the Ethics Committee of School of Psychology (Approval No. HR2022-1–050). Additional demographic details for both participant groups are provided in Table 1.

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Table 1.

Participant Demographics.

Scale		Older adults		Young adults
		N	%	N	%
Gender	Male	170	37.6	23	23.0
	Female	282	62.4	77	77.0
Daily use duration (hours)	<1	33	7.3	0	0.0
	1–2	113	25.0	4	4.0
	2–3	123	27.2	6	6.0
	3–5	104	23.0	26	26.0
	>5	79	17.5	64	64.0
Years of Use(years)	<1	12	2.7	2	2.0
	1–2	58	12.8	9	9.0
	2–4	122	27.0	16	16.0
	4–6	89	19.7	32	32.0
	>6	171	37.8	41	41.0
Monthly income (Chinese yuan)	<1000	84	18.6	59	59.0
	1000–2000	59	13.1	21	21.0
	2000–3000	110	24.3	5	5.0
	3000–5000	120	26.6	9	9.0
	>5000	79	17.5	6	6.0
Education level	Elementary School	42	9.3	0	0.0
	Middle School	157	34.7	1	1.0
	High School	139	30.8	7	7.0
	College	114	25.2	92	92.0

The primary target population of this questionnaire is older adult demographic, while the young adult group serves as a control group for subsequent validity assessment. Referring to previous studies (Roque & Boot, 2018), young adults generally possess higher proficiency, making them susceptible to the ceiling effect, meaning that the scores of the young adult group may reach the maximum level detectable by the questionnaire. Therefore, we recruited only 100 participants for the young adult group and administered an online questionnaire. In contrast, the older adult group, our primary focus, received paper-based questionnaires distributed offline, and the participants were supervised by the experimenters to complete the questionnaire on-site. This approach not only avoided technological requirements associated with online questionnaires for older adults but also ensured that older participants would have a comprehensive understanding of the operations and response options, minimizing the potential for invalid responses. Upon completion, all participants received appropriate rewards.

Distribution of the Questionnaire

For the young adult group, we deployed the online questionnaire through the platform powered by Sojump (https://www.wjx.cn/) and disseminated the link through social media channels. For the older adult group, we recruited participants in both Bengbu, Anhui Province and Xi’an, Shaanxi Province. In Bengbu, participants were sourced from congregational areas for older adults, such as the Guzhen Gymnasium and local neighborhoods. In Xi’an, participants were recruited from the vicinity of Shaanxi Normal University. We distributed paper-based questionnaires to older adults, both actively engaging passersby and employing fixed-location advertisement to attract nearby residents. Prior to the anonymous questionnaire, we explicitly declared that all participant responses would be exclusively used for academic research, ensuring strict confidentiality. Therefore, participants’ completion of the questionnaire implied their implicit consent.

Statistical Analysis

Data management and analysis were conducted using SPSS-24, encompassing evaluations of difficulty, discrimination, reliability, and validity. As for validity, we illustrated criteria validity of the questionnaire by formulating three hypotheses and examining the statistical data, including t-values from independent samples t-tests, F-values from one-way ANOVAs, and Pearson correlation coefficients.

Descriptive Statistics

Mean scores and standard deviations for the 30 items were calculated for both older adult and young adult groups. The overall average score for the older adult group was 3.34, with individual item averages ranging from 2.13 to 4.67. Conversely, the overall average score for the young adult group was 4.76, with individual item averages ranging from 4.57 to 4.94. Refer to Table 2 Columns 2–5 for a detailed presentation of the mean scores and standard deviations for each item.

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Table 2.

Mean Scores, Standard Deviations, T-test Results and Correlation Coefficients of Questionnaire Items.

Items	Older adults		Young adults
	M	SD	M	SD	t a	r b
1 Make phone calls, send messages	4.39	0.81	4.94	0.24	−12.15***	−0.13**
2 Add new contacts	3.88	1.38	4.89	0.31	−13.93***	−0.13**
3 Turn the device on/off, charge the device	4.67	0.61	4.95	0.22	−7.69***	−0.13**
4 Take photos and videos	4.25	1.05	4.84	0.42	−9.10***	−0.16**
5 Entertainment	4.02	1.19	4.82	0.39	−11.71***	−0.19**
6 Perform file operations	4.06	1.22	4.87	0.37	−11.97***	−0.16**
7 Send and receive messages	4.17	1.15	4.92	0.27	−12.37***	−0.16**
8 Make online payments	3.42	1.65	4.89	0.37	−17.14***	−0.25**
9 Input information on devices	3.52	1.49	4.90	0.33	−17.72***	−0.23**
10 Clean up device memory	3.48	1.50	4.61	0.65	−11.75***	−0.19**
11 Browse information on the screen	4.10	1.19	4.91	0.29	−12.89***	−0.18**
12 Make financial transactions with family and friends	3.37	1.63	4.89	0.35	−18.04***	−0.30**
13 Interact on social apps	3.65	1.51	4.83	0.40	−14.52***	−0.21**
14 Add new friends	3.50	1.52	4.87	0.39	−16.82***	−0.22**
15 Connect to a WIFI or other devices	3.26	1.48	4.70	0.54	−16.37***	−0.16**
16 Search for information about my interests online	3.43	1.51	4.81	0.44	−16.49***	−0.16**
17 Scan QR codes with devices	3.48	1.56	4.87	0.39	−16.62***	−0.20**
18 Adjust device system settings	4.02	1.19	4.82	0.41	−11.46***	−0.15**
19 Bookmark content	3.09	1.58	4.81	0.46	−19.62***	−0.11*
20 Extract information from devices	2.73	1.63	4.85	0.41	−24.42***	−0.20**
21 Set reminders and schedules	3.19	1.59	4.74	0.58	−16.37***	−0.17**
22 Make purchases online	2.61	1.63	4.85	0.39	−26.13***	−0.19**
23 Book an appointment online	2.40	1.56	4.59	0.67	−22.07***	−0.19**
24 Conduct payments of fees online	2.94	1.70	4.57	0.64	−15.98***	−0.19**
25 Use Email	2.13	1.52	4.66	0.55	−27.99***	−0.14**
26 Install and uninstall apps	2.49	1.59	4.89	0.35	−29.16***	−0.13**
27 Travel and navigate	2.32	1.57	4.71	0.56	−25.87***	−0.15**
28 Set or change passwords	2.53	1.65	4.84	0.42	−26.12***	−0.17**
29 Recover passwords	2.33	1.56	4.67	0.62	−24.35***	−0.16**
30 Erase using history	2.69	1.64	4.63	0.58	−20.14***	−0.18**

Item Analysis

Item analysis includes difficulty and discrimination. Difficulty was computed using the formula: (total score - actual score)/total score, where a smaller coefficient signifies higher scores and easier items. According to Table 3, for older adults, the following 10 items were relatively easy, with difficulty coefficients below 0.3: “Make phone calls, send messages,” “Add new contacts,” “Turn the device on/off, charge the device,” “Take photos and videos,” “Entertainment,” “Perform file operations,” “Send and receive messages,” “Browse information on the screen,” “Interact on social apps,” and “Adjust device system settings.” Conversely, items like “Book an appointment online,” “Use Email,” “Install and uninstall apps,” “Travel and navigate,” and “Recover passwords” posed higher difficulty for older adults, with coefficients surpassing 0.5.

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Table 3.

Item Analysis Results of the Questionnaire.

Items	Difficulty		Discrimination (Item-Total Correlation - r)		Discrimination (Critical Ratio - CR)
	Older adults	Young adults	Older adults	Young adults	Older adults	Young adults
1 Make phone calls, send messages	0.12	0.01	0.48	0.53	9.87	2.42
2 Add new contacts	0.22	0.02	0.67	0.59	16.48	3.27
3 Turn the device on/off, charge the device	0.07	0.01	0.35	0.45	6.19	2.12
4 Take photos and videos	0.15	0.03	0.57	0.74	11.24	4.40
5 Entertainment	0.20	0.04	0.54	0.64	10.74	5.11
6 Perform file operations	0.19	0.03	0.61	0.77	12.80	4.22
7 Send and receive messages	0.17	0.02	0.58	0.75	11.32	3.27
8 Make online payments	0.32	0.02	0.73	0.72	22.83	3.29
9 Input information on devices	0.30	0.02	0.74	0.68	21.66	3.09
10 Clean up device memory	0.30	0.08	0.71	0.64	19.46	7.13
11 Browse information on the screen	0.18	0.02	0.57	0.68	11.25	3.55
12 Make financial transactions with family and friends	0.33	0.02	0.75	0.60	25.25	3.64
13 Interact on social apps	0.27	0.03	0.75	0.75	24.76	5.56
14 Add new friends	0.30	0.03	0.78	0.74	26.47	3.82
15 Connect to a WIFI or other devices	0.35	0.06	0.66	0.75	18.55	7.81
16 Search for information about my interests online	0.31	0.04	0.73	0.68	21.80	5.03
17 Scan QR codes with devices	0.30	0.03	0.75	0.57	24.23	3.55
18 Adjust device system settings	0.20	0.04	0.56	0.76	11.17	5.19
19 Bookmark content	0.38	0.04	0.73	0.68	24.13	4.94
20 Extract information from devices	0.45	0.03	0.81	0.75	36.70	4.40
21 Set reminders and schedules	0.36	0.05	0.73	0.66	23.60	5.54
22 Make purchases online	0.48	0.03	0.77	0.70	26.57	4.53
23 Book an appointment online	0.52	0.08	0.77	0.63	26.67	7.16
24 Conduct payments of fees online	0.41	0.09	0.75	0.66	25.95	8.23
25 Use Email	0.57	0.07	0.72	0.66	20.44	8.45
26 Install and uninstall apps	0.50	0.02	0.76	0.62	26.92	3.36
27 Travel and navigate	0.54	0.06	0.74	0.69	23.69	5.88
28 Set or change passwords	0.49	0.03	0.75	0.73	24.79	4.40
29 Recover passwords	0.53	0.07	0.75	0.81	24.09	6.28
30 Erase using history	0.46	0.07	0.74	0.73	24.15	8.31

Discrimination includes the critical ratio (CR value) and item-total correlation coefficient (r). By sorting participants’ scores on the 30 items and selecting the top and bottom 27% as the high and low score groups respectively, independent samples t-tests were conducted for each item to determine CR values. The results revealed that all 30 items reached a significance level of 0.01 in both participant groups. Additionally, the item-total correlation coefficients, reflecting the correlation between individual item scores and total scores, were also significant at the 0.01 level for all 30 items in both participant groups. These findings affirm the questionnaire’s robust discrimination. Further details are outlined in Table 3.

Reliability

Cronbach’s α was used to assess the reliability of the 30 items, and the result suggested that the questionnaire is reliable (Cronbach’s α = 0.962).

Validity

Given the absence of localized questionnaires or scales pertaining to smartphone proficiency in China, criteria validity of this questionnaire could not be validated through existing instruments. Nonetheless, in line with previous studies (Roque & Boot, 2018), we proposed three hypotheses to demonstrate the criteria validity of the questionnaire:

H1

If the questionnaire is valid, the scores of the young adult group should be significantly higher than those of the older adult group.

Drawing on existing research and the digital divide theory, younger individuals generally exhibit higher proficiency in technology usage compared to older individuals. Therefore, we hypothesized that if the questionnaire would accurately measure participants’ smartphone usage proficiency, the scores of the older group should be lower than those of the young group for each item. Independent samples t-tests were conducted for each item to compare the scores of the older adult and young adult groups, revealing significantly lower scores for the older adult group across all 30 items (|t| ≥ 7.69, p < .001). The results can be seen in Table 2 Column 6, supporting H1.

H2

If the questionnaire is valid, the scores of the older adult group should be correlated with age, with higher age being associated with lower scores.

Individuals who were older in age were expected to have lower operational skills compared to those who were younger among the older adult group. We calculated the Pearson correlation coefficients (r) between the age of the older participants (a continuous variable) and the scores of the 30 items. The results as shown in Table 2 Column 7, indicated a significant negative correlation between age and scores for all items among the older adult group (r_max = −0.11, p < .05). This finding suggested that as age increased in the older population, their operational skill levels tended to be lower, supporting H2.

H3

If the questionnaire is valid, there should be differences in scores among the older adult group based on daily use duration, years of use, monthly income, and education level.

Longer daily use duration, more years of use, higher monthly income, and higher education level were expected to be associated with higher proficiency among older individuals. One-way ANOVAs were conducted between item scores and these factors (refer to Table 4). The F-test results revealed significant differences in daily use duration and years of use across all items (For all differences, p < .01), indicating that longer daily use and increased years of use positively influenced smartphone proficiency. Monthly income exhibited significant differences in 26 out of 30 items (p < .05), excluding specific areas such as “Turn the device on/off, charge the device,” “Entertainment,” “Browse information on the screen,” and “Adjust device system settings.” This suggested that higher monthly income was associated with increased proficiency. Education level displayed significant differences in all items except for “Entertainment” and “Adjust device system settings” (p < .05), indicating that enhanced education correlated with improved proficiency. Figure 1 illustrates how the score of each item varies with the above influences. Consequently, H3 was supported.

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Table 4.

F-test Results for Questionnaire Scores Across Daily Use Duration, Years of Use, Monthly Income, and Education Level.

Items	Daily Use Duration (hours)		Years of Use (years)		Monthly Income (CNY)		Education Level
	F	Sig.	F	Sig.	F	Sig.	F	Sig.
1 Make phone calls, send messages	4.33	**	12.35	***	4.75	**	5.2	**
2 Add new contacts	10.99	***	17.38	***	11.96	***	23.92	***
3 Turn the device on/off, charge the device	4.64	**	6.07	***	1.39	0.104	3.02	*
4 Take photos and videos	8.11	***	14.43	***	3.19	*	6.27	***
5 Entertainment	16.70	***	7.47	***	1.31	0.267	2.00	0.113
6 Perform file operations	13.07	***	11.50	***	4.20	**	7.49	***
7 Send and receive messages	8.89	***	12.05	***	5.15	***	6.63	***
8 Make online payments	7.50	***	19.70	***	6.25	***	16.96	***
9 Input information on devices	17.26	***	21.76	***	10.7	***	30.3	***
10 Clean up device memory	9.48	***	10.82	***	6.16	***	18.57	***
11 Browse information on the screen	10.71	***	7.82	***	1.93	0.104	3.32	*
12 Make financial transactions with family and friends	9.20	***	29.53	***	4.98	**	15.31	***
13 Interact on social apps	15.18	***	16.96	***	7.35	***	14.64	***
14 Add new friends	12.2	***	16.94	***	7.13	***	25.68	***
15 Connect to a WIFI or other devices	8.34	***	7.49	***	9.13	***	10.50	***
16 Search for information about my interests online	9.58	***	15.70	***	8.89	***	18.83	***
17 Scan QR codes with devices	14.78	***	18.16	***	7.17	***	16.73	***
18 Adjust device system settings	9.54	***	14.57	***	0.29	0.887	1.54	0.204
19 Bookmark content	8.96	***	15.08	***	13.06	***	32.85	***
20 Extract information from devices	10.98	***	16.17	***	10.69	***	30.33	***
21 Set reminders and schedules	10.46	***	9.34	***	6.06	***	18.42	***
22 Make purchases online	9.60	***	18.29	***	9.56	***	24.92	***
23 Book an appointment online	6.95	***	11.93	***	15.85	***	31.25	***
24 Conduct payments of fees online	9.98	***	14.69	***	8.8	***	23.49	***
25 Use Email	6.86	***	16.01	***	19.27	***	24.22	***
26 Install and uninstall apps	9.86	***	20.75	***	14.47	***	28.83	***
27 Travel and navigate	4.22	**	9.56	***	18.42	***	31.15	***
28 Set or change passwords	9.18	***	14.79	***	14.78	***	22.38	***
29 Recover passwords	6.23	***	8.34	***	14.44	***	22.80	***
30 Erase using history	7.39	***	9.69	***	11.21	***	28.09	***

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Figure 1.

The Score of Each Item Among Different Older Adults.

With all three hypotheses validated and aligned with existing research and recent data, we assert that the questionnaire demonstrates strong criteria validity.

Evaluation of the Questionnaire

Measuring the level of operational proficiency in using smartphones is a crucial step in improving digital literacy among older adults and helping them bridge the digital divide. This study is positioned to address this issue by developing a reliable and valid measurement questionnaire tailored for Chinese residents. The questionnaire consists of 30 self-assessment items on operational skills. Through rigorous validation, this questionnaire has demonstrated its ability to measure self-assessed operational proficiency.

A distinctive feature of our questionnaire, compared to counterparts like MDPQ, lies in its cultural alignment with Chinese older adults. In terms of content, our questionnaire includes daily operations commonly used by Chinese residents, such as “Scan QR codes with devices,” “Make purchases online,” “Conduct payments of fees online,” and “Travel and navigate.” Omitted are trivial and infrequently used tasks from previous questionnaires, such as “Send an email to multiple recipients simultaneously” and “Access my files from anywhere through cloud storage.” This is because, compared with email, Chinese older adults more commonly use WeChat as their communication medium (CASS, 2018). In terms of expression, language localization and life-oriented examples enhance comprehension, making the questionnaire a culturally sensitive tool. For instance, the explanation for “Interact on social apps” includes examples as “Give a thumbs-up on moments, share an article to other WeChat groups.”

Assessing the operational proficiency of smartphones among Chinese residents and investigating influencing factors contributes to the advancement of research on tailored skill training and product design for older adults, further providing personalized solutions to bridge the digital divide. Additionally, this questionnaire can function as a valuable tool to evaluate the effectiveness of skill training programs for older adults and provide insights for developing supportive policies that align with China’s national conditions to enhance digital literacy among older adults.

Findings on Smartphone Usage Patterns and Implications

In addition to developing a new questionnaire suitable for Chinese older adults, we have also verified the basic smartphone usage proficiency level among older adults in China and identified factors that influence their skill levels. The 5-point self-assessment revealed an average score of 3.34 for older adults and 4.76 for young adults, highlighting a significant proficiency gap. Lower scores on specific items reflected challenges faced by older adults. For example, the lower score on the item “Book an appointment online” aligns with previous findings that “only 12.1% of older adults can use mobile phones for online registration” (CASS, 2018), indicating that many older adults are unable to make appointments online. Correspondingly, the challenge of “being unable to book tickets or make registrations” has also been one of the common inconveniences among older individuals (CINIC, 2023). From an operational perspective, to complete the “Book an appointment online” task, users typically need to download or subscribe to a specific platform, which may involve multiple operations such as verifying personal information, entering verification codes, searching for services, and so on. These processes require flexibility and overall proficiency, thus posing higher technological demands on the older population. This also suggests that some commonly used operations with higher difficulty level should be a focal point for future skill training and product design for older adults. This analysis guides future interventions by pinpointing high-difficulty operations that demand enhanced technological proficiency.

Regarding influencing factors, we found differences in questionnaire scores based on age, daily use duration, years of use, monthly income, and education level. The relationship between proficiency and age is not only evident in the intergroup differences between the older and young adult groups but also within the older adult group, with a general trend that proficiency decreases as age increases. In the older adult population, longer daily use duration, more years of use, higher monthly income, and higher education level are associated with higher proficiency. These findings align with our initial hypothesis that there are variations in smartphone usage proficiency among older adults and that they are influenced by certain factors. These factors can serve as starting points for bridging the digital divide.

Limitations

We must acknowledge that we designed this questionnaire for pragmatic reasons, and therefore the process of development was not totally theory-driven, leading to a dimensionless structure. However, it is beneficial to extract dimensions in a questionnaire, as it enables further analysis of specific skill areas older adults need to focus on. A potential way is to conduct a cognitive task analysis (Knisely et al., 2021) of older adults’ interaction with smartphones and propose dimensions that can reflect more basic cognitive operations such as “information retrieval” and “navigation through apps” rather than the specific functions. In future research, we anticipate updating the questionnaire to enhance its stability, covering a broader range of fundamental and trainable abilities.

In terms of the factors that influence older adults’ proficiency in smartphone use, beyond the four considered here, future research should also consider whether the types of work older adults held before retirement will affect their attitudes and proficiency levels in using smartphones after retirement, and thus to better provide targeted support in smartphone usage for older adults.